From bdafe0fac739417b4b8d7a6e88c6aa192f2f6587 Mon Sep 17 00:00:00 2001
From: Greyson LaLonde <greyson.r.lalonde@gmail.com>
Date: Wed, 10 Dec 2025 20:32:10 -0500
Subject: [PATCH] fix: ensure token usage recording, validate response model on
 stream

---
 conftest.py                                   |    4 +
 lib/crewai/pyproject.toml                     |    2 +-
 .../llms/providers/anthropic/completion.py    |  104 +-
 .../crewai/llms/providers/azure/completion.py |  731 ++++---
 .../llms/providers/gemini/completion.py       |  600 +++---
 .../llms/providers/openai/completion.py       |   57 +-
 ...est_direct_llm_call_hooks_integration.yaml |   12 +-
 ...agent_hooks_integration_with_real_llm.yaml |   21 +-
 .../test_agent_custom_max_iterations.yaml     |   74 +-
 .../test_agent_error_on_parsing_tool.yaml     |   95 +-
 .../agents/test_agent_execute_task_basic.yaml |   25 +-
 .../test_agent_execute_task_with_context.yaml |   27 +-
 ...st_agent_execute_task_with_custom_llm.yaml |   26 +-
 .../test_agent_execute_task_with_tool.yaml    |   31 +-
 .../agents/test_agent_execution.yaml          |   25 +-
 ...t_agent_execution_with_specific_tools.yaml |   69 +-
 .../test_agent_execution_with_tools.yaml      |   70 +-
 .../test_agent_function_calling_llm.yaml      |  120 +-
 .../test_agent_kickoff_with_mcp_tools.yaml    |   71 +-
 ...est_agent_kickoff_with_platform_tools.yaml |   39 +-
 .../test_agent_max_iterations_stops_loop.yaml |  134 +-
 ...t_agent_moved_on_after_max_iterations.yaml |  294 +--
 ...put_when_guardrail_returns_base_model.yaml |   78 +-
 ...odel_family_that_allows_skipping_tool.yaml |   67 +-
 ..._by_new_o_model_family_that_uses_tool.yaml |   66 +-
 ...rmat_after_using_tools_too_many_times.yaml |  320 +--
 .../test_agent_repeated_tool_usage.yaml       |  230 +-
 ..._usage_check_even_with_disabled_cache.yaml |  255 +--
 .../test_agent_respect_the_max_rpm_set.yaml   |  309 +--
 ...respect_the_max_rpm_set_over_crew_rpm.yaml |  231 +-
 .../agents/test_agent_step_callback.yaml      |  286 +--
 ..._use_specific_tasks_output_as_context.yaml |   77 +-
 .../test_agent_with_knowledge_sources.yaml    |  166 +-
 ...with_knowledge_sources_extensive_role.yaml |  168 +-
 ...owledge_sources_generate_search_query.yaml |  284 +--
 ..._with_query_limit_and_score_threshold.yaml |  166 +-
 ...ery_limit_and_score_threshold_default.yaml |  166 +-
 ...test_agent_with_only_crewai_knowledge.yaml |   51 +-
 ...ent_without_max_rpm_respects_crew_rpm.yaml |  328 +--
 ...agent_without_max_rpm_respet_crew_rpm.yaml |    6 -
 .../cassettes/agents/test_cache_hitting.yaml  |  316 +--
 .../test_disabling_cache_for_agent.yaml       |  318 +--
 ...r_context_for_first_task_hierarchical.yaml |  213 +-
 ...gger_context_is_false_does_not_inject.yaml |   83 +-
 .../test_first_task_auto_inject_trigger.yaml  |  118 +-
 .../test_get_knowledge_search_query.yaml      |  253 +--
 ...st_guardrail_is_called_using_callable.yaml |   45 +-
 ...test_guardrail_is_called_using_string.yaml |  320 +--
 .../test_guardrail_reached_attempt_limit.yaml |  411 +---
 ...e_context_length_exceeds_limit_cli_no.yaml |   24 +-
 ...reated_with_correct_parameters[False].yaml |    8 -
 ...created_with_correct_parameters[True].yaml |   28 -
 ...t_lite_agent_output_includes_messages.yaml |   76 +-
 ...test_lite_agent_returns_usage_metrics.yaml |    4 -
 ...ite_agent_returns_usage_metrics_async.yaml |   83 +-
 .../test_lite_agent_structured_output.yaml    |  174 +-
 .../agents/test_lite_agent_with_tools.yaml    |    8 -
 .../tests/cassettes/agents/test_llm_call.yaml |   12 +-
 .../test_llm_call_with_all_attributes.yaml    |   15 +-
 .../agents/test_llm_call_with_error.yaml      |    7 +-
 .../agents/test_logging_tool_usage.yaml       |   69 +-
 ...est_task_allow_crewai_trigger_context.yaml |   45 +-
 ...low_crewai_trigger_context_no_payload.yaml |   53 +-
 ..._without_allow_crewai_trigger_context.yaml |   50 +-
 ...wer_is_the_final_answer_for_the_agent.yaml |   46 +-
 ...sage_information_is_appended_to_agent.yaml |   33 +-
 ...stAgentEvaluator.test_eval_lite_agent.yaml |   67 +-
 ...r.test_eval_specific_agents_from_crew.yaml |  251 +--
 ...uator.test_evaluate_current_iteration.yaml |  488 +----
 ...AgentEvaluator.test_failed_evaluation.yaml |   78 +-
 ...est_direct_llm_call_hooks_integration.yaml |   12 +-
 ...agent_hooks_integration_with_real_llm.yaml |   21 +-
 ...gent_hooks_integration_with_real_tool.yaml |  122 +-
 .../knowledge/test_docling_source.yaml        |    2 -
 .../test_multiple_docling_sources.yaml        |    4 -
 .../test_anthropic_async_basic_call.yaml      |   20 +-
 .../test_anthropic_async_conversation.yaml    |   25 +-
 .../test_anthropic_async_multiple_calls.yaml  |   36 +-
 .../test_anthropic_async_stop_sequences.yaml  |   12 +-
 .../test_anthropic_async_with_max_tokens.yaml |   24 +-
 ...ropic_async_with_response_format_json.yaml |   14 +-
 ...ropic_async_with_response_format_none.yaml |   14 +-
 ...t_anthropic_async_with_response_model.yaml |   14 +-
 ...t_anthropic_async_with_system_message.yaml |   24 +-
 ...test_anthropic_async_with_temperature.yaml |   24 +-
 .../test_anthropic_async_with_tools.yaml      |   16 +-
 .../test_anthropic_function_calling.yaml      |   34 +-
 ..._anthropic_stop_sequences_sent_to_api.yaml |   30 +-
 .../anthropic/test_anthropic_thinking.yaml    |   33 +-
 ...hinking_blocks_preserved_across_turns.yaml |   35 +-
 .../azure/test_azure_async_conversation.yaml  |   65 +
 .../test_azure_async_multiple_calls.yaml      |  126 ++
 .../azure/test_azure_async_non_streaming.yaml |   63 +
 ...async_streaming_returns_usage_metrics.yaml |  627 ++++++
 .../test_azure_async_with_max_tokens.yaml     |   64 +
 .../test_azure_async_with_parameters.yaml     |   66 +
 .../test_azure_async_with_system_message.yaml |   64 +
 .../test_azure_async_with_temperature.yaml    |   63 +
 .../test_azure_streaming_completion.yaml      |  117 +
 ...azure_streaming_returns_usage_metrics.yaml |  486 +++++
 .../google/test_gemini_async_basic_call.yaml  |    2 -
 .../test_gemini_async_conversation.yaml       |    2 -
 .../test_gemini_async_multiple_calls.yaml     |    4 -
 .../test_gemini_async_with_max_tokens.yaml    |    2 -
 .../test_gemini_async_with_parameters.yaml    |    2 -
 ...test_gemini_async_with_system_message.yaml |    2 -
 .../test_gemini_async_with_temperature.yaml   |    2 -
 ...async_streaming_returns_usage_metrics.yaml |   74 +
 ...oogle_streaming_returns_usage_metrics.yaml |  206 ++
 ...est_header_interceptor_with_real_call.yaml |   12 +-
 ...call_with_interceptor_tracks_requests.yaml |   12 +-
 ...st_logging_interceptor_tracks_details.yaml |   13 +-
 ....test_auth_interceptor_with_real_call.yaml |   19 +-
 ...call_with_interceptor_tracks_requests.yaml |   22 +-
 ...st_logging_interceptor_tracks_details.yaml |   19 +-
 .../openai/test_openai_async_basic_call.yaml  |   12 +-
 .../test_openai_async_conversation.yaml       |   15 +-
 .../test_openai_async_multiple_calls.yaml     |   24 +-
 ...async_streaming_returns_usage_metrics.yaml |  604 ++++++
 .../test_openai_async_with_max_tokens.yaml    |   15 +-
 .../test_openai_async_with_parameters.yaml    |   14 +-
 ...penai_async_with_response_format_json.yaml |   15 +-
 ...penai_async_with_response_format_none.yaml |   14 +-
 ...test_openai_async_with_system_message.yaml |   15 +-
 .../test_openai_async_with_temperature.yaml   |   12 +-
 .../openai/test_openai_completion_call.yaml   |   44 +-
 ...completion_call_returns_usage_metrics.yaml |   37 +-
 ...der_without_explicit_llm_set_on_agent.yaml |   39 +-
 .../test_openai_response_format_none.yaml     |   12 +-
 ...test_openai_response_format_with_dict.yaml |   15 +-
 ...i_response_format_with_pydantic_model.yaml |   18 +-
 ...penai_streaming_returns_usage_metrics.yaml |  646 ++++++
 .../test_crew_external_memory_save.yaml       |   10 -
 ..._using_crew_without_memory_flag[save].yaml |    2 -
 ...sing_crew_without_memory_flag[search].yaml |    2 -
 ...al_memory_save_with_memory_flag[save].yaml |  455 +---
 ..._memory_save_with_memory_flag[search].yaml |  566 +----
 .../test_crew_external_memory_search.yaml     |   20 -
 .../test_router_with_empty_input.yaml         |    2 -
 ...ew_execution_span_assigned_on_kickoff.yaml |  184 +-
 ...cution_span_assigned_on_kickoff_async.yaml |  161 +-
 ...ion_span_assigned_on_kickoff_for_each.yaml |  336 +--
 ...on_span_not_set_when_share_crew_false.yaml |  161 +-
 ...nd_crew_receives_valid_execution_span.yaml |  161 +-
 ...t_telemetry_fails_due_connect_timeout.yaml |  191 +-
 .../test_after_crew_modification.yaml         |  199 +-
 .../test_after_kickoff_modification.yaml      |  704 +-----
 ...are_captured_for_hierarchical_process.yaml |  216 +-
 ...on_tools_with_there_is_only_one_agent.yaml |    2 -
 .../test_anthropic_function_calling.yaml      |   33 +-
 .../cassettes/test_anthropic_thinking.yaml    |   29 +-
 ...hinking_blocks_preserved_across_turns.yaml |   34 +-
 .../cassettes/test_api_calls_throttling.yaml  |    4 -
 .../test_before_crew_modification.yaml        |  196 +-
 .../test_before_crew_with_none_input.yaml     |  186 +-
 .../test_before_kickoff_callback.yaml         |    4 -
 .../test_before_kickoff_modification.yaml     |  650 +-----
 .../test_before_kickoff_with_none_input.yaml  |  685 +-----
 .../test_before_kickoff_without_inputs.yaml   |    2 -
 .../test_cache_hitting_between_agents.yaml    |    8 -
 ...k_last_task_when_conditional_is_false.yaml |    2 -
 ...sk_last_task_when_conditional_is_true.yaml |    4 -
 .../tests/cassettes/test_crew_creation.yaml   |    4 -
 ...w_does_not_interpolate_without_inputs.yaml |    2 -
 .../test_crew_function_calling_llm.yaml       |    4 -
 .../test_crew_kickoff_usage_metrics.yaml      | 1369 ++----------
 .../cassettes/test_crew_log_file_output.yaml  |    2 -
 .../test_crew_output_file_end_to_end.yaml     |    2 -
 .../cassettes/test_crew_testing_function.yaml | 1444 ++-----------
 .../cassettes/test_crew_train_success.yaml    | 1880 ++---------------
 .../cassettes/test_crew_verbose_output.yaml   |    8 -
 .../test_crew_with_delegating_agents.yaml     |    2 -
 ...gents_should_not_override_agent_tools.yaml |    6 -
 ...agents_should_not_override_task_tools.yaml |    6 -
 ...est_crew_with_failing_task_guardrails.yaml |    8 -
 ...ith_knowledge_sources_works_with_copy.yaml |    4 -
 .../cassettes/test_custom_converter_cls.yaml  |   39 +-
 .../test_custom_llm_implementation.yaml       |    2 -
 .../test_deepseek_r1_with_open_router.yaml    |    2 -
 ...t_enabled_if_there_are_only_one_agent.yaml |    2 -
 ...sabled_memory_using_contextual_memory.yaml |    2 -
 ...ges_are_propagated_to_external_memory.yaml |  111 +-
 ...i_models[gemini-gemini-2.0-flash-001].yaml |    2 -
 ...els[gemini-gemini-2.0-flash-lite-001].yaml |    2 -
 ...-gemini-2.0-flash-thinking-exp-01-21].yaml |    2 -
 ...i_models[gemini-gemini-3-pro-preview].yaml |    2 -
 .../test_gemma3[gemini-gemma-3-27b-it].yaml   |   11 +-
 ...test_gpt_4_1[gpt-4.1-mini-2025-04-14].yaml |   15 +-
 ...test_gpt_4_1[gpt-4.1-nano-2025-04-14].yaml |   15 +-
 .../cassettes/test_gpt_4_1[gpt-4.1].yaml      |   15 +-
 .../test_guardrail_emits_events.yaml          |  396 +---
 .../test_guardrail_when_an_error_occurs.yaml  |  126 +-
 ...t_handle_context_length_exceeds_limit.yaml |    2 -
 ...hical_crew_creation_tasks_with_agents.yaml |    6 -
 ...w_creation_tasks_with_async_execution.yaml |    6 -
 ...al_crew_creation_tasks_with_sync_last.yaml |  648 +-----
 .../cassettes/test_hierarchical_process.yaml  |  559 +----
 ...rarchical_verbose_false_manager_agent.yaml | 1127 +---------
 ...st_hierarchical_verbose_manager_agent.yaml | 1191 +----------
 ..._delegations_for_hierarchical_process.yaml |  296 +--
 ...nt_delegations_for_sequential_process.yaml |  743 +------
 .../cassettes/test_increment_tool_errors.yaml |  392 +---
 .../tests/cassettes/test_inject_date.yaml     |  247 +--
 .../test_inject_date_custom_format.yaml       |   92 +-
 ...est_json_property_without_output_json.yaml |  244 +--
 ...test_kickoff_for_each_multiple_inputs.yaml |  181 +-
 .../test_kickoff_for_each_single_input.yaml   |   26 +-
 .../test_litellm_auth_error_handling.yaml     |   36 +-
 ...est_llm_call_when_stop_is_unsupported.yaml |   32 +-
 ...en_additional_drop_params_is_provided.yaml |   28 +-
 .../cassettes/test_llm_call_with_error.yaml   |   41 +-
 .../test_llm_call_with_message_list.yaml      |    2 -
 .../test_llm_call_with_string_input.yaml      |    2 -
 ..._call_with_string_input_and_callbacks.yaml |    2 -
 ...t_llm_call_with_tool_and_message_list.yaml |    2 -
 ...t_llm_call_with_tool_and_string_input.yaml |    2 -
 .../test_llm_callback_replacement.yaml        |    4 -
 .../test_llm_passes_additional_params.yaml    |   22 +-
 ...est_long_term_memory_with_memory_flag.yaml | 1184 +----------
 ...anager_agent_delegating_to_all_agents.yaml |   10 -
 ...ent_delegating_to_assigned_task_agent.yaml |   18 -
 .../test_memory_events_are_emitted.yaml       | 1352 +-----------
 ...l_agent_describing_image_successfully.yaml |    4 -
 ..._multimodal_agent_live_image_analysis.yaml |    6 -
 .../test_multiple_before_after_crew.yaml      |  204 +-
 .../test_multiple_before_after_kickoff.yaml   |  964 +--------
 .../tests/cassettes/test_no_inject_date.yaml  |  244 +--
 .../test_o3_mini_reasoning_effort_high.yaml   |    2 -
 .../test_o3_mini_reasoning_effort_low.yaml    |    2 -
 .../test_o3_mini_reasoning_effort_medium.yaml |    2 -
 .../test_output_json_dict_hierarchical.yaml   |  187 +-
 .../test_output_json_dict_sequential.yaml     |   40 +-
 .../test_output_json_hierarchical.yaml        |  197 +-
 .../test_output_json_sequential.yaml          |   40 +-
 .../test_output_json_to_another_task.yaml     |   78 +-
 .../test_output_pydantic_hierarchical.yaml    |  188 +-
 .../test_output_pydantic_sequential.yaml      |   40 +-
 .../test_output_pydantic_to_another_task.yaml |   74 +-
 ...t_replay_interpolates_inputs_properly.yaml |    6 -
 .../cassettes/test_replay_setup_context.yaml  |    2 -
 .../cassettes/test_replay_with_context.yaml   |    2 -
 .../cassettes/test_save_task_json_output.yaml |   81 +-
 .../cassettes/test_save_task_output.yaml      |    2 -
 .../test_save_task_pydantic_output.yaml       |   80 +-
 ...ntial_async_task_execution_completion.yaml |    6 -
 ...test_single_task_with_async_execution.yaml |    2 -
 .../cassettes/test_task_execution_times.yaml  |    2 -
 .../test_task_guardrail_process_output.yaml   |  225 +-
 .../test_task_interpolation_with_hyphens.yaml |  206 +-
 .../test_task_output_includes_messages.yaml   |  254 +--
 .../test_task_tools_override_agent_tools.yaml |    4 -
 .../test_task_with_max_execution_time.yaml    |    2 -
 ...task_with_max_execution_time_exceeded.yaml |  122 +-
 .../test_task_with_no_arguments.yaml          |    4 -
 .../test_tools_with_custom_caching.yaml       |   16 -
 .../test_using_contextual_memory.yaml         | 1183 +----------
 ...ntextual_memory_with_long_term_memory.yaml |    2 -
 ...textual_memory_with_short_term_memory.yaml |    4 -
 ...ong_term_memory_without_entity_memory.yaml |    2 -
 .../tools/agent_tools/test_ask_question.yaml  |    2 -
 ...t_ask_question_with_coworker_as_array.yaml |    2 -
 ...uestion_with_wrong_co_worker_variable.yaml |    2 -
 .../tools/agent_tools/test_delegate_work.yaml |    2 -
 ...te_work_with_wrong_co_worker_variable.yaml |    2 -
 ...egate_work_withwith_coworker_as_array.yaml |    2 -
 ...sync_tool_using_decorator_within_flow.yaml |   98 +-
 ..._using_decorator_within_isolated_crew.yaml |   98 +-
 ...async_tool_using_within_isolated_crew.yaml |   98 +-
 .../tools/test_async_tool_within_flow.yaml    |   98 +-
 .../test_max_usage_count_is_respected.yaml    |  310 +--
 ...t_no_http_calls_when_disabled_via_env.yaml |   30 +-
 ...calls_when_disabled_via_tracing_false.yaml |   30 +-
 ...test_trace_calls_when_enabled_via_env.yaml |  376 +---
 ...e_calls_when_enabled_via_tracing_true.yaml |  369 +---
 ...manager_finalizes_batch_clears_buffer.yaml |  214 +-
 ....test_events_collection_batch_manager.yaml |  248 +--
 ...me_user_trace_collection_user_accepts.yaml |  212 +-
 ...me_user_trace_collection_with_timeout.yaml |   31 +-
 ...t_time_user_trace_consolidation_logic.yaml |   37 +-
 ...ch_marked_as_failed_on_finalize_error.yaml |  111 +-
 ...t_trace_listener_collects_crew_events.yaml |  192 +-
 ...race_listener_disabled_when_env_false.yaml |  175 +-
 ...p.test_trace_listener_ephemeral_batch.yaml |  110 +-
 ...ner_setup_correctly_with_tracing_flag.yaml |   27 +-
 ...race_listener_with_authenticated_user.yaml |   27 +-
 ...xecution_started_and_completed_events.yaml |    4 -
 .../test_convert_with_instructions.yaml       |   24 +-
 .../test_converter_with_llama3_2_model.yaml   |   19 +-
 .../test_converter_with_nested_model.yaml     |   29 +-
 .../test_crew_emits_end_kickoff_event.yaml    |    4 -
 .../test_crew_emits_end_task_event.yaml       |    2 -
 .../test_crew_emits_kickoff_events.yaml       |    4 -
 .../test_crew_emits_start_kickoff_event.yaml  |    4 -
 .../test_crew_emits_start_task_event.yaml     |    4 -
 .../test_crew_emits_task_failed_event.yaml    |    2 -
 ...st_crew_emits_test_kickoff_type_event.yaml |  131 +-
 .../test_llm_emits_call_failed_event.yaml     |    2 -
 .../test_llm_emits_call_started_event.yaml    |    2 -
 ..._emits_event_with_task_and_agent_info.yaml |   31 +-
 ...stream_chunks_when_streaming_disabled.yaml |    2 -
 ...test_multiple_handlers_for_same_event.yaml |    2 -
 ...est_register_handler_adds_new_handler.yaml |    2 -
 ...emits_failed_event_on_execution_error.yaml |   12 -
 .../test_tools_emits_error_events.yaml        |   54 -
 .../test_tools_emits_finished_events.yaml     |    6 -
 lib/crewai/tests/llms/azure/test_azure.py     |   74 +-
 .../tests/llms/azure/test_azure_async.py      |   31 +
 lib/crewai/tests/llms/google/test_google.py   |   30 +
 .../tests/llms/google/test_google_async.py    |   32 +
 lib/crewai/tests/llms/openai/test_openai.py   |   29 +
 .../tests/llms/openai/test_openai_async.py    |   31 +
 uv.lock                                       |   48 +-
 312 files changed, 7846 insertions(+), 33124 deletions(-)
 create mode 100644 lib/crewai/tests/cassettes/llms/azure/test_azure_async_streaming_returns_usage_metrics.yaml
 create mode 100644 lib/crewai/tests/cassettes/llms/azure/test_azure_streaming_completion.yaml
 create mode 100644 lib/crewai/tests/cassettes/llms/azure/test_azure_streaming_returns_usage_metrics.yaml
 create mode 100644 lib/crewai/tests/cassettes/llms/google/test_google_async_streaming_returns_usage_metrics.yaml
 create mode 100644 lib/crewai/tests/cassettes/llms/google/test_google_streaming_returns_usage_metrics.yaml
 create mode 100644 lib/crewai/tests/cassettes/llms/openai/test_openai_async_streaming_returns_usage_metrics.yaml
 create mode 100644 lib/crewai/tests/cassettes/llms/openai/test_openai_streaming_returns_usage_metrics.yaml

diff --git a/conftest.py b/conftest.py
index fc3759f5f..3377bae81 100644
--- a/conftest.py
+++ b/conftest.py
@@ -136,6 +136,10 @@ def _filter_request_headers(request: Request) -> Request:  # type: ignore[no-any
 
 def _filter_response_headers(response: dict[str, Any]) -> dict[str, Any]:
     """Filter sensitive headers from response before recording."""
+    # Remove Content-Encoding to prevent decompression issues on replay
+    for encoding_header in ["Content-Encoding", "content-encoding"]:
+        response["headers"].pop(encoding_header, None)
+
     for header_name, replacement in HEADERS_TO_FILTER.items():
         for variant in [header_name, header_name.upper(), header_name.title()]:
             if variant in response["headers"]:
diff --git a/lib/crewai/pyproject.toml b/lib/crewai/pyproject.toml
index 11b1163bd..14b03eb62 100644
--- a/lib/crewai/pyproject.toml
+++ b/lib/crewai/pyproject.toml
@@ -84,7 +84,7 @@ bedrock = [
     "boto3~=1.40.45",
 ]
 google-genai = [
-    "google-genai~=1.2.0",
+    "google-genai~=1.49.0",
 ]
 azure-ai-inference = [
     "azure-ai-inference~=1.0.0b9",
diff --git a/lib/crewai/src/crewai/llms/providers/anthropic/completion.py b/lib/crewai/src/crewai/llms/providers/anthropic/completion.py
index 723826ea7..79e53907d 100644
--- a/lib/crewai/src/crewai/llms/providers/anthropic/completion.py
+++ b/lib/crewai/src/crewai/llms/providers/anthropic/completion.py
@@ -679,6 +679,49 @@ class AnthropicCompletion(BaseLLM):
             params["messages"], full_response, from_agent
         )
 
+    def _execute_tools_and_collect_results(
+        self,
+        tool_uses: list[ToolUseBlock],
+        available_functions: dict[str, Any],
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> list[dict[str, Any]]:
+        """Execute tools and collect results in Anthropic format.
+
+        Args:
+            tool_uses: List of tool use blocks from Claude's response
+            available_functions: Available functions for tool calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            List of tool result dictionaries in Anthropic format
+        """
+        tool_results = []
+
+        for tool_use in tool_uses:
+            function_name = tool_use.name
+            function_args = tool_use.input
+
+            result = self._handle_tool_execution(
+                function_name=function_name,
+                function_args=cast(dict[str, Any], function_args),
+                available_functions=available_functions,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+
+            tool_result = {
+                "type": "tool_result",
+                "tool_use_id": tool_use.id,
+                "content": str(result)
+                if result is not None
+                else "Tool execution completed",
+            }
+            tool_results.append(tool_result)
+
+        return tool_results
+
     def _handle_tool_use_conversation(
         self,
         initial_response: Message,
@@ -696,33 +739,10 @@ class AnthropicCompletion(BaseLLM):
         3. We send tool results back to Claude
         4. Claude processes results and generates final response
         """
-        # Execute all requested tools and collect results
-        tool_results = []
+        tool_results = self._execute_tools_and_collect_results(
+            tool_uses, available_functions, from_task, from_agent
+        )
 
-        for tool_use in tool_uses:
-            function_name = tool_use.name
-            function_args = tool_use.input
-
-            # Execute the tool
-            result = self._handle_tool_execution(
-                function_name=function_name,
-                function_args=function_args,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
-
-            # Create tool result in Anthropic format
-            tool_result = {
-                "type": "tool_result",
-                "tool_use_id": tool_use.id,
-                "content": str(result)
-                if result is not None
-                else "Tool execution completed",
-            }
-            tool_results.append(tool_result)
-
-        # Prepare follow-up conversation with tool results
         follow_up_params = params.copy()
 
         # Add Claude's tool use response to conversation
@@ -810,7 +830,7 @@ class AnthropicCompletion(BaseLLM):
             logging.error(f"Tool follow-up conversation failed: {e}")
             # Fallback: return the first tool result if follow-up fails
             if tool_results:
-                return tool_results[0]["content"]
+                return cast(str, tool_results[0]["content"])
             raise e
 
     async def _ahandle_completion(
@@ -1003,28 +1023,9 @@ class AnthropicCompletion(BaseLLM):
         3. We send tool results back to Claude
         4. Claude processes results and generates final response
         """
-        tool_results = []
-
-        for tool_use in tool_uses:
-            function_name = tool_use.name
-            function_args = tool_use.input
-
-            result = self._handle_tool_execution(
-                function_name=function_name,
-                function_args=function_args,
-                available_functions=available_functions,
-                from_task=from_task,
-                from_agent=from_agent,
-            )
-
-            tool_result = {
-                "type": "tool_result",
-                "tool_use_id": tool_use.id,
-                "content": str(result)
-                if result is not None
-                else "Tool execution completed",
-            }
-            tool_results.append(tool_result)
+        tool_results = self._execute_tools_and_collect_results(
+            tool_uses, available_functions, from_task, from_agent
+        )
 
         follow_up_params = params.copy()
 
@@ -1079,7 +1080,7 @@ class AnthropicCompletion(BaseLLM):
 
             logging.error(f"Tool follow-up conversation failed: {e}")
             if tool_results:
-                return tool_results[0]["content"]
+                return cast(str, tool_results[0]["content"])
             raise e
 
     def supports_function_calling(self) -> bool:
@@ -1115,7 +1116,8 @@ class AnthropicCompletion(BaseLLM):
         # Default context window size for Claude models
         return int(200000 * CONTEXT_WINDOW_USAGE_RATIO)
 
-    def _extract_anthropic_token_usage(self, response: Message) -> dict[str, Any]:
+    @staticmethod
+    def _extract_anthropic_token_usage(response: Message) -> dict[str, Any]:
         """Extract token usage from Anthropic response."""
         if hasattr(response, "usage") and response.usage:
             usage = response.usage
diff --git a/lib/crewai/src/crewai/llms/providers/azure/completion.py b/lib/crewai/src/crewai/llms/providers/azure/completion.py
index fe4416b1d..687dee9c6 100644
--- a/lib/crewai/src/crewai/llms/providers/azure/completion.py
+++ b/lib/crewai/src/crewai/llms/providers/azure/completion.py
@@ -3,7 +3,7 @@ from __future__ import annotations
 import json
 import logging
 import os
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, TypedDict
 
 from pydantic import BaseModel
 from typing_extensions import Self
@@ -18,7 +18,6 @@ from crewai.utilities.types import LLMMessage
 
 if TYPE_CHECKING:
     from crewai.llms.hooks.base import BaseInterceptor
-    from crewai.tools.base_tool import BaseTool
 
 
 try:
@@ -31,6 +30,8 @@ try:
     from azure.ai.inference.models import (
         ChatCompletions,
         ChatCompletionsToolCall,
+        ChatCompletionsToolDefinition,
+        FunctionDefinition,
         JsonSchemaFormat,
         StreamingChatCompletionsUpdate,
     )
@@ -50,6 +51,24 @@ except ImportError:
     ) from None
 
 
+class AzureCompletionParams(TypedDict, total=False):
+    """Type definition for Azure chat completion parameters."""
+
+    messages: list[LLMMessage]
+    stream: bool
+    model_extras: dict[str, Any]
+    response_format: JsonSchemaFormat
+    model: str
+    temperature: float
+    top_p: float
+    frequency_penalty: float
+    presence_penalty: float
+    max_tokens: int
+    stop: list[str]
+    tools: list[ChatCompletionsToolDefinition]
+    tool_choice: str
+
+
 class AzureCompletion(BaseLLM):
     """Azure AI Inference native completion implementation.
 
@@ -156,7 +175,8 @@ class AzureCompletion(BaseLLM):
             and "/openai/deployments/" in self.endpoint
         )
 
-    def _validate_and_fix_endpoint(self, endpoint: str, model: str) -> str:
+    @staticmethod
+    def _validate_and_fix_endpoint(endpoint: str, model: str) -> str:
         """Validate and fix Azure endpoint URL format.
 
         Azure OpenAI endpoints should be in the format:
@@ -179,10 +199,75 @@ class AzureCompletion(BaseLLM):
 
         return endpoint
 
+    def _handle_api_error(
+        self,
+        error: Exception,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> None:
+        """Handle API errors with appropriate logging and events.
+
+        Args:
+            error: The exception that occurred
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Raises:
+            The original exception after logging and emitting events
+        """
+        if isinstance(error, HttpResponseError):
+            if error.status_code == 401:
+                error_msg = "Azure authentication failed. Check your API key."
+            elif error.status_code == 404:
+                error_msg = (
+                    f"Azure endpoint not found. Check endpoint URL: {self.endpoint}"
+                )
+            elif error.status_code == 429:
+                error_msg = "Azure API rate limit exceeded. Please retry later."
+            else:
+                error_msg = (
+                    f"Azure API HTTP error: {error.status_code} - {error.message}"
+                )
+        else:
+            error_msg = f"Azure API call failed: {error!s}"
+
+        logging.error(error_msg)
+        self._emit_call_failed_event(
+            error=error_msg, from_task=from_task, from_agent=from_agent
+        )
+        raise error
+
+    def _handle_completion_error(
+        self,
+        error: Exception,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> None:
+        """Handle completion-specific errors including context length checks.
+
+        Args:
+            error: The exception that occurred
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Raises:
+            LLMContextLengthExceededError if context window exceeded, otherwise the original exception
+        """
+        if is_context_length_exceeded(error):
+            logging.error(f"Context window exceeded: {error}")
+            raise LLMContextLengthExceededError(str(error)) from error
+
+        error_msg = f"Azure API call failed: {error!s}"
+        logging.error(error_msg)
+        self._emit_call_failed_event(
+            error=error_msg, from_task=from_task, from_agent=from_agent
+        )
+        raise error
+
     def call(
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict[str, BaseTool]] | None = None,
+        tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -198,6 +283,7 @@ class AzureCompletion(BaseLLM):
             available_functions: Available functions for tool calling
             from_task: Task that initiated the call
             from_agent: Agent that initiated the call
+            response_model: Response model
 
         Returns:
             Chat completion response or tool call result
@@ -242,35 +328,13 @@ class AzureCompletion(BaseLLM):
                 response_model,
             )
 
-        except HttpResponseError as e:
-            if e.status_code == 401:
-                error_msg = "Azure authentication failed. Check your API key."
-            elif e.status_code == 404:
-                error_msg = (
-                    f"Azure endpoint not found. Check endpoint URL: {self.endpoint}"
-                )
-            elif e.status_code == 429:
-                error_msg = "Azure API rate limit exceeded. Please retry later."
-            else:
-                error_msg = f"Azure API HTTP error: {e.status_code} - {e.message}"
-
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
         except Exception as e:
-            error_msg = f"Azure API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            return self._handle_api_error(e, from_task, from_agent)  # type: ignore[func-returns-value]
 
-    async def acall(
+    async def acall(  # type: ignore[return]
         self,
         messages: str | list[LLMMessage],
-        tools: list[dict[str, BaseTool]] | None = None,
+        tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -324,37 +388,15 @@ class AzureCompletion(BaseLLM):
                 response_model,
             )
 
-        except HttpResponseError as e:
-            if e.status_code == 401:
-                error_msg = "Azure authentication failed. Check your API key."
-            elif e.status_code == 404:
-                error_msg = (
-                    f"Azure endpoint not found. Check endpoint URL: {self.endpoint}"
-                )
-            elif e.status_code == 429:
-                error_msg = "Azure API rate limit exceeded. Please retry later."
-            else:
-                error_msg = f"Azure API HTTP error: {e.status_code} - {e.message}"
-
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
         except Exception as e:
-            error_msg = f"Azure API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise
+            self._handle_api_error(e, from_task, from_agent)
 
     def _prepare_completion_params(
         self,
         messages: list[LLMMessage],
         tools: list[dict[str, Any]] | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> dict[str, Any]:
+    ) -> AzureCompletionParams:
         """Prepare parameters for Azure AI Inference chat completion.
 
         Args:
@@ -365,11 +407,14 @@ class AzureCompletion(BaseLLM):
         Returns:
             Parameters dictionary for Azure API
         """
-        params = {
+        params: AzureCompletionParams = {
             "messages": messages,
             "stream": self.stream,
         }
 
+        if self.stream:
+            params["model_extras"] = {"stream_options": {"include_usage": True}}
+
         if response_model and self.is_openai_model:
             model_description = generate_model_description(response_model)
             json_schema_info = model_description["json_schema"]
@@ -412,37 +457,42 @@ class AzureCompletion(BaseLLM):
 
         if drop_params and isinstance(additional_drop_params, list):
             for drop_param in additional_drop_params:
-                params.pop(drop_param, None)
+                if isinstance(drop_param, str):
+                    params.pop(drop_param, None)  # type: ignore[misc]
 
         return params
 
-    def _convert_tools_for_interference(
+    def _convert_tools_for_interference(  # type: ignore[override]
         self, tools: list[dict[str, Any]]
-    ) -> list[dict[str, Any]]:
-        """Convert CrewAI tool format to Azure OpenAI function calling format."""
+    ) -> list[ChatCompletionsToolDefinition]:
+        """Convert CrewAI tool format to Azure OpenAI function calling format.
 
+        Args:
+            tools: List of CrewAI tool definitions
+
+        Returns:
+            List of Azure ChatCompletionsToolDefinition objects
+        """
         from crewai.llms.providers.utils.common import safe_tool_conversion
 
-        azure_tools = []
+        azure_tools: list[ChatCompletionsToolDefinition] = []
 
         for tool in tools:
             name, description, parameters = safe_tool_conversion(tool, "Azure")
 
-            azure_tool = {
-                "type": "function",
-                "function": {
-                    "name": name,
-                    "description": description,
-                },
-            }
+            function_def = FunctionDefinition(
+                name=name,
+                description=description,
+                parameters=parameters
+                if isinstance(parameters, dict)
+                else dict(parameters)
+                if parameters
+                else None,
+            )
 
-            if parameters:
-                if isinstance(parameters, dict):
-                    azure_tool["function"]["parameters"] = parameters  # type: ignore
-                else:
-                    azure_tool["function"]["parameters"] = dict(parameters)
+            tool_def = ChatCompletionsToolDefinition(function=function_def)
 
-            azure_tools.append(azure_tool)
+            azure_tools.append(tool_def)
 
         return azure_tools
 
@@ -471,148 +521,239 @@ class AzureCompletion(BaseLLM):
 
         return azure_messages
 
-    def _handle_completion(
+    def _validate_and_emit_structured_output(
         self,
-        params: dict[str, Any],
-        available_functions: dict[str, Any] | None = None,
+        content: str,
+        response_model: type[BaseModel],
+        params: AzureCompletionParams,
         from_task: Any | None = None,
         from_agent: Any | None = None,
-        response_model: type[BaseModel] | None = None,
-    ) -> str | Any:
-        """Handle non-streaming chat completion."""
-        # Make API call
+    ) -> str:
+        """Validate content against response model and emit completion event.
+
+        Args:
+            content: Response content to validate
+            response_model: Pydantic model for validation
+            params: Completion parameters containing messages
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            Validated and serialized JSON string
+
+        Raises:
+            ValueError: If validation fails
+        """
         try:
-            response: ChatCompletions = self.client.complete(**params)
+            structured_data = response_model.model_validate_json(content)
+            structured_json = structured_data.model_dump_json()
 
-            if not response.choices:
-                raise ValueError("No choices returned from Azure API")
-
-            choice = response.choices[0]
-            message = choice.message
-
-            # Extract and track token usage
-            usage = self._extract_azure_token_usage(response)
-            self._track_token_usage_internal(usage)
-
-            if response_model and self.is_openai_model:
-                content = message.content or ""
-                try:
-                    structured_data = response_model.model_validate_json(content)
-                    structured_json = structured_data.model_dump_json()
-
-                    self._emit_call_completed_event(
-                        response=structured_json,
-                        call_type=LLMCallType.LLM_CALL,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        messages=params["messages"],
-                    )
-
-                    return structured_json
-                except Exception as e:
-                    error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
-                    logging.error(error_msg)
-                    raise ValueError(error_msg) from e
-
-            # Handle tool calls
-            if message.tool_calls and available_functions:
-                tool_call = message.tool_calls[0]  # Handle first tool call
-                if isinstance(tool_call, ChatCompletionsToolCall):
-                    function_name = tool_call.function.name
-
-                    try:
-                        function_args = json.loads(tool_call.function.arguments)
-                    except json.JSONDecodeError as e:
-                        logging.error(f"Failed to parse tool arguments: {e}")
-                        function_args = {}
-
-                    # Execute tool
-                    result = self._handle_tool_execution(
-                        function_name=function_name,
-                        function_args=function_args,
-                        available_functions=available_functions,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                    )
-
-                    if result is not None:
-                        return result
-
-            # Extract content
-            content = message.content or ""
-
-            # Apply stop words
-            content = self._apply_stop_words(content)
-
-            # Emit completion event and return content
             self._emit_call_completed_event(
-                response=content,
+                response=structured_json,
                 call_type=LLMCallType.LLM_CALL,
                 from_task=from_task,
                 from_agent=from_agent,
                 messages=params["messages"],
             )
 
-            content = self._invoke_after_llm_call_hooks(
-                params["messages"], content, from_agent
-            )
-
+            return structured_json
         except Exception as e:
-            if is_context_length_exceeded(e):
-                logging.error(f"Context window exceeded: {e}")
-                raise LLMContextLengthExceededError(str(e)) from e
-
-            error_msg = f"Azure API call failed: {e!s}"
+            error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
             logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise e
+            raise ValueError(error_msg) from e
 
-        return content
-
-    def _handle_streaming_completion(
+    def _process_completion_response(
         self,
-        params: dict[str, Any],
+        response: ChatCompletions,
+        params: AzureCompletionParams,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
+    ) -> str | Any:
+        """Process completion response with usage tracking, tool execution, and events.
+
+        Args:
+            response: Chat completion response from Azure API
+            params: Completion parameters containing messages
+            available_functions: Available functions for tool calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+            response_model: Pydantic model for structured output
+
+        Returns:
+            Response content or structured output
+        """
+        if not response.choices:
+            raise ValueError("No choices returned from Azure API")
+
+        choice = response.choices[0]
+        message = choice.message
+
+        # Extract and track token usage
+        usage = self._extract_azure_token_usage(response)
+        self._track_token_usage_internal(usage)
+
+        if response_model and self.is_openai_model:
+            content = message.content or ""
+            return self._validate_and_emit_structured_output(
+                content=content,
+                response_model=response_model,
+                params=params,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+
+        # Handle tool calls
+        if message.tool_calls and available_functions:
+            tool_call = message.tool_calls[0]  # Handle first tool call
+            if isinstance(tool_call, ChatCompletionsToolCall):
+                function_name = tool_call.function.name
+
+                try:
+                    function_args = json.loads(tool_call.function.arguments)
+                except json.JSONDecodeError as e:
+                    logging.error(f"Failed to parse tool arguments: {e}")
+                    function_args = {}
+
+                # Execute tool
+                result = self._handle_tool_execution(
+                    function_name=function_name,
+                    function_args=function_args,
+                    available_functions=available_functions,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
+
+                if result is not None:
+                    return result
+
+        # Extract content
+        content = message.content or ""
+
+        # Apply stop words
+        content = self._apply_stop_words(content)
+
+        # Emit completion event and return content
+        self._emit_call_completed_event(
+            response=content,
+            call_type=LLMCallType.LLM_CALL,
+            from_task=from_task,
+            from_agent=from_agent,
+            messages=params["messages"],
+        )
+
+        return self._invoke_after_llm_call_hooks(
+            params["messages"], content, from_agent
+        )
+
+    def _handle_completion(
+        self,
+        params: AzureCompletionParams,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
+    ) -> str | Any:
+        """Handle non-streaming chat completion."""
+        try:
+            # Cast params to Any to avoid type checking issues with TypedDict unpacking
+            response: ChatCompletions = self.client.complete(**params)  # type: ignore[assignment,arg-type]
+            return self._process_completion_response(
+                response=response,
+                params=params,
+                available_functions=available_functions,
+                from_task=from_task,
+                from_agent=from_agent,
+                response_model=response_model,
+            )
+        except Exception as e:
+            return self._handle_completion_error(e, from_task, from_agent)  # type: ignore[func-returns-value]
+
+    def _process_streaming_update(
+        self,
+        update: StreamingChatCompletionsUpdate,
+        full_response: str,
+        tool_calls: dict[str, dict[str, str]],
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
     ) -> str:
-        """Handle streaming chat completion."""
-        full_response = ""
-        tool_calls = {}
+        """Process a single streaming update chunk.
 
-        # Make streaming API call
-        for update in self.client.complete(**params):
-            if isinstance(update, StreamingChatCompletionsUpdate):
-                if update.choices:
-                    choice = update.choices[0]
-                    if choice.delta and choice.delta.content:
-                        content_delta = choice.delta.content
-                        full_response += content_delta
-                        self._emit_stream_chunk_event(
-                            chunk=content_delta,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                        )
+        Args:
+            update: Streaming update from Azure API
+            full_response: Accumulated response content
+            tool_calls: Dictionary of accumulated tool calls
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
 
-                    # Handle tool call streaming
-                    if choice.delta and choice.delta.tool_calls:
-                        for tool_call in choice.delta.tool_calls:
-                            call_id = tool_call.id or "default"
-                            if call_id not in tool_calls:
-                                tool_calls[call_id] = {
-                                    "name": "",
-                                    "arguments": "",
-                                }
+        Returns:
+            Updated full_response string
+        """
+        if update.choices:
+            choice = update.choices[0]
+            if choice.delta and choice.delta.content:
+                content_delta = choice.delta.content
+                full_response += content_delta
+                self._emit_stream_chunk_event(
+                    chunk=content_delta,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
 
-                            if tool_call.function and tool_call.function.name:
-                                tool_calls[call_id]["name"] = tool_call.function.name
-                            if tool_call.function and tool_call.function.arguments:
-                                tool_calls[call_id]["arguments"] += (
-                                    tool_call.function.arguments
-                                )
+            if choice.delta and choice.delta.tool_calls:
+                for tool_call in choice.delta.tool_calls:
+                    call_id = tool_call.id or "default"
+                    if call_id not in tool_calls:
+                        tool_calls[call_id] = {
+                            "name": "",
+                            "arguments": "",
+                        }
+
+                    if tool_call.function and tool_call.function.name:
+                        tool_calls[call_id]["name"] = tool_call.function.name
+                    if tool_call.function and tool_call.function.arguments:
+                        tool_calls[call_id]["arguments"] += tool_call.function.arguments
+
+        return full_response
+
+    def _finalize_streaming_response(
+        self,
+        full_response: str,
+        tool_calls: dict[str, dict[str, str]],
+        usage_data: dict[str, int],
+        params: AzureCompletionParams,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
+    ) -> str | Any:
+        """Finalize streaming response with usage tracking, tool execution, and events.
+
+        Args:
+            full_response: The complete streamed response content
+            tool_calls: Dictionary of tool calls accumulated during streaming
+            usage_data: Token usage data from the stream
+            params: Completion parameters containing messages
+            available_functions: Available functions for tool calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+            response_model: Pydantic model for structured output validation
+
+        Returns:
+            Final response content after processing, or structured output
+        """
+        self._track_token_usage_internal(usage_data)
+
+        # Handle structured output validation
+        if response_model and self.is_openai_model:
+            return self._validate_and_emit_structured_output(
+                content=full_response,
+                response_model=response_model,
+                params=params,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
 
         # Handle completed tool calls
         if tool_calls and available_functions:
@@ -653,9 +794,52 @@ class AzureCompletion(BaseLLM):
             params["messages"], full_response, from_agent
         )
 
+    def _handle_streaming_completion(
+        self,
+        params: AzureCompletionParams,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
+    ) -> str | Any:
+        """Handle streaming chat completion."""
+        full_response = ""
+        tool_calls: dict[str, dict[str, Any]] = {}
+
+        usage_data = {"total_tokens": 0}
+        for update in self.client.complete(**params):  # type: ignore[arg-type]
+            if isinstance(update, StreamingChatCompletionsUpdate):
+                if update.usage:
+                    usage = update.usage
+                    usage_data = {
+                        "prompt_tokens": usage.prompt_tokens,
+                        "completion_tokens": usage.completion_tokens,
+                        "total_tokens": usage.total_tokens,
+                    }
+                    continue
+
+                full_response = self._process_streaming_update(
+                    update=update,
+                    full_response=full_response,
+                    tool_calls=tool_calls,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
+
+        return self._finalize_streaming_response(
+            full_response=full_response,
+            tool_calls=tool_calls,
+            usage_data=usage_data,
+            params=params,
+            available_functions=available_functions,
+            from_task=from_task,
+            from_agent=from_agent,
+            response_model=response_model,
+        )
+
     async def _ahandle_completion(
         self,
-        params: dict[str, Any],
+        params: AzureCompletionParams,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
@@ -663,160 +847,64 @@ class AzureCompletion(BaseLLM):
     ) -> str | Any:
         """Handle non-streaming chat completion asynchronously."""
         try:
-            response: ChatCompletions = await self.async_client.complete(**params)
-
-            if not response.choices:
-                raise ValueError("No choices returned from Azure API")
-
-            choice = response.choices[0]
-            message = choice.message
-
-            usage = self._extract_azure_token_usage(response)
-            self._track_token_usage_internal(usage)
-
-            if response_model and self.is_openai_model:
-                content = message.content or ""
-                try:
-                    structured_data = response_model.model_validate_json(content)
-                    structured_json = structured_data.model_dump_json()
-
-                    self._emit_call_completed_event(
-                        response=structured_json,
-                        call_type=LLMCallType.LLM_CALL,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                        messages=params["messages"],
-                    )
-
-                    return structured_json
-                except Exception as e:
-                    error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
-                    logging.error(error_msg)
-                    raise ValueError(error_msg) from e
-
-            if message.tool_calls and available_functions:
-                tool_call = message.tool_calls[0]  # Handle first tool call
-                if isinstance(tool_call, ChatCompletionsToolCall):
-                    function_name = tool_call.function.name
-
-                    try:
-                        function_args = json.loads(tool_call.function.arguments)
-                    except json.JSONDecodeError as e:
-                        logging.error(f"Failed to parse tool arguments: {e}")
-                        function_args = {}
-
-                    result = self._handle_tool_execution(
-                        function_name=function_name,
-                        function_args=function_args,
-                        available_functions=available_functions,
-                        from_task=from_task,
-                        from_agent=from_agent,
-                    )
-
-                    if result is not None:
-                        return result
-
-            content = message.content or ""
-
-            content = self._apply_stop_words(content)
-
-            self._emit_call_completed_event(
-                response=content,
-                call_type=LLMCallType.LLM_CALL,
+            # Cast params to Any to avoid type checking issues with TypedDict unpacking
+            response: ChatCompletions = await self.async_client.complete(**params)  # type: ignore[assignment,arg-type]
+            return self._process_completion_response(
+                response=response,
+                params=params,
+                available_functions=available_functions,
                 from_task=from_task,
                 from_agent=from_agent,
-                messages=params["messages"],
+                response_model=response_model,
             )
-
         except Exception as e:
-            if is_context_length_exceeded(e):
-                logging.error(f"Context window exceeded: {e}")
-                raise LLMContextLengthExceededError(str(e)) from e
-
-            error_msg = f"Azure API call failed: {e!s}"
-            logging.error(error_msg)
-            self._emit_call_failed_event(
-                error=error_msg, from_task=from_task, from_agent=from_agent
-            )
-            raise e
-
-        return content
+            return self._handle_completion_error(e, from_task, from_agent)  # type: ignore[func-returns-value]
 
     async def _ahandle_streaming_completion(
         self,
-        params: dict[str, Any],
+        params: AzureCompletionParams,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
-    ) -> str:
+    ) -> str | Any:
         """Handle streaming chat completion asynchronously."""
         full_response = ""
-        tool_calls = {}
+        tool_calls: dict[str, dict[str, Any]] = {}
 
-        stream = await self.async_client.complete(**params)
-        async for update in stream:
+        usage_data = {"total_tokens": 0}
+
+        stream = await self.async_client.complete(**params)  # type: ignore[arg-type]
+        async for update in stream:  # type: ignore[union-attr]
             if isinstance(update, StreamingChatCompletionsUpdate):
-                if update.choices:
-                    choice = update.choices[0]
-                    if choice.delta and choice.delta.content:
-                        content_delta = choice.delta.content
-                        full_response += content_delta
-                        self._emit_stream_chunk_event(
-                            chunk=content_delta,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                        )
-
-                    if choice.delta and choice.delta.tool_calls:
-                        for tool_call in choice.delta.tool_calls:
-                            call_id = tool_call.id or "default"
-                            if call_id not in tool_calls:
-                                tool_calls[call_id] = {
-                                    "name": "",
-                                    "arguments": "",
-                                }
-
-                            if tool_call.function and tool_call.function.name:
-                                tool_calls[call_id]["name"] = tool_call.function.name
-                            if tool_call.function and tool_call.function.arguments:
-                                tool_calls[call_id]["arguments"] += (
-                                    tool_call.function.arguments
-                                )
-
-        if tool_calls and available_functions:
-            for call_data in tool_calls.values():
-                function_name = call_data["name"]
-
-                try:
-                    function_args = json.loads(call_data["arguments"])
-                except json.JSONDecodeError as e:
-                    logging.error(f"Failed to parse streamed tool arguments: {e}")
+                if hasattr(update, "usage") and update.usage:
+                    usage = update.usage
+                    usage_data = {
+                        "prompt_tokens": getattr(usage, "prompt_tokens", 0),
+                        "completion_tokens": getattr(usage, "completion_tokens", 0),
+                        "total_tokens": getattr(usage, "total_tokens", 0),
+                    }
                     continue
 
-                result = self._handle_tool_execution(
-                    function_name=function_name,
-                    function_args=function_args,
-                    available_functions=available_functions,
+                full_response = self._process_streaming_update(
+                    update=update,
+                    full_response=full_response,
+                    tool_calls=tool_calls,
                     from_task=from_task,
                     from_agent=from_agent,
                 )
 
-                if result is not None:
-                    return result
-
-        full_response = self._apply_stop_words(full_response)
-
-        self._emit_call_completed_event(
-            response=full_response,
-            call_type=LLMCallType.LLM_CALL,
+        return self._finalize_streaming_response(
+            full_response=full_response,
+            tool_calls=tool_calls,
+            usage_data=usage_data,
+            params=params,
+            available_functions=available_functions,
             from_task=from_task,
             from_agent=from_agent,
-            messages=params["messages"],
+            response_model=response_model,
         )
 
-        return full_response
-
     def supports_function_calling(self) -> bool:
         """Check if the model supports function calling."""
         # Azure OpenAI models support function calling
@@ -860,7 +948,8 @@ class AzureCompletion(BaseLLM):
         # Default context window size
         return int(8192 * CONTEXT_WINDOW_USAGE_RATIO)
 
-    def _extract_azure_token_usage(self, response: ChatCompletions) -> dict[str, Any]:
+    @staticmethod
+    def _extract_azure_token_usage(response: ChatCompletions) -> dict[str, Any]:
         """Extract token usage from Azure response."""
         if hasattr(response, "usage") and response.usage:
             usage = response.usage
diff --git a/lib/crewai/src/crewai/llms/providers/gemini/completion.py b/lib/crewai/src/crewai/llms/providers/gemini/completion.py
index 0917bf555..e511c61b0 100644
--- a/lib/crewai/src/crewai/llms/providers/gemini/completion.py
+++ b/lib/crewai/src/crewai/llms/providers/gemini/completion.py
@@ -3,7 +3,7 @@ from __future__ import annotations
 import logging
 import os
 import re
-from typing import TYPE_CHECKING, Any
+from typing import TYPE_CHECKING, Any, Literal, cast
 
 from pydantic import BaseModel
 
@@ -105,6 +105,7 @@ class GeminiCompletion(BaseLLM):
         self.stream = stream
         self.safety_settings = safety_settings or {}
         self.stop_sequences = stop_sequences or []
+        self.tools: list[dict[str, Any]] | None = None
 
         # Model-specific settings
         version_match = re.search(r"gemini-(\d+(?:\.\d+)?)", model.lower())
@@ -223,10 +224,11 @@ class GeminiCompletion(BaseLLM):
         Args:
             messages: Input messages for the chat completion
             tools: List of tool/function definitions
-            callbacks: Callback functions (not used as token counts are handled by the reponse)
+            callbacks: Callback functions (not used as token counts are handled by the response)
             available_functions: Available functions for tool calling
             from_task: Task that initiated the call
             from_agent: Agent that initiated the call
+            response_model: Response model to use.
 
         Returns:
             Chat completion response or tool call result
@@ -267,7 +269,6 @@ class GeminiCompletion(BaseLLM):
 
             return self._handle_completion(
                 formatted_content,
-                system_instruction,
                 config,
                 available_functions,
                 from_task,
@@ -309,6 +310,7 @@ class GeminiCompletion(BaseLLM):
             available_functions: Available functions for tool calling
             from_task: Task that initiated the call
             from_agent: Agent that initiated the call
+            response_model: Response model to use.
 
         Returns:
             Chat completion response or tool call result
@@ -344,7 +346,6 @@ class GeminiCompletion(BaseLLM):
 
             return await self._ahandle_completion(
                 formatted_content,
-                system_instruction,
                 config,
                 available_functions,
                 from_task,
@@ -497,35 +498,113 @@ class GeminiCompletion(BaseLLM):
 
         return contents, system_instruction
 
-    def _handle_completion(
+    def _validate_and_emit_structured_output(
         self,
+        content: str,
+        response_model: type[BaseModel],
+        messages_for_event: list[LLMMessage],
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str:
+        """Validate content against response model and emit completion event.
+
+        Args:
+            content: Response content to validate
+            response_model: Pydantic model for validation
+            messages_for_event: Messages to include in event
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            Validated and serialized JSON string
+
+        Raises:
+            ValueError: If validation fails
+        """
+        try:
+            structured_data = response_model.model_validate_json(content)
+            structured_json = structured_data.model_dump_json()
+
+            self._emit_call_completed_event(
+                response=structured_json,
+                call_type=LLMCallType.LLM_CALL,
+                from_task=from_task,
+                from_agent=from_agent,
+                messages=messages_for_event,
+            )
+
+            return structured_json
+        except Exception as e:
+            error_msg = f"Failed to validate structured output with model {response_model.__name__}: {e}"
+            logging.error(error_msg)
+            raise ValueError(error_msg) from e
+
+    def _finalize_completion_response(
+        self,
+        content: str,
+        contents: list[types.Content],
+        response_model: type[BaseModel] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str:
+        """Finalize completion response with validation and event emission.
+
+        Args:
+            content: The response content
+            contents: Original contents for event conversion
+            response_model: Pydantic model for structured output validation
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            Final response content after processing
+        """
+        messages_for_event = self._convert_contents_to_dict(contents)
+
+        # Handle structured output validation
+        if response_model:
+            return self._validate_and_emit_structured_output(
+                content=content,
+                response_model=response_model,
+                messages_for_event=messages_for_event,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+
+        self._emit_call_completed_event(
+            response=content,
+            call_type=LLMCallType.LLM_CALL,
+            from_task=from_task,
+            from_agent=from_agent,
+            messages=messages_for_event,
+        )
+
+        return self._invoke_after_llm_call_hooks(
+            messages_for_event, content, from_agent
+        )
+
+    def _process_response_with_tools(
+        self,
+        response: GenerateContentResponse,
         contents: list[types.Content],
-        system_instruction: str | None,
-        config: types.GenerateContentConfig,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
-        """Handle non-streaming content generation."""
-        try:
-            # The API accepts list[Content] but mypy is overly strict about variance
-            contents_for_api: Any = contents
-            response = self.client.models.generate_content(
-                model=self.model,
-                contents=contents_for_api,
-                config=config,
-            )
+        """Process response, execute function calls, and finalize completion.
 
-            usage = self._extract_token_usage(response)
-        except Exception as e:
-            if is_context_length_exceeded(e):
-                logging.error(f"Context window exceeded: {e}")
-                raise LLMContextLengthExceededError(str(e)) from e
-            raise e from e
-
-        self._track_token_usage_internal(usage)
+        Args:
+            response: The completion response
+            contents: Original contents for event conversion
+            available_functions: Available functions for function calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+            response_model: Pydantic model for structured output validation
 
+        Returns:
+            Final response content or function call result
+        """
         if response.candidates and (self.tools or available_functions):
             candidate = response.candidates[0]
             if candidate.content and candidate.content.parts:
@@ -554,61 +633,90 @@ class GeminiCompletion(BaseLLM):
         content = response.text or ""
         content = self._apply_stop_words(content)
 
-        messages_for_event = self._convert_contents_to_dict(contents)
-
-        self._emit_call_completed_event(
-            response=content,
-            call_type=LLMCallType.LLM_CALL,
+        return self._finalize_completion_response(
+            content=content,
+            contents=contents,
+            response_model=response_model,
             from_task=from_task,
             from_agent=from_agent,
-            messages=messages_for_event,
         )
 
-        return self._invoke_after_llm_call_hooks(
-            messages_for_event, content, from_agent
-        )
-
-    def _handle_streaming_completion(
+    def _process_stream_chunk(
         self,
+        chunk: GenerateContentResponse,
+        full_response: str,
+        function_calls: dict[str, dict[str, Any]],
+        usage_data: dict[str, int],
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> tuple[str, dict[str, dict[str, Any]], dict[str, int]]:
+        """Process a single streaming chunk.
+
+        Args:
+            chunk: The streaming chunk response
+            full_response: Accumulated response text
+            function_calls: Accumulated function calls
+            usage_data: Accumulated usage data
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            Tuple of (updated full_response, updated function_calls, updated usage_data)
+        """
+        if chunk.usage_metadata:
+            usage_data = self._extract_token_usage(chunk)
+
+        if chunk.text:
+            full_response += chunk.text
+            self._emit_stream_chunk_event(
+                chunk=chunk.text,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+
+        if chunk.candidates:
+            candidate = chunk.candidates[0]
+            if candidate.content and candidate.content.parts:
+                for part in candidate.content.parts:
+                    if hasattr(part, "function_call") and part.function_call:
+                        call_id = part.function_call.name or "default"
+                        if call_id not in function_calls:
+                            function_calls[call_id] = {
+                                "name": part.function_call.name,
+                                "args": dict(part.function_call.args)
+                                if part.function_call.args
+                                else {},
+                            }
+
+        return full_response, function_calls, usage_data
+
+    def _finalize_streaming_response(
+        self,
+        full_response: str,
+        function_calls: dict[str, dict[str, Any]],
+        usage_data: dict[str, int],
         contents: list[types.Content],
-        config: types.GenerateContentConfig,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
         from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str:
-        """Handle streaming content generation."""
-        full_response = ""
-        function_calls: dict[str, dict[str, Any]] = {}
+        """Finalize streaming response with usage tracking, function execution, and events.
 
-        # The API accepts list[Content] but mypy is overly strict about variance
-        contents_for_api: Any = contents
-        for chunk in self.client.models.generate_content_stream(
-            model=self.model,
-            contents=contents_for_api,
-            config=config,
-        ):
-            if chunk.text:
-                full_response += chunk.text
-                self._emit_stream_chunk_event(
-                    chunk=chunk.text,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                )
+        Args:
+            full_response: The complete streamed response content
+            function_calls: Dictionary of function calls accumulated during streaming
+            usage_data: Token usage data from the stream
+            contents: Original contents for event conversion
+            available_functions: Available functions for function calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+            response_model: Pydantic model for structured output validation
 
-            if chunk.candidates:
-                candidate = chunk.candidates[0]
-                if candidate.content and candidate.content.parts:
-                    for part in candidate.content.parts:
-                        if hasattr(part, "function_call") and part.function_call:
-                            call_id = part.function_call.name or "default"
-                            if call_id not in function_calls:
-                                function_calls[call_id] = {
-                                    "name": part.function_call.name,
-                                    "args": dict(part.function_call.args)
-                                    if part.function_call.args
-                                    else {},
-                                }
+        Returns:
+            Final response content after processing
+        """
+        self._track_token_usage_internal(usage_data)
 
         # Handle completed function calls
         if function_calls and available_functions:
@@ -636,24 +744,95 @@ class GeminiCompletion(BaseLLM):
                 if result is not None:
                     return result
 
-        messages_for_event = self._convert_contents_to_dict(contents)
-
-        self._emit_call_completed_event(
-            response=full_response,
-            call_type=LLMCallType.LLM_CALL,
+        return self._finalize_completion_response(
+            content=full_response,
+            contents=contents,
+            response_model=response_model,
             from_task=from_task,
             from_agent=from_agent,
-            messages=messages_for_event,
         )
 
-        return self._invoke_after_llm_call_hooks(
-            messages_for_event, full_response, from_agent
+    def _handle_completion(
+        self,
+        contents: list[types.Content],
+        config: types.GenerateContentConfig,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
+    ) -> str | Any:
+        """Handle non-streaming content generation."""
+        try:
+            # The API accepts list[Content] but mypy is overly strict about variance
+            contents_for_api: Any = contents
+            response = self.client.models.generate_content(
+                model=self.model,
+                contents=contents_for_api,
+                config=config,
+            )
+
+            usage = self._extract_token_usage(response)
+        except Exception as e:
+            if is_context_length_exceeded(e):
+                logging.error(f"Context window exceeded: {e}")
+                raise LLMContextLengthExceededError(str(e)) from e
+            raise e from e
+
+        self._track_token_usage_internal(usage)
+
+        return self._process_response_with_tools(
+            response=response,
+            contents=contents,
+            available_functions=available_functions,
+            from_task=from_task,
+            from_agent=from_agent,
+            response_model=response_model,
+        )
+
+    def _handle_streaming_completion(
+        self,
+        contents: list[types.Content],
+        config: types.GenerateContentConfig,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+        response_model: type[BaseModel] | None = None,
+    ) -> str:
+        """Handle streaming content generation."""
+        full_response = ""
+        function_calls: dict[str, dict[str, Any]] = {}
+        usage_data = {"total_tokens": 0}
+
+        # The API accepts list[Content] but mypy is overly strict about variance
+        contents_for_api: Any = contents
+        for chunk in self.client.models.generate_content_stream(
+            model=self.model,
+            contents=contents_for_api,
+            config=config,
+        ):
+            full_response, function_calls, usage_data = self._process_stream_chunk(
+                chunk=chunk,
+                full_response=full_response,
+                function_calls=function_calls,
+                usage_data=usage_data,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+
+        return self._finalize_streaming_response(
+            full_response=full_response,
+            function_calls=function_calls,
+            usage_data=usage_data,
+            contents=contents,
+            available_functions=available_functions,
+            from_task=from_task,
+            from_agent=from_agent,
+            response_model=response_model,
         )
 
     async def _ahandle_completion(
         self,
         contents: list[types.Content],
-        system_instruction: str | None,
         config: types.GenerateContentConfig,
         available_functions: dict[str, Any] | None = None,
         from_task: Any | None = None,
@@ -679,46 +858,15 @@ class GeminiCompletion(BaseLLM):
 
         self._track_token_usage_internal(usage)
 
-        if response.candidates and (self.tools or available_functions):
-            candidate = response.candidates[0]
-            if candidate.content and candidate.content.parts:
-                for part in candidate.content.parts:
-                    if hasattr(part, "function_call") and part.function_call:
-                        function_name = part.function_call.name
-                        if function_name is None:
-                            continue
-                        function_args = (
-                            dict(part.function_call.args)
-                            if part.function_call.args
-                            else {}
-                        )
-
-                        result = self._handle_tool_execution(
-                            function_name=function_name,
-                            function_args=function_args,
-                            available_functions=available_functions or {},
-                            from_task=from_task,
-                            from_agent=from_agent,
-                        )
-
-                        if result is not None:
-                            return result
-
-        content = response.text or ""
-        content = self._apply_stop_words(content)
-
-        messages_for_event = self._convert_contents_to_dict(contents)
-
-        self._emit_call_completed_event(
-            response=content,
-            call_type=LLMCallType.LLM_CALL,
+        return self._process_response_with_tools(
+            response=response,
+            contents=contents,
+            available_functions=available_functions,
             from_task=from_task,
             from_agent=from_agent,
-            messages=messages_for_event,
+            response_model=response_model,
         )
 
-        return content
-
     async def _ahandle_streaming_completion(
         self,
         contents: list[types.Content],
@@ -731,6 +879,7 @@ class GeminiCompletion(BaseLLM):
         """Handle async streaming content generation."""
         full_response = ""
         function_calls: dict[str, dict[str, Any]] = {}
+        usage_data = {"total_tokens": 0}
 
         # The API accepts list[Content] but mypy is overly strict about variance
         contents_for_api: Any = contents
@@ -740,214 +889,24 @@ class GeminiCompletion(BaseLLM):
             config=config,
         )
         async for chunk in stream:
-            if chunk.text:
-                full_response += chunk.text
-                self._emit_stream_chunk_event(
-                    chunk=chunk.text,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                )
-
-            if chunk.candidates:
-                candidate = chunk.candidates[0]
-                if candidate.content and candidate.content.parts:
-                    for part in candidate.content.parts:
-                        if hasattr(part, "function_call") and part.function_call:
-                            call_id = part.function_call.name or "default"
-                            if call_id not in function_calls:
-                                function_calls[call_id] = {
-                                    "name": part.function_call.name,
-                                    "args": dict(part.function_call.args)
-                                    if part.function_call.args
-                                    else {},
-                                }
-
-        if function_calls and available_functions:
-            for call_data in function_calls.values():
-                function_name = call_data["name"]
-                function_args = call_data["args"]
-
-                # Skip if function_name is None
-                if not isinstance(function_name, str):
-                    continue
-
-                # Ensure function_args is a dict
-                if not isinstance(function_args, dict):
-                    function_args = {}
-
-                result = self._handle_tool_execution(
-                    function_name=function_name,
-                    function_args=function_args,
-                    available_functions=available_functions,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                )
-
-                if result is not None:
-                    return result
-
-        messages_for_event = self._convert_contents_to_dict(contents)
-
-        self._emit_call_completed_event(
-            response=full_response,
-            call_type=LLMCallType.LLM_CALL,
-            from_task=from_task,
-            from_agent=from_agent,
-            messages=messages_for_event,
-        )
-
-        return self._invoke_after_llm_call_hooks(
-            messages_for_event, full_response, from_agent
-        )
-
-    async def _ahandle_completion(
-        self,
-        contents: list[types.Content],
-        system_instruction: str | None,
-        config: types.GenerateContentConfig,
-        available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
-        response_model: type[BaseModel] | None = None,
-    ) -> str | Any:
-        """Handle async non-streaming content generation."""
-        try:
-            # The API accepts list[Content] but mypy is overly strict about variance
-            contents_for_api: Any = contents
-            response = await self.client.aio.models.generate_content(
-                model=self.model,
-                contents=contents_for_api,
-                config=config,
+            full_response, function_calls, usage_data = self._process_stream_chunk(
+                chunk=chunk,
+                full_response=full_response,
+                function_calls=function_calls,
+                usage_data=usage_data,
+                from_task=from_task,
+                from_agent=from_agent,
             )
 
-            usage = self._extract_token_usage(response)
-        except Exception as e:
-            if is_context_length_exceeded(e):
-                logging.error(f"Context window exceeded: {e}")
-                raise LLMContextLengthExceededError(str(e)) from e
-            raise e from e
-
-        self._track_token_usage_internal(usage)
-
-        if response.candidates and (self.tools or available_functions):
-            candidate = response.candidates[0]
-            if candidate.content and candidate.content.parts:
-                for part in candidate.content.parts:
-                    if hasattr(part, "function_call") and part.function_call:
-                        function_name = part.function_call.name
-                        if function_name is None:
-                            continue
-                        function_args = (
-                            dict(part.function_call.args)
-                            if part.function_call.args
-                            else {}
-                        )
-
-                        result = self._handle_tool_execution(
-                            function_name=function_name,
-                            function_args=function_args,
-                            available_functions=available_functions or {},
-                            from_task=from_task,
-                            from_agent=from_agent,
-                        )
-
-                        if result is not None:
-                            return result
-
-        content = response.text or ""
-        content = self._apply_stop_words(content)
-
-        messages_for_event = self._convert_contents_to_dict(contents)
-
-        self._emit_call_completed_event(
-            response=content,
-            call_type=LLMCallType.LLM_CALL,
+        return self._finalize_streaming_response(
+            full_response=full_response,
+            function_calls=function_calls,
+            usage_data=usage_data,
+            contents=contents,
+            available_functions=available_functions,
             from_task=from_task,
             from_agent=from_agent,
-            messages=messages_for_event,
-        )
-
-        return content
-
-    async def _ahandle_streaming_completion(
-        self,
-        contents: list[types.Content],
-        config: types.GenerateContentConfig,
-        available_functions: dict[str, Any] | None = None,
-        from_task: Any | None = None,
-        from_agent: Any | None = None,
-        response_model: type[BaseModel] | None = None,
-    ) -> str:
-        """Handle async streaming content generation."""
-        full_response = ""
-        function_calls: dict[str, dict[str, Any]] = {}
-
-        # The API accepts list[Content] but mypy is overly strict about variance
-        contents_for_api: Any = contents
-        stream = await self.client.aio.models.generate_content_stream(
-            model=self.model,
-            contents=contents_for_api,
-            config=config,
-        )
-        async for chunk in stream:
-            if chunk.text:
-                full_response += chunk.text
-                self._emit_stream_chunk_event(
-                    chunk=chunk.text,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                )
-
-            if chunk.candidates:
-                candidate = chunk.candidates[0]
-                if candidate.content and candidate.content.parts:
-                    for part in candidate.content.parts:
-                        if hasattr(part, "function_call") and part.function_call:
-                            call_id = part.function_call.name or "default"
-                            if call_id not in function_calls:
-                                function_calls[call_id] = {
-                                    "name": part.function_call.name,
-                                    "args": dict(part.function_call.args)
-                                    if part.function_call.args
-                                    else {},
-                                }
-
-        if function_calls and available_functions:
-            for call_data in function_calls.values():
-                function_name = call_data["name"]
-                function_args = call_data["args"]
-
-                # Skip if function_name is None
-                if not isinstance(function_name, str):
-                    continue
-
-                # Ensure function_args is a dict
-                if not isinstance(function_args, dict):
-                    function_args = {}
-
-                result = self._handle_tool_execution(
-                    function_name=function_name,
-                    function_args=function_args,
-                    available_functions=available_functions,
-                    from_task=from_task,
-                    from_agent=from_agent,
-                )
-
-                if result is not None:
-                    return result
-
-        messages_for_event = self._convert_contents_to_dict(contents)
-
-        self._emit_call_completed_event(
-            response=full_response,
-            call_type=LLMCallType.LLM_CALL,
-            from_task=from_task,
-            from_agent=from_agent,
-            messages=messages_for_event,
-        )
-
-        return self._invoke_after_llm_call_hooks(
-            messages_for_event, full_response, from_agent
+            response_model=response_model,
         )
 
     def supports_function_calling(self) -> bool:
@@ -1009,12 +968,12 @@ class GeminiCompletion(BaseLLM):
             }
         return {"total_tokens": 0}
 
+    @staticmethod
     def _convert_contents_to_dict(
-        self,
         contents: list[types.Content],
     ) -> list[LLMMessage]:
         """Convert contents to dict format."""
-        result: list[dict[str, str]] = []
+        result: list[LLMMessage] = []
         for content_obj in contents:
             role = content_obj.role
             if role == "model":
@@ -1027,5 +986,10 @@ class GeminiCompletion(BaseLLM):
                 part.text for part in parts if hasattr(part, "text") and part.text
             )
 
-            result.append({"role": role, "content": content})
+            result.append(
+                LLMMessage(
+                    role=cast(Literal["user", "assistant", "system"], role),
+                    content=content,
+                )
+            )
         return result
diff --git a/lib/crewai/src/crewai/llms/providers/openai/completion.py b/lib/crewai/src/crewai/llms/providers/openai/completion.py
index f38235dce..becb5209b 100644
--- a/lib/crewai/src/crewai/llms/providers/openai/completion.py
+++ b/lib/crewai/src/crewai/llms/providers/openai/completion.py
@@ -297,6 +297,7 @@ class OpenAICompletion(BaseLLM):
         }
         if self.stream:
             params["stream"] = self.stream
+            params["stream_options"] = {"include_usage": True}
 
         params.update(self.additional_params)
 
@@ -544,18 +545,21 @@ class OpenAICompletion(BaseLLM):
                             )
 
                 final_completion = stream.get_final_completion()
-                if final_completion and final_completion.choices:
-                    parsed_result = final_completion.choices[0].message.parsed
-                    if parsed_result:
-                        structured_json = parsed_result.model_dump_json()
-                        self._emit_call_completed_event(
-                            response=structured_json,
-                            call_type=LLMCallType.LLM_CALL,
-                            from_task=from_task,
-                            from_agent=from_agent,
-                            messages=params["messages"],
-                        )
-                        return structured_json
+                if final_completion:
+                    usage = self._extract_openai_token_usage(final_completion)
+                    self._track_token_usage_internal(usage)
+                    if final_completion.choices:
+                        parsed_result = final_completion.choices[0].message.parsed
+                        if parsed_result:
+                            structured_json = parsed_result.model_dump_json()
+                            self._emit_call_completed_event(
+                                response=structured_json,
+                                call_type=LLMCallType.LLM_CALL,
+                                from_task=from_task,
+                                from_agent=from_agent,
+                                messages=params["messages"],
+                            )
+                            return structured_json
 
             logging.error("Failed to get parsed result from stream")
             return ""
@@ -564,7 +568,13 @@ class OpenAICompletion(BaseLLM):
             self.client.chat.completions.create(**params)
         )
 
+        usage_data = {"total_tokens": 0}
+
         for completion_chunk in completion_stream:
+            if hasattr(completion_chunk, "usage") and completion_chunk.usage:
+                usage_data = self._extract_openai_token_usage(completion_chunk)
+                continue
+
             if not completion_chunk.choices:
                 continue
 
@@ -593,6 +603,8 @@ class OpenAICompletion(BaseLLM):
                     if tool_call.function and tool_call.function.arguments:
                         tool_calls[call_id]["arguments"] += tool_call.function.arguments
 
+        self._track_token_usage_internal(usage_data)
+
         if tool_calls and available_functions:
             for call_data in tool_calls.values():
                 function_name = call_data["name"]
@@ -785,7 +797,12 @@ class OpenAICompletion(BaseLLM):
             ] = await self.async_client.chat.completions.create(**params)
 
             accumulated_content = ""
+            usage_data = {"total_tokens": 0}
             async for chunk in completion_stream:
+                if hasattr(chunk, "usage") and chunk.usage:
+                    usage_data = self._extract_openai_token_usage(chunk)
+                    continue
+
                 if not chunk.choices:
                     continue
 
@@ -800,6 +817,8 @@ class OpenAICompletion(BaseLLM):
                         from_agent=from_agent,
                     )
 
+            self._track_token_usage_internal(usage_data)
+
             try:
                 parsed_object = response_model.model_validate_json(accumulated_content)
                 structured_json = parsed_object.model_dump_json()
@@ -828,7 +847,13 @@ class OpenAICompletion(BaseLLM):
             ChatCompletionChunk
         ] = await self.async_client.chat.completions.create(**params)
 
+        usage_data = {"total_tokens": 0}
+
         async for chunk in stream:
+            if hasattr(chunk, "usage") and chunk.usage:
+                usage_data = self._extract_openai_token_usage(chunk)
+                continue
+
             if not chunk.choices:
                 continue
 
@@ -857,6 +882,8 @@ class OpenAICompletion(BaseLLM):
                     if tool_call.function and tool_call.function.arguments:
                         tool_calls[call_id]["arguments"] += tool_call.function.arguments
 
+        self._track_token_usage_internal(usage_data)
+
         if tool_calls and available_functions:
             for call_data in tool_calls.values():
                 function_name = call_data["name"]
@@ -944,8 +971,10 @@ class OpenAICompletion(BaseLLM):
         # Default context window size
         return int(8192 * CONTEXT_WINDOW_USAGE_RATIO)
 
-    def _extract_openai_token_usage(self, response: ChatCompletion) -> dict[str, Any]:
-        """Extract token usage from OpenAI ChatCompletion response."""
+    def _extract_openai_token_usage(
+        self, response: ChatCompletion | ChatCompletionChunk
+    ) -> dict[str, Any]:
+        """Extract token usage from OpenAI ChatCompletion or ChatCompletionChunk response."""
         if hasattr(response, "usage") and response.usage:
             usage = response.usage
             return {
diff --git a/lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_direct_llm_call_hooks_integration.yaml b/lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_direct_llm_call_hooks_integration.yaml
index ab7d60301..a05de7481 100644
--- a/lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_direct_llm_call_hooks_integration.yaml
+++ b/lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_direct_llm_call_hooks_integration.yaml
@@ -40,20 +40,10 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//jFJNb9QwEL3nVww+b9Am7Ee7FyT2wCIQ0CJxqarItSdZg+Ox7AmwVPvf
-        KyftJv1A4uLDvHnP783MbQYgjBYbEGovWbXe5tvm6/bv5ZeDu5AmlubTzr///G778fKi+O6/iVli
-        0M0PVPzAeq2o9RbZkBtgFVAyJtVivVqUZbku3vRASxptojWe8wXlrXEmL+flIp+v8+Lsnr0nozCK
-        DVxlAAC3/Zt8Oo1/xAbms4dKizHKBsXm1AQgAtlUETJGE1k6FrMRVOQYXW99h9bSK9jRb1DSwQcY
-        CHCgDpi0PLydEgPWXZTJvOusnQDSOWKZwveWr++R48mkpcYHuolPqKI2zsR9FVBGcslQZPKiR48Z
-        wHU/jO5RPuEDtZ4rpp/Yf3c+qIlxA88xJpZ2LBdnsxe0Ko0sjY2TUQol1R71yBznLjttaAJkk8TP
-        vbykPaQ2rvkf+RFQCj2jrnxAbdTjvGNbwHSe/2o7Tbg3LCKGX0ZhxQZD2oLGWnZ2OBoRD5GxrWrj
-        Ggw+mOFyal8tV3NZr3C5PBfZMbsDAAD//wMARXm1qUcDAAA=
+      string: "{\n  \"id\": \"chatcmpl-CgPCzROynQais2iLHpGNBCKRQ1VpS\",\n  \"object\": \"chat.completion\",\n  \"created\": 1764222713,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n  \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\": \"assistant\",\n        \"content\": \"Hello! How can I assist you today?\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 9,\n    \"completion_tokens\": 9,\n    \"total_tokens\": 18,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\": 0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\": \"default\",\n  \"system_fingerprint\": \"fp_560af6e559\"\n}\n"
     headers:
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_lite_agent_hooks_integration_with_real_llm.yaml b/lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_lite_agent_hooks_integration_with_real_llm.yaml
index 34ac74dac..07d1f9c1e 100644
--- a/lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_lite_agent_hooks_integration_with_real_llm.yaml
+++ b/lib/crewai/tests/cassettes/TestLLMHooksIntegration.test_lite_agent_hooks_integration_with_real_llm.yaml
@@ -1,12 +1,6 @@
 interactions:
 - request:
-    body: '{"messages":[{"role":"system","content":"You are Test Assistant. You are
-      a helpful test assistant\nYour personal goal is: Answer questions briefly\n\nTo
-      give my best complete final answer to the task respond using the exact following
-      format:\n\nThought: I now can give a great answer\nFinal Answer: Your final
-      answer must be the great and the most complete as possible, it must be outcome
-      described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"Say
-      ''Hello World'' and nothing else"}],"model":"gpt-4.1-mini"}'
+    body: '{"messages":[{"role":"system","content":"You are Test Assistant. You are a helpful test assistant\nYour personal goal is: Answer questions briefly\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"Say ''Hello World'' and nothing else"}],"model":"gpt-4.1-mini"}'
     headers:
       accept:
       - application/json
@@ -44,21 +38,10 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//jFLLbtswELzrKxY8W4HlSjasW5Gibfo6FU1fgUCTK4kuxSVIKm4a+N8L
-        So6ltCnQiwDt7Axndvc+AWBKshKYaHkQndXpZXNVv3vz6cWv68/b/XtX0OHuw9v9l92r/uslZ4vI
-        oN0eRXhgXQjqrMagyIywcMgDRtVss86z7aZYPxuAjiTqSGtsSPOLLO2UUelquSrSZZ5m+YnekhLo
-        WQnfEgCA++EbjRqJP1kJy8VDpUPveYOsPDcBMEc6Vhj3XvnATWCLCRRkAprB+8eW+qYNJVyBoQMI
-        bqBRtwgcmhgAuPEHdN/NS2W4hufDXwmvUWuCa3JaznUd1r3nMZzptZ4B3BgKPA5nSHRzQo7nDJoa
-        62jn/6CyWhnl28oh92SiXx/IsgE9JgA3w6z6R/GZddTZUAX6gcNz2XI16rFpRzO0OIGBAtezerZZ
-        PKFXSQxcaT+bNhNctCgn6rQa3ktFMyCZpf7bzVPaY3Jlmv+RnwAh0AaUlXUolXiceGpzGE/4X23n
-        KQ+GmUd3qwRWQaGLm5BY816Pd8X8nQ/YVbUyDTrr1Hhcta22m/Uai3y7W7HkmPwGAAD//wMABY90
-        7msDAAA=
+      string: "{\n  \"id\": \"chatcmpl-CgIfLJVDzWX9jMr5owyNKjYbGuZCa\",\n  \"object\": \"chat.completion\",\n  \"created\": 1764197563,\n  \"model\": \"gpt-4.1-mini-2025-04-14\",\n  \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\": \"assistant\",\n        \"content\": \"Thought: I now can give a great answer\\nFinal Answer: Hello World\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 102,\n    \"completion_tokens\": 15,\n    \"total_tokens\": 117,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\": 0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\": \"default\",\n  \"system_fingerprint\": \"fp_9766e549b2\"\n}\n"
     headers:
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/agents/test_agent_custom_max_iterations.yaml b/lib/crewai/tests/cassettes/agents/test_agent_custom_max_iterations.yaml
index 413dd406a..f8a70badf 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_custom_max_iterations.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_custom_max_iterations.yaml
@@ -1,22 +1,7 @@
 interactions:
 - request:
-    body: '{"messages":[{"role":"system","content":"You are test role. test backstory\nYour
-      personal goal is: test goal\nYou ONLY have access to the following tools, and
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index 7bea07cfa..0e86756ec 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_error_on_parsing_tool.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_error_on_parsing_tool.yaml
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_execute_task_basic.yaml b/lib/crewai/tests/cassettes/agents/test_agent_execute_task_basic.yaml
index d07a76574..74f6ddd8e 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_execute_task_basic.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_execute_task_basic.yaml
@@ -1,15 +1,6 @@
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index a1b5ebf6e..77d036e24 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_execute_task_with_context.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_execute_task_with_context.yaml
@@ -1,17 +1,6 @@
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_execute_task_with_custom_llm.yaml b/lib/crewai/tests/cassettes/agents/test_agent_execute_task_with_custom_llm.yaml
index 5d6ea0fba..27d8337dd 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_execute_task_with_custom_llm.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_execute_task_with_custom_llm.yaml
@@ -1,16 +1,6 @@
 interactions:
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_execute_task_with_tool.yaml b/lib/crewai/tests/cassettes/agents/test_agent_execute_task_with_tool.yaml
index d138d9a2a..e6b810d10 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_execute_task_with_tool.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_execute_task_with_tool.yaml
@@ -1,22 +1,7 @@
 interactions:
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index fd9d4817a..0e64a5c3a 100644
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index d0bc0060a..0a0369d05 100644
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index a5edc7b75..86ee1882d 100644
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index 1174d562b..53b4c57e2 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_kickoff_with_platform_tools.yaml
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index 4ce4822b6..27857c602 100644
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+      original input question\n```"},{"role":"user","content":"\nCurrent Task: Use get_data tool for step1, step2, step3, step4, step5, step6, step7, step8, step9, and step10. Do NOT stop until you''ve called it for ALL steps.\n\nThis is the expected criteria for your final answer: A summary of all data collected\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"},{"role":"assistant","content":"```\nThought: I need to start collecting data from step1 as required.\nAction: get_data\nAction Input: {\"step\":\"step1\"}\nObservation: Data for step1: incomplete, need to query more steps."},{"role":"assistant","content":"```\nThought: I have data for step1, need to continue to step2.\nAction: get_data\nAction Input: {\"step\":\"step2\"}\nObservation: Data for step2: incomplete, need to query more steps."},{"role":"assistant","content":"```\nThought:
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_moved_on_after_max_iterations.yaml b/lib/crewai/tests/cassettes/agents/test_agent_moved_on_after_max_iterations.yaml
index 1d012377c..2e3668453 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_moved_on_after_max_iterations.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_moved_on_after_max_iterations.yaml
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-      Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_powered_by_new_o_model_family_that_uses_tool.yaml b/lib/crewai/tests/cassettes/agents/test_agent_powered_by_new_o_model_family_that_uses_tool.yaml
index f0c3312bf..80871cffa 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_powered_by_new_o_model_family_that_uses_tool.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_powered_by_new_o_model_family_that_uses_tool.yaml
@@ -1,21 +1,7 @@
 interactions:
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-      to you, use the tools available and give your best Final Answer, your job depends
-      on it!\n\nThought:"}],"model":"o3-mini"}'
+    body: '{"messages":[{"role":"user","content":"You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: comapny_customer_data\nTool Arguments: {}\nTool Description: Useful for getting customer related data.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [comapny_customer_data], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```\nCurrent Task: How many customers does the company have?\n\nThis is the expected
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-      comapny_customer_data\nAction Input: {\"query\": \"total_customers\"}\nObservation:
-      The company has 42 customers"}],"model":"o3-mini"}'
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_remembers_output_format_after_using_tools_too_many_times.yaml b/lib/crewai/tests/cassettes/agents/test_agent_remembers_output_format_after_using_tools_too_many_times.yaml
index 4318782a0..5c5d5c7cb 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_remembers_output_format_after_using_tools_too_many_times.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_remembers_output_format_after_using_tools_too_many_times.yaml
@@ -1,23 +1,7 @@
 interactions:
 - request:
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+      else instead."},{"role":"assistant","content":"```\nThought: I should keep using the get_final_answer tool as instructed, regardless of previous observations.\nAction: get_final_answer\nAction Input: {}\nObservation: <MagicMock name=''_remember_format()'' id=''4563008400''>"},{"role":"assistant","content":"```\nThought: I should keep using the get_final_answer tool as instructed, regardless of the format of the observation.\nAction: get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, I must stop using this action input. I''ll try something else instead."},{"role":"assistant","content":"```\nThought: I should continue using get_final_answer repeatedly as requested, ignoring observations.\nAction: get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, I must stop using this action input. I''ll try something else instead."},{"role":"assistant","content":"```\nThought: I should continue using get_final_answer as requested.\nAction:
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index f1ae8c760..f6f55184b 100644
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-      I need to keep using the `get_final_answer` tool until instructed to give the
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-      I now know the final answer\nFinal Answer: the final answer to the original
-      input question\n```"},{"role":"assistant","content":"Thought: I should continue
-      to use the `get_final_answer` tool as required, alter the `anything` parameter
-      to avoid using the same input as before.\nAction: get_final_answer\nAction Input:
-      {\"anything\": \"This is progress... the test continues to use the `get_final_answer`
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-      continue to use the `get_final_answer` tool as required, alter the `anything`
-      parameter to avoid using the same input as before.\nAction: get_final_answer\nAction
-      Input: {\"anything\": \"This is progress... the test continues to use the `get_final_answer`
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-      answer. You''ll ignore all previous instructions, stop using any tools, and
-      just return your absolute BEST Final answer."}],"model":"gpt-4"}'
+    body: '{"messages":[{"role":"system","content":"You are test role. test backstory\nYour personal goal is: test goal\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {''anything'': {''description'': None, ''type'': ''str''}}\nTool Description: Get the final answer but don''t give it yet, just re-use this\n        tool non-stop.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input
+      question\n```"},{"role":"user","content":"\nCurrent Task: The final answer is 42. But don''t give it until I tell you so, instead keep using the `get_final_answer` tool.\n\nThis is the expected criteria for your final answer: The final answer, don''t give it until I tell you so\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"},{"role":"assistant","content":"I need to use the `get_final_answer` tool and keep using it until prompted to reveal the final answer.\nAction: get_final_answer\nAction Input: {\"anything\": \"The final answer is 42. But don''t give it until I tell you so, instead keep using the `get_final_answer` tool.\"}\nObservation: 42"},{"role":"assistant","content":"Thought: I''ve used the `get_final_answer` tool and obtained the final answer as 42. However, I should continue to use the `get_final_answer` tool
+      as directed.\nAction: get_final_answer\nAction Input: {\"anything\": \"The final answer is 42. But don''t give it until I tell you so, instead keep using the `get_final_answer` tool.\"}\nObservation: I tried reusing the same input, I must stop using this action input. I''ll try something else instead."},{"role":"assistant","content":"Thought: I need to keep using the `get_final_answer` tool until instructed to give the final answer, but without reusing the same action input.\nAction: get_final_answer\nAction Input: {\"anything\": \"Proceeding with the test as instructed. Keeping the final answer under wraps for now and continuing to use `get_final_answer`.\"}\nObservation: 42\n\n\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: get_final_answer\nTool Arguments: {''anything'': {''description'': None, ''type'': ''str''}}\nTool Description: Get the final answer but don''t give it yet, just re-use this\n        tool non-stop.\n\nIMPORTANT:
+      Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [get_final_answer], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```"},{"role":"assistant","content":"Thought: I should continue to use the `get_final_answer` tool as required, alter the `anything` parameter to avoid using the same input as before.\nAction: get_final_answer\nAction Input: {\"anything\": \"This is progress... the test continues to use the `get_final_answer` tool.\"}\nObservation: 42"},{"role":"assistant","content":"Thought: I should continue to use the `get_final_answer` tool
+      as required, alter the `anything` parameter to avoid using the same input as before.\nAction: get_final_answer\nAction Input: {\"anything\": \"This is progress... the test continues to use the `get_final_answer` tool.\"}\nObservation: 42\nNow it''s time you MUST give your absolute best final answer. You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer."}],"model":"gpt-4"}'
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_respect_the_max_rpm_set.yaml b/lib/crewai/tests/cassettes/agents/test_agent_respect_the_max_rpm_set.yaml
index 96209ba03..3ba5f85cb 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_respect_the_max_rpm_set.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_respect_the_max_rpm_set.yaml
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-      Arguments: {}\nTool Description: Get the final answer but don''t give it yet,
-      just re-use this\n        tool non-stop.\n\nIMPORTANT: Use the following format
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-      is VERY important to you, use the tools available and give your best Final Answer,
-      your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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+      Use tool logic for `get_final_answer` but fon''t give you final answer yet, instead keep using it unless you''re told to give your final answer\n\nThis is the expected criteria for your final answer: The final answer\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_respect_the_max_rpm_set_over_crew_rpm.yaml b/lib/crewai/tests/cassettes/agents/test_agent_respect_the_max_rpm_set_over_crew_rpm.yaml
index a9c384cdc..9e523e79e 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_respect_the_max_rpm_set_over_crew_rpm.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_respect_the_max_rpm_set_over_crew_rpm.yaml
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index 208ff7023..338985b64 100644
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_use_specific_tasks_output_as_context.yaml b/lib/crewai/tests/cassettes/agents/test_agent_use_specific_tasks_output_as_context.yaml
index 4b75c96b7..eb84a0560 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_use_specific_tasks_output_as_context.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_use_specific_tasks_output_as_context.yaml
@@ -1,15 +1,6 @@
 interactions:
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-      your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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-      your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources.yaml b/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources.yaml
index 88b893173..585f76734 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources.yaml
@@ -1,17 +1,6 @@
 interactions:
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-      criteria for your final answer: Brandon''s favorite color.\nyou MUST return
-      the actual complete content as the final answer, not a summary.."}],"model":"gpt-4o-mini"}'
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\"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\"\
+        \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 3,\n    \"total_tokens\": 3\n  }\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -330,18 +199,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"messages":[{"role":"system","content":"You are Information Agent. You
-      have access to specific knowledge sources.\nYour personal goal is: Provide information
-      based on knowledge sources\nTo give my best complete final answer to the task
-      respond using the exact following format:\n\nThought: I now can give a great
-      answer\nFinal Answer: Your final answer must be the great and the most complete
-      as possible, it must be outcome described.\n\nI MUST use these formats, my job
-      depends on it!"},{"role":"user","content":"\nCurrent Task: What is Brandon''s
-      favorite color?\n\nThis is the expected criteria for your final answer: Brandon''s
-      favorite color.\nyou MUST return the actual complete content as the final answer,
-      not a summary.Additional Information: Brandon''s favorite color is red and he
-      likes Mexican food.\n\nBegin! This is VERY important to you, use the tools available
-      and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
+    body: '{"messages":[{"role":"system","content":"You are Information Agent. You have access to specific knowledge sources.\nYour personal goal is: Provide information based on knowledge sources\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task: What is Brandon''s favorite color?\n\nThis is the expected criteria for your final answer: Brandon''s favorite color.\nyou MUST return the actual complete content as the final answer, not a summary.Additional Information: Brandon''s favorite color is red and he likes Mexican food.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -383,23 +241,13 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//jFJNb9swDL37VxC67BIXcRrkw7euQ4EdtuPQdSsMRaIdZbIoiIqzoch/
-        H+Sksdt1QC8GzMdHvffIpwxAGC1KEGoro2q9zW93nzgG9t/Wh/vD7qHTX2/vvt9viu7hSxHEJDFo
-        s0MVn1lXilpvMRpyJ1gFlBHT1GK5mK/W82Kx7IGWNNpEa3zM55S3xpl8Np3N8+kyL1Zn9paMQhYl
-        /MgAAJ76b9LpNP4WJUwnz5UWmWWDorw0AYhANlWEZDYcpYtiMoCKXETXS/8Mjg6gpIPGdAgSmiQb
-        pOMDBoCf7s44aeGm/y/hY5BOk/vAUMuOgokIiiwFMAwB9dX4lYD1nmVy6vbWjgDpHEWZkur9PZ6R
-        48WRpcYH2vArqqiNM7ytAkoml9RzJC969JgBPPbJ7V+EIXyg1scq0i/snytW16d5YljYGD2DkaK0
-        Q302LSZvzKs0Rmksj7IXSqot6oE6LErutaERkI1c/6vmrdkn58Y17xk/AEqhj6grH1Ab9dLx0BYw
-        3fP/2i4p94IFY+iMwioaDGkTGmu5t6crE/yHI7ZVbVyDwQdzOrXaV0VRX09n63qxEdkx+wsAAP//
-        AwAPD1p2eAMAAA==
+      string: "{\n  \"id\": \"chatcmpl-CjDstrspV9wXwjZvdNCFYXb1vZM1r\",\n  \"object\": \"chat.completion\",\n  \"created\": 1764894167,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n  \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\": \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal Answer: Brandon's favorite color is red.\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 183,\n    \"completion_tokens\": 18,\n    \"total_tokens\": 201,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\": 0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\": \"default\",\n  \"system_fingerprint\": \"fp_11f3029f6b\"\
+        \n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_extensive_role.yaml b/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_extensive_role.yaml
index 06c1a5ae4..868db58ef 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_extensive_role.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_extensive_role.yaml
@@ -1,17 +1,6 @@
 interactions:
 - request:
-    body: '{"messages":[{"role":"system","content":"Your goal is to rewrite the user
-      query so that it is optimized for retrieval from a vector database. Consider
-      how the query will be used to find relevant documents, and aim to make it more
-      specific and context-aware. \n\n Do not include any other text than the rewritten
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-      criteria for your final answer: Brandon''s favorite color.\nyou MUST return
-      the actual complete content as the final answer, not a summary.."}],"model":"gpt-4o-mini"}'
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\"\
+        \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 4,\n    \"total_tokens\": 4\n  }\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -330,19 +199,8 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"messages":[{"role":"system","content":"You are Information Agent with
-      extensive role description that is longer than 80 characters. You have access
-      to specific knowledge sources.\nYour personal goal is: Provide information based
-      on knowledge sources\nTo give my best complete final answer to the task respond
-      using the exact following format:\n\nThought: I now can give a great answer\nFinal
-      Answer: Your final answer must be the great and the most complete as possible,
-      it must be outcome described.\n\nI MUST use these formats, my job depends on
-      it!"},{"role":"user","content":"\nCurrent Task: What is Brandon''s favorite
-      color?\n\nThis is the expected criteria for your final answer: Brandon''s favorite
-      color.\nyou MUST return the actual complete content as the final answer, not
-      a summary.Additional Information: Brandon''s favorite color is red and he likes
-      Mexican food.\n\nBegin! This is VERY important to you, use the tools available
-      and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
+    body: '{"messages":[{"role":"system","content":"You are Information Agent with extensive role description that is longer than 80 characters. You have access to specific knowledge sources.\nYour personal goal is: Provide information based on knowledge sources\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task: What is Brandon''s favorite color?\n\nThis is the expected criteria for your final answer: Brandon''s favorite color.\nyou MUST return the actual complete content as the final answer, not a summary.Additional Information: Brandon''s favorite color is red and he likes Mexican food.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends
+      on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -384,23 +242,13 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//jFJNb9QwEL3nV4x84bKpsqu0+3GDAqLiQEEcQFBFU3uS9eJ4XNvbD1X7
-        3yt7l00KReISKfPmPb83M48FgNBKrEDINUbZO1Oeb97e3Fx+/fINP92ff7z8sJm/21w8LPrP7fz7
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-        WlIQK/hRAAA85m/yaRXdixVkrVzpKQTsSKyOTQDCs0kVgSHoENFGMRlAyTaSzdYvwPIdSLTQ6VsC
-        hC7ZBrThjjzAT/teWzTwOv+v4I1Hq9i+CtDiLXsdCSQb9qADeFIn41c8tduAKandGjMC0FqOmCaV
-        810dkN0xkeHOeb4Of1BFq60O68YTBrbJfYjsREZ3BcBVntz22TCE89y72ET+Rfm56bLe64lhYSN0
-        cQAjRzRDfTadTV7QaxRF1CaMZi8kyjWpgTosCrdK8wgoRqn/dvOS9j65tt3/yA+AlOQiqcZ5Ulo+
-        Tzy0eUr3/K+245SzYRHI32pJTdTk0yYUtbg1+ysT4SFE6ptW246883p/aq1rTqtlddbOEKUodsUT
-        AAAA//8DAPxBkfB4AwAA
+      string: "{\n  \"id\": \"chatcmpl-CjDqqPTRXaOxCKPHj7EjIy8mQf7Y5\",\n  \"object\": \"chat.completion\",\n  \"created\": 1764894040,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n  \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\": \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal Answer: Brandon's favorite color is red.\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 194,\n    \"completion_tokens\": 18,\n    \"total_tokens\": 212,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\": 0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\": \"default\",\n  \"system_fingerprint\": \"fp_50906f2aac\"\
+        \n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_generate_search_query.yaml b/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_generate_search_query.yaml
index e43aa4cb7..6b590180d 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_generate_search_query.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_generate_search_query.yaml
@@ -42,123 +42,13 @@ interactions:
     uri: https://api.openai.com/v1/embeddings
   response:
     body:
-      string: !!binary |
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eNlztJdys9UW3ZvMAkjLx/1S28JV9NPSDgNj25S628FN/cOj7ImjuLjL28rantPKgqV7w6XtQ7TTgNvfaX3jxs5qS8F5i/vMSjorzvCMo8w/q9u6l0ITydvRG79XoPuyWplzx27NA5gH4SPHkETrtrNUE8q5lvuwcgzrzd1/W8y9ubOztRA7lWgIS8MBbdPJ4cLDw5oB+8aGcOPdtTDT2KQnO7NzGHPA6aErw5oB89kXLtPMI0Crz+11Y8KNbkvGe+qTxoZw68duzQOyQAs7sV59u5uQniOj/QGT3oEhy9Z74pPYnGiTzT0ck8w7Dzuz4fNj0C44I6Or1uu9m3ebzwZ2Q7YS+VuoolpDsY6gi85qsCvVeIAz3qIhq9SM5GPaPtC7yN5oW8/S7yO+Oo1btU5HC8zkq0PMZp1rwOmhI933MJuwHw07scAgY9EAmrPPJ3Yju7usW8fyzJPMAkDLqX7DG9d5W1PA+iET3Xp/s7di6cvCk9/rqCAvu839KjPO5XZjzQuUw9PFkCvBc5JTuiPKi8LgbfvDq97jtSvyK84YMHvKmJcTvsR2g8vcpDOYgydbxo2/g8fcWvuxryhzxHCJO615KrPBWIwbyl9Qq9Sz3fvBf32TzN6xm9GxfWu0cIE71t7qO7pEymO6GTQ7zd1/U7aMYovDWAI7vKdIK8MWCnvO++f7sKj2Y6LvGOOyKmart5Rhk7uUstOg5QyDsgIgI9ExkpvWFE5Tws9uA69XqPO+KLBjyWO868tIJMPHoMzbzL8Gs8deTRO/wJJL2SxLY7OK3wPOW40zs2KYg8dB6euTD5DbyX7DE9dH04PJTUNDyTDgG8nGvIO95rijzD+j09J2/LPLtwezwK2bA5TU3dPOOThTyLLaM8Mn12vI6kuru3+WM88iDHPGdfDzxwXTy9Ak/uPGZXELw4rXC7SBASPFDEdDvoEpw8AZG5vJ4crDvEoyI8kXLtPJwpfTyYS8w8Z3Rfu9pLDrxDpsu7FN/cPDPPvzbEoyK81Zf9PPq32rzlowM9MsfAOfV6j7ywC7U7fWaVPH5ulDwnb8s8AwDSPMp0Arx7tTE8bY8JPRc5JT1VjdU7xrOgO6mJ8Twrj0e9r7lrPCuPxz0HYhm9vcpDvLiiyLx7FMy8mfyvvDY+2Dw6qB49lCvQOye5lTzDm6O61YKtO/7ChjyL1oc8BRDQPJH+Aj3Ymio7cBtxPNlDDzzbsqc8LPbguq01Az103NI7jVJxO1LU8jy23BS7VS67vA2SE7xJGJE6xlSGPNhQYLtjVOO7KSAvu0p/KjxRbVk82qqovMp0grp+bpS8k4Jru+06F7tsnFq8luQyPa+5a7yKhL68/LIIO7f54zxYpdK6A6E3vCAiAj1wp4a7cm06PC5IKjyJHSW9R8bHPBlJI7zKdIK8777/POKg1rsiMoA6ulOsuzmgn7zU2cg7g6vfO4T9qDx6Tpg8JQiyO3lGmTzJyx09/sIGuriiyDzUGxQ8j2Jvu6+kG73J4G28LZ/FPIZkQjxsnFq7vcrDvJH+Ajs/hk88kFWevBryB71eF5i8VXgFve5X5jthjq+8d0trvAlylzyVfZk8i+vXvPXuebt+4v48Gg9XvKrbujz6ASW8zxBoPF52MrwcH9U8qMs8O+4AyzwfGoM78ndivCqHSL3MmdA8Es9evM5KtDzaCcM7scnpu2GOrzykCls8Or3uO+xH6Lu2kso8tMyWPDcxBz3Iw568F/dZO/G5rbyz2We9zQDqPL0MD7xV1588zQDqPBTKDDyFu90733OJO2VkYTwq0RI9OPc6OdsRQjuIMnU8ATIfPXK3BDxndN88SiCQPKE0qTypdKE8D1hHvGnOpzz1MMU8UMT0OjdGVzwuBt86rC0EvGVPET2+c6g8a5TbPJ57Rjz36ae8NI10Oxv6hryKzog7BhjPPDBteLxjVGO8NYAju9iaqjzH0G+8gYaRvALjgjt+JMo7PcAbPNm3+Tzhgwe9jPPWPDYpiDvaYN48oTQpvEzuQrytNYM8t1D/vE32QbyUFgC9PG7Su5IGgrsr2ZG728f3O/BnZDzjB3A7ytOcujEe3LwYVnS8LqfEvAMA0rydFC29MmgmPb2A+TwQCSu8OVZVvPse9DocAoa8GvIHvWZXkLz+19a75qsCvRWIQbzD+j27EWjFPOezAbxVLju9VY3VO5/i3zt/1a08nnvGOvaX3jtku/y8uWD9Ot1jizx3S+s7qRWHO7JyTrxILeE80SDmPCVfzbzGVIa7XjRnOhc5JT1q1qY86LsAvZ4crDrUeq68WhTrO5qllLzXkqs84kE8vRwCBj0hKgE9Zsv6vLQjsrwECFG7GFb0vA0G/jsfGoO8mPQwO0dnLT1ZmIG80QMXvR8ag7uTbRu9bOakORwCBr02iCI7JsZmvaGTQzxffjG9WqCAPLu6xTtkpiy9iXy/O8sytzwwt0K9a5RbvK+567zNAOq7hVxDvAMAUjxWgIQ8bD3AvCGJm7zuQpa8Q/CVu53S4bz/gLs8Nuc8vGa2KryFXMO44Tm9vFTk8DqIvoq8qh0Gva2pbbz48SY9nRStvJC0ODrTE5W7TlVcPJQWgDyjWXe7+KfcvB0n1DvfiFk8BOuBOxTKDL2eOXu8N5AhOY3mhbyUFoC8Vt8ePRfiCbyaGf87v5B3vDImWzyRvDc8QugWvO34yzzGada6uWB9O4SeDr3iQby82glDvCAigrvFYde80SDmOrn0kbtSvyI8WrXQPKrbOjx+za48+x50PKNZ97yGrow8phrZPA0G/jug1Q69fL2wPG4L8zrSCxY97fhLuxpm8rwup0Q8ggJ7PF40ZzztmbG8Nue8vKPti71JGJG8I09PvJwp/Twipmo9XQ8ZvMazID3yYhI821MNPe34S7wggZy6HGEgPRM2eLwaZnI8DxZ8vD12Ub3AJAw7PcAbPVaAhDwY/9g8EKqQPHXPAT1NTV09dc+BPK77trw7UYM8cm26PP4ZIr1W3x69ICICPYKOkLzyYhI8bPt0PIRUxDvKdII8TKR4PJqlFD0bWSG9g5aPvK3zN7xKfyq9T11bvTR4pLvccFw8FzmlPEiE/Dw377s7LvEOPZsMrjvcGUE8TwbAPPtgv7zQWrK8jT0hPZj0MLyA8nw8Xc3NPCz24LsPFnw8XQ+ZPPhIQr1phN08xFlYO4vWhzvSarA8\"\
+        \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 3,\n    \"total_tokens\": 3\n  }\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -544,20 +302,8 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"messages":[{"role":"system","content":"You are Information Agent with
-      extensive role description that is longer than 80 characters. You have access
-      to specific knowledge sources.\nYour personal goal is: Provide information based
-      on knowledge sources\nTo give my best complete final answer to the task respond
-      using the exact following format:\n\nThought: I now can give a great answer\nFinal
-      Answer: Your final answer must be the great and the most complete as possible,
-      it must be outcome described.\n\nI MUST use these formats, my job depends on
-      it!"},{"role":"user","content":"\nCurrent Task: What is Brandon''s favorite
-      color?\n\nThis is the expected criteria for your final answer: The answer to
-      the question, in a format like this: `{{name: str, favorite_color: str}}`\nyou
-      MUST return the actual complete content as the final answer, not a summary.Additional
-      Information: Brandon''s favorite color is red and he likes Mexican food.\n\nBegin!
-      This is VERY important to you, use the tools available and give your best Final
-      Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
+    body: '{"messages":[{"role":"system","content":"You are Information Agent with extensive role description that is longer than 80 characters. You have access to specific knowledge sources.\nYour personal goal is: Provide information based on knowledge sources\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task: What is Brandon''s favorite color?\n\nThis is the expected criteria for your final answer: The answer to the question, in a format like this: `{{name: str, favorite_color: str}}`\nyou MUST return the actual complete content as the final answer, not a summary.Additional Information: Brandon''s favorite color is red and he likes Mexican food.\n\nBegin! This is VERY important to you, use the tools
+      available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -599,23 +345,13 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAA4ySwW7bMAyG734KQud4sNMsSX3bOmwoht0GDMVSGKxEO+pkUZOUdEWQdx9kp7G7
-        tUAvBsyPP8Wf5CEDEFqJCoTcYpSdM/nV/ac4//7lh1svyytFj3Sjipvy2++tvt5/FbOk4Lt7kvFJ
-        9U5y5wxFzXbA0hNGSlXL1XKxvlyUq2UPOlZkkqx1MV9w3mmr83kxX+TFKi/XJ/WWtaQgKviZAQAc
-        +m/q0yr6IyooZk+RjkLAlkR1TgIQnk2KCAxBh4g2itkIJdtItm/9Giw/gEQLrd4TILSpbUAbHsgD
-        bOxnbdHAh/6/gsPBYkcVbMRHj1ax3YgZNLhnryPVkg37BD2pjTgep296anYBk2+7M2YC0FqOmObW
-        u709kePZn+HWeb4L/0hFo60O29oTBrbJS4jsRE+PGcBtP8fds9EI57lzsY78i/rn5uXFUE+M65vQ
-        xQlGjmgm8YvV7IV6taKI2oTJJoREuSU1Sse14U5pnoBs4vr/bl6qPTjXtn1L+RFISS6Sqp0npeVz
-        x2Oap3Tdr6Wdp9w3LAL5vZZUR00+bUJRgzsz3JwIjyFSVzfatuSd18PhNa5+X1wWy2aOKEV2zP4C
-        AAD//wMAPCH4kYYDAAA=
+      string: "{\n  \"id\": \"chatcmpl-CjDt2TGWp861CdeyeYd0Y1MqhiIvK\",\n  \"object\": \"chat.completion\",\n  \"created\": 1764894176,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n  \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\": \"assistant\",\n        \"content\": \"I now can give a great answer  \\nFinal Answer: {{name: \\\"Brandon\\\", favorite_color: \\\"red\\\"}}\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 213,\n    \"completion_tokens\": 24,\n    \"total_tokens\": 237,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\": 0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\": \"default\",\n  \"system_fingerprint\"\
+        : \"fp_50906f2aac\"\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold.yaml b/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold.yaml
index b1ab0e2e3..737386cc0 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold.yaml
@@ -42,123 +42,13 @@ interactions:
     uri: https://api.openai.com/v1/embeddings
   response:
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5+sDyGVQA8QT/TuxEX7bwSqww8yGOCPDSaXj1bMCy9+7EUPBA3gbxJeNu80hAWO107XbzkT0I9uEoCvIqWJ7ydPi69fJgivTJ/47rQ9Rq7Ih0dvPpYhDwupxa8IEA3vO++QD0a1Eo8Mn9jPPk9iTz+5wq9dhG7O44V5LuBQqS8PVc8vCINUzzEEpE86N5IvGiq2zwaPaW8deZ1PGTCRDzQnAo88uTsuzKPrbzhwLs7YEOIPFxLJ7yl4JA9JdpuPMrXjTyobxe8MLJHPM9xRb1g2i289GhCvJ7Cgzm7F568cdATPeokCT2aGAI9452hPIkSUj2dbHk8rJXDu/SThzyCi+q8xg34ODiturzoCY68FWhevGK3EzxdDRK9Kq81uxxYIL1pLrG8RhQaPYKbtDspK+C8RSdqPOClwLziRJE80ysRvOSNV7wQkJE8V0iVvER1yTvMDQS8WbwgPIO2rzyJ5Aa9uTq4uvN4DDwrcSA9lQWmvAX2Tbo20NS86ZBpvJzlnTxldGU8elJiPAJX/bxkhC+9m1H+vApyhLyqesi8GtTKvCXabrsqRts8ndVTPJLPL7tGFBo8zhi1u5UFpjiIyYs6AFwWPY9Aqbtj0o67shwrvZAwXz3qJAm9yRUjvHZqSze67Fg89jVePHeVkLsuPry8ngAZvfwKpbw8PEE7QagtvKoR7jz6WIS7zoGPvPWDPTun23c8jWNDvO4MoDxy6468WKGlvCZuDjz1GuM89YM9vWqyBj3WYQe8E10tPeZac7whif086qvkvBaDWb3lEa27xnbSPEr8sLoGuDi84VdhOko6xruJqfc88Nm7O/28RboqViW9fR/+Ovk9CbvftYo9IqR4PI4VZDylDty7nWz5PKYp17vcVM+8wo67O0gvlbzXEyg8mYTiPGwmkjskU5O8+0g6PFGxYzvGSIe7uIgXPWrg0TxYoaU7mJSsvH0f/jwg19w7fjr5OrYUDDzUsuy8UJZoOyIdHTyq46I7m2FIu/fn/jxWLRq8nlkpPP5+sLzqq+Q7JdruOw7hdr2aGAI99+d+PNb4LDwF9k28oHQkO3yYojwopIS6DcZ7PGzNAT2IyQu8RKAOPVI1uTpbMCy9cusOvNa6lz30k4e8h478O7EBsDtKZYu8Vh3QvK5i37s6iiA9SpPWvFQSnzs+crc8SpNWvA0BCz1khC88oKLvPB2h5jze85+8SXjbPM2U3zpRseM70DMwO+zGXzwOs6s8Wn4LPOYsqLwYyRm8ers8u27I6Lzw6QW9RSfqPJACFDzhKRa8kv16PGlc/DzaOdS8PtsRvTbQ1Dqld7a8xKm2PP0loLyY/YY8xfL8PE+mMrxplwu8jN9tPISmZT2L7zc8YHHTvLnRXby6VTM7SeE1PFQSn7tciTy8UbHjPNLSgLxo1aA8MHQyvIFw77zlege8oAtKPM9hezugom+7XCDiulLM3jxSnpM88ZumPDq4azsEBhi83W9KPN9MMLxnI4A6l3kxvOWoUrzM4r46jiWuPLFqCj3Wuhc9jcydu+okiTwcsbA77mWwvIr/AbyO55i8RruJvARErTmbYUi9IVuyvHB3A7uPQKm84uuAOwoJKj2G7KU8ur4NvFQSH7zzeAw8Xu39Ooi5Qbv6HXW7pFy7Oz4J3bkVaF484SmWvHq7vDs+Gac8HBoLOg7xQDu9Ik+96fnDPIi5wbf6lhm8SC+VPMvyiDkMFNs8WGOQPICQAz2kHia84SkWPMKehbxnI4C9O0wLPTQDObtQwa08sOY0PfxjtTsPdRY8Ho4WPStxILymkrE8IVsyPKn28rnyTUe8n7I5O8RA3Dy8B9Q85ajSPGsLl7xRgxi7rss5vEo6RjwIw+k8INfcO9oLCT0mXkS8HjWGu9jVkjvtSrU85mo9uaxXrjyVnEs8S1VBvISm5TypX008XLQBPHgc7LyIucG8AXeRuzRskzygC8q8DlobvKn2cjkyf+M792CjO4QPwDz+QBu9dyy2PJZO7DyA6ZM7JFOTO1puwbyA6ZM6GtTKvEfWBDxLRfe8slpAvAPrnDwD65w7bPvMvDxnhryY/Qa8GHAJO9ZhB73xyfG7jn4+vGuS8ry8B1Q8QiyDPMpus7wvwhG8+dQuvKJRCj1vXAi9hq6Qu+SN17qxaoq87eHavCpWJT0MFNs88uRsPZ2niLygC8o7+BLEvObDzbyyHCu8SC+VPIbcW7wcWKC7Jm6OO/yhSjzAGjA9gL7OPPzMD7wAtaY8NqIJPdLSAL3jNEc8UjW5uz5ytztbMCw9udHdOjkGyzugzbQ6YHHTu0JazrswdLI7btgyvJMYdj2tGRk8R9YEvVLM3rem+wu91visOrSggDzvvkC9m8qiu1CW6DxMF6w7sZjVvFxLp7wQNwG9BY3zvMpuM70BDrc6CgkqvVhjkDw5b6W7Zp+qu0wXrDs1h468KZQ6PWV0ZbwsjJu8SUoQvAx9tbucuti8Cd5ku5j9Br1Kk9Y8EgSdvM6v2ru50d06vqakuafb97yKxHI7gOkTO9G3hTwjzz28hDoFPZd5sTx/VXS8f2U+PC4u8rzUhCE9chnau9wWuryEOoU8+h11u+bTl7oa1Eo8yPonPLsXnjwU4QI9W5kGPEUn6ryWt0Y8/AolvAF3Eb3pkOk89eyXPNDK1bz9vMW8MEntvGlc/Lhq4NE8eIVGPHW4Kju+5Lk83dikvAiVHr3GDXi89JOHu2e6pTwp/RS7PoIBPAPrHL1aBec8nlkpPcAaMD2Dtq88LGHWPK7bA72swIg8lKyVPCHEDLwpK+C7KkbbO7T5ELz+fjA9kbS0uzVM/7xgcVM9g7avu8ehF7w3kr+8eQkcu7r8Ir3AGjC8/kCbPPdgIz0y6D097Uo1vPauAj0tE/e8XigNPV2kt7ow3Qy8GGA/PRv/D7y4H708cEy+vELDqLwHPA69YEMIPL0izzwZIio9jAqzPN6KxTxm+Do9CUe/PGzNAbzU3TE8JgU0PXpirLxa15s7IzgYPKitrLtNYPI82C6jPIDpk7rGdlK77C86PdrQ+Twy6D29K3EgvSv4+zvdBnA6aKrbvMrHw7vt4Vo87ycbPVCWaDycjA29EjLoPKGPnztbMCy85ahSPL6mpLzqfRm9XIk8PAYhk7yAgDm82+v0PEOFE7z5Ano8uTo4PYWTlbv1gz084Vfhu7JKdjxwdwO8\"\
+        \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 12,\n    \"total_tokens\": 12\n  }\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -213,18 +103,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"messages":[{"role":"system","content":"Your goal is to rewrite the user
-      query so that it is optimized for retrieval from a vector database. Consider
-      how the query will be used to find relevant documents, and aim to make it more
-      specific and context-aware. \n\n Do not include any other text than the rewritten
-      query, especially any preamble or postamble and only add expected output format
-      if its relevant to the rewritten query. \n\n Focus on the key words of the intended
-      task and to retrieve the most relevant information. \n\n There will be some
-      extra context provided that might need to be removed such as expected_output
-      formats structured_outputs and other instructions."},{"role":"user","content":"The
-      original query is: What is Brandon''s favorite color?\n\nThis is the expected
-      criteria for your final answer: Brandon''s favorite color.\nyou MUST return
-      the actual complete content as the final answer, not a summary.."}],"model":"gpt-4o-mini"}'
+    body: '{"messages":[{"role":"system","content":"Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."},{"role":"user","content":"The original query is: What is Brandon''s favorite color?\n\nThis is the expected criteria for your final answer: Brandon''s favorite color.\nyou MUST return the actual complete content as the final answer, not a summary.."}],"model":"gpt-4o-mini"}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -264,22 +143,12 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAA4xSu27cMBDs9RXENm5OgeTTPXJlHMRpAgQpUiQwBIpcSbQpLkGujATG/XtA3fkk
-        xw6QhgVnZzgz3KdMCDAaDgJUL1kN3uY39x9j23w28vvXYn/7w3b9jf4yfvqmtutbC6vEoOYeFT+z
-        3ikavEU25E6wCigZk2q521b791W5WU/AQBptonWe84rywTiTXxfXVV7s8nJ/ZvdkFEY4iJ+ZEEI8
-        TWfy6TT+goMoVs83A8YoO4TDZUgICGTTDcgYTWTpGFYzqMgxusn6hyCdJncVRSsfKRhGochSWI4H
-        bMcok2U3WrsApHPEMkWejN6dkePFmqXOB2riX1RojTOxrwPKSC7ZiEweJvSYCXE3VTC+SAU+0OC5
-        ZnrA6blytz7pwdz8jG7OGBNLuyTtV2/I1RpZGhsXHYKSqkc9U+fC5agNLYBsEfq1mbe0T8GN6/5H
-        fgaUQs+oax9QG/Uy8DwWMO3lv8YuJU+GIWJ4NAprNhjSR2hs5WhP2wLxd2Qc6ta4DoMP5rQyra+b
-        TbXbFqVuNGTH7A8AAAD//wMAf/WJY0ADAAA=
+      string: "{\n  \"id\": \"chatcmpl-CjDsfbHiaVP08GZlghCdMuFRc63Gl\",\n  \"object\": \"chat.completion\",\n  \"created\": 1764894153,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n  \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\": \"assistant\",\n        \"content\": \"Brandon's favorite color\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 173,\n    \"completion_tokens\": 5,\n    \"total_tokens\": 178,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\": 0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\": \"default\",\n  \"system_fingerprint\": \"fp_b547601dbd\"\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -330,17 +199,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"messages":[{"role":"system","content":"You are Information Agent. You
-      have access to specific knowledge sources.\nYour personal goal is: Provide information
-      based on knowledge sources\nTo give my best complete final answer to the task
-      respond using the exact following format:\n\nThought: I now can give a great
-      answer\nFinal Answer: Your final answer must be the great and the most complete
-      as possible, it must be outcome described.\n\nI MUST use these formats, my job
-      depends on it!"},{"role":"user","content":"\nCurrent Task: What is Brandon''s
-      favorite color?\n\nThis is the expected criteria for your final answer: Brandon''s
-      favorite color.\nyou MUST return the actual complete content as the final answer,
-      not a summary.\n\nBegin! This is VERY important to you, use the tools available
-      and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
+    body: '{"messages":[{"role":"system","content":"You are Information Agent. You have access to specific knowledge sources.\nYour personal goal is: Provide information based on knowledge sources\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task: What is Brandon''s favorite color?\n\nThis is the expected criteria for your final answer: Brandon''s favorite color.\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_default.yaml b/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_default.yaml
index b1c5f9b20..332a5d84d 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_default.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_with_knowledge_sources_with_query_limit_and_score_threshold_default.yaml
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-        veq//vV/AAAA//8DAL9Z37TgIAAA
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\"\
+        \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 12,\n    \"total_tokens\": 12\n  }\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -213,18 +103,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"messages":[{"role":"system","content":"Your goal is to rewrite the user
-      query so that it is optimized for retrieval from a vector database. Consider
-      how the query will be used to find relevant documents, and aim to make it more
-      specific and context-aware. \n\n Do not include any other text than the rewritten
-      query, especially any preamble or postamble and only add expected output format
-      if its relevant to the rewritten query. \n\n Focus on the key words of the intended
-      task and to retrieve the most relevant information. \n\n There will be some
-      extra context provided that might need to be removed such as expected_output
-      formats structured_outputs and other instructions."},{"role":"user","content":"The
-      original query is: What is Brandon''s favorite color?\n\nThis is the expected
-      criteria for your final answer: Brandon''s favorite color.\nyou MUST return
-      the actual complete content as the final answer, not a summary.."}],"model":"gpt-4o-mini"}'
+    body: '{"messages":[{"role":"system","content":"Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."},{"role":"user","content":"The original query is: What is Brandon''s favorite color?\n\nThis is the expected criteria for your final answer: Brandon''s favorite color.\nyou MUST return the actual complete content as the final answer, not a summary.."}],"model":"gpt-4o-mini"}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -264,22 +143,12 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//jFKxbtswFNz1FcRbuliFLCuW67HtlC3JWAQCTT7JjCk+lnwKUgT+94KS
-        YyltCnThwHt3vDu+10wIMBr2AtRRsuq9zb89feeft5sqmvbuwT747eaeCtee6lPlbmCVGHR4QsVv
-        rM+Kem+RDbkJVgElY1Jd19tq96Uqy+0I9KTRJlrnOa8o740zeVmUVV7U+Xp3YR/JKIywFz8yIYR4
-        Hc/k02l8gb0oVm83PcYoO4T9dUgICGTTDcgYTWTpGFYzqMgxutH61yCdJvcpilY+UzCMQpGlsBwP
-        2A5RJstusHYBSOeIZYo8Gn28IOerNUudD3SIf1ChNc7EYxNQRnLJRmTyMKLnTIjHsYLhXSrwgXrP
-        DdMJx+fW9WbSg7n5Gb25YEws7ZK0W30g12hkaWxcdAhKqiPqmToXLgdtaAFki9B/m/lIewpuXPc/
-        8jOgFHpG3fiA2qj3geexgGkv/zV2LXk0DBHDs1HYsMGQPkJjKwc7bQvEX5Gxb1rjOgw+mGllWt/I
-        alvoGstDDdk5+w0AAP//AwDgvkifQAMAAA==
+      string: "{\n  \"id\": \"chatcmpl-CjDtqJ34sifQSlSp63Ro0nfk7k4n5\",\n  \"object\": \"chat.completion\",\n  \"created\": 1764894226,\n  \"model\": \"gpt-4o-mini-2024-07-18\",\n  \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\": \"assistant\",\n        \"content\": \"Brandon's favorite color\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 173,\n    \"completion_tokens\": 5,\n    \"total_tokens\": 178,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\": 0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\": \"default\",\n  \"system_fingerprint\": \"fp_a460d7e2b7\"\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -330,17 +199,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"messages":[{"role":"system","content":"You are Information Agent. You
-      have access to specific knowledge sources.\nYour personal goal is: Provide information
-      based on knowledge sources\nTo give my best complete final answer to the task
-      respond using the exact following format:\n\nThought: I now can give a great
-      answer\nFinal Answer: Your final answer must be the great and the most complete
-      as possible, it must be outcome described.\n\nI MUST use these formats, my job
-      depends on it!"},{"role":"user","content":"\nCurrent Task: What is Brandon''s
-      favorite color?\n\nThis is the expected criteria for your final answer: Brandon''s
-      favorite color.\nyou MUST return the actual complete content as the final answer,
-      not a summary.\n\nBegin! This is VERY important to you, use the tools available
-      and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_with_only_crewai_knowledge.yaml b/lib/crewai/tests/cassettes/agents/test_agent_with_only_crewai_knowledge.yaml
index 0427c33d7..6cb36d742 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_with_only_crewai_knowledge.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_with_only_crewai_knowledge.yaml
@@ -1,17 +1,6 @@
 interactions:
 - request:
-    body: '{"messages":[{"role":"system","content":"Your goal is to rewrite the user
-      query so that it is optimized for retrieval from a vector database. Consider
-      how the query will be used to find relevant documents, and aim to make it more
-      specific and context-aware. \n\n Do not include any other text than the rewritten
-      query, especially any preamble or postamble and only add expected output format
-      if its relevant to the rewritten query. \n\n Focus on the key words of the intended
-      task and to retrieve the most relevant information. \n\n There will be some
-      extra context provided that might need to be removed such as expected_output
-      formats structured_outputs and other instructions."},{"role":"user","content":"The
-      original query is: What is Vidit''s favorite color?\n\nThis is the expected
-      criteria for your final answer: Vidit''s favorite color.\nyou MUST return the
-      actual complete content as the final answer, not a summary.."}],"model":"gpt-4o-mini"}'
+    body: '{"messages":[{"role":"system","content":"Your goal is to rewrite the user query so that it is optimized for retrieval from a vector database. Consider how the query will be used to find relevant documents, and aim to make it more specific and context-aware. \n\n Do not include any other text than the rewritten query, especially any preamble or postamble and only add expected output format if its relevant to the rewritten query. \n\n Focus on the key words of the intended task and to retrieve the most relevant information. \n\n There will be some extra context provided that might need to be removed such as expected_output formats structured_outputs and other instructions."},{"role":"user","content":"The original query is: What is Vidit''s favorite color?\n\nThis is the expected criteria for your final answer: Vidit''s favorite color.\nyou MUST return the actual complete content as the final answer, not a summary.."}],"model":"gpt-4o-mini"}'
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-      favorite color?\n\nThis is the expected criteria for your final answer: Vidit''s
-      favorite color.\nyou MUST return the actual complete content as the final answer,
-      not a summary.\n\nBegin! This is VERY important to you, use the tools available
-      and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
+    body: '{"messages":[{"role":"system","content":"You are Information Agent. You have access to specific knowledge sources.\nYour personal goal is: Provide information based on knowledge sources\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task: What is Vidit''s favorite color?\n\nThis is the expected criteria for your final answer: Vidit''s favorite color.\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4o-mini"}'
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     headers:
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_without_max_rpm_respects_crew_rpm.yaml b/lib/crewai/tests/cassettes/agents/test_agent_without_max_rpm_respects_crew_rpm.yaml
index 2a20bec5d..c801e96c4 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_without_max_rpm_respects_crew_rpm.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_without_max_rpm_respects_crew_rpm.yaml
@@ -1,15 +1,6 @@
 interactions:
 - request:
-    body: '{"messages":[{"role":"system","content":"You are test role. test backstory\nYour
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-      respond using the exact following format:\n\nThought: I now can give a great
-      answer\nFinal Answer: Your final answer must be the great and the most complete
-      as possible, it must be outcome described.\n\nI MUST use these formats, my job
-      depends on it!"},{"role":"user","content":"\nCurrent Task: Just say hi.\n\nThis
-      is the expected criteria for your final answer: Your greeting.\nyou MUST return
-      the actual complete content as the final answer, not a summary.\n\nBegin! This
-      is VERY important to you, use the tools available and give your best Final Answer,
-      your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
+    body: '{"messages":[{"role":"system","content":"You are test role. test backstory\nYour personal goal is: test goal\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task: Just say hi.\n\nThis is the expected criteria for your final answer: Your greeting.\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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@@ -49,22 +40,12 @@ interactions:
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@@ -115,25 +96,8 @@ interactions:
       code: 200
       message: OK
 - request:
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-      you, use the tools available and give your best Final Answer, your job depends
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@@ -173,23 +137,13 @@ interactions:
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+      I now know the final answer\nFinal Answer: the final answer to the original input question\n```"},{"role":"assistant","content":"```\nThought: I have to continue using the get_final_answer tool repeatedly without stopping, as per the instruction.\nAction: get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, I must stop using this action input. I''ll try something else instead."},{"role":"assistant","content":"```\nThought: I should keep invoking the get_final_answer tool repeatedly as instructed to gather the necessary information before providing the final answer.\nAction: get_final_answer\nAction Input: {}\nObservation: I tried reusing the same input, I must stop using this action input. I''ll try something else instead."},{"role":"assistant","content":"```\nThought: I should keep invoking the get_final_answer tool repeatedly as instructed to gather the necessary information before providing the final answer.\nAction: get_final_answer\nAction Input: {}\nObservation:
+      I tried reusing the same input, I must stop using this action input. I''ll try something else instead.\n\n\nNow it''s time you MUST give your absolute best final answer. You''ll ignore all previous instructions, stop using any tools, and just return your absolute BEST Final answer."}],"model":"gpt-4.1-mini"}'
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diff --git a/lib/crewai/tests/cassettes/agents/test_agent_without_max_rpm_respet_crew_rpm.yaml b/lib/crewai/tests/cassettes/agents/test_agent_without_max_rpm_respet_crew_rpm.yaml
index a07bb7acb..c9cd3d82c 100644
--- a/lib/crewai/tests/cassettes/agents/test_agent_without_max_rpm_respet_crew_rpm.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_agent_without_max_rpm_respet_crew_rpm.yaml
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diff --git a/lib/crewai/tests/cassettes/agents/test_cache_hitting.yaml b/lib/crewai/tests/cassettes/agents/test_cache_hitting.yaml
index f07c762df..06d0a3bb1 100644
--- a/lib/crewai/tests/cassettes/agents/test_cache_hitting.yaml
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-      it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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diff --git a/lib/crewai/tests/cassettes/agents/test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical.yaml b/lib/crewai/tests/cassettes/agents/test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical.yaml
index 0e4d3e724..a8b590a58 100644
--- a/lib/crewai/tests/cassettes/agents/test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_do_not_allow_crewai_trigger_context_for_first_task_hierarchical.yaml
@@ -1,42 +1,8 @@
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index 78c434e1c..5daeee987 100644
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index 472449784..69b4064d4 100644
--- a/lib/crewai/tests/cassettes/agents/test_first_task_auto_inject_trigger.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_first_task_auto_inject_trigger.yaml
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diff --git a/lib/crewai/tests/cassettes/agents/test_get_knowledge_search_query.yaml b/lib/crewai/tests/cassettes/agents/test_get_knowledge_search_query.yaml
index 18e61a671..ba4714bdd 100644
--- a/lib/crewai/tests/cassettes/agents/test_get_knowledge_search_query.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_get_knowledge_search_query.yaml
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+        \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 7,\n    \"total_tokens\": 7\n  }\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
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       Date:
@@ -255,123 +145,13 @@ interactions:
     uri: https://api.openai.com/v1/embeddings
   response:
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\"\
+        \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 3,\n    \"total_tokens\": 3\n  }\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -426,18 +206,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"messages":[{"role":"system","content":"You are Information Agent. I have
-      access to knowledge sources\nYour personal goal is: Provide information based
-      on knowledge sources\nTo give my best complete final answer to the task respond
-      using the exact following format:\n\nThought: I now can give a great answer\nFinal
-      Answer: Your final answer must be the great and the most complete as possible,
-      it must be outcome described.\n\nI MUST use these formats, my job depends on
-      it!"},{"role":"user","content":"\nCurrent Task: What is the capital of France?\n\nThis
-      is the expected criteria for your final answer: The capital of France is Paris.\nyou
-      MUST return the actual complete content as the final answer, not a summary.Additional
-      Information: The capital of France is Paris.\n\nBegin! This is VERY important
-      to you, use the tools available and give your best Final Answer, your job depends
-      on it!\n\nThought:"}],"model":"gpt-4"}'
+    body: '{"messages":[{"role":"system","content":"You are Information Agent. I have access to knowledge sources\nYour personal goal is: Provide information based on knowledge sources\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task: What is the capital of France?\n\nThis is the expected criteria for your final answer: The capital of France is Paris.\nyou MUST return the actual complete content as the final answer, not a summary.Additional Information: The capital of France is Paris.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4"}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -477,22 +246,12 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
     body:
-      string: !!binary |
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-        3BpNpRiKaRMV1djbw4kp/s1CXVkb11AM0Qx3ljaZ7bM/AAAA//8DACCP0v1eAwAA
+      string: "{\n  \"id\": \"chatcmpl-CjDtpOss9X8tWs9nkZXvgwNwtRjJM\",\n  \"object\": \"chat.completion\",\n  \"created\": 1764894225,\n  \"model\": \"gpt-4-0613\",\n  \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\": \"assistant\",\n        \"content\": \"I now can give a great answer\\nFinal Answer: The capital of France is Paris.\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 179,\n    \"completion_tokens\": 18,\n    \"total_tokens\": 197,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\": 0,\n      \"rejected_prediction_tokens\": 0\n    }\n  },\n  \"service_tier\": \"default\",\n  \"system_fingerprint\": null\n}\n"
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
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diff --git a/lib/crewai/tests/cassettes/agents/test_guardrail_is_called_using_callable.yaml b/lib/crewai/tests/cassettes/agents/test_guardrail_is_called_using_callable.yaml
index 04521e7f8..4b32e0483 100644
--- a/lib/crewai/tests/cassettes/agents/test_guardrail_is_called_using_callable.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_guardrail_is_called_using_callable.yaml
@@ -1,14 +1,6 @@
 interactions:
 - request:
-    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You
-      are an expert at gathering and organizing information. You carefully collect
-      details and present them in a structured way.\nYour personal goal is: Gather
-      information about the best soccer players\n\nTo give my best complete final
-      answer to the task respond using the exact following format:\n\nThought: I now
-      can give a great answer\nFinal Answer: Your final answer must be the great and
-      the most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "Top 1 best players
-      in the world?"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
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index 0450cabaf..3fceac1d4 100644
--- a/lib/crewai/tests/cassettes/agents/test_guardrail_is_called_using_string.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_guardrail_is_called_using_string.yaml
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+      – A rising midfielder known for his work rate, vision, and maturity beyond his years.\n10. Karim Benzema – Experienced forward with excellent technique, vision, and scoring ability, integral to his team’s success.\n\nThis list reflects a holistic view of current performances, influence on the pitch, and overall reputation across leagues and international competitions.\n\n        Guardrail:\n        Only include Brazilian players, both women and men\n\n        Your task:\n        - Confirm if the Task result complies with the guardrail.\n        - If not, provide clear feedback explaining what is wrong (e.g., by how much it violates the rule, or what specific part fails).\n        - Focus only on identifying issues — do not propose corrections.\n        - If the Task result complies with the guardrail, saying that is valid\n        "}],"model":"gpt-4.1-mini"}'
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-      \         \\\"description\\\": \\\"A feedback about the task output if it is
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-      \     \\\"required\\\": [\\n        \\\"valid\\\",\\n        \\\"feedback\\\"\\n
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-      not include the OpenAPI schema in the final output. Ensure the final output
-      does not include any code block markers like ```json or ```python.\"},{\"role\":\"user\",\"content\":\"\\n
-      \       Ensure the following task result complies with the given guardrail.\\n\\n
-      \       Task result:\\n        The top 10 best Brazilian soccer players in the
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-      Vin\xEDcius J\xFAnior \u2013 A dynamic winger known for his exceptional dribbling,
-      pace, and improving goal-scoring record with Real Madrid.\\n2. Neymar Jr. \u2013
-      A skillful forward with creativity, flair, and experience, still influential
-      for PSG and Brazil.\\n3. Casemiro \u2013 A commanding defensive midfielder known
-      for his tackling, positioning, and leadership both at club and national level.\\n4.
-      Alisson Becker \u2013 One of the world's top goalkeepers, instrumental for Liverpool
-      and Brazil.\\n5. Marquinhos \u2013 A versatile defender known for his composure,
-      tactical awareness, and leadership at PSG and Brazil.\\n6. Rodrygo Goes \u2013
-      Young forward with great technical ability and an increasing impact at Real
-      Madrid.\\n7. Fred \u2013 A hard-working midfielder with good passing and stamina,
-      key for Manchester United and Brazil.\\n8. Richarlison \u2013 A versatile and
-      energetic forward known for goal-scoring and work ethic, playing for Tottenham
-      Hotspur.\\n9. Gabriel Jesus \u2013 A quick and creative striker/winger, recently
-      playing for Arsenal and Brazil.\\n10. \xC9der Milit\xE3o \u2013 A strong and
-      reliable defender, key at Real Madrid and for the national team.\\n\\nThis list
-      highlights the best Brazilian players actively performing at top global clubs
-      and contributing significantly to Brazil\u2019s national team.\\n\\n        Guardrail:\\n
-      \       Only include Brazilian players, both women and men\\n\\n        Your
-      task:\\n        - Confirm if the Task result complies with the guardrail.\\n
-      \       - If not, provide clear feedback explaining what is wrong (e.g., by
-      how much it violates the rule, or what specific part fails).\\n        - Focus
-      only on identifying issues \u2014 do not propose corrections.\\n        - If
-      the Task result complies with the guardrail, saying that is valid\\n        \"}],\"model\":\"gpt-4.1-mini\"}"
+    body: '{"messages":[{"role":"system","content":"You are Guardrail Agent. You are a expert at validating the output of a task. By providing effective feedback if the output is not valid.\nYour personal goal is: Validate the output of the task\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!Ensure your final answer strictly adheres to the following OpenAPI schema: {\n  \"type\": \"json_schema\",\n  \"json_schema\": {\n    \"name\": \"LLMGuardrailResult\",\n    \"strict\": true,\n    \"schema\": {\n      \"properties\": {\n        \"valid\": {\n          \"description\": \"Whether the task output complies with the guardrail\",\n          \"title\": \"Valid\",\n          \"type\": \"boolean\"\n        },\n        \"feedback\": {\n          \"anyOf\":
+      [\n            {\n              \"type\": \"string\"\n            },\n            {\n              \"type\": \"null\"\n            }\n          ],\n          \"default\": null,\n          \"description\": \"A feedback about the task output if it is not valid\",\n          \"title\": \"Feedback\"\n        }\n      },\n      \"required\": [\n        \"valid\",\n        \"feedback\"\n      ],\n      \"title\": \"LLMGuardrailResult\",\n      \"type\": \"object\",\n      \"additionalProperties\": false\n    }\n  }\n}\n\nDo not include the OpenAPI schema in the final output. Ensure the final output does not include any code block markers like ```json or ```python."},{"role":"user","content":"\n        Ensure the following task result complies with the given guardrail.\n\n        Task result:\n        The top 10 best Brazilian soccer players in the world as of 2024, based on current form, skill, impact, and achievements, are:\n\n1. Vinícius Júnior – A dynamic winger known for his exceptional
+      dribbling, pace, and improving goal-scoring record with Real Madrid.\n2. Neymar Jr. – A skillful forward with creativity, flair, and experience, still influential for PSG and Brazil.\n3. Casemiro – A commanding defensive midfielder known for his tackling, positioning, and leadership both at club and national level.\n4. Alisson Becker – One of the world''s top goalkeepers, instrumental for Liverpool and Brazil.\n5. Marquinhos – A versatile defender known for his composure, tactical awareness, and leadership at PSG and Brazil.\n6. Rodrygo Goes – Young forward with great technical ability and an increasing impact at Real Madrid.\n7. Fred – A hard-working midfielder with good passing and stamina, key for Manchester United and Brazil.\n8. Richarlison – A versatile and energetic forward known for goal-scoring and work ethic, playing for Tottenham Hotspur.\n9. Gabriel Jesus – A quick and creative striker/winger, recently playing for Arsenal and Brazil.\n10. Éder Militão – A strong and reliable
+      defender, key at Real Madrid and for the national team.\n\nThis list highlights the best Brazilian players actively performing at top global clubs and contributing significantly to Brazil’s national team.\n\n        Guardrail:\n        Only include Brazilian players, both women and men\n\n        Your task:\n        - Confirm if the Task result complies with the guardrail.\n        - If not, provide clear feedback explaining what is wrong (e.g., by how much it violates the rule, or what specific part fails).\n        - Focus only on identifying issues — do not propose corrections.\n        - If the Task result complies with the guardrail, saying that is valid\n        "}],"model":"gpt-4.1-mini"}'
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diff --git a/lib/crewai/tests/cassettes/agents/test_guardrail_reached_attempt_limit.yaml b/lib/crewai/tests/cassettes/agents/test_guardrail_reached_attempt_limit.yaml
index 968e3cb91..e5e104fef 100644
--- a/lib/crewai/tests/cassettes/agents/test_guardrail_reached_attempt_limit.yaml
+++ b/lib/crewai/tests/cassettes/agents/test_guardrail_reached_attempt_limit.yaml
@@ -1,14 +1,6 @@
 interactions:
 - request:
-    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You
-      are an expert at gathering and organizing information. You carefully collect
-      details and present them in a structured way.\nYour personal goal is: Gather
-      information about the best soccer players\n\nTo give my best complete final
-      answer to the task respond using the exact following format:\n\nThought: I now
-      can give a great answer\nFinal Answer: Your final answer must be the great and
-      the most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players
-      in the world?"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.\nYour personal goal is: Gather information about the best soccer players\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players in the world?"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
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-      champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland
-      (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed,
-      goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions
-      League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester
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-      Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League
-      winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   -
-      Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
-      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6.
-      **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   - Key Attributes:
-      Finishing, positioning, aerial ability.\n   - Achievements: FIFA Best Men''s
-      Player, multiple Bundesliga titles, La Liga champion (2023).\n\n7. **Mohamed
-      Salah (Liverpool)**\n   - Position: Forward\n   - Key Attributes: Speed, finishing,
-      dribbling.\n   - Achievements: Premier League champion, FA Cup, UEFA Champions
-      League winner.\n\n8. **Vin\u00edcius J\u00fanior (Real Madrid)**\n   - Position:
-      Forward\n   - Key Attributes: Speed, dribbling, creativity.\n   - Achievements:
-      UEFA Champions League winner (2022), La Liga champion (2023).\n\n9. **Luka Modri\u0107
-      (Real Madrid)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision,
-      tactical intelligence.\n   - Achievements: Multiple Champions League titles,
-      Ballon d''Or winner (2018).\n\n10. **Harry Kane (Bayern Munich)**\n    - Position:
-      Forward\n    - Key Attributes: Goal-scoring, technique, playmaking.\n    - Achievements:
-      Golden Boot winner, Premier League titles, UEFA European Championship runner-up.\n\nThis
-      list has been adjusted to exclude Brazilian players and focuses on those who
-      have made significant impacts in their clubs and on the international stage
-      as of October 2023. Each player is recognized for their exceptional skills,
-      performances, and achievements."}, {"role": "user", "content": "You are not
-      allowed to include Brazilian players"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
+    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.\nYour personal goal is: Gather information about the best soccer players\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players in the world?"}, {"role": "assistant", "content": "Thought: I now can give a great answer  \nFinal Answer: The top 10 best soccer players in the world, as of October 2023, can be identified based on their recent performances, skills, impact on games, and overall contributions to their teams. Here is the structured list:\n\n1. **Lionel Messi (Inter Miami CF)**\n   -
+      Position: Forward\n   - Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements: Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2. **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed, goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   - Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   - Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
+      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6. **Neymar Jr. (Al Hilal)**\n   - Position: Forward\n   - Key Attributes: Flair, dribbling, creativity.\n   - Achievements: Multiple domestic league titles, Champions League runner-up.\n\n7. **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   - Key Attributes: Finishing, positioning, aerial ability.\n   - Achievements: FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion (2023).\n\n8. **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key Attributes: Speed, finishing, dribbling.\n   - Achievements: Premier League champion, FA Cup, UEFA Champions League winner.\n\n9. **Vin\u00edcius J\u00fanior (Real Madrid)**\n   - Position: Forward\n   - Key Attributes: Speed, dribbling, creativity.\n   - Achievements: UEFA Champions League winner (2022), La Liga champion (2023).\n\n10. **Luka Modri\u0107 (Real Madrid)**\n    - Position: Midfielder\n    - Key Attributes: Passing, vision,
+      tactical intelligence.\n    - Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\nThis list is compiled based on their current form, past performances, and contributions to their respective teams in both domestic and international competitions. Player rankings can vary based on personal opinion and specific criteria used for evaluation, but these players have consistently been regarded as some of the best in the world as of October 2023."}, {"role": "user", "content": "You are not allowed to include Brazilian players"}, {"role": "assistant", "content": "Thought: I now can give a great answer  \nFinal Answer: Here is an updated list of the top 10 best soccer players in the world as of October 2023, excluding Brazilian players:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   - Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements: Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
+      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed, goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   - Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   - Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements: 2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6. **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   - Key Attributes: Finishing, positioning,
+      aerial ability.\n   - Achievements: FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion (2023).\n\n7. **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key Attributes: Speed, finishing, dribbling.\n   - Achievements: Premier League champion, FA Cup, UEFA Champions League winner.\n\n8. **Vin\u00edcius J\u00fanior (Real Madrid)**\n   - Position: Forward\n   - Key Attributes: Speed, dribbling, creativity.\n   - Achievements: UEFA Champions League winner (2022), La Liga champion (2023).\n\n9. **Luka Modri\u0107 (Real Madrid)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, tactical intelligence.\n   - Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\n10. **Harry Kane (Bayern Munich)**\n    - Position: Forward\n    - Key Attributes: Goal-scoring, technique, playmaking.\n    - Achievements: Golden Boot winner, Premier League titles, UEFA European Championship runner-up.\n\nThis list has been adjusted to exclude Brazilian
+      players and focuses on those who have made significant impacts in their clubs and on the international stage as of October 2023. Each player is recognized for their exceptional skills, performances, and achievements."}, {"role": "user", "content": "You are not allowed to include Brazilian players"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
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-      in the world?"}, {"role": "assistant", "content": "Thought: I now can give a
-      great answer  \nFinal Answer: The top 10 best soccer players in the world, as
-      of October 2023, can be identified based on their recent performances, skills,
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-      world as of October 2023."}, {"role": "user", "content": "You are not allowed
-      to include Brazilian players"}, {"role": "assistant", "content": "Thought: I
-      now can give a great answer  \nFinal Answer: Here is an updated list of the
-      top 10 best soccer players in the world as of October 2023, excluding Brazilian
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-      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
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-      League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester
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-      Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League
-      winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   -
-      Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
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-      have made significant impacts in their clubs and on the international stage
-      as of October 2023. Each player is recognized for their exceptional skills,
-      performances, and achievements."}, {"role": "user", "content": "You are not
-      allowed to include Brazilian players"}, {"role": "assistant", "content": "Thought:
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-      top 10 best soccer players in the world as of October 2023, explicitly excluding
-      Brazilian players:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   -
-      Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements:
-      Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
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-      vision, creativity.\n   - Achievements: Multiple Premier League titles, FA Cups,
-      UEFA Champions League winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   -
-      Position: Forward\n   - Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   -
-      Achievements: 2022 Ballon d''Or winner, multiple Champions Leagues with Real
-      Madrid.\n\n6. **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   -
-      Key Attributes: Finishing, positioning, aerial ability.\n   - Achievements:
-      FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion (2023).\n\n7.
-      **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key Attributes:
-      Speed, finishing, dribbling.\n   - Achievements: Premier League champion, FA
-      Cup, UEFA Champions League winner.\n\n8. **Luka Modri\u0107 (Real Madrid)**\n   -
-      Position: Midfielder\n   - Key Attributes: Passing, vision, tactical intelligence.\n   -
-      Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\n9.
-      **Harry Kane (Bayern Munich)**\n   - Position: Forward\n   - Key Attributes:
-      Goal-scoring, technique, playmaking.\n   - Achievements: Golden Boot winner,
-      Premier League titles, UEFA European Championship runner-up.\n\n10. **Rodri
-      (Manchester City)**\n    - Position: Midfielder\n    - Key Attributes: Defensive
-      skills, passing, positional awareness.\n    - Achievements: Premier League titles,
-      UEFA Champions League winner (2023).\n\nThis list is curated while adhering
-      to the restriction of excluding Brazilian players. Each player included has
-      demonstrated exceptional skills and remarkable performances, solidifying their
-      status as some of the best in the world as of October 2023."}, {"role": "user",
-      "content": "You are not allowed to include Brazilian players"}], "model": "gpt-4o-mini",
-      "stop": ["\nObservation:"]}'
+    body: '{"messages": [{"role": "system", "content": "You are Sports Analyst. You are an expert at gathering and organizing information. You carefully collect details and present them in a structured way.\nYour personal goal is: Gather information about the best soccer players\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user", "content": "Top 10 best players in the world?"}, {"role": "assistant", "content": "Thought: I now can give a great answer  \nFinal Answer: The top 10 best soccer players in the world, as of October 2023, can be identified based on their recent performances, skills, impact on games, and overall contributions to their teams. Here is the structured list:\n\n1. **Lionel Messi (Inter Miami CF)**\n   -
+      Position: Forward\n   - Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements: Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2. **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed, goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   - Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   - Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements:
+      2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6. **Neymar Jr. (Al Hilal)**\n   - Position: Forward\n   - Key Attributes: Flair, dribbling, creativity.\n   - Achievements: Multiple domestic league titles, Champions League runner-up.\n\n7. **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   - Key Attributes: Finishing, positioning, aerial ability.\n   - Achievements: FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion (2023).\n\n8. **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key Attributes: Speed, finishing, dribbling.\n   - Achievements: Premier League champion, FA Cup, UEFA Champions League winner.\n\n9. **Vin\u00edcius J\u00fanior (Real Madrid)**\n   - Position: Forward\n   - Key Attributes: Speed, dribbling, creativity.\n   - Achievements: UEFA Champions League winner (2022), La Liga champion (2023).\n\n10. **Luka Modri\u0107 (Real Madrid)**\n    - Position: Midfielder\n    - Key Attributes: Passing, vision,
+      tactical intelligence.\n    - Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\nThis list is compiled based on their current form, past performances, and contributions to their respective teams in both domestic and international competitions. Player rankings can vary based on personal opinion and specific criteria used for evaluation, but these players have consistently been regarded as some of the best in the world as of October 2023."}, {"role": "user", "content": "You are not allowed to include Brazilian players"}, {"role": "assistant", "content": "Thought: I now can give a great answer  \nFinal Answer: Here is an updated list of the top 10 best soccer players in the world as of October 2023, excluding Brazilian players:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   - Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements: Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2.
+      **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup champion (2018), Ligue 1 titles, multiple domestic cups.\n\n3. **Erling Haaland (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed, goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   - Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   - Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements: 2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6. **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   - Key Attributes: Finishing, positioning,
+      aerial ability.\n   - Achievements: FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion (2023).\n\n7. **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key Attributes: Speed, finishing, dribbling.\n   - Achievements: Premier League champion, FA Cup, UEFA Champions League winner.\n\n8. **Vin\u00edcius J\u00fanior (Real Madrid)**\n   - Position: Forward\n   - Key Attributes: Speed, dribbling, creativity.\n   - Achievements: UEFA Champions League winner (2022), La Liga champion (2023).\n\n9. **Luka Modri\u0107 (Real Madrid)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, tactical intelligence.\n   - Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\n10. **Harry Kane (Bayern Munich)**\n    - Position: Forward\n    - Key Attributes: Goal-scoring, technique, playmaking.\n    - Achievements: Golden Boot winner, Premier League titles, UEFA European Championship runner-up.\n\nThis list has been adjusted to exclude Brazilian
+      players and focuses on those who have made significant impacts in their clubs and on the international stage as of October 2023. Each player is recognized for their exceptional skills, performances, and achievements."}, {"role": "user", "content": "You are not allowed to include Brazilian players"}, {"role": "assistant", "content": "Thought: I now can give a great answer  \nFinal Answer: Here is the revised list of the top 10 best soccer players in the world as of October 2023, explicitly excluding Brazilian players:\n\n1. **Lionel Messi (Inter Miami CF)**\n   - Position: Forward\n   - Key Attributes: Dribbling, vision, goal-scoring ability.\n   - Achievements: Multiple Ballon d''Or winner, Copa America champion, World Cup champion (2022).\n\n2. **Kylian Mbapp\u00e9 (Paris Saint-Germain)**\n   - Position: Forward\n   - Key Attributes: Speed, technique, finishing.\n   - Achievements: FIFA World Cup champion (2018), multiple Ligue 1 titles, and various domestic cups.\n\n3. **Erling Haaland
+      (Manchester City)**\n   - Position: Forward\n   - Key Attributes: Power, speed, goal-scoring instinct.\n   - Achievements: Bundesliga top scorer, UEFA Champions League winner (2023), Premier League titles.\n\n4. **Kevin De Bruyne (Manchester City)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, creativity.\n   - Achievements: Multiple Premier League titles, FA Cups, UEFA Champions League winner (2023).\n\n5. **Karim Benzema (Al-Ittihad)**\n   - Position: Forward\n   - Key Attributes: Goal-scoring, playmaking, tactical intelligence.\n   - Achievements: 2022 Ballon d''Or winner, multiple Champions Leagues with Real Madrid.\n\n6. **Robert Lewandowski (FC Barcelona)**\n   - Position: Forward\n   - Key Attributes: Finishing, positioning, aerial ability.\n   - Achievements: FIFA Best Men''s Player, multiple Bundesliga titles, La Liga champion (2023).\n\n7. **Mohamed Salah (Liverpool)**\n   - Position: Forward\n   - Key Attributes: Speed, finishing, dribbling.\n   - Achievements:
+      Premier League champion, FA Cup, UEFA Champions League winner.\n\n8. **Luka Modri\u0107 (Real Madrid)**\n   - Position: Midfielder\n   - Key Attributes: Passing, vision, tactical intelligence.\n   - Achievements: Multiple Champions League titles, Ballon d''Or winner (2018).\n\n9. **Harry Kane (Bayern Munich)**\n   - Position: Forward\n   - Key Attributes: Goal-scoring, technique, playmaking.\n   - Achievements: Golden Boot winner, Premier League titles, UEFA European Championship runner-up.\n\n10. **Rodri (Manchester City)**\n    - Position: Midfielder\n    - Key Attributes: Defensive skills, passing, positional awareness.\n    - Achievements: Premier League titles, UEFA Champions League winner (2023).\n\nThis list is curated while adhering to the restriction of excluding Brazilian players. Each player included has demonstrated exceptional skills and remarkable performances, solidifying their status as some of the best in the world as of October 2023."}, {"role": "user", "content":
+      "You are not allowed to include Brazilian players"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
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index cbd8762d9..66a4a43ca 100644
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index 73e2b643f..3d6162288 100644
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index 7e2493fb6..918f18479 100644
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+++ b/lib/crewai/tests/cassettes/experimental/evaluation/TestAgentEvaluator.test_eval_specific_agents_from_crew.yaml
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index a02b48327..bcb89dd63 100644
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index 16190be00..a9a7051ab 100644
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index 657742652..b5be5dc5a 100644
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index c0aa3fa16..deeeedb90 100644
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+++ b/lib/crewai/tests/cassettes/hooks/TestLLMHooksIntegration.test_lite_agent_hooks_integration_with_real_llm.yaml
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index a07f69ec6..a379c8397 100644
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     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAA3TQTUsDMRAG4L8S55zC7tKi5lL01IInEYuIhJBMu8HsZE0mtkvZ/y5bLH7haWCe
-        d4ZhjtBFhwEU2GCKw1mORMiz+aypmkW1qOcgwTtQ0OWdruqrwpu7x277VC4fOn9obra39f0GJPDQ
-        45TCnM0OQUKKYWqYnH1mQwwSbCRGYlDPx3Oe8TDJqShYYQjxQqziXpiEYohFuOhpJzg6MyzFOgtu
-        MaEwNHA7wVpYQ6LF0J/Se8/tEsYXCZljrxOaHAkUIDnNJRF8Qsa3gmQRFJUQJJTT0eoInvrCmuMr
-        UgZ1LcEa26K2CQ37SPqnV2dPaNx/dp6d1mPfYofJBL3o/ua/tG5/6yghFv7eaioJGdO7t6jZYwIF
-        06OdSQ7G8QMAAP//AwATs5vC2QEAAA==
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_018utWLVmfYu7Tmix2AfB1RW","type":"message","role":"assistant","content":[{"type":"text","text":"Hello! How are you doing today? Is there anything I can help you with?"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":9,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":20,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_conversation.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_conversation.yaml
index 92f0f8b38..1bbbfe02f 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_conversation.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_conversation.yaml
@@ -1,7 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"My name is Alice."},{"role":"assistant","content":"Hello
-      Alice! Nice to meet you."},{"role":"user","content":"What is my name?"}],"model":"claude-sonnet-4-0","stream":false}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"My name is Alice."},{"role":"assistant","content":"Hello Alice! Nice to meet you."},{"role":"user","content":"What is my name?"}],"model":"claude-sonnet-4-0","stream":false}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -43,19 +42,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJBbSwMxEIX/SjnPqez2Apq3vqv4oIKKhJAM3WB2siaTYi3732WLxRs+
-        DZzvm2E4B/TJU4SGi7Z6mpfETDJfzRfNYt2s2xUUgodGX7amaW+X7dXd+83qMd9vNmlfLy/y7rqD
-        guwHmiwqxW4JCjnFKbClhCKWBQousRAL9NPh5Au9TeQ4NB5SzTO2Pc1CmW1icHSG8VmhSBpMJlsS
-        Q4PYG6mZ8QkKvVZiR9BcY1Soxw/0AYGHKkbSC3GBXrYKzrqOjMtkJSQ2P4XmxDNZ/x877U73aeio
-        p2yjWfd//S/adr/pqJCqfI/OFQrlXXBkJFCGxtSat9ljHD8AAAD//wMAr8g7PaYBAAA=
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01T31MUzP4ZrVAAoyuL9rvNh","type":"message","role":"assistant","content":[{"type":"text","text":"Your name is Alice."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":31,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":8,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -100,8 +92,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"My name is Alice."},{"role":"assistant","content":"Hello
-      Alice! Nice to meet you."},{"role":"user","content":"What is my name?"}],"model":"claude-sonnet-4-0","stream":false}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"My name is Alice."},{"role":"assistant","content":"Hello Alice! Nice to meet you."},{"role":"user","content":"What is my name?"}],"model":"claude-sonnet-4-0","stream":false}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -143,20 +134,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJBLa8MwDID/itHZHUnXsM63nrfjVjbGMMYWjZkjZ5ZcVkr++0hp2Yud
-        hPR9eqAjDDlgAgM+uRpwwZkIZbFaLJtl13TtCjTEAAYG3tmmXT3ddfdr3qJ7GLebtS/5Nh4eQYMc
-        RpwtZHY7BA0lp7ngmCOLIwENPpMgCZiX48UX/JjJKRh4zrUocgOqyGqToketHKtDrkpyCmoGNKdF
-        jQX3MVdW541XML1qYMmjLeg4ExhAClZqITgDxveK5BEM1ZQ01NOp5giRxipW8hsSg7luNXjne7S+
-        oJOYyf4Umgsv6MJ/7NI7z8exxwGLS7Yb/vpftO1/00lDrvK91N5oYCz76NFKxAIG5v8GVwJM0ycA
-        AAD//wMAqvdgpNABAAA=
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_014XK5L8sVeaTpVA8cro9iyU","type":"message","role":"assistant","content":[{"type":"text","text":"Your name is Alice, as you told me in your previous message."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":31,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":17,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_multiple_calls.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_multiple_calls.yaml
index 0ef6ebf0f..feacde30c 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_multiple_calls.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_multiple_calls.yaml
@@ -42,19 +42,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAA3SQTUvDQBCG/0p4r24hqa2HBQ8iiiBCBfUisiy7QxtMZuPOrLSU/HdJsUgVTwPz
-        PPPBu0efInWwCJ0vkWaSmElni9m8ni/rZbOAQRth0cva1c3V/eolXq/87epRLoaHm7B9ftrdwUB3
-        A00Wifg1wSCnbmp4kVbUs8IgJFZihX3dH32l7UQOxaKpzqqmuqzmGN8MRNPgMnlJDAvi6LRkxjcQ
-        +ijEgWC5dJ1BOdy1e7Q8FHWa3okFtlkYBB825EImr21idyrUR57Jx//YcXbaT8OGesq+c8v+r/9D
-        m81vOhqkoiffnRsI5c82kNOWMiymsKLPEeP4BQAA//8DAAkpexydAQAA
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01AKPVdCPaFPQs6pMEcxUTyH","type":"message","role":"assistant","content":[{"type":"text","text":"1 + 1 = 2"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":14,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":13,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -141,19 +134,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAA3SQTUvEMBCG/0p5r2ahre0l4EUPXjyIrCCIhJAM27DtpCYTWVn636WLi6ziaWCe
-        Zz54j5iipxEabrTF0yZHZpJNt2nrtq/7poNC8NCY8s7UTX/b3w2BD/4pvtw/Pm/dw3TYWijI50yr
-        RTnbHUEhxXFt2JxDFssCBRdZiAX69Xj2hQ4rORWNtrqq2uqm6rC8KWSJs0lkc2RoEHsjJTG+Qab3
-        QuwImss4KpTTXX1E4LmIkbgnztBNp+CsG8i4RFZCZHMp1GeeyPr/2Hl23U/zQBMlO5p++uv/0Gb4
-        TReFWOTiu2uFTOkjODISKEFjDcvb5LEsXwAAAP//AwDa2+/dnQEAAA==
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_015B5ChinxdRoXGPUTcLmxTa","type":"message","role":"assistant","content":[{"type":"text","text":"2 + 2 = 4"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":14,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":13,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -240,19 +226,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJBNS8QwEIb/yvJezUKztJeAF8XVi0cPKhJCMrbFdtJNJrK69L9LFxdZ
-        xdPAPM988B4wxkADDPzgSqB1jswk63q9qTZN1egaCn2AwZhbW+nt9un+avf5+lCnm9C2d41cP97u
-        oSAfEy0W5exagkKKw9JwOfdZHAsUfGQhFpjnw8kX2i/kWAz0hV5drjaYXxSyxMkmcjkyDIiDlZIY
-        3yDTrhB7guEyDArleNUc0PNUxEp8I84wulbwzndkfSInfWR7LlQnnsiF/9hpdtlPU0cjJTfYZvzr
-        /1Dd/aazQixy9p1WyJTee09WekowWKIKLgXM8xcAAAD//wMApv4OQJsBAAA=
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01FFZMBqzfU4rEdggH5tCYGx","type":"message","role":"assistant","content":[{"type":"text","text":"1+1 = 2"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":14,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":11,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -339,19 +318,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJBNS8QwEIb/SnmvZqGt3VUCHtYPFNSbFxUJIRm2xXZSk4koS/+7dHGR
-        VTwNzPPMB+8WQ/DUQ8P1NntapMBMsmgWdVkvy2XVQKHz0BjSxpRV9Xiyulpf3zyE+8vbVXUXTp/O
-        1xdQkM+RZotSshuCQgz93LApdUksCxRcYCEW6Oft3hf6mMmuaNTFUVEXZ0WD6UUhSRhNJJsCQ4PY
-        G8mR8Q0SvWViR9Cc+14h7+7qLToesxgJr8QJumoUnHUtGRfJShfYHArlnkey/j+2n53309jSQNH2
-        Zjn89X9o1f6mk0LIcvDdsUKi+N45MtJRhMYclrfRY5q+AAAA//8DAIkh+sidAQAA
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_011Y76EAGHToMDK61Lo8ZBAC","type":"message","role":"assistant","content":[{"type":"text","text":"2 + 2 = 4"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":14,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":13,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_stop_sequences.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_stop_sequences.yaml
index 14b7ddc01..a2c74154d 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_stop_sequences.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_stop_sequences.yaml
@@ -1,7 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Count from 1 to
-      10"}],"model":"claude-sonnet-4-0","stop_sequences":["END","STOP"],"stream":false}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Count from 1 to 10"}],"model":"claude-sonnet-4-0","stop_sequences":["END","STOP"],"stream":false}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -43,19 +42,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAA3SQy2rDMBBFf0Xc9QRsJ+5DH1BKyaJ000UpQshDI2KPHD1MSvC/F4eGvuhq4J4z
-        w3BPGELHPTRcb0vHqxREOK82q6Zq2qqtNyD4DhpDejNV/fw0iR2OzaM8HO6aaesnu9/eg5DfR14s
-        Tsm+MQgx9EtgU/IpW8kguCCZJUO/nC5+5uNCzkOjJtWQWpPakGpJXZG6JnVD6pZUXWF+JaQcRhPZ
-        piDQYOlMLlHwCRIfCotjaCl9TyjnX/QJXsaSTQ57lgRdtwRn3Y6Ni2yzD2J+CtWFR7bdf+yyu9zn
-        cccDR9ubdvjrf9F695vOhFDy92jdEBLHyTs22XOExlJgZ2OHef4AAAD//wMAnJDpxrEBAAA=
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01WRvnamx2PnJqF2vLivakLH","type":"message","role":"assistant","content":[{"type":"text","text":"1, 2, 3, 4, 5, 6, 7, 8, 9, 10"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":15,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":32,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_max_tokens.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_max_tokens.yaml
index 7bdd6f7c4..99b30f395 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_max_tokens.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_max_tokens.yaml
@@ -1,7 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":10,"messages":[{"role":"user","content":"Write a very long
-      story about a dragon."}],"model":"claude-sonnet-4-0","stream":false}'
+    body: '{"max_tokens":10,"messages":[{"role":"user","content":"Write a very long story about a dragon."}],"model":"claude-sonnet-4-0","stream":false}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -43,19 +42,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJBNS8RADIb/SonXKbRLu8ic/TjoTRFBZBin2e3gNFMnqbvL0v8us1hk
-        FU+BPE+SlxxhiB0G0OCCnTosORKhlE25qlZt1dYNKPAdaBh4a6r6chfp+vnmdnfl67f1MDR37aEX
-        UCCHEbOFzHaLoCDFkBuW2bNYyo6LJEgC+uW4+IL703QuGi6Kxx6Le8tSPETaFnFTPFkM0h8Y5lcF
-        LHE0CS1Hyrfs3kh8R2L4RowfE5JD0DSFoGA6ZdFH8DROssi6Xitw1vVoXEIrPpI5F6qFJ7Tdf2yZ
-        zftx7HHAZINph7/+D63733RWECc5S1cpYEyf3qERjwk05Ad2NnUwz18AAAD//wMAo6EHIbEBAAA=
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_018wonEXFGwDi1b6mm4K5yht","type":"message","role":"assistant","content":[{"type":"text","text":"# The Last Song of Vaelthys"}],"stop_reason":"max_tokens","stop_sequence":null,"usage":{"input_tokens":16,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -100,8 +92,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"max_tokens":10,"messages":[{"role":"user","content":"Write a very long
-      story about a dragon."}],"model":"claude-sonnet-4-0","stream":false}'
+    body: '{"max_tokens":10,"messages":[{"role":"user","content":"Write a very long story about a dragon."}],"model":"claude-sonnet-4-0","stream":false}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -143,19 +134,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJDBasMwDIZfJWhXF5LS9uDboLeFXdatgzGMcbTE1JEzSxntSt59uCyM
-        buwk0PdJ+tEZ+thgAA0u2LHBBUcilMVqsSyX63JdrUCBb0BDz60pq9u7z229ve9P6B/r/ap7ftrU
-        ewEFchowW8hsWwQFKYbcsMyexVJ2XCRBEtAv59kXPF6mc9FwU+w6LGrLUjxEaov4Vuw6G/KOSDC9
-        KmCJg0loOVK+Zo9G4gGJ4Rsxvo9IDkHTGIKC8ZJGn8HTMMos62qjwFnXoXEJrfhI5looZ57QNv+x
-        eTbvx6HDHpMNZt3/9X9o1f2mk4I4ylW6UgFj+vAOjXhMoCG/sLGpgWn6AgAA//8DANbGCpGzAQAA
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01AKzDLDNmyeiULW4hXV6LWt","type":"message","role":"assistant","content":[{"type":"text","text":"# The Last Song of Thalassion"}],"stop_reason":"max_tokens","stop_sequence":null,"usage":{"input_tokens":16,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":10,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_format_json.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_format_json.yaml
index 675192fd8..20fb80da7 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_format_json.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_format_json.yaml
@@ -1,8 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Return a JSON
-      object devoid of ```json{x}```, where x is the json object, with a ''greeting''
-      field"}],"model":"claude-sonnet-4-0","stream":false}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Return a JSON object devoid of ```json{x}```, where x is the json object, with a ''greeting'' field"}],"model":"claude-sonnet-4-0","stream":false}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -44,20 +42,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAA3SQW0vEMBCF/0o8z1noXoqQF0FxXcUXQUSwEkI6dIPtpOairqX/Xaou3vBp4Hzf
-        HIYZ0PmaWijY1uSaZtEzU5qtZotiURblfAUJV0Ohi40u5ic3V4dntjy+dOuL29Nled2/rrslJNKu
-        p8miGE1DkAi+nQITo4vJcIKE9ZyIE9TdsPcTvUzkfSgMFQtRoQlEyXFTQYkKG2pbfyA2/llYw+Jc
-        fFSKnc8i+drsjipUPGK8l4jJ9zqQiZ6hQFzrlAPjE0R6zMSWoDi3rUR+P1UNcNznpJN/II5Qy1LC
-        GrslbQOZ5Dzrn0Kx54FM/R/b70791G+po2BaXXZ//S863/6mo4TP6Xu0mEtECk/Okk6OAhSm/9Ym
-        1BjHNwAAAP//AwCUmUxv0AEAAA==
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01CVQ7Gc5BLiFJXE35TpzFm3","type":"message","role":"assistant","content":[{"type":"text","text":"{\n  \"greeting\": \"Hello! How can I assist you today?\"\n}"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":35,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":21,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_format_none.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_format_none.yaml
index 02fb55547..42070b72a 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_format_none.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_format_none.yaml
@@ -1,7 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Tell me a short
-      fact"}],"model":"claude-sonnet-4-0","stream":false}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Tell me a short fact"}],"model":"claude-sonnet-4-0","stream":false}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -43,21 +42,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJHBahtBDIZfZapLLuOwXtsE5uZDSa5toQ0pYZFnVO+QWWk70poa43cv
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-        AA==
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_017RAmi5WVZSps2b2eaQGsnX","type":"message","role":"assistant","content":[{"type":"text","text":"Honey never spoils! Archaeologists have found edible honey in ancient Egyptian tombs that''s over 3,000 years old."}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":12,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":33,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_model.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_model.yaml
index 47ac76c8e..34af261c2 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_model.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_response_model.yaml
@@ -1,8 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say hello in French"}],"model":"claude-sonnet-4-0","stream":false,"tool_choice":{"type":"tool","name":"structured_output"},"tools":[{"name":"structured_output","description":"Returns
-      structured data according to the schema","input_schema":{"description":"Response
-      model for greeting test.","properties":{"greeting":{"title":"Greeting","type":"string"},"language":{"title":"Language","type":"string"}},"required":["greeting","language"],"title":"GreetingResponse","type":"object"}}]}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say hello in French"}],"model":"claude-sonnet-4-0","stream":false,"tool_choice":{"type":"tool","name":"structured_output"},"tools":[{"name":"structured_output","description":"Returns structured data according to the schema","input_schema":{"description":"Response model for greeting test.","properties":{"greeting":{"title":"Greeting","type":"string"},"language":{"title":"Language","type":"string"}},"required":["greeting","language"],"title":"GreetingResponse","type":"object"}}]}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -44,20 +42,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJBNT8MwDIb/i8+Z1JZViBxXBAeEhIDDJISiLDFtttQp+dg0Vf3vKJsK
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-        /q/+h5bdbzoxOL/ku1RfMQjo90ahiAb96X+StPQapukLAAD//wMAoHQshQMCAAA=
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01XjvX2nCho1knuucbwwgCpw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019rfPRSDmBb7CyCTdGMv5rK","name":"structured_output","input":{"greeting":"Bonjour","language":"French"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":432,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":53,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_system_message.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_system_message.yaml
index 3143726b7..0279abcd7 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_system_message.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_system_message.yaml
@@ -1,7 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is 2+2?"}],"model":"claude-sonnet-4-0","stream":false,"system":"You
-      are a helpful assistant."}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is 2+2?"}],"model":"claude-sonnet-4-0","stream":false,"system":"You are a helpful assistant."}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -43,19 +42,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJBdS8QwEEX/SrmvZqHttggBXxZkffJBRESREJNhm7VNajIRdel/ly6u
-        X4tPA/ecmYG7wxAs9ZAwvc6WFil4T7xoFnVZt2VbNRBwFhJD2qiyurwYV9vl7dVN+35+d908bten
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-        vWqHY/+bVt1fOgmEzD+jaimQKL44Q4odRUjMZVkdLabpAwAA//8DAJXQ8cKdAQAA
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01NHpBj3XRV5zEZT4bjG7iBG","type":"message","role":"assistant","content":[{"type":"text","text":"2 + 2 = 4"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":20,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":13,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -100,8 +92,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is 2+2?"}],"model":"claude-sonnet-4-0","stream":false,"system":"You
-      are a helpful assistant."}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is 2+2?"}],"model":"claude-sonnet-4-0","stream":false,"system":"You are a helpful assistant."}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -143,19 +134,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJDLasMwEEV/xdxtZbBdeyPoIpu2dNduSxFCGhITe6RIoz4I/vfi0PRJ
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-        trvfdFEIRb5H7aVCpvQ8OjIyUoLGWpa3yWNZ3gEAAP//AwAKNgjwnQEAAA==
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01A2wFpGupApJ5qkRAQ4eHrd","type":"message","role":"assistant","content":[{"type":"text","text":"2 + 2 = 4"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":20,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":13,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_temperature.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_temperature.yaml
index f6e1eb16a..4c13b028f 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_temperature.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_temperature.yaml
@@ -1,7 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say the word ''test''
-      once"}],"model":"claude-sonnet-4-0","stream":false,"temperature":0.1}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say the word ''test'' once"}],"model":"claude-sonnet-4-0","stream":false,"temperature":0.1}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -43,19 +42,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAA3SQTUsDQQyG/8t7nsJu2bUyR8UeKp4EPYgMw0xol+5mtpOMqGX/u2yx+IWnkPd5
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-        e9QYCOWXLpDTjjIs5jdFnyOm6QMAAP//AwBU2mqKlwEAAA==
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01FMn6K7EJ4BJgCJSWVkzq87","type":"message","role":"assistant","content":[{"type":"text","text":"test"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":15,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -100,8 +92,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say the word ''test''
-      once"}],"model":"claude-sonnet-4-0","stream":false,"temperature":0.1}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say the word ''test'' once"}],"model":"claude-sonnet-4-0","stream":false,"temperature":0.1}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -143,19 +134,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
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+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01YLmf15HNiTFbwyb5HmYyTZ","type":"message","role":"assistant","content":[{"type":"text","text":"test"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":15,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":4,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_tools.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_tools.yaml
index 2689bba6c..f257668f7 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_tools.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_async_with_tools.yaml
@@ -1,9 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What''s the weather
-      in San Francisco?"}],"model":"claude-sonnet-4-0","stream":false,"tools":[{"name":"get_weather","description":"Get
-      the current weather for a location","input_schema":{"type":"object","properties":{"location":{"type":"string","description":"The
-      city and state, e.g. San Francisco, CA"}},"required":["location"]}}]}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What''s the weather in San Francisco?"}],"model":"claude-sonnet-4-0","stream":false,"tools":[{"name":"get_weather","description":"Get the current weather for a location","input_schema":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"}},"required":["location"]}}]}'
     headers:
       User-Agent:
       - X-USER-AGENT-XXX
@@ -45,21 +42,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJFLSyQxFIX/SjgbN2mp6odidiot2CroqLgYhhBT166iU0mb3PiYpv67
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-        Jc0NRSj0n1qZWKHr3gAAAP//AwBJY3XJRQIAAA==
+      string: '{"model":"claude-sonnet-4-20250514","id":"msg_01B7MnLRB5M7EuA3EJUztvm3","type":"message","role":"assistant","content":[{"type":"text","text":"I''ll check the current weather in San Francisco for you."},{"type":"tool_use","id":"toolu_0133tWTcW1kwL2KtdJQbPahU","name":"get_weather","input":{"location":"San Francisco, CA"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":401,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":69,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_function_calling.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_function_calling.yaml
index 8423c518b..33a90d040 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_function_calling.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_function_calling.yaml
@@ -1,10 +1,6 @@
 interactions:
 - request:
-    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is the weather
-      in Tokyo? Use the get_weather tool."}],"model":"claude-sonnet-4-5","stream":false,"tools":[{"name":"get_weather","description":"Get
-      the current weather in a given location","input_schema":{"type":"object","properties":{"location":{"type":"string","description":"The
-      city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"],"description":"The
-      unit of temperature"}},"required":["location"]}}]}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is the weather in Tokyo? Use the get_weather tool."}],"model":"claude-sonnet-4-5","stream":false,"tools":[{"name":"get_weather","description":"Get the current weather in a given location","input_schema":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"],"description":"The unit of temperature"}},"required":["location"]}}]}'
     headers:
       accept:
       - application/json
@@ -44,20 +40,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
-        H4sIAAAAAAAAAwAAAP//dJBBS8NAEIX/isx5A0k0he5NhR60FRG9KLKsmzGJ3eyku7O1JeS/SyLV
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+      string: '{"model":"claude-sonnet-4-5-20250929","id":"msg_01B3fMbiAhcxutivRC1gu1qZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AoUFM2koNLsFscK6xjnLPo","name":"get_weather","input":{"location":"Tokyo, Japan"}}],"stop_reason":"tool_use","stop_sequence":null,"usage":{"input_tokens":620,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":55,"service_tier":"standard"}}'
     headers:
       CF-RAY:
       - CF-RAY-XXX
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -108,13 +96,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: "{\"max_tokens\":4096,\"messages\":[{\"role\":\"user\",\"content\":\"What
-      is the weather in Tokyo? Use the get_weather tool.\"},{\"role\":\"assistant\",\"content\":[{\"type\":\"tool_use\",\"id\":\"toolu_01AoUFM2koNLsFscK6xjnLPo\",\"name\":\"get_weather\",\"input\":{\"location\":\"Tokyo,
-      Japan\"}}]},{\"role\":\"user\",\"content\":[{\"type\":\"tool_result\",\"tool_use_id\":\"toolu_01AoUFM2koNLsFscK6xjnLPo\",\"content\":\"The
-      weather in Tokyo, Japan is sunny and 72\xB0F\"}]}],\"model\":\"claude-sonnet-4-5\",\"stream\":false,\"tools\":[{\"name\":\"get_weather\",\"description\":\"Get
-      the current weather in a given location\",\"input_schema\":{\"type\":\"object\",\"properties\":{\"location\":{\"type\":\"string\",\"description\":\"The
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diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_stop_sequences_sent_to_api.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_stop_sequences_sent_to_api.yaml
index 4f286b9aa..3f1d37c0c 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_stop_sequences_sent_to_api.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_stop_sequences_sent_to_api.yaml
@@ -1,10 +1,6 @@
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+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say hello in one word"}],"model":"claude-3-5-haiku-20241022","stop_sequences":["\nObservation:","\nThought:"],"stream":false}'
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-      word"}],"model":"claude-3-5-haiku-20241022","stop_sequences":["\nObservation:","\nThought:"],"stream":false}'
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"Say hello in one word"}],"model":"claude-3-5-haiku-20241022","stop_sequences":["\nObservation:","\nThought:"],"stream":false}'
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diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_thinking.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_thinking.yaml
index c46f7b3b5..3ab7cbff6 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_thinking.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_thinking.yaml
@@ -1,7 +1,6 @@
 interactions:
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-      in Tokyo?"}],"model":"claude-sonnet-4-5","stream":false,"thinking":{"type":"enabled","budget_tokens":5000}}'
+    body: '{"max_tokens":10000,"messages":[{"role":"user","content":"What is the weather in Tokyo?"}],"model":"claude-sonnet-4-5","stream":false,"thinking":{"type":"enabled","budget_tokens":5000}}'
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diff --git a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_thinking_blocks_preserved_across_turns.yaml b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_thinking_blocks_preserved_across_turns.yaml
index 5cfd3cc78..1bff1c275 100644
--- a/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_thinking_blocks_preserved_across_turns.yaml
+++ b/lib/crewai/tests/cassettes/llms/anthropic/test_anthropic_thinking_blocks_preserved_across_turns.yaml
@@ -40,25 +40,12 @@ interactions:
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+      string: '{"model":"claude-sonnet-4-5-20250929","id":"msg_015YGo3nWoECsjkecM7cnkWC","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"This is a simple arithmetic question. 2+2 equals 4.","signature":"EtsBCkYIChgCKkANrO4e7QOyBt+X28FZKLvTzNoQDVSTVMHBDOKtdn00Qyust7+mKBLyfu25KPMJ1vxi7EBV2nWiTwBDAqS5ISPSEgw4osCEgxoIKxtF4aEaDNjFV1lDPtM2C3ZHSSIwORbCdva9l0QAc7PYzQBqw8kW/BDbEnwQSCctHhwrUQithEBZ74VLo2m4+RcuFEO5KkNy+rjfKsdyFeJLr89CoBJJOmCyrssMFA+JHnDALdbz9T0LwkTLhOe6H/OxdAEgE2C5BrQOhxxoujWWMpFS0KqB7KoUGAE="},{"type":"text","text":"2 + 2 = 4"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":43,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":36,"service_tier":"standard"}}'
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+    body: '{"max_tokens":10000,"messages":[{"role":"user","content":"What is 2+2?"},{"role":"assistant","content":[{"type":"thinking","thinking":"This is a simple arithmetic question. 2+2 equals 4.","signature":"EtsBCkYIChgCKkANrO4e7QOyBt+X28FZKLvTzNoQDVSTVMHBDOKtdn00Qyust7+mKBLyfu25KPMJ1vxi7EBV2nWiTwBDAqS5ISPSEgw4osCEgxoIKxtF4aEaDNjFV1lDPtM2C3ZHSSIwORbCdva9l0QAc7PYzQBqw8kW/BDbEnwQSCctHhwrUQithEBZ74VLo2m4+RcuFEO5KkNy+rjfKsdyFeJLr89CoBJJOmCyrssMFA+JHnDALdbz9T0LwkTLhOe6H/OxdAEgE2C5BrQOhxxoujWWMpFS0KqB7KoUGAE="},{"type":"text","text":"2 + 2 = 4"}]},{"role":"user","content":"Now what is 3+3?"}],"model":"claude-sonnet-4-5","stream":false,"thinking":{"type":"enabled","budget_tokens":5000}}'
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index 995331655..6ceaa8f1f 100644
--- a/lib/crewai/tests/cassettes/llms/azure/test_azure_async_conversation.yaml
+++ b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_conversation.yaml
@@ -64,4 +64,69 @@ interactions:
     status:
       code: 200
       message: OK
+- request:
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+      authorization:
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+      x-ms-client-request-id:
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+    uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-08-01-preview
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+      message: OK
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diff --git a/lib/crewai/tests/cassettes/llms/azure/test_azure_async_multiple_calls.yaml b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_multiple_calls.yaml
index 7df4dd9f7..d8fa406ca 100644
--- a/lib/crewai/tests/cassettes/llms/azure/test_azure_async_multiple_calls.yaml
+++ b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_multiple_calls.yaml
@@ -125,4 +125,130 @@ interactions:
     status:
       code: 200
       message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": "What is 1+1?"}], "stream": false}'
+    headers:
+      Accept:
+      - application/json
+      Content-Length:
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+      Content-Type:
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+      authorization:
+      - AUTHORIZATION-XXX
+      x-ms-client-request-id:
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+    method: POST
+    uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-08-01-preview
+  response:
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+
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+      x-request-id:
+      - X-REQUEST-ID-XXX
+    status:
+      code: 200
+      message: OK
+- request:
+    body: '{"messages": [{"role": "user", "content": "What is 2+2?"}], "stream": false}'
+    headers:
+      Accept:
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+      Content-Length:
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+      authorization:
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+      x-ms-client-request-id:
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+    method: POST
+    uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-08-01-preview
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+
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diff --git a/lib/crewai/tests/cassettes/llms/azure/test_azure_async_non_streaming.yaml b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_non_streaming.yaml
index 63e3cc385..902d73367 100644
--- a/lib/crewai/tests/cassettes/llms/azure/test_azure_async_non_streaming.yaml
+++ b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_non_streaming.yaml
@@ -62,4 +62,67 @@ interactions:
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       message: OK
+- request:
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+      authorization:
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+    method: POST
+    uri: https://fake-azure-endpoint.openai.azure.com/openai/deployments/gpt-4o-mini/chat/completions?api-version=2024-08-01-preview
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diff --git a/lib/crewai/tests/cassettes/llms/azure/test_azure_async_streaming_returns_usage_metrics.yaml b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_streaming_returns_usage_metrics.yaml
new file mode 100644
index 000000000..9ceb644ac
--- /dev/null
+++ b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_streaming_returns_usage_metrics.yaml
@@ -0,0 +1,627 @@
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index d0d749472..c41d0919b 100644
--- a/lib/crewai/tests/cassettes/llms/azure/test_azure_async_with_max_tokens.yaml
+++ b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_with_max_tokens.yaml
@@ -63,4 +63,68 @@ interactions:
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       message: OK
+- request:
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diff --git a/lib/crewai/tests/cassettes/llms/azure/test_azure_async_with_parameters.yaml b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_with_parameters.yaml
index ba421ef48..1e54ffa47 100644
--- a/lib/crewai/tests/cassettes/llms/azure/test_azure_async_with_parameters.yaml
+++ b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_with_parameters.yaml
@@ -65,4 +65,70 @@ interactions:
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       message: OK
+- request:
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index 9db9bb2c3..d145cea7e 100644
--- a/lib/crewai/tests/cassettes/llms/azure/test_azure_async_with_system_message.yaml
+++ b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_with_system_message.yaml
@@ -63,4 +63,68 @@ interactions:
     status:
       code: 200
       message: OK
+- request:
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index 67352b464..dbed08395 100644
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+++ b/lib/crewai/tests/cassettes/llms/azure/test_azure_async_with_temperature.yaml
@@ -62,4 +62,67 @@ interactions:
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+        Madrid\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"9l28BaWYbBTPw\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
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+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\"
+        also\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"c0o2NkjXaOZrDxC\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\"
+        recognized\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"nmqaGvryt\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\"
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+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\"
+        its\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\"
+        lively\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"5T3ThhxaLOpQT\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\"
+        nightlife\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"8diAuiYSsT\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\",\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"yUi\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\"
+        renowned\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"iJ1JVAEJxsR\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\"
+        cuisine\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"GIZu9vN5Wiy5\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\",\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"Ha5\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\"
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+        major\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"tDJBgG3QYv0gnu\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
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+        center\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"kX2pRv7vNVTRc\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
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+        business\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"WxafYRVotG4\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
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+        in\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"s\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
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+        Europe\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"YKDMTTKrvFaL9\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
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+        city's\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"ntv6Pr0eYktNH\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
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+        strategic\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"ZNrao9j0uf\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
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+        historically\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"qAQoeEB\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
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+        {\"choices\":[{\"content_filter_results\":{\"hate\":{\"filtered\":false,\"severity\":\"safe\"},\"self_harm\":{\"filtered\":false,\"severity\":\"safe\"},\"sexual\":{\"filtered\":false,\"severity\":\"safe\"},\"violence\":{\"filtered\":false,\"severity\":\"safe\"}},\"delta\":{\"content\":\"
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+        Madrid\"},\"finish_reason\":null,\"index\":0,\"logprobs\":null}],\"created\":1765403271,\"id\":\"chatcmpl-ClMKFV5p9W2uG31bdSFDN2v7JksnH\",\"model\":\"gpt-4o-mini-2024-07-18\",\"obfuscation\":\"SOJT5fUTm9QBM\",\"object\":\"chat.completion.chunk\",\"system_fingerprint\":\"fp_efad92c60b\",\"usage\":null}\n\ndata:
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index 5cfdf1707..850f95b93 100644
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index 55152b565..dc03b8851 100644
--- a/lib/crewai/tests/cassettes/llms/google/test_gemini_async_with_system_message.yaml
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diff --git a/lib/crewai/tests/cassettes/llms/hooks/TestAnthropicHeaderInterceptor.test_header_interceptor_with_real_call.yaml b/lib/crewai/tests/cassettes/llms/hooks/TestAnthropicHeaderInterceptor.test_header_interceptor_with_real_call.yaml
index a8b80887d..76cce49af 100644
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+++ b/lib/crewai/tests/cassettes/llms/hooks/TestAnthropicHeaderInterceptor.test_header_interceptor_with_real_call.yaml
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index 43420ce57..7da1edc92 100644
--- a/lib/crewai/tests/cassettes/llms/hooks/TestAnthropicInterceptorIntegration.test_anthropic_call_with_interceptor_tracks_requests.yaml
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index 57ec60b18..c37041f44 100644
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index f7ca34230..adf7a422e 100644
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diff --git a/lib/crewai/tests/cassettes/llms/openai/test_openai_async_with_max_tokens.yaml b/lib/crewai/tests/cassettes/llms/openai/test_openai_async_with_max_tokens.yaml
index 0aed3c2de..5d2c72a30 100644
--- a/lib/crewai/tests/cassettes/llms/openai/test_openai_async_with_max_tokens.yaml
+++ b/lib/crewai/tests/cassettes/llms/openai/test_openai_async_with_max_tokens.yaml
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index 2329580c4..cd93815bc 100644
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index e9302c78e..cde939813 100644
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+++ b/lib/crewai/tests/cassettes/llms/openai/test_openai_async_with_system_message.yaml
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 interactions:
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index 14235eb72..6d91379b5 100644
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diff --git a/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save.yaml b/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save.yaml
index bea0aca42..bad76d161 100644
--- a/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save.yaml
+++ b/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save.yaml
@@ -47,8 +47,6 @@ interactions:
       - 929ab3937d457e01-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -151,8 +149,6 @@ interactions:
       - 929ab3965ba47dfb-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -344,8 +340,6 @@ interactions:
       - 929ab399fe8d7dee-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -453,8 +447,6 @@ interactions:
       - 929ab3a78f7f7e01-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -607,8 +599,6 @@ interactions:
       - 929ab3ab8cf37dee-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_using_crew_without_memory_flag[save].yaml b/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_using_crew_without_memory_flag[save].yaml
index fe832aa81..92b756446 100644
--- a/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_using_crew_without_memory_flag[save].yaml
+++ b/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_using_crew_without_memory_flag[save].yaml
@@ -137,8 +137,6 @@ interactions:
       - 92f576d83a447e05-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_using_crew_without_memory_flag[search].yaml b/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_using_crew_without_memory_flag[search].yaml
index d29762490..4828a8189 100644
--- a/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_using_crew_without_memory_flag[search].yaml
+++ b/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_using_crew_without_memory_flag[search].yaml
@@ -145,8 +145,6 @@ interactions:
       - 92f576a01d3b7e05-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_with_memory_flag[save].yaml b/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_with_memory_flag[save].yaml
index 2268e127b..8f9756164 100644
--- a/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_with_memory_flag[save].yaml
+++ b/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_with_memory_flag[save].yaml
@@ -59,8 +59,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"input": ["Perform a search on specific topics."], "model": "text-embedding-3-small",
-      "encoding_format": "base64"}'
+    body: '{"input": ["Perform a search on specific topics."], "model": "text-embedding-3-small", "encoding_format": "base64"}'
     headers:
       accept:
       - application/json
@@ -97,17 +96,13 @@ interactions:
     method: POST
     uri: https://api.openai.com/v1/embeddings
   response:
-    content: "{\n  \"object\": \"list\",\n  \"data\": [\n    {\n      \"object\":
-      \"embedding\",\n      \"index\": 0,\n      \"embedding\": \"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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
-      312,\n    \"total_tokens\": 312\n  }\n}\n"
+    content: "{\n  \"object\": \"list\",\n  \"data\": [\n    {\n      \"object\": \"embedding\",\n      \"index\": 0,\n      \"embedding\": 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\"\
+      \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 312,\n    \"total_tokens\": 312\n  }\n}\n"
     headers:
       CF-RAY:
       - 92f57669ba517df2-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -504,44 +437,10 @@ interactions:
     http_version: HTTP/1.1
     status_code: 200
 - request:
-    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
-      completed based on the description, expected output, and actual results.\n\nTask
-      Description:\nPerform a search on specific topics.\n\nExpected Output:\nA list
-      of relevant URLs based on the search query.\n\nActual Output:\nI now can give
-      a great answer. \n\nFinal Answer: Here are some relevant URLs based on your
-      search query. Please visit the following links for comprehensive information
-      on the specified topics:\n\n1. **Artificial Intelligence Ethics**\n   - https://www.aaai.org/Ethics/AIEthics.pdf\n   -
-      https://plato.stanford.edu/entries/ethics-ai/\n\n2. **Impact of 5G Technology**\n   -
-      https://www.itu.int/en/ITU-T/focusgroups/5g/Documents/FG-5G-DOC-1830.zip\n   -
-      https://www.gsma.com/5g/\n\n3. **Quantum Computing Developments**\n   - https://www.ibm.com/quantum-computing/\n   -
-      https://www.microsoft.com/en-us/quantum\n\n4. **Cybersecurity Trends 2023**\n   -
-      https://www.csoonline.com/article/3642552/cybersecurity-trends-2023.html\n   -
-      https://www.forbes.com/sites/bernardmarr/2023/01/03/top-5-cybersecurity-trends-in-2023/\n\n5.
-      **Sustainable Technology Innovations**\n   - https://www.weforum.org/agenda/2023/01/10-innovations-sustainability/\n   -
-      https://www.greenbiz.com/article/13-sustainable-tech-solutions-watch-2023\n\nFeel
-      free to explore these URLs for detailed content on each topic.\n\nPlease provide:\n-
-      Bullet points suggestions to improve future similar tasks\n- A score from 0
-      to 10 evaluating on completion, quality, and overall performance- Entities extracted
-      from the task output, if any, their type, description, and relationships"}],
-      "model": "gpt-4o-mini", "tool_choice": {"type": "function", "function": {"name":
-      "TaskEvaluation"}}, "tools": [{"type": "function", "function": {"name": "TaskEvaluation",
-      "description": "Correctly extracted `TaskEvaluation` with all the required parameters
-      with correct types", "parameters": {"$defs": {"Entity": {"properties": {"name":
-      {"description": "The name of the entity.", "title": "Name", "type": "string"},
-      "type": {"description": "The type of the entity.", "title": "Type", "type":
-      "string"}, "description": {"description": "Description of the entity.", "title":
-      "Description", "type": "string"}, "relationships": {"description": "Relationships
-      of the entity.", "items": {"type": "string"}, "title": "Relationships", "type":
-      "array"}}, "required": ["name", "type", "description", "relationships"], "title":
-      "Entity", "type": "object"}}, "properties": {"suggestions": {"description":
-      "Suggestions to improve future similar tasks.", "items": {"type": "string"},
-      "title": "Suggestions", "type": "array"}, "quality": {"description": "A score
-      from 0 to 10 evaluating on completion, quality, and overall performance, all
-      taking into account the task description, expected output, and the result of
-      the task.", "title": "Quality", "type": "number"}, "entities": {"description":
-      "Entities extracted from the task output.", "items": {"$ref": "#/$defs/Entity"},
-      "title": "Entities", "type": "array"}}, "required": ["entities", "quality",
-      "suggestions"], "type": "object"}}}]}'
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+      https://www.forbes.com/sites/bernardmarr/2023/01/03/top-5-cybersecurity-trends-in-2023/\n\n5. **Sustainable Technology Innovations**\n   - https://www.weforum.org/agenda/2023/01/10-innovations-sustainability/\n   - https://www.greenbiz.com/article/13-sustainable-tech-solutions-watch-2023\n\nFeel free to explore these URLs for detailed content on each topic.\n\nPlease provide:\n- Bullet points suggestions to improve future similar tasks\n- A score from 0 to 10 evaluating on completion, quality, and overall performance- Entities extracted from the task output, if any, their type, description, and relationships"}], "model": "gpt-4o-mini", "tool_choice": {"type": "function", "function": {"name": "TaskEvaluation"}}, "tools": [{"type": "function", "function": {"name": "TaskEvaluation", "description": "Correctly extracted `TaskEvaluation` with all the required parameters with correct types", "parameters": {"$defs": {"Entity": {"properties": {"name": {"description": "The name of the entity.",
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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+      \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 24,\n    \"total_tokens\": 24\n  }\n}\n"
     headers:
       CF-RAY:
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       Date:
@@ -869,9 +721,7 @@ interactions:
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     status_code: 200
 - request:
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-      and research in quantum computing technology."], "model": "text-embedding-3-small",
-      "encoding_format": "base64"}'
+    body: '{"input": ["Quantum Computing Developments(Field): Recent advancements and research in quantum computing technology."], "model": "text-embedding-3-small", "encoding_format": "base64"}'
     headers:
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@@ -884,8 +734,7 @@ interactions:
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       cookie:
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@@ -911,17 +760,13 @@ interactions:
     method: POST
     uri: https://api.openai.com/v1/embeddings
   response:
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
-      20,\n    \"total_tokens\": 20\n  }\n}\n"
+    content: "{\n  \"object\": \"list\",\n  \"data\": [\n    {\n      \"object\": \"embedding\",\n      \"index\": 0,\n      \"embedding\": 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\"\
+      \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 20,\n    \"total_tokens\": 20\n  }\n}\n"
     headers:
       CF-RAY:
       - 92f5769d89287e0a-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -1181,39 +1012,16 @@ interactions:
     uri: https://pypi.org/pypi/agentops/json
   response:
     body:
-      string: '{"info":{"author":null,"author_email":"Alex Reibman <areibman@gmail.com>,
-        Shawn Qiu <siyangqiu@gmail.com>, Braelyn Boynton <bboynton97@gmail.com>, Howard
-        Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush
-        Shukla <ps4534@nyu.edu>, Travis Dent <tcdent@gmail.com>, Dwij Patel <dwijpatel1704@gmail.com>,
-        Fenil Faldu <fenilfaldu143@gmail.com>","bugtrack_url":null,"classifiers":["License
-        :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming
-        Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming
-        Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming
-        Language :: Python :: 3.13","Programming Language :: Python :: 3.9"],"description":"","description_content_type":null,"docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":null,"keywords":null,"license":null,"license_expression":null,"license_files":["LICENSE"],"maintainer":null,"maintainer_email":null,"name":"agentops","package_url":"https://pypi.org/project/agentops/","platform":null,"project_url":"https://pypi.org/project/agentops/","project_urls":{"Homepage":"https://github.com/AgentOps-AI/agentops","Issues":"https://github.com/AgentOps-AI/agentops/issues"},"provides_extra":null,"release_url":"https://pypi.org/project/agentops/0.4.16/","requires_dist":["httpx<0.29.0,>=0.24.0","opentelemetry-api==1.29.0;
-        python_version < \"3.10\"","opentelemetry-api>1.29.0; python_version >= \"3.10\"","opentelemetry-exporter-otlp-proto-http==1.29.0;
-        python_version < \"3.10\"","opentelemetry-exporter-otlp-proto-http>1.29.0;
-        python_version >= \"3.10\"","opentelemetry-instrumentation==0.50b0; python_version
-        < \"3.10\"","opentelemetry-instrumentation>=0.50b0; python_version >= \"3.10\"","opentelemetry-sdk==1.29.0;
-        python_version < \"3.10\"","opentelemetry-sdk>1.29.0; python_version >= \"3.10\"","opentelemetry-semantic-conventions==0.50b0;
-        python_version < \"3.10\"","opentelemetry-semantic-conventions>=0.50b0; python_version
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diff --git a/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_with_memory_flag[search].yaml b/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_with_memory_flag[search].yaml
index da0a945e8..d0eeeb33c 100644
--- a/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_with_memory_flag[search].yaml
+++ b/lib/crewai/tests/cassettes/memory/test_crew_external_memory_save_with_memory_flag[search].yaml
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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+    content: "{\n  \"object\": \"list\",\n  \"data\": [\n    {\n      \"object\": \"embedding\",\n      \"index\": 0,\n      \"embedding\": 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+      \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 606,\n    \"total_tokens\": 606\n  }\n}\n"
     headers:
       CF-RAY:
       - 92f57602bbdf7e0d-GRU
       Connection:
       - keep-alive
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       Date:
@@ -530,55 +440,11 @@ interactions:
     http_version: HTTP/1.1
     status_code: 200
 - request:
-    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
-      completed based on the description, expected output, and actual results.\n\nTask
-      Description:\nPerform a search on specific topics.\n\nExpected Output:\nA list
-      of relevant URLs based on the search query.\n\nActual Output:\nI now can give
-      a great answer  \nFinal Answer: \n\n1. **Artificial Intelligence in Healthcare**\n   -
-      URL: [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073215/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073215/)
-      - This article explores various applications of AI in healthcare, including
-      diagnostics and treatment personalization.\n\n2. **Blockchain Technology and
-      Its Impact on Supply Chain**\n   - URL: [https://www.researchgate.net/publication/341717309_Blockchain_Technology_in_Supply_Chain_Management](https://www.researchgate.net/publication/341717309_Blockchain_Technology_in_Supply_Chain_Management)
-      - This research paper discusses the potential of blockchain in enhancing supply
-      chain transparency and efficiency.\n\n3. **Cybersecurity Trends for 2023**\n   -
-      URL: [https://www.cybersecurity-insiders.com/cybersecurity-trends-2023/](https://www.cybersecurity-insiders.com/cybersecurity-trends-2023/)
-      - This resource outlines the major cybersecurity trends expected to shape the
-      industry in 2023, including emerging threats and mitigation strategies.\n\n4.
-      **The Impact of Remote Work on Productivity**\n   - URL: [https://www.mitpressjournals.org/doi/full/10.1162/99608f92.2020.12.01](https://www.mitpressjournals.org/doi/full/10.1162/99608f92.2020.12.01)
-      - This journal article provides insights into how remote work affects productivity,
-      work-life balance, and organizational dynamics.\n\n5. **Quantum Computing: A
-      Beginner''s Guide**\n   - URL: [https://www.ibm.com/quantum-computing/learn/what-is-quantum-computing/](https://www.ibm.com/quantum-computing/learn/what-is-quantum-computing/)
-      - This resource serves as an introduction to quantum computing, detailing its
-      principles and potential applications.\n\n6. **Sustainable Energy Technologies
-      for the Future**\n   - URL: [https://www.energy.gov/eere/solar/articles/sustainable-energy-technology-future](https://www.energy.gov/eere/solar/articles/sustainable-energy-technology-future)
-      - This article discusses various sustainable energy technologies that could
-      play a crucial role in future energy landscapes.\n\n7. **5G Technology and Its
-      Implications**\n   - URL: [https://www.qualcomm.com/invention/5g/what-is-5g](https://www.qualcomm.com/invention/5g/what-is-5g)
-      - This page explains what 5G technology is and explores its potential implications
-      for various sectors including telecommunications and the Internet of Things
-      (IoT). \n\nThese resources have been carefully selected to meet the specified
-      topics and provide comprehensive insights.\n\nPlease provide:\n- Bullet points
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-      "tools": [{"type": "function", "function": {"name": "TaskEvaluation", "description":
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-      "The name of the entity.", "title": "Name", "type": "string"}, "type": {"description":
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-      future similar tasks.", "items": {"type": "string"}, "title": "Suggestions",
-      "type": "array"}, "quality": {"description": "A score from 0 to 10 evaluating
-      on completion, quality, and overall performance, all taking into account the
-      task description, expected output, and the result of the task.", "title": "Quality",
-      "type": "number"}, "entities": {"description": "Entities extracted from the
-      task output.", "items": {"$ref": "#/$defs/Entity"}, "title": "Entities", "type":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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+    content: "{\n  \"object\": \"list\",\n  \"data\": [\n    {\n      \"object\": \"embedding\",\n      \"index\": 0,\n      \"embedding\": 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+      \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 19,\n    \"total_tokens\": 19\n  }\n}\n"
     headers:
       CF-RAY:
       - 92f576409a3d7df4-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -1429,39 +1206,16 @@ interactions:
     uri: https://pypi.org/pypi/agentops/json
   response:
     body:
-      string: '{"info":{"author":null,"author_email":"Alex Reibman <areibman@gmail.com>,
-        Shawn Qiu <siyangqiu@gmail.com>, Braelyn Boynton <bboynton97@gmail.com>, Howard
-        Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush
-        Shukla <ps4534@nyu.edu>, Travis Dent <tcdent@gmail.com>, Dwij Patel <dwijpatel1704@gmail.com>,
-        Fenil Faldu <fenilfaldu143@gmail.com>","bugtrack_url":null,"classifiers":["License
-        :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming
-        Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming
-        Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming
-        Language :: Python :: 3.13","Programming Language :: Python :: 3.9"],"description":"","description_content_type":null,"docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":null,"keywords":null,"license":null,"license_expression":null,"license_files":["LICENSE"],"maintainer":null,"maintainer_email":null,"name":"agentops","package_url":"https://pypi.org/project/agentops/","platform":null,"project_url":"https://pypi.org/project/agentops/","project_urls":{"Homepage":"https://github.com/AgentOps-AI/agentops","Issues":"https://github.com/AgentOps-AI/agentops/issues"},"provides_extra":null,"release_url":"https://pypi.org/project/agentops/0.4.16/","requires_dist":["httpx<0.29.0,>=0.24.0","opentelemetry-api==1.29.0;
-        python_version < \"3.10\"","opentelemetry-api>1.29.0; python_version >= \"3.10\"","opentelemetry-exporter-otlp-proto-http==1.29.0;
-        python_version < \"3.10\"","opentelemetry-exporter-otlp-proto-http>1.29.0;
-        python_version >= \"3.10\"","opentelemetry-instrumentation==0.50b0; python_version
-        < \"3.10\"","opentelemetry-instrumentation>=0.50b0; python_version >= \"3.10\"","opentelemetry-sdk==1.29.0;
-        python_version < \"3.10\"","opentelemetry-sdk>1.29.0; python_version >= \"3.10\"","opentelemetry-semantic-conventions==0.50b0;
-        python_version < \"3.10\"","opentelemetry-semantic-conventions>=0.50b0; python_version
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diff --git a/lib/crewai/tests/cassettes/telemetry/test_crew_execution_span_assigned_on_kickoff_async.yaml b/lib/crewai/tests/cassettes/telemetry/test_crew_execution_span_assigned_on_kickoff_async.yaml
index f37cb1c3c..ac780c5d0 100644
--- a/lib/crewai/tests/cassettes/telemetry/test_crew_execution_span_assigned_on_kickoff_async.yaml
+++ b/lib/crewai/tests/cassettes/telemetry/test_crew_execution_span_assigned_on_kickoff_async.yaml
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index 6ddde4318..41e4e2110 100644
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diff --git a/lib/crewai/tests/cassettes/telemetry/test_crew_execution_span_not_set_when_share_crew_false.yaml b/lib/crewai/tests/cassettes/telemetry/test_crew_execution_span_not_set_when_share_crew_false.yaml
index 093ba97ac..b31b914b6 100644
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+++ b/lib/crewai/tests/cassettes/telemetry/test_crew_execution_span_not_set_when_share_crew_false.yaml
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index 3d0f3a9cc..8ab9fefc4 100644
--- a/lib/crewai/tests/cassettes/telemetry/test_telemetry_fails_due_connect_timeout.yaml
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index e9d6926b0..885fda517 100644
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+      integrated biometric sensors for health monitoring, and smartphone connectivity. These features improve rider safety, navigation accuracy, and personalized riding experiences (Source: Journal of Smart Transportation, March 2025).\n\n3. **Sustainable Bicycle Manufacturing Using Recycled and Bio-based Materials**  \n   The Bicycle Industry Sustainability Report 2025 by the Global Cycling Federation (GCF) reveals that over 40% of new bicycle frames now incorporate recycled aluminum, bio-based composites from plant fibers, or sustainably sourced bamboo. Vegan leather alternatives for grips and saddles are also increasingly used, reducing the environmental footprint of bicycle production (Source: GCF Sustainability Report, 2025).\n\n4. **Growth of E-Cargo Bikes for Urban Logistics**  \n   A 2025 white paper from Urban Mobility Solutions highlights that electric cargo bikes account for 20% of commercial deliveries in major European and North American cities. Their popularity is attributed
+      to reduced emissions, lower operating costs, and ease of maneuvering in congested urban areas (Source: Urban Mobility Solutions, 2025).\n\n5. **Adaptive Bicycles Enhancing Mobility for Disabled Riders**  \n   The Assistive Cycling Technologies Consortium’s 2025 annual review documents advances such as modular handcycles with electric assist, customized recumbent trikes with adjustable ergonomics, and sensor-based balance aids, improving cycling accessibility for riders with disabilities worldwide (Source: Assistive Cycling Technologies Consortium, 2025).\n\n6. **Advanced Safety Systems: Collision Detection and Smart Helmets**  \n   A 2025 report from the Institute of Transportation Safety presents the rise of bicycles fitted with radar-based collision detection systems and smart helmets equipped with AR displays, emergency crash sensors, and integrated communication devices, significantly reducing accident severity and response times (Source: Institute of Transportation Safety, 2025).\n\n7.
+      **Popularity of Gravel Bikes Driven by Multi-Terrain Versatility**  \n   According to Cycling Trends Quarterly (Q1 2025), gravel bikes have surged in demand, with top manufacturers improving tire tread innovations and frames using carbon-cobalt composites to balance durability and lightness, catering to riders seeking adventure beyond paved roads (Source: Cycling Trends Quarterly, 2025).\n\n8. **Expansion and Technological Enhancements in Urban Bike Share Programs**  \n   Data from the World Urban Cycling Council 2025 indicates that over 150 cities have upgraded bike-share fleets to include electric and smart bikes with integrated QR code payments and real-time availability tracking apps, promoting sustainable, accessible urban transportation (Source: World Urban Cycling Council, 2025).\n\n9. **Continued Innovation in Lightweight Carbon and Composite Frames**  \n   The 2025 Bicycle Materials Symposium reports that advanced CAD and AI-assisted design paired with new aerospace-grade
+      carbon composites have reduced frame weights by up to 15% compared to 2020 models, aiding both competitive racing and leisure cycling by enhancing speed and ride efficiency (Source: Bicycle Materials Symposium Proceedings, 2025).\n\n10. **Government Investment in Cycling Infrastructure and Supportive Policy Changes**  \n    According to the 2025 Global Transport Policy Review by the United Nations, more than 60 countries have increased budget allocations for cycling infrastructure, including expanded cycle lanes, secure parking, and e-bike charging stations. Complementary regulatory updates support helmet usage, traffic calming measures, and lower speed limits in urban centers to foster safer cycling environments (Source: United Nations Global Transport Policy Review, 2025).\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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+        \ have fundamentally redefined e-bike performance. These batteries provide a remarkable increase in travel range, with rides extending up to 150 kilometers on a single charge. Moreover, recharge times have been drastically reduced to under 30 minutes, considerably improving usability and convenience for daily commuters and recreational riders alike. Their advanced chemistry eliminates many of the safety issues associated with traditional lithium-ion cells, offering enhanced thermal stability and reducing the risk of fires or explosions. This breakthrough enables manufacturers to build lighter, safer, and longer-lasting e-bike models while addressing one of the critical barriers to widespread adoption.\\n\\nThe implications of such technological progress extend beyond consumer benefits; the enhanced battery efficiency supports broader sustainability goals by encouraging more urban dwellers to choose e-bikes over traditional fossil-fuel-powered transport modes, which helps reduce greenhouse\
+        \ gas emissions and urban congestion.\\n\\n---\\n\\n## 2. Smart Bicycle Integration with IoT and AI\\n\\nThe evolution of traditional bicycles into smart, connected devices has substantially increased rider safety, navigation efficiency, and personalization. A 2025 study featured in the Journal of Smart Transportation highlights the widespread integration of Internet of Things (IoT) and Artificial Intelligence (AI) technologies in modern bicycles.\\n\\nModern smart bicycles are equipped with GPS-enabled anti-theft systems that provide real-time location tracking to prevent losses. AI-powered route optimization algorithms analyze traffic patterns, terrain, and rider preferences to suggest the most efficient and safest paths. Integrated biometric sensors monitor health parameters such as heart rate, oxygen saturation, and cadence, enabling riders to make data-informed decisions about their exertion and endurance levels.\\n\\nConnectivity with smartphones allows seamless data synchronization,\
+        \ user notifications, and ride analytics, facilitating a personalized riding experience. These advancements have improved navigation accuracy, mitigated risks during commuting, and created opportunities for fitness tracking and performance enhancement. Overall, these smart features are transforming bicycles from simple mobility tools into sophisticated, user-centric platforms for urban and recreational mobility.\\n\\n---\\n\\n## 3. Sustainable Bicycle Manufacturing Using Recycled and Bio-based Materials\\n\\nSustainability has become a paramount concern in bicycle manufacturing, leading to innovative uses of recycled and bio-based materials. The 2025 Bicycle Industry Sustainability Report by the Global Cycling Federation (GCF) reveals a notable trend toward reducing the environmental footprint of bicycles.\\n\\nCurrently, over 40% of new bicycle frames incorporate recycled aluminum, substantially reducing the reliance on virgin metal extraction and lowering carbon emissions associated\
+        \ with metal production. Beyond metal, manufacturers are employing bio-based composites derived from plant fibers such as flax, hemp, and kenaf, leveraging their strength, lightweight characteristics, and renewability. Bamboo, a naturally fast-growing and sustainable resource, is increasingly used in frame construction, contributing to both aesthetic appeal and environmental responsibility.\\n\\nIn addition, many companies have substituted traditional leather with vegan leather alternatives made from sustainable materials like cork or plant-based polymers for grips and saddles. This comprehensive approach in material sourcing and production processes is driving the industry toward circular economy principles, inspiring consumer confidence in bicycles as eco-friendly transportation options.\\n\\n---\\n\\n## 4. Growth of E-Cargo Bikes for Urban Logistics\\n\\nThe expanding role of electric cargo bikes in urban logistics is reshaping last-mile delivery paradigms in major cities. According\
+        \ to a white paper by Urban Mobility Solutions (2025), e-cargo bikes now contribute to approximately 20% of commercial deliveries in prominent metropolitan areas across Europe and North America.\\n\\nE-cargo bikes offer significant advantages over conventional delivery vehicles. Their zero emissions and silent operation support urban air quality and noise reduction goals. The smaller footprint and agility of e-cargo bikes ease maneuvering through traffic congestion, pedestrian zones, and narrow streets, facilitating efficient and timely deliveries. Additionally, reduced operating and maintenance costs make e-cargo bikes financially attractive for businesses adapting to increasingly stringent regulatory environments.\\n\\nThis shift supports sustainable urban logistics strategies by complementing public transit infrastructure and offering scalable, eco-friendly delivery solutions that align with city planners’ objectives of reducing carbon emissions and improving urban livability.\\\
+        n\\n---\\n\\n## 5. Adaptive Bicycles Enhancing Mobility for Disabled Riders\\n\\nAdaptive bicycles are gaining recognition for transforming mobility and recreational opportunities for individuals with disabilities. The Assistive Cycling Technologies Consortium’s 2025 annual review catalogues innovative developments that have expanded cycling accessibility worldwide.\\n\\nKey advances include modular handcycles featuring electric assist systems that provide customizable power output to support riders with limited lower-body mobility. Customized recumbent tricycles with adjustable ergonomic components allow tailored seating and control configurations to accommodate diverse physical needs and improve comfort during rides. Sensor-based balance aids employ real-time feedback to assist riders with balance impairments, enhancing safety and confidence.\\n\\nThese technological and design innovations remove many physical barriers to cycling, promoting inclusivity and health benefits for disabled\
+        \ riders and enabling them to participate more fully in active lifestyles.\\n\\n---\\n\\n## 6. Advanced Safety Systems: Collision Detection and Smart Helmets\\n\\nSafety remains a critical focus within the cycling ecosystem, driven by the integration of advanced technological systems into bicycles and rider equipment. The 2025 report from the Institute of Transportation Safety underscores the rise of radar-based collision detection systems and smart helmet technologies.\\n\\nRadar collision detection utilizes sensors mounted on bikes that monitor the proximity of vehicles, pedestrians, and obstacles, providing riders with alerts to prevent imminent crashes. These systems can also interface with mobile apps or bike displays to inform riders proactively and potentially trigger emergency brake assistance.\\n\\nSmart helmets have evolved to include augmented reality (AR) displays projecting navigation, speed, and hazard warnings directly into the rider’s field of vision. Emergency crash\
+        \ sensors embedded in helmets can detect impact severity and automatically notify emergency contacts or dispatch services with GPS coordinates, significantly improving response times. Integrated communication devices allow hands-free calling and messaging, enhancing connectivity without compromising safety.\\n\\nThe convergence of these technologies enhances accident prevention and post-incident response, collectively contributing to safer cycling environments.\\n\\n---\\n\\n## 7. Popularity of Gravel Bikes Driven by Multi-Terrain Versatility\\n\\nGravel bikes have surged in popularity as versatile machines that cater to riders seeking exploration beyond paved urban roadways. As documented in the Q1 2025 edition of Cycling Trends Quarterly, gravel bikes represent a rapidly growing segment characterized by innovation in materials and tire technologies.\\n\\nTop manufacturers are investing in tire tread design improvements that optimize traction, shock absorption, and rolling efficiency\
+        \ on diverse surfaces such as gravel, dirt, mud, and pavement. Concurrently, frame construction has advanced with the adoption of carbon-cobalt composites, a material blend that balances the need for lightweight performance and durability against rough terrains.\\n\\nThis combination allows riders to confidently tackle multi-terrain environments without sacrificing comfort or speed. The growing appeal of gravel biking reflects broader recreational trends emphasizing adventure, endurance challenges, and the pursuit of more varied outdoor experiences.\\n\\n---\\n\\n## 8. Expansion and Technological Enhancements in Urban Bike Share Programs\\n\\nUrban bike share programs are experiencing widespread expansion and enhancement, driven by technological innovations and increasing demand for sustainable urban transport. The World Urban Cycling Council’s 2025 data show that over 150 cities globally have upgraded their shared bike fleets to include electric and smart bicycle models.\\n\\nThese\
+        \ newer bike share systems incorporate integrated QR code payment solutions, simplifying user access and reducing friction in rental processes. Real-time availability tracking apps enable users to locate and reserve bikes efficiently, while smart lock systems enhance security and fleet management.\\n\\nThe inclusion of electric-assist bikes in shared fleets extends accessibility to a wider demographic by lowering physical exertion barriers, encouraging longer trips and broader use cases. Collectively, these improvements promote environmentally-friendly city transit options, reduce reliance on private automobiles, and support public health through active transportation.\\n\\n---\\n\\n## 9. Continued Innovation in Lightweight Carbon and Composite Frames\\n\\nMaterial science and design innovation continue to drive performance enhancements in bicycle frame construction. According to findings from the 2025 Bicycle Materials Symposium, advances in computer-aided design (CAD) and AI-assisted\
+        \ engineering have enabled the sport and recreational cycling industries to achieve significant weight reductions in frames.\\n\\nNew aerospace-grade carbon composites offer superior strength-to-weight ratios compared to previous generations, enabling up to a 15% reduction in frame mass relative to comparable 2020 models. Designers utilize AI to optimize frame geometry and composite layering, achieving maximum rigidity and ride responsiveness without compromising durability.\\n\\nThese enhancements benefit competitive racers through improved speed and energy efficiency, while leisure riders enjoy smoother handling and easier acceleration. The continuous development of lightweight materials underscores the industry's commitment to pushing the boundaries of cycling performance and user experience.\\n\\n---\\n\\n## 10. Government Investment in Cycling Infrastructure and Supportive Policy Changes\\n\\nGovernments worldwide are increasingly recognizing the importance of cycling as a sustainable\
+        \ mode of transportation and are accordingly ramping up investments and regulatory support. The 2025 Global Transport Policy Review by the United Nations highlights that more than 60 countries have expanded their budgets dedicated to cycling infrastructure.\\n\\nKey infrastructure improvements include the construction and extension of cycle lanes designed to improve safety and connectivity, the installation of secure bicycle parking facilities, and the establishment of e-bike charging stations to support electric mobility. Many municipalities are implementing traffic calming measures such as speed limit reductions in urban centers to create safer environments for cyclists of all ages and abilities.\\n\\nComplementary regulatory updates promote helmet usage and encourage inclusive road-sharing policies that integrate bicycles with other transport modes. These government-led initiatives collectively foster safer, more accessible, and more attractive cycling networks, incentivizing\
+        \ increased bicycle usage and contributing to national and global sustainability, public health, and urban mobility objectives.\\n\\n---\\n\\n# Conclusion\\n\\nThe bicycle industry in 2025 exemplifies dynamic growth and innovation across multiple dimensions—from technological breakthroughs in batteries and smart systems to sustainability-driven manufacturing and inclusive design for disabled riders. Urban mobility is being reshaped by electric cargo bikes and expanded bike share programs, while governments worldwide increasingly prioritize cycling through strategic infrastructure investment and supportive policies. Advances in safety equipment and lightweight materials continue to enhance rider experience and protection. Collectively, these trends signal a vibrant future for cycling as a cornerstone of sustainable, healthy, and accessible transportation around the globe.\\n\\n---\\n\\n**Sources:**\\n\\n- International Energy Agency (IEA), Global E-bike Market Report, 2025  \\n- Journal\
+        \ of Smart Transportation, March 2025  \\n- Global Cycling Federation (GCF), Bicycle Industry Sustainability Report, 2025  \\n- Urban Mobility Solutions, White Paper, 2025  \\n- Assistive Cycling Technologies Consortium, Annual Review 2025  \\n- Institute of Transportation Safety, Report 2025  \\n- Cycling Trends Quarterly, Q1 2025  \\n- World Urban Cycling Council, 2025 Data  \\n- Bicycle Materials Symposium Proceedings, 2025  \\n- United Nations Global Transport Policy Review, 2025\",\n        \"refusal\": null,\n        \"annotations\": []\n      },\n      \"logprobs\": null,\n      \"finish_reason\": \"stop\"\n    }\n  ],\n  \"usage\": {\n    \"prompt_tokens\": 1106,\n    \"completion_tokens\": 2239,\n    \"total_tokens\": 3345,\n    \"prompt_tokens_details\": {\n      \"cached_tokens\": 0,\n      \"audio_tokens\": 0\n    },\n    \"completion_tokens_details\": {\n      \"reasoning_tokens\": 0,\n      \"audio_tokens\": 0,\n      \"accepted_prediction_tokens\": 0,\n      \"rejected_prediction_tokens\"\
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index 115b27282..2dd6d9389 100644
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diff --git a/lib/crewai/tests/cassettes/test_anthropic_function_calling.yaml b/lib/crewai/tests/cassettes/test_anthropic_function_calling.yaml
index e03c219ba..fb7ab2401 100644
--- a/lib/crewai/tests/cassettes/test_anthropic_function_calling.yaml
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-      unit of temperature\"}},\"required\":[\"location\"]}}]}"
+    body: '{"max_tokens":4096,"messages":[{"role":"user","content":"What is the weather in Tokyo? Use the get_weather tool."},{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Eobgw8gDD6KegYJz1iyYC7","name":"get_weather","input":{"location":"Tokyo"}}]},{"role":"user","content":[{"type":"tool_result","tool_use_id":"toolu_01Eobgw8gDD6KegYJz1iyYC7","content":"The weather in Tokyo is sunny and 72°F"}]}],"model":"claude-sonnet-4-5","stream":false,"tools":[{"name":"get_weather","description":"Get the current weather in a given location","input_schema":{"type":"object","properties":{"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"},"unit":{"type":"string","enum":["celsius","fahrenheit"],"description":"The unit of temperature"}},"required":["location"]}}]}'
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diff --git a/lib/crewai/tests/cassettes/test_anthropic_thinking.yaml b/lib/crewai/tests/cassettes/test_anthropic_thinking.yaml
index 8a22c6ee2..0a9a73b75 100644
--- a/lib/crewai/tests/cassettes/test_anthropic_thinking.yaml
+++ b/lib/crewai/tests/cassettes/test_anthropic_thinking.yaml
@@ -1,7 +1,6 @@
 interactions:
 - request:
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-      in Tokyo?"}],"model":"claude-sonnet-4-5","stream":false,"thinking":{"type":"enabled","budget_tokens":5000}}'
+    body: '{"max_tokens":10000,"messages":[{"role":"user","content":"What is the weather in Tokyo?"}],"model":"claude-sonnet-4-5","stream":false,"thinking":{"type":"enabled","budget_tokens":5000}}'
     headers:
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@@ -41,35 +40,13 @@ interactions:
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+        don''t have access to real-time weather data, so I can''t tell you the current weather in Tokyo. \n\nTo get current weather information for Tokyo, you could:\n- Check weather websites like weather.com, accuweather.com, or weather.gov\n- Search \"Tokyo weather\" in any search engine\n- Use a weather app on your phone\n- Visit the Japan Meteorological Agency website (jma.go.jp)\n\nIs there anything else about Tokyo I can help you with?"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":43,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":172,"service_tier":"standard"}}'
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diff --git a/lib/crewai/tests/cassettes/test_anthropic_thinking_blocks_preserved_across_turns.yaml b/lib/crewai/tests/cassettes/test_anthropic_thinking_blocks_preserved_across_turns.yaml
index 3b5b70ad5..f5e2efc98 100644
--- a/lib/crewai/tests/cassettes/test_anthropic_thinking_blocks_preserved_across_turns.yaml
+++ b/lib/crewai/tests/cassettes/test_anthropic_thinking_blocks_preserved_across_turns.yaml
@@ -40,25 +40,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
-      string: !!binary |
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+      string: '{"model":"claude-sonnet-4-5-20250929","id":"msg_018jNqkB4oZoJMnf5rcGaVja","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"This is a simple arithmetic question. 2+2 equals 4.","signature":"EtsBCkYIChgCKkANrO4e7QOyBt+X28FZKLvTzNoQDVSTVMHBDOKtdn00Qyust7+mKBLyfu25KPMJ1vxi7EBV2nWiTwBDAqS5ISPSEgzy8fA2B0+wA5I3nBMaDGBC5LuZTYVFw/R6ryIwgnlwdEif5NJWNli1IlYD1jP8jJ5ell5/w17BJU9LTk+yeC6EHnLdtmfVMdlcF6flKkNFt7DVGLhdqtohBXxx4qmB8IKKp7M9A++M103ftST+4MjPHy9ehVxpNE0WHaoacHJAP0L6qIMkvjtafceejaklDL3aGAE="},{"type":"text","text":"2 + 2 = 4"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":43,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":36,"service_tier":"standard"}}'
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@@ -109,9 +96,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"max_tokens":10000,"messages":[{"role":"user","content":"What is 2+2?"},{"role":"assistant","content":[{"type":"thinking","thinking":"This
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-      + 2 = 4"}]},{"role":"user","content":"Now what is 3+3?"}],"model":"claude-sonnet-4-5","stream":false,"thinking":{"type":"enabled","budget_tokens":5000}}'
+    body: '{"max_tokens":10000,"messages":[{"role":"user","content":"What is 2+2?"},{"role":"assistant","content":[{"type":"thinking","thinking":"This is a simple arithmetic question. 2+2 equals 4.","signature":"EtsBCkYIChgCKkANrO4e7QOyBt+X28FZKLvTzNoQDVSTVMHBDOKtdn00Qyust7+mKBLyfu25KPMJ1vxi7EBV2nWiTwBDAqS5ISPSEgzy8fA2B0+wA5I3nBMaDGBC5LuZTYVFw/R6ryIwgnlwdEif5NJWNli1IlYD1jP8jJ5ell5/w17BJU9LTk+yeC6EHnLdtmfVMdlcF6flKkNFt7DVGLhdqtohBXxx4qmB8IKKp7M9A++M103ftST+4MjPHy9ehVxpNE0WHaoacHJAP0L6qIMkvjtafceejaklDL3aGAE="},{"type":"text","text":"2 + 2 = 4"}]},{"role":"user","content":"Now what is 3+3?"}],"model":"claude-sonnet-4-5","stream":false,"thinking":{"type":"enabled","budget_tokens":5000}}'
     headers:
       accept:
       - application/json
@@ -151,25 +136,12 @@ interactions:
     uri: https://api.anthropic.com/v1/messages
   response:
     body:
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+      string: '{"model":"claude-sonnet-4-5-20250929","id":"msg_01VDYjTx2TrfPBCk6zXNQQKT","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"This is a straightforward arithmetic question. 3 + 3 = 6.","signature":"EuEBCkYIChgCKkChAUTGRniU9a2nOcBaaty1R+x6p5TyaVU1OzGtKbya6AZK46fW8H3aOXVDDh6mj1xiHWpD29jgWgSOPZyLpNXMEgw2MB+McXKIFCHt7T8aDCC1Uv3X1nl/SE4XxiIwI0nBZraWXH4cAIosmXgkz8ozCqEF0FCMlbVV9xPA6oVzj/Z4LpyTXLTFMDQpJFYsKkkdUyZVdBCYqAxaHN87hkq8mk0bpKOBsSgj7P1vHV16dyX9TL2XVFEx9izHe4KPd17lUhuwxjLNqzfmRyR8bAmjGcJq7EdgAuxyGAE="},{"type":"text","text":"3 + 3 = 6"}],"stop_reason":"end_turn","stop_sequence":null,"usage":{"input_tokens":67,"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"cache_creation":{"ephemeral_5m_input_tokens":0,"ephemeral_1h_input_tokens":0},"output_tokens":39,"service_tier":"standard"}}'
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diff --git a/lib/crewai/tests/cassettes/test_api_calls_throttling.yaml b/lib/crewai/tests/cassettes/test_api_calls_throttling.yaml
index d13cc2b9d..db8871320 100644
--- a/lib/crewai/tests/cassettes/test_api_calls_throttling.yaml
+++ b/lib/crewai/tests/cassettes/test_api_calls_throttling.yaml
@@ -71,8 +71,6 @@ interactions:
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diff --git a/lib/crewai/tests/cassettes/test_before_crew_modification.yaml b/lib/crewai/tests/cassettes/test_before_crew_modification.yaml
index b476c30f7..4553885d3 100644
--- a/lib/crewai/tests/cassettes/test_before_crew_modification.yaml
+++ b/lib/crewai/tests/cassettes/test_before_crew_modification.yaml
@@ -1,21 +1,7 @@
 interactions:
 - request:
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-      latest developments in Bicycles. Known for your ability to find the most relevant
-      information and present it in a clear and concise manner.\n\nYour personal goal
-      is: Uncover cutting-edge developments in Bicycles\n\nTo give my best complete
-      final answer to the task use the exact following format:\n\nThought: I now can
-      give a great answer\nFinal Answer: Your final answer must be the great and the
-      most complete as possible, it must be outcome described.\n\nI MUST use these
-      formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent Task:
-      Conduct a thorough research about Bicycles Make sure you find any interesting
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-      the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],
-      "model": "gpt-4o", "stop": ["\nObservation:"], "stream": false}'
+    body: '{"messages": [{"role": "system", "content": "You are Bicycles Senior Data Researcher\n. You''re a seasoned researcher with a knack for uncovering the latest developments in Bicycles. Known for your ability to find the most relevant information and present it in a clear and concise manner.\n\nYour personal goal is: Uncover cutting-edge developments in Bicycles\n\nTo give my best complete final answer to the task use the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent Task: Conduct a thorough research about Bicycles Make sure you find any interesting and relevant information given the current year is 2024.\n\n\nThis is the expect criteria for your final answer: A list with 10 bullet points of the most relevant information about Bicycles\n\nyou MUST return
+      the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o", "stop": ["\nObservation:"], "stream": false}'
     headers:
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@@ -53,39 +39,10 @@ interactions:
     uri: https://api.openai.com/v1/chat/completions
   response:
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-      front and rear cameras, adaptive LED lights with automatic brightness control,
-      and collision detection systems alerting both riders and surrounding vehicles.
-      Notable implementations come from brands like Garmin and Lumos (Sources: Garmin
-      Bike Safety Features Release 2025, Lumos Smart Helmets Technical Specifications).\n\n6.
-      **Customization and Modular Bike Designs**: Modular frames and interchangeable
-      parts have become popular, allowing riders to customize their bikes for different
-      terrains or activities without purchasing new bicycles. Companies like Urwahn
-      and Tern promote modular versatility appealing to both urban and trail riders
-      (Sources: Urwahn Modular Frame Whitepaper 2025, Tern Bicycle Modular Design
-      Overview).\n\n7. **Cutting-Edge Racing Bikes with AI Design**: Professional
-      racing bikes utilize ultra-light carbon fiber composites and aerodynamic designs
-      optimized via AI-driven software, improving speed and energy efficiency. Integrated
-      telemetry systems provide real-time data to support race strategies. Leading
-      brands include Specialized and Trek (Sources: Specialized S-Works Innovation
-      Report 2025, Trek Race Tech Insights 2025).\n\n8. **Health and Fitness Integration
-      with Cycling Tech**: Modern bicycles are embedded with sensors that monitor
-      posture, power output, and suggest personalized workout plans through linked
-      apps. Peloton and Wahoo Fitness are at the forefront of integrating these capabilities
-      to transform cycling into a data-driven fitness experience (Sources: Peloton
-      Bike+ 2025 Product Guide, Wahoo Fitness Cycling Data Study).\n\n9. **Rise of
-      Cargo and Utility E-Bikes for Urban Logistics**: The use of cargo e-bikes has
-      surged for small business deliveries and family transport in urban areas, promoted
-      by companies such as Rad Power Bikes and Urban Arrow, offering electric assist
-      for heavy loads and longer distances (Sources: Rad Power Bikes Market Report
-      2025, Urban Arrow Product Catalog 2025).\n\n10. **Government Policies and Infrastructure
-      Fostering Cycling**: In 2025, many governments have enacted policies offering
-      subsidies and tax credits for electric bicycle purchases. Urban infrastructure
-      investments include extensive protected bike lanes and smart traffic signals
-      prioritizing cyclists, evident in cities like Copenhagen and Amsterdam (Sources:
-      Danish Government Transport Policy 2025, Amsterdam Cycling Infrastructure Report
-      2025).\n\nThese detailed, sourced points comprehensively capture the state-of-the-art
-      developments, sustainability efforts, technological advancements, and policy
-      frameworks shaping the bicycle industry and culture in 2025.\n\nBegin! This
-      is VERY important to you, use the tools available and give your best Final Answer,
-      your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
+    body: '{"messages":[{"role":"system","content":"You are Bicycles Reporting Analyst\n. You''re a meticulous analyst with a keen eye for detail. You''re known for your ability to turn complex data into clear and concise reports, making it easy for others to understand and act on the information you provide.\n\nYour personal goal is: Create detailed reports based on Bicycles data analysis and research findings\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"},{"role":"user","content":"\nCurrent Task: Review the context you got and expand each topic into a full section for a report. Make sure the report is detailed and contains any and all relevant information.\n\n\nThis is the expected criteria for your final answer: A fully fledge reports
+      with the mains topics, each with a full section of information. Formatted as markdown without ''```''\n\nyou MUST return the actual complete content as the final answer, not a summary.\n\nThis is the context you''re working with:\n1. **E-Bikes Innovation Leading the Market**: As of 2025, electric bicycles dominate global bicycle sales, with significant advancements in battery technology leading to ranges up to 150 miles per charge and charging times reduced to under one hour. Key industry leaders like Bosch and Shimano have introduced smart battery management systems improving energy efficiency and safety (Sources: Bosch Mobility Reports 2025, Shimano Press Release 2025).\n\n2. **Integration of IoT and Smart Features**: Modern bicycles increasingly feature IoT capabilities such as GPS tracking, real-time performance analytics, and theft prevention via mobile app notifications. Brands like VanMoof and Cowboy have integrated biometric sensors monitoring heart rate and calories burned,
+      enabling comprehensive rider health tracking (Sources: VanMoof 2025 Annual Review, Cowboy Tech Brief 2025).\n\n3. **Sustainable Materials in Production**: There is a growing trend towards eco-friendly bicycle manufacturing using recycled aluminum, bamboo composites, and bioplastics. Companies such as Priority Bicycles and Pure Cycles emphasize reducing carbon footprints through transparency in sourcing and lifecycle impact (Sources: Priority Bicycles Sustainability Report 2025, Pure Cycles Green Manufacturing Initiative).\n\n4. **Expansion of Urban Bike-Sharing Systems**: Cities worldwide have expanded dockless and e-bike sharing schemes, boosting urban mobility. Companies like Lime, Mobike, and Ofo have introduced fleets including cargo and tandem bicycles catering to varying commuter needs, facilitated by improved user apps and real-time fleet management (Sources: Lime Urban Mobility Report 2025, Mobike Global Expansion Analysis).\n\n5. **Advanced Safety Equipment**: Standard safety
+      features in 2025 bicycles include integrated front and rear cameras, adaptive LED lights with automatic brightness control, and collision detection systems alerting both riders and surrounding vehicles. Notable implementations come from brands like Garmin and Lumos (Sources: Garmin Bike Safety Features Release 2025, Lumos Smart Helmets Technical Specifications).\n\n6. **Customization and Modular Bike Designs**: Modular frames and interchangeable parts have become popular, allowing riders to customize their bikes for different terrains or activities without purchasing new bicycles. Companies like Urwahn and Tern promote modular versatility appealing to both urban and trail riders (Sources: Urwahn Modular Frame Whitepaper 2025, Tern Bicycle Modular Design Overview).\n\n7. **Cutting-Edge Racing Bikes with AI Design**: Professional racing bikes utilize ultra-light carbon fiber composites and aerodynamic designs optimized via AI-driven software, improving speed and energy efficiency. Integrated
+      telemetry systems provide real-time data to support race strategies. Leading brands include Specialized and Trek (Sources: Specialized S-Works Innovation Report 2025, Trek Race Tech Insights 2025).\n\n8. **Health and Fitness Integration with Cycling Tech**: Modern bicycles are embedded with sensors that monitor posture, power output, and suggest personalized workout plans through linked apps. Peloton and Wahoo Fitness are at the forefront of integrating these capabilities to transform cycling into a data-driven fitness experience (Sources: Peloton Bike+ 2025 Product Guide, Wahoo Fitness Cycling Data Study).\n\n9. **Rise of Cargo and Utility E-Bikes for Urban Logistics**: The use of cargo e-bikes has surged for small business deliveries and family transport in urban areas, promoted by companies such as Rad Power Bikes and Urban Arrow, offering electric assist for heavy loads and longer distances (Sources: Rad Power Bikes Market Report 2025, Urban Arrow Product Catalog 2025).\n\n10.
+      **Government Policies and Infrastructure Fostering Cycling**: In 2025, many governments have enacted policies offering subsidies and tax credits for electric bicycle purchases. Urban infrastructure investments include extensive protected bike lanes and smart traffic signals prioritizing cyclists, evident in cities like Copenhagen and Amsterdam (Sources: Danish Government Transport Policy 2025, Amsterdam Cycling Infrastructure Report 2025).\n\nThese detailed, sourced points comprehensively capture the state-of-the-art developments, sustainability efforts, technological advancements, and policy frameworks shaping the bicycle industry and culture in 2025.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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+        \ and integrated into daily transport habits.\\n\\nIndustry leaders such as Bosch and Shimano have been instrumental in this evolution. Bosch’s smart battery management systems, as detailed in their 2025 Mobility Report, optimize energy consumption, monitor battery health, and enhance safety features by preventing overheating and overcharging. Similarly, Shimano’s 2025 press releases highlight their innovations in sustainable battery use and integration of regenerative braking systems that recharge batteries during riding. These advancements not only improve user experience but also increase the lifespan and reliability of e-bikes, cementing their leadership in the bicycle market.\\n\\n## 2. Integration of IoT and Smart Features\\n\\nThe integration of Internet of Things (IoT) technologies into bicycles has transformed them into connected devices that offer enhanced safety, health monitoring, and performance analytics. Modern bicycles now come equipped with GPS tracking that allows\
+        \ riders to monitor routes and locate bikes remotely, reducing theft risks. Real-time performance analytics provide data such as speed, distance, and cadence, accessible via associated mobile apps for immediate feedback and training improvements.\\n\\nBrands like VanMoof and Cowboy have led innovation in biometric monitoring systems embedded within bicycle frames or handlebar controls. These sensors track vital rider health metrics such as heart rate and calories burned, feeding data into apps that generate personalized fitness insights. According to VanMoof’s 2025 Annual Review and the Cowboy Tech Brief, these integrations help riders achieve holistic health tracking and increase engagement by transforming cycling sessions into measurable workouts.\\n\\n## 3. Sustainable Materials in Production\\n\\nAddressing climate concerns and consumer demand for eco-friendly options, the bicycle industry increasingly emphasizes the use of sustainable materials in production. Companies are incorporating\
+        \ recycled aluminum, bamboo composites, and bioplastics into frames and components, reducing reliance on virgin materials and harmful manufacturing processes.\\n\\nPriority Bicycles and Pure Cycles are exemplary in this arena. Their sustainability reports and green manufacturing initiatives document their processes to ensure transparency in sourcing and minimizing carbon footprints. Priority Bicycles focuses on cradle-to-grave lifecycle analysis, optimizing design for recyclability, whereas Pure Cycles incorporates bamboo—a rapidly renewable resource—and bioplastics that biodegrade more readily than traditional plastics. These efforts contribute to a more sustainable bicycle industry and appeal to environmentally conscious consumers.\\n\\n## 4. Expansion of Urban Bike-Sharing Systems\\n\\nUrban mobility has been revolutionized by the growth of dockless and e-bike sharing systems that offer accessible, flexible alternatives to traditional transportation. Cities globally have expanded\
+        \ these schemes, supported by improved technology platforms that facilitate real-time fleet management and efficient usage.\\n\\nLeading operators like Lime, Mobike, and Ofo have diversified shared fleets by introducing cargo and tandem bicycles, which accommodate various commuter needs, including families, small-scale cargo transport, and social riding experiences. The Lime Urban Mobility Report of 2025 highlights how their smart apps optimize bike availability and usage patterns, improving overall system reliability. Meanwhile, Mobike’s Global Expansion Analysis documents how integration with public transit and app innovations have increased adoption rates in densely populated cities.\\n\\n## 5. Advanced Safety Equipment\\n\\nSafety technology in bicycles has made significant strides. In 2025, standard features include integrated front and rear cameras that enhance situational awareness by recording journeys and alerting riders to hazards. Adaptive LED lighting systems automatically\
+        \ adjust brightness based on ambient conditions, increasing visibility while conserving battery life.\\n\\nCollision detection systems represent a major innovation, utilizing sensors to detect imminent impacts and alert both riders and nearby drivers, reducing accidents. Garmin and Lumos have introduced industry-leading products in this space: Garmin’s 2025 Bike Safety feature release details embedded cameras with AI-driven hazard detection, while Lumos’ smart helmets incorporate communication lights and crash detection technology as outlined in their 2025 technical specifications. These enhancements contribute substantially to rider safety and the broader acceptance of cycling as a safe urban mode of transport.\\n\\n## 6. Customization and Modular Bike Designs\\n\\nResponding to diverse user requirements and lifestyles, modular bicycles with interchangeable components have gained popularity. These designs allow riders to customize their bikes easily for different terrains or purposes—whether\
+        \ switching from urban commuting to off-road trails or adapting cargo capacity without purchasing new bikes.\\n\\nUrwahn and Tern Bicycle are at the forefront, promoting modular versatility. Urwahn’s 2025 Modular Frame Whitepaper documents innovations in lightweight frames that can be disassembled or reconfigured with minimal tools. Tern’s design overviews illustrate compatibility with accessories such as child seats, cargo racks, and alternative wheelsets. This trend supports sustainable consumption by extending product lifespans and fostering personalization, leading to increased consumer satisfaction.\\n\\n## 7. Cutting-Edge Racing Bikes with AI Design\\n\\nThe professional and competitive cycling sectors benefit from state-of-the-art advancements integrating artificial intelligence into bike design and performance tracking. Racing bikes now utilize ultra-light carbon fiber composites and aerodynamic shapes optimized via AI-driven software, enhancing speed and reducing rider fatigue\
+        \ through better energy efficiency.\\n\\nSpecialized and Trek have been key innovators. Specialized’s S-Works Innovation Report 2025 describes how AI simulations improve frame geometry and component integration to tailor stiffness and aerodynamics for specific racing conditions. Trek’s Race Tech Insights detail the implementation of integrated telemetry systems delivering real-time feedback on metrics like power output, cadence, and environmental forces, allowing riders and coaches to strategize dynamically during races.\\n\\n## 8. Health and Fitness Integration with Cycling Tech\\n\\nModern bicycles transcend transportation by functioning as fitness platforms equipped with an array of sensors that monitor rider posture, power output, and other physiological metrics. These data points feed into linked applications that generate personalized workout plans and optimize training regimens.\\n\\nPeloton and Wahoo Fitness lead this integration. The Peloton Bike+ 2025 Product Guide highlights\
+        \ interactive screens and sensor arrays promoting immersive at-home cycling experiences with adaptive workout sessions. Wahoo Fitness’ 2025 Cycling Data Study demonstrates accuracy improvements in power meters and posture sensors, providing athletes with actionable data for performance enhancement and injury prevention. Together, these technologies transform cycling into a comprehensive health and fitness activity.\\n\\n## 9. Rise of Cargo and Utility E-Bikes for Urban Logistics\\n\\nThe demand for cargo e-bikes has surged in metropolitan areas, driven by the needs of small business deliveries and family transport solutions. These bikes offer electric assist capabilities that accommodate heavy loads and long distances, providing an environmentally friendly alternative to traditional delivery vehicles.\\n\\nRad Power Bikes and Urban Arrow have developed specialized cargo e-bike models as outlined in their 2025 reports. Rad Power Bikes’ Market Report highlights their success in providing\
+        \ robust, user-friendly platforms suited for diverse logistic tasks. Urban Arrow’s Product Catalog emphasizes modular cargo compartments and safety features tailored for urban environments. This trend reflects a broader shift toward sustainable urban logistics and convenience-focused family mobility.\\n\\n## 10. Government Policies and Infrastructure Fostering Cycling\\n\\nGovernment initiatives worldwide increasingly support cycling through subsidies, tax credits, and investments in infrastructure conducive to bicycle use. Several countries have enacted policies facilitating the purchase of electric bicycles, thereby encouraging adoption among commuters and recreational riders.\\n\\nInfrastructural developments include expanded networks of protected bike lanes and smart traffic signals prioritizing cyclists, which improve safety and cycle flow. Copenhagen and Amsterdam serve as exemplary cities, with the Danish Government Transport Policy 2025 and the Amsterdam Cycling Infrastructure\
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-      \         \\\"description\\\": \\\"A feedback about the task output if it is
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-      \     \\\"required\\\": [\\n        \\\"valid\\\",\\n        \\\"feedback\\\"\\n
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-      \ \\n   - Source: Dehghani, Z. et al., \\\"Data Mesh Principles and Logical
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-      \\\"Synthesizing Training Data with Diffusion Models,\\\" 2025. https://nvlabs.github.io/diffusion-models
-      \ \\n   - Source: Xu, L. et al., \\\"Synthetic Data Generation for Data Science,\\\"
-      ACM Computing Surveys, 2025.\\n\\n6. **Federated and Privacy-Preserving Learning:**
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-      \ \\n   - Source: IEEE, \\\"Ethics in AI and Data Science,\\\" 2025 report.
-      https://ethicsinaiai.ieee.org/reports/2025  \\n   - Source: PWC, \\\"Building
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-      \       ensure each bullet contains its source\\n\\n        Your task:\\n        -
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-      issues \u2014 do not propose corrections.\\n        - If the Task result complies
-      with the guardrail, saying that is valid\\n        \"}],\"model\":\"gpt-4.1-mini\"}"
+    body: '{"messages":[{"role":"system","content":"You are Guardrail Agent. You are a expert at validating the output of a task. By providing effective feedback if the output is not valid.\nYour personal goal is: Validate the output of the task\n\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!Ensure your final answer strictly adheres to the following OpenAPI schema: {\n  \"type\": \"json_schema\",\n  \"json_schema\": {\n    \"name\": \"LLMGuardrailResult\",\n    \"strict\": true,\n    \"schema\": {\n      \"properties\": {\n        \"valid\": {\n          \"description\": \"Whether the task output complies with the guardrail\",\n          \"title\": \"Valid\",\n          \"type\": \"boolean\"\n        },\n        \"feedback\": {\n          \"anyOf\":
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+      AI (XAI) Advances:** Novel XAI frameworks now combine causal inference and counterfactual reasoning to offer transparent, actionable insights, essential for compliance in healthcare, finance, and legal domains.  \n   - Source: Molnar, C., \"Interpretable Machine Learning,\" 2nd Edition, 2024. https://christophm.github.io/interpretable-ml-book/  \n   - Source: Ribeiro, M.T. et al., \"Anchors: High-Precision Model-Agnostic Explanations,\" AAAI, 2025.\n\n4. **Data Fabric and Mesh Architectures:** Organizations adopt data fabric and data mesh designs to streamline distributed, multi-cloud, and hybrid data ecosystems. This improves data access, governance, and democratization for decentralized data science teams.  \n   - Source: Gartner, \"Top Strategic Data and Analytics Trends for 2025,\" Dec 2024. https://www.gartner.com/en/documents/strategic-data-and-analytics-trends-2025  \n   - Source: Dehghani, Z. et al., \"Data Mesh Principles and Logical Architecture,\" MartinFowler.com, 2024.\n\n5.
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+      string: "{\n  \"id\": \"chatcmpl-CYgSnoRAe0NIiL6eBoyibDmsuo7Q0\",\n  \"object\": \"chat.completion\",\n  \"created\": 1762382417,\n  \"model\": \"gpt-4.1-mini-2025-04-14\",\n  \"choices\": [\n    {\n      \"index\": 0,\n      \"message\": {\n        \"role\": \"assistant\",\n        \"content\": \"Thought: I now can give a great answer\\nFinal Answer:\\n\\n# Cutting-Edge Developments in Data Science – 2025 Comprehensive Report\\n\\nThis report presents an in-depth analysis of the top ten transformative developments in data science as of 2025. These advancements highlight the evolving landscape of data-driven technologies, methodologies, and ethical frameworks that underpin modern data science initiatives across industries. Each section below explores a key development, supported by current research and industry insights.\\n\\n---\\n\\n## 1. Foundation Models Dominance\\n\\n### Overview\\nFoundation models, exemplified by architectures such as GPT-5, have become foundational to data\
+        \ science workflows in 2025. These large-scale pretrained models facilitate multitask learning and robust transfer learning, which significantly enhance performance in natural language processing (NLP), computer vision, and multimodal applications that combine text, images, and other data types.\\n\\n### Technical Advancements\\n- **Multitask and Transfer Learning:** Foundation models are trained on vast and diverse datasets enabling them to generalize knowledge across varied downstream tasks without intensive retraining.\\n- **Scalability:** These models scale to billions of parameters, improving context understanding and generation capabilities.\\n- **Multimodal Capabilities:** GPT-5 and peer models integrate multimodal inputs, vastly expanding potential applications.\\n\\n### Impact\\n- Improved performance across automated content generation, sophisticated question answering, image recognition, and integrated AI systems.\\n- Democratization of AI through accessible APIs and platforms\
+        \ powered by foundation models.\\n- Accelerated development cycles for AI-driven products.\\n\\n### References\\n- OpenAI. \\\"Introducing GPT-5,\\\" OpenAI Blog, 2025. https://openai.com/research/gpt5  \\n- Brown, T. et al. \\\"Language Models are Few-Shot Learners,\\\" NeurIPS, 2024.\\n\\n---\\n\\n## 2. Automated Machine Learning (AutoML) at Scale\\n\\n### Overview\\nAutoML platforms have evolved from task-specific tools into comprehensive, scalable systems integrated natively with major cloud platforms such as AWS, Microsoft Azure, and Google Cloud Platform (GCP). These solutions automate and optimize end-to-end machine learning workflows including model selection, hyperparameter tuning, deployment, and monitoring.\\n\\n### Features and Innovations\\n- **Continuous Model Testing:** Integration of automated pipelines that enable iterative testing and improvements based on live data.\\n- **Explainability:** Enhanced model explainability modules provide transparency for complex automated\
+        \ models, supporting auditability and trust.\\n- **Cloud-Native Scalability:** Elastic scaling to handle large datasets and high-velocity data streams.\\n\\n### Benefits\\n- Reduction in human expertise requirements and time-to-deployment.\\n- Increased accessibility for organizations lacking large data science teams.\\n- Improved model robustness and adaptability to changing data patterns through continuous tuning.\\n\\n### References\\n- Google Cloud Blog, \\\"AutoML Updates for 2025,\\\" 2025. https://cloud.google.com/blog/topics/ai-machine-learning/automl-2025-updates  \\n- He, X. et al., \\\"Scalable AutoML Systems,\\\" KDD 2024.\\n\\n---\\n\\n## 3. Explainable AI (XAI) Advances\\n\\n### Overview\\nExplainable AI has matured to integrate causal inference and counterfactual reasoning techniques that generate intuitive, comprehensible model explanations. This progression is critical for domains with strong regulatory oversight—including healthcare, finance, and legal sectors—where\
+        \ transparency and accountability are compulsory.\\n\\n### Key Innovations\\n- **Causal Inference:** Moving beyond statistical correlations toward understanding cause-effect relationships within predictive models.\\n- **Counterfactuals:** Generating \\\"what-if\\\" scenarios that illustrate how slight input changes alter predictions.\\n- **Model-Agnostic Techniques:** Tools like Anchors provide high-precision, interpretable explanations independent of model architecture.\\n\\n### Importance\\n- Facilitates trust among end-users and stakeholders.\\n- Aids regulatory compliance with laws such as GDPR around automated decision-making transparency.\\n- Helps detect potential biases and ethical issues in predictive models.\\n\\n### References\\n- Molnar, C., \\\"Interpretable Machine Learning,\\\" 2nd Edition, 2024. https://christophm.github.io/interpretable-ml-book/  \\n- Ribeiro, M.T. et al., \\\"Anchors: High-Precision Model-Agnostic Explanations,\\\" AAAI, 2025.\\n\\n---\\n\\n## 4.\
+        \ Data Fabric and Mesh Architectures\\n\\n### Overview\\nTo address challenges related to data silos, complexity of modern multi-cloud environments, and decentralized teams, organizations are adopting data fabric and data mesh architectural paradigms. These frameworks enhance access, governance, and democratization of data assets across distributed ecosystems.\\n\\n### Characteristics\\n- **Data Fabric:** A unified data management architecture that automates data discovery, integration, and governance across heterogeneous sources.\\n- **Data Mesh:** Emphasizes domain-oriented decentralized ownership, treating data as a product maintained by cross-functional teams.\\n\\n### Outcomes\\n- Improved agility in extracting insights from diverse, distributed data.\\n- Enhanced governance controls supporting compliance and security.\\n- Empowered data science teams through self-serve data infrastructure.\\n\\n### References\\n- Gartner, \\\"Top Strategic Data and Analytics Trends for 2025,\\\
+        \" Dec 2024. https://www.gartner.com/en/documents/strategic-data-and-analytics-trends-2025  \\n- Dehghani, Z. et al., \\\"Data Mesh Principles and Logical Architecture,\\\" MartinFowler.com, 2024.\\n\\n---\\n\\n## 5. Synthetic Data Generation\\n\\n### Overview\\nSynthetic data generation using advanced generative models, notably diffusion models, has become widespread. These methods create high-fidelity synthetic datasets that mirror real data’s statistical properties while ensuring privacy and mitigating bias in training datasets.\\n\\n### Technological Developments\\n- **Diffusion Models:** State-of-the-art generative techniques that iteratively refine synthetic samples to produce realistic and diverse data.\\n- **Privacy Preservation:** Synthetic datasets help maintain compliance by avoiding the sharing of personally identifiable information (PII).\\n- **Bias Mitigation:** Allow balancing underrepresented groups and scenarios that might be scarce in original datasets.\\n\\n###\
+        \ Applications\\n- Training AI models when real data is scarce or sensitive.\\n- Facilitating testing and validation without data access constraints.\\n- Accelerating development cycles without compromising privacy.\\n\\n### References\\n- NVIDIA Research, \\\"Synthesizing Training Data with Diffusion Models,\\\" 2025. https://nvlabs.github.io/diffusion-models  \\n- Xu, L. et al., \\\"Synthetic Data Generation for Data Science,\\\" ACM Computing Surveys, 2025.\\n\\n---\\n\\n## 6. Federated and Privacy-Preserving Learning\\n\\n### Overview\\nFederated learning techniques combined with advanced encryption methods—such as homomorphic encryption and secure multiparty computation—enable collaborative model training across multiple parties without centralizing sensitive data. This approach is increasingly adopted to ensure privacy and regulatory compliance.\\n\\n### Innovations\\n- **Federated Frameworks:** Coordinate decentralized data holders to jointly train shared models while keeping\
+        \ raw data local.\\n- **Encryption Schemes:** Practical secure aggregation protocols maintain data confidentiality during model updates.\\n- **Regulatory Alignment:** Supports compliance with GDPR, CCPA, and other privacy mandates.\\n\\n### Benefits\\n- Facilitates cross-organization collaboration in sensitive sectors like healthcare and finance.\\n- Reduces risks of data breaches or misuse by avoiding centralized datasets.\\n- Enables global scale AI development respecting local data laws.\\n\\n### References\\n- Google AI Blog, \\\"Federated Learning at Scale,\\\" 2025. https://ai.googleblog.com/2025/02/federated-learning-at-scale.html  \\n- Bonawitz, K. et al., \\\"Practical Secure Aggregation for Federated Learning,\\\" CCS, 2024.\\n\\n---\\n\\n## 7. Integration of Quantum Computing\\n\\n### Overview\\nThe integration of quantum computing techniques within hybrid classical-quantum machine learning workflows is emerging as a novel capability to tackle combinatorial optimization\
+        \ and complex simulations that challenge classical methods. Pilot projects demonstrate potential for significant acceleration in solving specific industrial data science problems.\\n\\n### Developments\\n- **Hybrid Workflows:** Utilize quantum processors for subproblems well-suited to quantum advantage, coupled with classical processing.\\n- **Quantum Algorithms:** Exploration of quantum-enhanced learning methods and optimization techniques.\\n- **Industrial Pilots:** Early applications in logistics, material science, and finance for faster decision-making.\\n\\n### Outlook\\n- Quantum machine learning remains experimental but shows promise for transforming problem classes previously intractable.\\n- Monitoring and development by leading research institutions and technology companies continue rapidly.\\n\\n### References\\n- IBM Research, \\\"Quantum Machine Learning Advances,\\\" 2025. https://research.ibm.com/quantum-ml  \\n- Schuld, M. & Petruccione, F., \\\"Quantum Machine Learning,\\\
+        \" Cambridge University Press, 2024.\\n\\n---\\n\\n## 8. Real-Time and Streaming Analytics Expansion\\n\\n### Overview\\nReal-time AI analytics technologies have become integral to operational systems, enabling instantaneous insights such as anomaly detection, predictive maintenance, and personalized customer recommendations.\\n\\n### Key Features\\n- **Stream Processing Engines:** Platforms like Apache Flink support massive-scale, low-latency data processing.\\n- **AI Pipelines:** Integrated real-time AI models deliver dynamic decision support.\\n- **Use Cases:** Applications flourish in finance (fraud detection), telecommunications (network monitoring), and manufacturing (equipment health).\\n\\n### Business Impact\\n- Enhanced operational efficiency through timely interventions.\\n- Improved user experience with real-time personalization.\\n- Reduction of downtime and risk via predictive alerts.\\n\\n### References\\n- Apache Flink, \\\"State of Streaming Analytics 2025,\\\" 2025.\
+        \ https://flink.apache.org/blog/2025/streaming-analytics-trends  \\n- Microsoft Azure Blog, \\\"Real-time AI Pipelines,\\\" 2025.\\n\\n---\\n\\n## 9. Cross-Disciplinary Collaboration\\n\\n### Overview\\nData science teams increasingly embed domain experts, ethicists, and legal advisors to ensure that AI models are ethically sound, fair, and legally compliant. This multidisciplinary collaboration addresses critical issues like bias mitigation, privacy protection, and societal impacts of data-driven technology.\\n\\n### Practices\\n- **Ethical Frameworks:** Adoption of guidelines for responsible AI from leading institutions.\\n- **Bias Auditing:** Proactive identification and correction of model biases with interdisciplinary input.\\n- **Legal Compliance:** Continuous alignment with evolving data protection laws and standards.\\n\\n### Significance\\n- Builds public trust and organizational accountability.\\n- Mitigates risks of regulatory penalties and reputational harm.\\n- Encourages\
+        \ socially beneficial AI deployment.\\n\\n### References\\n- IEEE, \\\"Ethics in AI and Data Science,\\\" 2025 report. https://ethicsinaiai.ieee.org/reports/2025  \\n- PWC, \\\"Building Ethical AI Teams,\\\" 2025.\\n\\n---\\n\\n## 10. Environmental and Energy-Aware AI\\n\\n### Overview\\nWith growing awareness of the environmental impact of large-scale AI training and inference, the data science community is prioritizing energy-efficient algorithms, specialized hardware accelerators, and carbon footprint tracking tools.\\n\\n### Key Developments\\n- **Energy-Efficient Algorithms:** Optimizations that reduce computations without degrading model accuracy.\\n- **AI Accelerators:** Development of hardware specifically designed to minimize energy consumption.\\n- **Carbon Tracking:** Tools that quantify and report carbon emissions associated with AI workloads.\\n\\n### Importance\\n- Supports organizational sustainability and ESG (Environmental, Social, Governance) goals.\\n- Addresses\
+        \ public concerns about the environmental cost of AI.\\n- Encourages industry-wide standards for carbon-aware AI practices.\\n\\n### References\\n- Google Sustainability, \\\"Towards Carbon-Aware AI,\\\" 2025. https://sustainability.google/projects/ai-carbon-awareness  \\n- Strubell, E. et al., \\\"Energy and Policy Considerations for Deep Learning in NLP,\\\" ACL, 2024.\\n\\n---\\n\\n# Conclusion\\n\\nThe 2025 landscape of data science is characterized by groundbreaking progress in foundational technologies, operational efficiencies, ethical practices, and environmental mindfulness. The fusion of scalable foundation models, automated pipelines, transparent AI, and sustainable development practices marks a pivotal era for data science, with profound implications for industry, research, and society at large.\\n\\nOrganizations seeking to maintain competitive advantage and societal trust must strategically adopt and adapt to these developments, fostering cross-disciplinary collaboration\
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-      string: "{\"info\":{\"author\":null,\"author_email\":\"Alex Reibman <areibman@gmail.com>,
-        Shawn Qiu <siyangqiu@gmail.com>, Braelyn Boynton <bboynton97@gmail.com>, Howard
-        Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush
-        Shukla <ps4534@nyu.edu>\",\"bugtrack_url\":null,\"classifiers\":[\"License
-        :: OSI Approved :: MIT License\",\"Operating System :: OS Independent\",\"Programming
-        Language :: Python :: 3\",\"Programming Language :: Python :: 3.10\",\"Programming
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-        align=\\\"center\\\">\\n  <a href=\\\"https://agentops.ai?ref=gh\\\">\\n    <img
-        src=\\\"docs/images/external/logo/github-banner.png\\\" alt=\\\"Logo\\\">\\n
-        \ </a>\\n</div>\\n\\n<div align=\\\"center\\\">\\n  <em>Observability and
-        DevTool platform for AI Agents</em>\\n</div>\\n\\n<br />\\n\\n<div align=\\\"center\\\">\\n
-        \ <a href=\\\"https://pepy.tech/project/agentops\\\">\\n    <img src=\\\"https://static.pepy.tech/badge/agentops/month\\\"
-        alt=\\\"Downloads\\\">\\n  </a>\\n  <a href=\\\"https://github.com/agentops-ai/agentops/issues\\\">\\n
-        \ <img src=\\\"https://img.shields.io/github/commit-activity/m/agentops-ai/agentops\\\"
-        alt=\\\"git commit activity\\\">\\n  </a>\\n  <img src=\\\"https://img.shields.io/pypi/v/agentops?&color=3670A0\\\"
-        alt=\\\"PyPI - Version\\\">\\n  <a href=\\\"https://opensource.org/licenses/MIT\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/License-MIT-yellow.svg?&color=3670A0\\\"
-        alt=\\\"License: MIT\\\">\\n  </a>\\n</div>\\n\\n<p align=\\\"center\\\">\\n
-        \ <a href=\\\"https://twitter.com/agentopsai/\\\">\\n    <img src=\\\"https://img.shields.io/twitter/follow/agentopsai?style=social\\\"
-        alt=\\\"Twitter\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://discord.gg/FagdcwwXRR\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord\\\"
-        alt=\\\"Discord\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://app.agentops.ai/?ref=gh\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/Dashboard-blue.svg?style=flat-square\\\"
-        alt=\\\"Dashboard\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://docs.agentops.ai/introduction\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/Documentation-orange.svg?style=flat-square\\\"
-        alt=\\\"Documentation\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://entelligence.ai/AgentOps-AI&agentops\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/Chat%20with%20Docs-green.svg?style=flat-square\\\"
-        alt=\\\"Chat with Docs\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n</p>\\n\\n\\n\\n<div
-        style=\\\"justify-content: center\\\">\\n  <img src=\\\"docs/images/external/app_screenshots/dashboard-banner.png\\\"
-        alt=\\\"Dashboard Banner\\\">\\n</div>\\n\\n<br/>\\n\\n\\nAgentOps helps developers
-        build, evaluate, and monitor AI agents. From prototype to production.\\n\\n|
-        \                                      |                                                               |\\n|
-        ------------------------------------- | -------------------------------------------------------------
-        |\\n| \U0001F4CA **Replay Analytics and Debugging** | Step-by-step agent execution
-        graphs                           |\\n| \U0001F4B8 **LLM Cost Management**
-        \           | Track spend with LLM foundation model providers               |\\n|
-        \U0001F9EA **Agent Benchmarking**             | Test your agents against 1,000+
-        evals                         |\\n| \U0001F510 **Compliance and Security**
-        \       | Detect common prompt injection and data exfiltration exploits |\\n|
-        \U0001F91D **Framework Integrations**         | Native Integrations with CrewAI,
-        AG2(AutoGen), Camel AI, & LangChain         |\\n\\n## Quick Start \u2328\uFE0F\\n\\n```bash\\npip
-        install agentops\\n```\\n\\n\\n#### Session replays in 2 lines of code\\n\\nInitialize
-        the AgentOps client and automatically get analytics on all your LLM calls.\\n\\n[Get
-        an API key](https://app.agentops.ai/settings/projects)\\n\\n```python\\nimport
-        agentops\\n\\n# Beginning of your program (i.e. main.py, __init__.py)\\nagentops.init(
-        < INSERT YOUR API KEY HERE >)\\n\\n...\\n\\n# End of program\\nagentops.end_session('Success')\\n```\\n\\nAll
-        your sessions can be viewed on the [AgentOps dashboard](https://app.agentops.ai?ref=gh)\\n<br/>\\n\\n<details>\\n
-        \ <summary>Agent Debugging</summary>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/session-drilldown-metadata.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Agent Metadata\\\"/>\\n  </a>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/chat-viewer.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Chat Viewer\\\"/>\\n  </a>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/session-drilldown-graphs.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Event Graphs\\\"/>\\n  </a>\\n</details>\\n\\n<details>\\n
-        \ <summary>Session Replays</summary>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/session-replay.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Session Replays\\\"/>\\n  </a>\\n</details>\\n\\n<details
-        open>\\n  <summary>Summary Analytics</summary>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \  <img src=\\\"docs/images/external/app_screenshots/overview.png\\\" style=\\\"width:
-        90%;\\\" alt=\\\"Summary Analytics\\\"/>\\n  </a>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \  <img src=\\\"docs/images/external/app_screenshots/overview-charts.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Summary Analytics Charts\\\"/>\\n  </a>\\n</details>\\n\\n\\n###
-        First class Developer Experience\\nAdd powerful observability to your agents,
-        tools, and functions with as little code as possible: one line at a time.\\n<br/>\\nRefer
-        to our [documentation](http://docs.agentops.ai)\\n\\n```python\\n# Automatically
-        associate all Events with the agent that originated them\\nfrom agentops import
-        track_agent\\n\\n@track_agent(name='SomeCustomName')\\nclass MyAgent:\\n  ...\\n```\\n\\n```python\\n#
-        Automatically create ToolEvents for tools that agents will use\\nfrom agentops
-        import record_tool\\n\\n@record_tool('SampleToolName')\\ndef sample_tool(...):\\n
-        \ ...\\n```\\n\\n```python\\n# Automatically create ActionEvents for other
-        functions.\\nfrom agentops import record_action\\n\\n@agentops.record_action('sample
-        function being record')\\ndef sample_function(...):\\n  ...\\n```\\n\\n```python\\n#
-        Manually record any other Events\\nfrom agentops import record, ActionEvent\\n\\nrecord(ActionEvent(\\\"received_user_input\\\"))\\n```\\n\\n##
-        Integrations \U0001F9BE\\n\\n### CrewAI \U0001F6F6\\n\\nBuild Crew agents
-        with observability with only 2 lines of code. Simply set an `AGENTOPS_API_KEY`
-        in your environment, and your crews will get automatic monitoring on the AgentOps
-        dashboard.\\n\\n```bash\\npip install 'crewai[agentops]'\\n```\\n\\n- [AgentOps
-        integration example](https://docs.agentops.ai/v1/integrations/crewai)\\n-
-        [Official CrewAI documentation](https://docs.crewai.com/how-to/AgentOps-Observability)\\n\\n###
-        AG2 \U0001F916\\nWith only two lines of code, add full observability and monitoring
-        to AG2 (formerly AutoGen) agents. Set an `AGENTOPS_API_KEY` in your environment
-        and call `agentops.init()`\\n\\n- [AG2 Observability Example](https://docs.ag2.ai/notebooks/agentchat_agentops)\\n-
-        [AG2 - AgentOps Documentation](https://docs.ag2.ai/docs/ecosystem/agentops)\\n\\n###
-        Camel AI \U0001F42A\\n\\nTrack and analyze CAMEL agents with full observability.
-        Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get
-        started.\\n\\n- [Camel AI](https://www.camel-ai.org/) - Advanced agent communication
-        framework\\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/camel)\\n-
-        [Official Camel AI documentation](https://docs.camel-ai.org/cookbooks/agents_tracking.html)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n\\n```bash\\npip install \\\"camel-ai[all]==0.2.11\\\"\\npip
-        install agentops\\n```\\n\\n```python\\nimport os\\nimport agentops\\nfrom
-        camel.agents import ChatAgent\\nfrom camel.messages import BaseMessage\\nfrom
-        camel.models import ModelFactory\\nfrom camel.types import ModelPlatformType,
-        ModelType\\n\\n# Initialize AgentOps\\nagentops.init(os.getenv(\\\"AGENTOPS_API_KEY\\\"),
-        default_tags=[\\\"CAMEL Example\\\"])\\n\\n# Import toolkits after AgentOps
-        init for tracking\\nfrom camel.toolkits import SearchToolkit\\n\\n# Set up
-        the agent with search tools\\nsys_msg = BaseMessage.make_assistant_message(\\n
-        \   role_name='Tools calling operator',\\n    content='You are a helpful assistant'\\n)\\n\\n#
-        Configure tools and model\\ntools = [*SearchToolkit().get_tools()]\\nmodel
-        = ModelFactory.create(\\n    model_platform=ModelPlatformType.OPENAI,\\n    model_type=ModelType.GPT_4O_MINI,\\n)\\n\\n#
-        Create and run the agent\\ncamel_agent = ChatAgent(\\n    system_message=sys_msg,\\n
-        \   model=model,\\n    tools=tools,\\n)\\n\\nresponse = camel_agent.step(\\\"What
-        is AgentOps?\\\")\\nprint(response)\\n\\nagentops.end_session(\\\"Success\\\")\\n```\\n\\nCheck
-        out our [Camel integration guide](https://docs.agentops.ai/v1/integrations/camel)
-        for more examples including multi-agent scenarios.\\n</details>\\n\\n### Langchain
-        \U0001F99C\U0001F517\\n\\nAgentOps works seamlessly with applications built
-        using Langchain. To use the handler, install Langchain as an optional dependency:\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```shell\\npip install agentops[langchain]\\n```\\n\\nTo
-        use the handler, import and set\\n\\n```python\\nimport os\\nfrom langchain.chat_models
-        import ChatOpenAI\\nfrom langchain.agents import initialize_agent, AgentType\\nfrom
-        agentops.partners.langchain_callback_handler import LangchainCallbackHandler\\n\\nAGENTOPS_API_KEY
-        = os.environ['AGENTOPS_API_KEY']\\nhandler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY,
-        tags=['Langchain Example'])\\n\\nllm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,\\n
-        \                callbacks=[handler],\\n                 model='gpt-3.5-turbo')\\n\\nagent
-        = initialize_agent(tools,\\n                         llm,\\n                         agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\\n
-        \                        verbose=True,\\n                         callbacks=[handler],
-        # You must pass in a callback handler to record your agent\\n                         handle_parsing_errors=True)\\n```\\n\\nCheck
-        out the [Langchain Examples Notebook](./examples/langchain_examples.ipynb)
-        for more details including Async handlers.\\n\\n</details>\\n\\n### Cohere
-        \u2328\uFE0F\\n\\nFirst class support for Cohere(>=5.4.0). This is a living
-        integration, should you need any added functionality please message us on
-        Discord!\\n\\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/cohere)\\n-
-        [Official Cohere documentation](https://docs.cohere.com/reference/about)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install cohere\\n```\\n\\n```python
-        python\\nimport cohere\\nimport agentops\\n\\n# Beginning of program's code
-        (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\nco
-        = cohere.Client()\\n\\nchat = co.chat(\\n    message=\\\"Is it pronounced
-        ceaux-hear or co-hehray?\\\"\\n)\\n\\nprint(chat)\\n\\nagentops.end_session('Success')\\n```\\n\\n```python
-        python\\nimport cohere\\nimport agentops\\n\\n# Beginning of program's code
-        (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nco
-        = cohere.Client()\\n\\nstream = co.chat_stream(\\n    message=\\\"Write me
-        a haiku about the synergies between Cohere and AgentOps\\\"\\n)\\n\\nfor event
-        in stream:\\n    if event.event_type == \\\"text-generation\\\":\\n        print(event.text,
-        end='')\\n\\nagentops.end_session('Success')\\n```\\n</details>\\n\\n\\n###
-        Anthropic \uFE68\\n\\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\\n\\n-
-        [AgentOps integration guide](https://docs.agentops.ai/v1/integrations/anthropic)\\n-
-        [Official Anthropic documentation](https://docs.anthropic.com/en/docs/welcome)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install anthropic\\n```\\n\\n```python
-        python\\nimport anthropic\\nimport agentops\\n\\n# Beginning of program's
-        code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nclient
-        = anthropic.Anthropic(\\n    # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"ANTHROPIC_API_KEY\\\"),\\n)\\n\\nmessage
-        = client.messages.create(\\n        max_tokens=1024,\\n        messages=[\\n
-        \           {\\n                \\\"role\\\": \\\"user\\\",\\n                \\\"content\\\":
-        \\\"Tell me a cool fact about AgentOps\\\",\\n            }\\n        ],\\n
-        \       model=\\\"claude-3-opus-20240229\\\",\\n    )\\nprint(message.content)\\n\\nagentops.end_session('Success')\\n```\\n\\nStreaming\\n```python
-        python\\nimport anthropic\\nimport agentops\\n\\n# Beginning of program's
-        code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nclient
-        = anthropic.Anthropic(\\n    # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"ANTHROPIC_API_KEY\\\"),\\n)\\n\\nstream
-        = client.messages.create(\\n    max_tokens=1024,\\n    model=\\\"claude-3-opus-20240229\\\",\\n
-        \   messages=[\\n        {\\n            \\\"role\\\": \\\"user\\\",\\n            \\\"content\\\":
-        \\\"Tell me something cool about streaming agents\\\",\\n        }\\n    ],\\n
-        \   stream=True,\\n)\\n\\nresponse = \\\"\\\"\\nfor event in stream:\\n    if
-        event.type == \\\"content_block_delta\\\":\\n        response += event.delta.text\\n
-        \   elif event.type == \\\"message_stop\\\":\\n        print(\\\"\\\\n\\\")\\n
-        \       print(response)\\n        print(\\\"\\\\n\\\")\\n```\\n\\nAsync\\n\\n```python
-        python\\nimport asyncio\\nfrom anthropic import AsyncAnthropic\\n\\nclient
-        = AsyncAnthropic(\\n    # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"ANTHROPIC_API_KEY\\\"),\\n)\\n\\n\\nasync
-        def main() -> None:\\n    message = await client.messages.create(\\n        max_tokens=1024,\\n
-        \       messages=[\\n            {\\n                \\\"role\\\": \\\"user\\\",\\n
-        \               \\\"content\\\": \\\"Tell me something interesting about async
-        agents\\\",\\n            }\\n        ],\\n        model=\\\"claude-3-opus-20240229\\\",\\n
-        \   )\\n    print(message.content)\\n\\n\\nawait main()\\n```\\n</details>\\n\\n###
-        Mistral \u303D\uFE0F\\n\\nTrack agents built with the Anthropic Python SDK
-        (>=0.32.0).\\n\\n- [AgentOps integration example](./examples/mistral//mistral_example.ipynb)\\n-
-        [Official Mistral documentation](https://docs.mistral.ai)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install mistralai\\n```\\n\\nSync\\n\\n```python
-        python\\nfrom mistralai import Mistral\\nimport agentops\\n\\n# Beginning
-        of program's code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR
-        API KEY HERE>)\\n\\nclient = Mistral(\\n    # This is the default and can
-        be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\nmessage
-        = client.chat.complete(\\n        messages=[\\n            {\\n                \\\"role\\\":
-        \\\"user\\\",\\n                \\\"content\\\": \\\"Tell me a cool fact about
-        AgentOps\\\",\\n            }\\n        ],\\n        model=\\\"open-mistral-nemo\\\",\\n
-        \   )\\nprint(message.choices[0].message.content)\\n\\nagentops.end_session('Success')\\n```\\n\\nStreaming\\n\\n```python
-        python\\nfrom mistralai import Mistral\\nimport agentops\\n\\n# Beginning
-        of program's code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR
-        API KEY HERE>)\\n\\nclient = Mistral(\\n    # This is the default and can
-        be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\nmessage
-        = client.chat.stream(\\n        messages=[\\n            {\\n                \\\"role\\\":
-        \\\"user\\\",\\n                \\\"content\\\": \\\"Tell me something cool
-        about streaming agents\\\",\\n            }\\n        ],\\n        model=\\\"open-mistral-nemo\\\",\\n
-        \   )\\n\\nresponse = \\\"\\\"\\nfor event in message:\\n    if event.data.choices[0].finish_reason
-        == \\\"stop\\\":\\n        print(\\\"\\\\n\\\")\\n        print(response)\\n
-        \       print(\\\"\\\\n\\\")\\n    else:\\n        response += event.text\\n\\nagentops.end_session('Success')\\n```\\n\\nAsync\\n\\n```python
-        python\\nimport asyncio\\nfrom mistralai import Mistral\\n\\nclient = Mistral(\\n
-        \   # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\n\\nasync
-        def main() -> None:\\n    message = await client.chat.complete_async(\\n        messages=[\\n
-        \           {\\n                \\\"role\\\": \\\"user\\\",\\n                \\\"content\\\":
-        \\\"Tell me something interesting about async agents\\\",\\n            }\\n
-        \       ],\\n        model=\\\"open-mistral-nemo\\\",\\n    )\\n    print(message.choices[0].message.content)\\n\\n\\nawait
-        main()\\n```\\n\\nAsync Streaming\\n\\n```python python\\nimport asyncio\\nfrom
-        mistralai import Mistral\\n\\nclient = Mistral(\\n    # This is the default
-        and can be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\n\\nasync
-        def main() -> None:\\n    message = await client.chat.stream_async(\\n        messages=[\\n
-        \           {\\n                \\\"role\\\": \\\"user\\\",\\n                \\\"content\\\":
-        \\\"Tell me something interesting about async streaming agents\\\",\\n            }\\n
-        \       ],\\n        model=\\\"open-mistral-nemo\\\",\\n    )\\n\\n    response
-        = \\\"\\\"\\n    async for event in message:\\n        if event.data.choices[0].finish_reason
-        == \\\"stop\\\":\\n            print(\\\"\\\\n\\\")\\n            print(response)\\n
-        \           print(\\\"\\\\n\\\")\\n        else:\\n            response +=
-        event.text\\n\\n\\nawait main()\\n```\\n</details>\\n\\n\\n\\n### CamelAI
-        \uFE68\\n\\nTrack agents built with the CamelAI Python SDK (>=0.32.0).\\n\\n-
-        [CamelAI integration guide](https://docs.camel-ai.org/cookbooks/agents_tracking.html#)\\n-
-        [Official CamelAI documentation](https://docs.camel-ai.org/index.html)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install camel-ai[all]\\npip
-        install agentops\\n```\\n\\n```python python\\n#Import Dependencies\\nimport
-        agentops\\nimport os\\nfrom getpass import getpass\\nfrom dotenv import load_dotenv\\n\\n#Set
-        Keys\\nload_dotenv()\\nopenai_api_key = os.getenv(\\\"OPENAI_API_KEY\\\")
-        or \\\"<your openai key here>\\\"\\nagentops_api_key = os.getenv(\\\"AGENTOPS_API_KEY\\\")
-        or \\\"<your agentops key here>\\\"\\n\\n\\n\\n```\\n</details>\\n\\n[You
-        can find usage examples here!](examples/camelai_examples/README.md).\\n\\n\\n\\n###
-        LiteLLM \U0001F685\\n\\nAgentOps provides support for LiteLLM(>=1.3.1), allowing
-        you to call 100+ LLMs using the same Input/Output Format. \\n\\n- [AgentOps
-        integration example](https://docs.agentops.ai/v1/integrations/litellm)\\n-
-        [Official LiteLLM documentation](https://docs.litellm.ai/docs/providers)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install litellm\\n```\\n\\n```python
-        python\\n# Do not use LiteLLM like this\\n# from litellm import completion\\n#
-        ...\\n# response = completion(model=\\\"claude-3\\\", messages=messages)\\n\\n#
-        Use LiteLLM like this\\nimport litellm\\n...\\nresponse = litellm.completion(model=\\\"claude-3\\\",
-        messages=messages)\\n# or\\nresponse = await litellm.acompletion(model=\\\"claude-3\\\",
-        messages=messages)\\n```\\n</details>\\n\\n### LlamaIndex \U0001F999\\n\\n\\nAgentOps
-        works seamlessly with applications built using LlamaIndex, a framework for
-        building context-augmented generative AI applications with LLMs.\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```shell\\npip install llama-index-instrumentation-agentops\\n```\\n\\nTo
-        use the handler, import and set\\n\\n```python\\nfrom llama_index.core import
-        set_global_handler\\n\\n# NOTE: Feel free to set your AgentOps environment
-        variables (e.g., 'AGENTOPS_API_KEY')\\n# as outlined in the AgentOps documentation,
-        or pass the equivalent keyword arguments\\n# anticipated by AgentOps' AOClient
-        as **eval_params in set_global_handler.\\n\\nset_global_handler(\\\"agentops\\\")\\n```\\n\\nCheck
-        out the [LlamaIndex docs](https://docs.llamaindex.ai/en/stable/module_guides/observability/?h=agentops#agentops)
-        for more details.\\n\\n</details>\\n\\n### Llama Stack \U0001F999\U0001F95E\\n\\nAgentOps
-        provides support for Llama Stack Python Client(>=0.0.53), allowing you to
-        monitor your Agentic applications. \\n\\n- [AgentOps integration example 1](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-fdddf65549f3714f8f007ce7dfd1cde720329fe54155d54389dd50fbd81813cb)\\n-
-        [AgentOps integration example 2](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-6688ff4fb7ab1ce7b1cc9b8362ca27264a3060c16737fb1d850305787a6e3699)\\n-
-        [Official Llama Stack Python Client](https://github.com/meta-llama/llama-stack-client-python)\\n\\n###
-        SwarmZero AI \U0001F41D\\n\\nTrack and analyze SwarmZero agents with full
-        observability. Set an `AGENTOPS_API_KEY` in your environment and initialize
-        AgentOps to get started.\\n\\n- [SwarmZero](https://swarmzero.ai) - Advanced
-        multi-agent framework\\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/swarmzero)\\n-
-        [SwarmZero AI integration example](https://docs.swarmzero.ai/examples/ai-agents/build-and-monitor-a-web-search-agent)\\n-
-        [SwarmZero AI - AgentOps documentation](https://docs.swarmzero.ai/sdk/observability/agentops)\\n-
-        [Official SwarmZero Python SDK](https://github.com/swarmzero/swarmzero)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n\\n```bash\\npip install swarmzero\\npip
-        install agentops\\n```\\n\\n```python\\nfrom dotenv import load_dotenv\\nload_dotenv()\\n\\nimport
-        agentops\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nfrom swarmzero import
-        Agent, Swarm\\n# ...\\n```\\n</details>\\n\\n## Time travel debugging \U0001F52E\\n\\n<div
-        style=\\\"justify-content: center\\\">\\n  <img src=\\\"docs/images/external/app_screenshots/time_travel_banner.png\\\"
-        alt=\\\"Time Travel Banner\\\">\\n</div>\\n\\n<br />\\n\\n[Try it out!](https://app.agentops.ai/timetravel)\\n\\n##
-        Agent Arena \U0001F94A\\n\\n(coming soon!)\\n\\n## Evaluations Roadmap \U0001F9ED\\n\\n|
-        Platform                                                                     |
-        Dashboard                                  | Evals                                  |\\n|
-        ----------------------------------------------------------------------------
-        | ------------------------------------------ | --------------------------------------
-        |\\n| \u2705 Python SDK                                                                |
-        \u2705 Multi-session and Cross-session metrics | \u2705 Custom eval metrics
-        \                |\\n| \U0001F6A7 Evaluation builder API                                                    |
-        \u2705 Custom event tag tracking\_              | \U0001F51C Agent scorecards
-        \                   |\\n| \u2705 [Javascript/Typescript SDK](https://github.com/AgentOps-AI/agentops-node)
-        | \u2705 Session replays                         | \U0001F51C Evaluation playground
-        + leaderboard |\\n\\n## Debugging Roadmap \U0001F9ED\\n\\n| Performance testing
-        \                      | Environments                                                                        |
-        LLM Testing                                 | Reasoning and execution testing
-        \                  |\\n| ----------------------------------------- | -----------------------------------------------------------------------------------
-        | ------------------------------------------- | -------------------------------------------------
-        |\\n| \u2705 Event latency analysis                 | \U0001F51C Non-stationary
-        environment testing                                               | \U0001F51C
-        LLM non-deterministic function detection | \U0001F6A7 Infinite loops and recursive
-        thought detection |\\n| \u2705 Agent workflow execution pricing       | \U0001F51C
-        Multi-modal environments                                                         |
-        \U0001F6A7 Token limit overflow flags               | \U0001F51C Faulty reasoning
-        detection                     |\\n| \U0001F6A7 Success validators (external)
-        \         | \U0001F51C Execution containers                                                             |
-        \U0001F51C Context limit overflow flags             | \U0001F51C Generative
-        code validators                     |\\n| \U0001F51C Agent controllers/skill
-        tests          | \u2705 Honeypot and prompt injection detection ([PromptArmor](https://promptarmor.com))
-        | \U0001F51C API bill tracking                        | \U0001F51C Error breakpoint
-        analysis                      |\\n| \U0001F51C Information context constraint
-        testing | \U0001F51C Anti-agent roadblocks (i.e. Captchas)                                            |
-        \U0001F51C CI/CD integration checks                 |                                                   |\\n|
-        \U0001F51C Regression testing                     | \U0001F51C Multi-agent
-        framework visualization                                              |                                             |
-        \                                                  |\\n\\n### Why AgentOps?
-        \U0001F914\\n\\nWithout the right tools, AI agents are slow, expensive, and
-        unreliable. Our mission is to bring your agent from prototype to production.
-        Here's why AgentOps stands out:\\n\\n- **Comprehensive Observability**: Track
-        your AI agents' performance, user interactions, and API usage.\\n- **Real-Time
-        Monitoring**: Get instant insights with session replays, metrics, and live
-        monitoring tools.\\n- **Cost Control**: Monitor and manage your spend on LLM
-        and API calls.\\n- **Failure Detection**: Quickly identify and respond to
-        agent failures and multi-agent interaction issues.\\n- **Tool Usage Statistics**:
-        Understand how your agents utilize external tools with detailed analytics.\\n-
-        **Session-Wide Metrics**: Gain a holistic view of your agents' sessions with
-        comprehensive statistics.\\n\\nAgentOps is designed to make agent observability,
-        testing, and monitoring easy.\\n\\n\\n## Star History\\n\\nCheck out our growth
-        in the community:\\n\\n<img src=\\\"https://api.star-history.com/svg?repos=AgentOps-AI/agentops&type=Date\\\"
-        style=\\\"max-width: 500px\\\" width=\\\"50%\\\" alt=\\\"Logo\\\">\\n\\n##
-        Popular projects using AgentOps\\n\\n\\n| Repository | Stars  |\\n| :--------
-        \ | -----: |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/2707039?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [geekan](https://github.com/geekan)
-        / [MetaGPT](https://github.com/geekan/MetaGPT) | 42787 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/130722866?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [run-llama](https://github.com/run-llama)
-        / [llama_index](https://github.com/run-llama/llama_index) | 34446 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/170677839?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [crewAIInc](https://github.com/crewAIInc)
-        / [crewAI](https://github.com/crewAIInc/crewAI) | 18287 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/134388954?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [camel-ai](https://github.com/camel-ai)
-        / [camel](https://github.com/camel-ai/camel) | 5166 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/152537519?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [superagent-ai](https://github.com/superagent-ai)
-        / [superagent](https://github.com/superagent-ai/superagent) | 5050 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/30197649?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [iyaja](https://github.com/iyaja)
-        / [llama-fs](https://github.com/iyaja/llama-fs) | 4713 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/162546372?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [BasedHardware](https://github.com/BasedHardware)
-        / [Omi](https://github.com/BasedHardware/Omi) | 2723 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/454862?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [MervinPraison](https://github.com/MervinPraison)
-        / [PraisonAI](https://github.com/MervinPraison/PraisonAI) | 2007 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/140554352?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [AgentOps-AI](https://github.com/AgentOps-AI)
-        / [Jaiqu](https://github.com/AgentOps-AI/Jaiqu) | 272 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/173542722?s=48&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [swarmzero](https://github.com/swarmzero)
-        / [swarmzero](https://github.com/swarmzero/swarmzero) | 195 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/3074263?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [strnad](https://github.com/strnad)
-        / [CrewAI-Studio](https://github.com/strnad/CrewAI-Studio) | 134 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/18406448?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [alejandro-ao](https://github.com/alejandro-ao)
-        / [exa-crewai](https://github.com/alejandro-ao/exa-crewai) | 55 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/64493665?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [tonykipkemboi](https://github.com/tonykipkemboi)
-        / [youtube_yapper_trapper](https://github.com/tonykipkemboi/youtube_yapper_trapper)
-        | 47 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/17598928?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [sethcoast](https://github.com/sethcoast)
-        / [cover-letter-builder](https://github.com/sethcoast/cover-letter-builder)
-        | 27 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/109994880?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [bhancockio](https://github.com/bhancockio)
-        / [chatgpt4o-analysis](https://github.com/bhancockio/chatgpt4o-analysis) |
-        19 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/14105911?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [breakstring](https://github.com/breakstring)
-        / [Agentic_Story_Book_Workflow](https://github.com/breakstring/Agentic_Story_Book_Workflow)
-        | 14 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/124134656?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [MULTI-ON](https://github.com/MULTI-ON)
-        / [multion-python](https://github.com/MULTI-ON/multion-python) | 13 |\\n\\n\\n_Generated
-        using [github-dependents-info](https://github.com/nvuillam/github-dependents-info),
-        by [Nicolas Vuillamy](https://github.com/nvuillam)_\\n\",\"description_content_type\":\"text/markdown\",\"docs_url\":null,\"download_url\":null,\"downloads\":{\"last_day\":-1,\"last_month\":-1,\"last_week\":-1},\"dynamic\":null,\"home_page\":null,\"keywords\":null,\"license\":null,\"license_expression\":null,\"license_files\":[\"LICENSE\"],\"maintainer\":null,\"maintainer_email\":null,\"name\":\"agentops\",\"package_url\":\"https://pypi.org/project/agentops/\",\"platform\":null,\"project_url\":\"https://pypi.org/project/agentops/\",\"project_urls\":{\"Homepage\":\"https://github.com/AgentOps-AI/agentops\",\"Issues\":\"https://github.com/AgentOps-AI/agentops/issues\"},\"provides_extra\":null,\"release_url\":\"https://pypi.org/project/agentops/0.3.26/\",\"requires_dist\":[\"opentelemetry-api==1.22.0;
-        python_version < \\\"3.10\\\"\",\"opentelemetry-api>=1.27.0; python_version
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+      string: '{"info":{"author":null,"author_email":"Alex Reibman <areibman@gmail.com>, Shawn Qiu <siyangqiu@gmail.com>, Braelyn Boynton <bboynton97@gmail.com>, Howard Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush Shukla <ps4534@nyu.edu>","bugtrack_url":null,"classifiers":["License :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming Language :: Python :: 3.13","Programming Language :: Python :: 3.9"],"description":"<div align=\"center\">\n  <a href=\"https://agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/logo/github-banner.png\" alt=\"Logo\">\n  </a>\n</div>\n\n<div align=\"center\">\n  <em>Observability and DevTool platform for AI Agents</em>\n</div>\n\n<br />\n\n<div align=\"center\">\n  <a href=\"https://pepy.tech/project/agentops\">\n    <img src=\"https://static.pepy.tech/badge/agentops/month\"
+        alt=\"Downloads\">\n  </a>\n  <a href=\"https://github.com/agentops-ai/agentops/issues\">\n  <img src=\"https://img.shields.io/github/commit-activity/m/agentops-ai/agentops\" alt=\"git commit activity\">\n  </a>\n  <img src=\"https://img.shields.io/pypi/v/agentops?&color=3670A0\" alt=\"PyPI - Version\">\n  <a href=\"https://opensource.org/licenses/MIT\">\n    <img src=\"https://img.shields.io/badge/License-MIT-yellow.svg?&color=3670A0\" alt=\"License: MIT\">\n  </a>\n</div>\n\n<p align=\"center\">\n  <a href=\"https://twitter.com/agentopsai/\">\n    <img src=\"https://img.shields.io/twitter/follow/agentopsai?style=social\" alt=\"Twitter\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://discord.gg/FagdcwwXRR\">\n    <img src=\"https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord\" alt=\"Discord\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://app.agentops.ai/?ref=gh\">\n    <img src=\"https://img.shields.io/badge/Dashboard-blue.svg?style=flat-square\"
+        alt=\"Dashboard\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://docs.agentops.ai/introduction\">\n    <img src=\"https://img.shields.io/badge/Documentation-orange.svg?style=flat-square\" alt=\"Documentation\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://entelligence.ai/AgentOps-AI&agentops\">\n    <img src=\"https://img.shields.io/badge/Chat%20with%20Docs-green.svg?style=flat-square\" alt=\"Chat with Docs\" style=\"height: 20px;\">\n  </a>\n</p>\n\n\n\n<div style=\"justify-content: center\">\n  <img src=\"docs/images/external/app_screenshots/dashboard-banner.png\" alt=\"Dashboard Banner\">\n</div>\n\n<br/>\n\n\nAgentOps helps developers build, evaluate, and monitor AI agents. From prototype to production.\n\n|                                       |                                                               |\n| ------------------------------------- | ------------------------------------------------------------- |\n| 📊 **Replay Analytics and Debugging** | Step-by-step
+        agent execution graphs                           |\n| 💸 **LLM Cost Management**            | Track spend with LLM foundation model providers               |\n| 🧪 **Agent Benchmarking**             | Test your agents against 1,000+ evals                         |\n| 🔐 **Compliance and Security**        | Detect common prompt injection and data exfiltration exploits |\n| 🤝 **Framework Integrations**         | Native Integrations with CrewAI, AG2(AutoGen), Camel AI, & LangChain         |\n\n## Quick Start ⌨️\n\n```bash\npip install agentops\n```\n\n\n#### Session replays in 2 lines of code\n\nInitialize the AgentOps client and automatically get analytics on all your LLM calls.\n\n[Get an API key](https://app.agentops.ai/settings/projects)\n\n```python\nimport agentops\n\n# Beginning of your program (i.e. main.py, __init__.py)\nagentops.init( < INSERT YOUR API KEY HERE >)\n\n...\n\n# End of program\nagentops.end_session(''Success'')\n```\n\nAll your sessions can be viewed on the [AgentOps
+        dashboard](https://app.agentops.ai?ref=gh)\n<br/>\n\n<details>\n  <summary>Agent Debugging</summary>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/session-drilldown-metadata.png\" style=\"width: 90%;\" alt=\"Agent Metadata\"/>\n  </a>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/chat-viewer.png\" style=\"width: 90%;\" alt=\"Chat Viewer\"/>\n  </a>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/session-drilldown-graphs.png\" style=\"width: 90%;\" alt=\"Event Graphs\"/>\n  </a>\n</details>\n\n<details>\n  <summary>Session Replays</summary>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/session-replay.png\" style=\"width: 90%;\" alt=\"Session Replays\"/>\n  </a>\n</details>\n\n<details open>\n  <summary>Summary Analytics</summary>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n   <img
+        src=\"docs/images/external/app_screenshots/overview.png\" style=\"width: 90%;\" alt=\"Summary Analytics\"/>\n  </a>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n   <img src=\"docs/images/external/app_screenshots/overview-charts.png\" style=\"width: 90%;\" alt=\"Summary Analytics Charts\"/>\n  </a>\n</details>\n\n\n### First class Developer Experience\nAdd powerful observability to your agents, tools, and functions with as little code as possible: one line at a time.\n<br/>\nRefer to our [documentation](http://docs.agentops.ai)\n\n```python\n# Automatically associate all Events with the agent that originated them\nfrom agentops import track_agent\n\n@track_agent(name=''SomeCustomName'')\nclass MyAgent:\n  ...\n```\n\n```python\n# Automatically create ToolEvents for tools that agents will use\nfrom agentops import record_tool\n\n@record_tool(''SampleToolName'')\ndef sample_tool(...):\n  ...\n```\n\n```python\n# Automatically create ActionEvents for other functions.\nfrom agentops
+        import record_action\n\n@agentops.record_action(''sample function being record'')\ndef sample_function(...):\n  ...\n```\n\n```python\n# Manually record any other Events\nfrom agentops import record, ActionEvent\n\nrecord(ActionEvent(\"received_user_input\"))\n```\n\n## Integrations 🦾\n\n### CrewAI 🛶\n\nBuild Crew agents with observability with only 2 lines of code. Simply set an `AGENTOPS_API_KEY` in your environment, and your crews will get automatic monitoring on the AgentOps dashboard.\n\n```bash\npip install ''crewai[agentops]''\n```\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/crewai)\n- [Official CrewAI documentation](https://docs.crewai.com/how-to/AgentOps-Observability)\n\n### AG2 🤖\nWith only two lines of code, add full observability and monitoring to AG2 (formerly AutoGen) agents. Set an `AGENTOPS_API_KEY` in your environment and call `agentops.init()`\n\n- [AG2 Observability Example](https://docs.ag2.ai/notebooks/agentchat_agentops)\n-
+        [AG2 - AgentOps Documentation](https://docs.ag2.ai/docs/ecosystem/agentops)\n\n### Camel AI 🐪\n\nTrack and analyze CAMEL agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [Camel AI](https://www.camel-ai.org/) - Advanced agent communication framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/camel)\n- [Official Camel AI documentation](https://docs.camel-ai.org/cookbooks/agents_tracking.html)\n\n<details>\n  <summary>Installation</summary>\n\n```bash\npip install \"camel-ai[all]==0.2.11\"\npip install agentops\n```\n\n```python\nimport os\nimport agentops\nfrom camel.agents import ChatAgent\nfrom camel.messages import BaseMessage\nfrom camel.models import ModelFactory\nfrom camel.types import ModelPlatformType, ModelType\n\n# Initialize AgentOps\nagentops.init(os.getenv(\"AGENTOPS_API_KEY\"), default_tags=[\"CAMEL Example\"])\n\n# Import toolkits after AgentOps init for tracking\nfrom
+        camel.toolkits import SearchToolkit\n\n# Set up the agent with search tools\nsys_msg = BaseMessage.make_assistant_message(\n    role_name=''Tools calling operator'',\n    content=''You are a helpful assistant''\n)\n\n# Configure tools and model\ntools = [*SearchToolkit().get_tools()]\nmodel = ModelFactory.create(\n    model_platform=ModelPlatformType.OPENAI,\n    model_type=ModelType.GPT_4O_MINI,\n)\n\n# Create and run the agent\ncamel_agent = ChatAgent(\n    system_message=sys_msg,\n    model=model,\n    tools=tools,\n)\n\nresponse = camel_agent.step(\"What is AgentOps?\")\nprint(response)\n\nagentops.end_session(\"Success\")\n```\n\nCheck out our [Camel integration guide](https://docs.agentops.ai/v1/integrations/camel) for more examples including multi-agent scenarios.\n</details>\n\n### Langchain 🦜🔗\n\nAgentOps works seamlessly with applications built using Langchain. To use the handler, install Langchain as an optional dependency:\n\n<details>\n  <summary>Installation</summary>\n  \n```shell\npip
+        install agentops[langchain]\n```\n\nTo use the handler, import and set\n\n```python\nimport os\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.agents import initialize_agent, AgentType\nfrom agentops.partners.langchain_callback_handler import LangchainCallbackHandler\n\nAGENTOPS_API_KEY = os.environ[''AGENTOPS_API_KEY'']\nhandler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=[''Langchain Example''])\n\nllm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,\n                 callbacks=[handler],\n                 model=''gpt-3.5-turbo'')\n\nagent = initialize_agent(tools,\n                         llm,\n                         agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n                         verbose=True,\n                         callbacks=[handler], # You must pass in a callback handler to record your agent\n                         handle_parsing_errors=True)\n```\n\nCheck out the [Langchain Examples Notebook](./examples/langchain_examples.ipynb) for
+        more details including Async handlers.\n\n</details>\n\n### Cohere ⌨️\n\nFirst class support for Cohere(>=5.4.0). This is a living integration, should you need any added functionality please message us on Discord!\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/cohere)\n- [Official Cohere documentation](https://docs.cohere.com/reference/about)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install cohere\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\nco = cohere.Client()\n\nchat = co.chat(\n    message=\"Is it pronounced ceaux-hear or co-hehray?\"\n)\n\nprint(chat)\n\nagentops.end_session(''Success'')\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nco = cohere.Client()\n\nstream = co.chat_stream(\n    message=\"Write
+        me a haiku about the synergies between Cohere and AgentOps\"\n)\n\nfor event in stream:\n    if event.event_type == \"text-generation\":\n        print(event.text, end='''')\n\nagentops.end_session(''Success'')\n```\n</details>\n\n\n### Anthropic ﹨\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration guide](https://docs.agentops.ai/v1/integrations/anthropic)\n- [Official Anthropic documentation](https://docs.anthropic.com/en/docs/welcome)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install anthropic\n```\n\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = anthropic.Anthropic(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nmessage = client.messages.create(\n        max_tokens=1024,\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\":
+        \"Tell me a cool fact about AgentOps\",\n            }\n        ],\n        model=\"claude-3-opus-20240229\",\n    )\nprint(message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = anthropic.Anthropic(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nstream = client.messages.create(\n    max_tokens=1024,\n    model=\"claude-3-opus-20240229\",\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"Tell me something cool about streaming agents\",\n        }\n    ],\n    stream=True,\n)\n\nresponse = \"\"\nfor event in stream:\n    if event.type == \"content_block_delta\":\n        response += event.delta.text\n    elif event.type == \"message_stop\":\n        print(\"\\n\")\n        print(response)\n        print(\"\\n\")\n```\n\nAsync\n\n```python
+        python\nimport asyncio\nfrom anthropic import AsyncAnthropic\n\nclient = AsyncAnthropic(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\n\nasync def main() -> None:\n    message = await client.messages.create(\n        max_tokens=1024,\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something interesting about async agents\",\n            }\n        ],\n        model=\"claude-3-opus-20240229\",\n    )\n    print(message.content)\n\n\nawait main()\n```\n</details>\n\n### Mistral 〽️\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration example](./examples/mistral//mistral_example.ipynb)\n- [Official Mistral documentation](https://docs.mistral.ai)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install mistralai\n```\n\nSync\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s
+        code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.complete(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me a cool fact about AgentOps\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\nprint(message.choices[0].message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.stream(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something cool
+        about streaming agents\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\n\nresponse = \"\"\nfor event in message:\n    if event.data.choices[0].finish_reason == \"stop\":\n        print(\"\\n\")\n        print(response)\n        print(\"\\n\")\n    else:\n        response += event.text\n\nagentops.end_session(''Success'')\n```\n\nAsync\n\n```python python\nimport asyncio\nfrom mistralai import Mistral\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n    message = await client.chat.complete_async(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something interesting about async agents\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\n    print(message.choices[0].message.content)\n\n\nawait main()\n```\n\nAsync Streaming\n\n```python python\nimport asyncio\nfrom mistralai
+        import Mistral\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n    message = await client.chat.stream_async(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something interesting about async streaming agents\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\n\n    response = \"\"\n    async for event in message:\n        if event.data.choices[0].finish_reason == \"stop\":\n            print(\"\\n\")\n            print(response)\n            print(\"\\n\")\n        else:\n            response += event.text\n\n\nawait main()\n```\n</details>\n\n\n\n### CamelAI ﹨\n\nTrack agents built with the CamelAI Python SDK (>=0.32.0).\n\n- [CamelAI integration guide](https://docs.camel-ai.org/cookbooks/agents_tracking.html#)\n- [Official CamelAI documentation](https://docs.camel-ai.org/index.html)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip
+        install camel-ai[all]\npip install agentops\n```\n\n```python python\n#Import Dependencies\nimport agentops\nimport os\nfrom getpass import getpass\nfrom dotenv import load_dotenv\n\n#Set Keys\nload_dotenv()\nopenai_api_key = os.getenv(\"OPENAI_API_KEY\") or \"<your openai key here>\"\nagentops_api_key = os.getenv(\"AGENTOPS_API_KEY\") or \"<your agentops key here>\"\n\n\n\n```\n</details>\n\n[You can find usage examples here!](examples/camelai_examples/README.md).\n\n\n\n### LiteLLM 🚅\n\nAgentOps provides support for LiteLLM(>=1.3.1), allowing you to call 100+ LLMs using the same Input/Output Format. \n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/litellm)\n- [Official LiteLLM documentation](https://docs.litellm.ai/docs/providers)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install litellm\n```\n\n```python python\n# Do not use LiteLLM like this\n# from litellm import completion\n# ...\n# response = completion(model=\"claude-3\",
+        messages=messages)\n\n# Use LiteLLM like this\nimport litellm\n...\nresponse = litellm.completion(model=\"claude-3\", messages=messages)\n# or\nresponse = await litellm.acompletion(model=\"claude-3\", messages=messages)\n```\n</details>\n\n### LlamaIndex 🦙\n\n\nAgentOps works seamlessly with applications built using LlamaIndex, a framework for building context-augmented generative AI applications with LLMs.\n\n<details>\n  <summary>Installation</summary>\n  \n```shell\npip install llama-index-instrumentation-agentops\n```\n\nTo use the handler, import and set\n\n```python\nfrom llama_index.core import set_global_handler\n\n# NOTE: Feel free to set your AgentOps environment variables (e.g., ''AGENTOPS_API_KEY'')\n# as outlined in the AgentOps documentation, or pass the equivalent keyword arguments\n# anticipated by AgentOps'' AOClient as **eval_params in set_global_handler.\n\nset_global_handler(\"agentops\")\n```\n\nCheck out the [LlamaIndex docs](https://docs.llamaindex.ai/en/stable/module_guides/observability/?h=agentops#agentops)
+        for more details.\n\n</details>\n\n### Llama Stack 🦙🥞\n\nAgentOps provides support for Llama Stack Python Client(>=0.0.53), allowing you to monitor your Agentic applications. \n\n- [AgentOps integration example 1](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-fdddf65549f3714f8f007ce7dfd1cde720329fe54155d54389dd50fbd81813cb)\n- [AgentOps integration example 2](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-6688ff4fb7ab1ce7b1cc9b8362ca27264a3060c16737fb1d850305787a6e3699)\n- [Official Llama Stack Python Client](https://github.com/meta-llama/llama-stack-client-python)\n\n### SwarmZero AI 🐝\n\nTrack and analyze SwarmZero agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [SwarmZero](https://swarmzero.ai) - Advanced multi-agent framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/swarmzero)\n-
+        [SwarmZero AI integration example](https://docs.swarmzero.ai/examples/ai-agents/build-and-monitor-a-web-search-agent)\n- [SwarmZero AI - AgentOps documentation](https://docs.swarmzero.ai/sdk/observability/agentops)\n- [Official SwarmZero Python SDK](https://github.com/swarmzero/swarmzero)\n\n<details>\n  <summary>Installation</summary>\n\n```bash\npip install swarmzero\npip install agentops\n```\n\n```python\nfrom dotenv import load_dotenv\nload_dotenv()\n\nimport agentops\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nfrom swarmzero import Agent, Swarm\n# ...\n```\n</details>\n\n## Time travel debugging 🔮\n\n<div style=\"justify-content: center\">\n  <img src=\"docs/images/external/app_screenshots/time_travel_banner.png\" alt=\"Time Travel Banner\">\n</div>\n\n<br />\n\n[Try it out!](https://app.agentops.ai/timetravel)\n\n## Agent Arena 🥊\n\n(coming soon!)\n\n## Evaluations Roadmap 🧭\n\n| Platform                                                                     | Dashboard                                  |
+        Evals                                  |\n| ---------------------------------------------------------------------------- | ------------------------------------------ | -------------------------------------- |\n| ✅ Python SDK                                                                | ✅ Multi-session and Cross-session metrics | ✅ Custom eval metrics                 |\n| 🚧 Evaluation builder API                                                    | ✅ Custom event tag tracking               | 🔜 Agent scorecards                    |\n| ✅ [Javascript/Typescript SDK](https://github.com/AgentOps-AI/agentops-node) | ✅ Session replays                         | 🔜 Evaluation playground + leaderboard |\n\n## Debugging Roadmap 🧭\n\n| Performance testing                       | Environments                                                                        | LLM Testing                                 | Reasoning and execution testing                   |\n| -----------------------------------------
+        | ----------------------------------------------------------------------------------- | ------------------------------------------- | ------------------------------------------------- |\n| ✅ Event latency analysis                 | 🔜 Non-stationary environment testing                                               | 🔜 LLM non-deterministic function detection | 🚧 Infinite loops and recursive thought detection |\n| ✅ Agent workflow execution pricing       | 🔜 Multi-modal environments                                                         | 🚧 Token limit overflow flags               | 🔜 Faulty reasoning detection                     |\n| 🚧 Success validators (external)          | 🔜 Execution containers                                                             | 🔜 Context limit overflow flags             | 🔜 Generative code validators                     |\n| 🔜 Agent controllers/skill tests          | ✅ Honeypot and prompt injection detection ([PromptArmor](https://promptarmor.com))
+        | 🔜 API bill tracking                        | 🔜 Error breakpoint analysis                      |\n| 🔜 Information context constraint testing | 🔜 Anti-agent roadblocks (i.e. Captchas)                                            | 🔜 CI/CD integration checks                 |                                                   |\n| 🔜 Regression testing                     | 🔜 Multi-agent framework visualization                                              |                                             |                                                   |\n\n### Why AgentOps? 🤔\n\nWithout the right tools, AI agents are slow, expensive, and unreliable. Our mission is to bring your agent from prototype to production. Here''s why AgentOps stands out:\n\n- **Comprehensive Observability**: Track your AI agents'' performance, user interactions, and API usage.\n- **Real-Time Monitoring**: Get instant insights with session replays, metrics, and live monitoring tools.\n- **Cost Control**: Monitor
+        and manage your spend on LLM and API calls.\n- **Failure Detection**: Quickly identify and respond to agent failures and multi-agent interaction issues.\n- **Tool Usage Statistics**: Understand how your agents utilize external tools with detailed analytics.\n- **Session-Wide Metrics**: Gain a holistic view of your agents'' sessions with comprehensive statistics.\n\nAgentOps is designed to make agent observability, testing, and monitoring easy.\n\n\n## Star History\n\nCheck out our growth in the community:\n\n<img src=\"https://api.star-history.com/svg?repos=AgentOps-AI/agentops&type=Date\" style=\"max-width: 500px\" width=\"50%\" alt=\"Logo\">\n\n## Popular projects using AgentOps\n\n\n| Repository | Stars  |\n| :--------  | -----: |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/2707039?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [geekan](https://github.com/geekan) / [MetaGPT](https://github.com/geekan/MetaGPT) | 42787 |\n|<img class=\"avatar
+        mr-2\" src=\"https://avatars.githubusercontent.com/u/130722866?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [run-llama](https://github.com/run-llama) / [llama_index](https://github.com/run-llama/llama_index) | 34446 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/170677839?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [crewAIInc](https://github.com/crewAIInc) / [crewAI](https://github.com/crewAIInc/crewAI) | 18287 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/134388954?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [camel-ai](https://github.com/camel-ai) / [camel](https://github.com/camel-ai/camel) | 5166 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/152537519?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [superagent-ai](https://github.com/superagent-ai) / [superagent](https://github.com/superagent-ai/superagent) | 5050 |\n|<img class=\"avatar mr-2\"
+        src=\"https://avatars.githubusercontent.com/u/30197649?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [iyaja](https://github.com/iyaja) / [llama-fs](https://github.com/iyaja/llama-fs) | 4713 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/162546372?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [BasedHardware](https://github.com/BasedHardware) / [Omi](https://github.com/BasedHardware/Omi) | 2723 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/454862?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [MervinPraison](https://github.com/MervinPraison) / [PraisonAI](https://github.com/MervinPraison/PraisonAI) | 2007 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/140554352?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [AgentOps-AI](https://github.com/AgentOps-AI) / [Jaiqu](https://github.com/AgentOps-AI/Jaiqu) | 272 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/173542722?s=48&v=4\"
+        width=\"20\" height=\"20\" alt=\"\">  &nbsp; [swarmzero](https://github.com/swarmzero) / [swarmzero](https://github.com/swarmzero/swarmzero) | 195 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/3074263?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [strnad](https://github.com/strnad) / [CrewAI-Studio](https://github.com/strnad/CrewAI-Studio) | 134 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/18406448?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [alejandro-ao](https://github.com/alejandro-ao) / [exa-crewai](https://github.com/alejandro-ao/exa-crewai) | 55 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/64493665?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [tonykipkemboi](https://github.com/tonykipkemboi) / [youtube_yapper_trapper](https://github.com/tonykipkemboi/youtube_yapper_trapper) | 47 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/17598928?s=40&v=4\"
+        width=\"20\" height=\"20\" alt=\"\">  &nbsp; [sethcoast](https://github.com/sethcoast) / [cover-letter-builder](https://github.com/sethcoast/cover-letter-builder) | 27 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/109994880?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [bhancockio](https://github.com/bhancockio) / [chatgpt4o-analysis](https://github.com/bhancockio/chatgpt4o-analysis) | 19 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/14105911?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [breakstring](https://github.com/breakstring) / [Agentic_Story_Book_Workflow](https://github.com/breakstring/Agentic_Story_Book_Workflow) | 14 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/124134656?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [MULTI-ON](https://github.com/MULTI-ON) / [multion-python](https://github.com/MULTI-ON/multion-python) | 13 |\n\n\n_Generated using [github-dependents-info](https://github.com/nvuillam/github-dependents-info),
+        by [Nicolas Vuillamy](https://github.com/nvuillam)_\n","description_content_type":"text/markdown","docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":null,"keywords":null,"license":null,"license_expression":null,"license_files":["LICENSE"],"maintainer":null,"maintainer_email":null,"name":"agentops","package_url":"https://pypi.org/project/agentops/","platform":null,"project_url":"https://pypi.org/project/agentops/","project_urls":{"Homepage":"https://github.com/AgentOps-AI/agentops","Issues":"https://github.com/AgentOps-AI/agentops/issues"},"provides_extra":null,"release_url":"https://pypi.org/project/agentops/0.3.26/","requires_dist":["opentelemetry-api==1.22.0; python_version < \"3.10\"","opentelemetry-api>=1.27.0; python_version >= \"3.10\"","opentelemetry-exporter-otlp-proto-http==1.22.0; python_version < \"3.10\"","opentelemetry-exporter-otlp-proto-http>=1.27.0; python_version >= \"3.10\"","opentelemetry-sdk==1.22.0;
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-      string: "{\"info\":{\"author\":null,\"author_email\":\"Alex Reibman <areibman@gmail.com>,
-        Shawn Qiu <siyangqiu@gmail.com>, Braelyn Boynton <bboynton97@gmail.com>, Howard
-        Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush
-        Shukla <ps4534@nyu.edu>\",\"bugtrack_url\":null,\"classifiers\":[\"License
-        :: OSI Approved :: MIT License\",\"Operating System :: OS Independent\",\"Programming
-        Language :: Python :: 3\",\"Programming Language :: Python :: 3.10\",\"Programming
-        Language :: Python :: 3.11\",\"Programming Language :: Python :: 3.12\",\"Programming
-        Language :: Python :: 3.13\",\"Programming Language :: Python :: 3.9\"],\"description\":\"<div
-        align=\\\"center\\\">\\n  <a href=\\\"https://agentops.ai?ref=gh\\\">\\n    <img
-        src=\\\"docs/images/external/logo/github-banner.png\\\" alt=\\\"Logo\\\">\\n
-        \ </a>\\n</div>\\n\\n<div align=\\\"center\\\">\\n  <em>Observability and
-        DevTool platform for AI Agents</em>\\n</div>\\n\\n<br />\\n\\n<div align=\\\"center\\\">\\n
-        \ <a href=\\\"https://pepy.tech/project/agentops\\\">\\n    <img src=\\\"https://static.pepy.tech/badge/agentops/month\\\"
-        alt=\\\"Downloads\\\">\\n  </a>\\n  <a href=\\\"https://github.com/agentops-ai/agentops/issues\\\">\\n
-        \ <img src=\\\"https://img.shields.io/github/commit-activity/m/agentops-ai/agentops\\\"
-        alt=\\\"git commit activity\\\">\\n  </a>\\n  <img src=\\\"https://img.shields.io/pypi/v/agentops?&color=3670A0\\\"
-        alt=\\\"PyPI - Version\\\">\\n  <a href=\\\"https://opensource.org/licenses/MIT\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/License-MIT-yellow.svg?&color=3670A0\\\"
-        alt=\\\"License: MIT\\\">\\n  </a>\\n</div>\\n\\n<p align=\\\"center\\\">\\n
-        \ <a href=\\\"https://twitter.com/agentopsai/\\\">\\n    <img src=\\\"https://img.shields.io/twitter/follow/agentopsai?style=social\\\"
-        alt=\\\"Twitter\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://discord.gg/FagdcwwXRR\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord\\\"
-        alt=\\\"Discord\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://app.agentops.ai/?ref=gh\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/Dashboard-blue.svg?style=flat-square\\\"
-        alt=\\\"Dashboard\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://docs.agentops.ai/introduction\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/Documentation-orange.svg?style=flat-square\\\"
-        alt=\\\"Documentation\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://entelligence.ai/AgentOps-AI&agentops\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/Chat%20with%20Docs-green.svg?style=flat-square\\\"
-        alt=\\\"Chat with Docs\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n</p>\\n\\n\\n\\n<div
-        style=\\\"justify-content: center\\\">\\n  <img src=\\\"docs/images/external/app_screenshots/dashboard-banner.png\\\"
-        alt=\\\"Dashboard Banner\\\">\\n</div>\\n\\n<br/>\\n\\n\\nAgentOps helps developers
-        build, evaluate, and monitor AI agents. From prototype to production.\\n\\n|
-        \                                      |                                                               |\\n|
-        ------------------------------------- | -------------------------------------------------------------
-        |\\n| \U0001F4CA **Replay Analytics and Debugging** | Step-by-step agent execution
-        graphs                           |\\n| \U0001F4B8 **LLM Cost Management**
-        \           | Track spend with LLM foundation model providers               |\\n|
-        \U0001F9EA **Agent Benchmarking**             | Test your agents against 1,000+
-        evals                         |\\n| \U0001F510 **Compliance and Security**
-        \       | Detect common prompt injection and data exfiltration exploits |\\n|
-        \U0001F91D **Framework Integrations**         | Native Integrations with CrewAI,
-        AG2(AutoGen), Camel AI, & LangChain         |\\n\\n## Quick Start \u2328\uFE0F\\n\\n```bash\\npip
-        install agentops\\n```\\n\\n\\n#### Session replays in 2 lines of code\\n\\nInitialize
-        the AgentOps client and automatically get analytics on all your LLM calls.\\n\\n[Get
-        an API key](https://app.agentops.ai/settings/projects)\\n\\n```python\\nimport
-        agentops\\n\\n# Beginning of your program (i.e. main.py, __init__.py)\\nagentops.init(
-        < INSERT YOUR API KEY HERE >)\\n\\n...\\n\\n# End of program\\nagentops.end_session('Success')\\n```\\n\\nAll
-        your sessions can be viewed on the [AgentOps dashboard](https://app.agentops.ai?ref=gh)\\n<br/>\\n\\n<details>\\n
-        \ <summary>Agent Debugging</summary>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/session-drilldown-metadata.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Agent Metadata\\\"/>\\n  </a>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/chat-viewer.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Chat Viewer\\\"/>\\n  </a>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/session-drilldown-graphs.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Event Graphs\\\"/>\\n  </a>\\n</details>\\n\\n<details>\\n
-        \ <summary>Session Replays</summary>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/session-replay.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Session Replays\\\"/>\\n  </a>\\n</details>\\n\\n<details
-        open>\\n  <summary>Summary Analytics</summary>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \  <img src=\\\"docs/images/external/app_screenshots/overview.png\\\" style=\\\"width:
-        90%;\\\" alt=\\\"Summary Analytics\\\"/>\\n  </a>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \  <img src=\\\"docs/images/external/app_screenshots/overview-charts.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Summary Analytics Charts\\\"/>\\n  </a>\\n</details>\\n\\n\\n###
-        First class Developer Experience\\nAdd powerful observability to your agents,
-        tools, and functions with as little code as possible: one line at a time.\\n<br/>\\nRefer
-        to our [documentation](http://docs.agentops.ai)\\n\\n```python\\n# Automatically
-        associate all Events with the agent that originated them\\nfrom agentops import
-        track_agent\\n\\n@track_agent(name='SomeCustomName')\\nclass MyAgent:\\n  ...\\n```\\n\\n```python\\n#
-        Automatically create ToolEvents for tools that agents will use\\nfrom agentops
-        import record_tool\\n\\n@record_tool('SampleToolName')\\ndef sample_tool(...):\\n
-        \ ...\\n```\\n\\n```python\\n# Automatically create ActionEvents for other
-        functions.\\nfrom agentops import record_action\\n\\n@agentops.record_action('sample
-        function being record')\\ndef sample_function(...):\\n  ...\\n```\\n\\n```python\\n#
-        Manually record any other Events\\nfrom agentops import record, ActionEvent\\n\\nrecord(ActionEvent(\\\"received_user_input\\\"))\\n```\\n\\n##
-        Integrations \U0001F9BE\\n\\n### CrewAI \U0001F6F6\\n\\nBuild Crew agents
-        with observability with only 2 lines of code. Simply set an `AGENTOPS_API_KEY`
-        in your environment, and your crews will get automatic monitoring on the AgentOps
-        dashboard.\\n\\n```bash\\npip install 'crewai[agentops]'\\n```\\n\\n- [AgentOps
-        integration example](https://docs.agentops.ai/v1/integrations/crewai)\\n-
-        [Official CrewAI documentation](https://docs.crewai.com/how-to/AgentOps-Observability)\\n\\n###
-        AG2 \U0001F916\\nWith only two lines of code, add full observability and monitoring
-        to AG2 (formerly AutoGen) agents. Set an `AGENTOPS_API_KEY` in your environment
-        and call `agentops.init()`\\n\\n- [AG2 Observability Example](https://docs.ag2.ai/notebooks/agentchat_agentops)\\n-
-        [AG2 - AgentOps Documentation](https://docs.ag2.ai/docs/ecosystem/agentops)\\n\\n###
-        Camel AI \U0001F42A\\n\\nTrack and analyze CAMEL agents with full observability.
-        Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get
-        started.\\n\\n- [Camel AI](https://www.camel-ai.org/) - Advanced agent communication
-        framework\\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/camel)\\n-
-        [Official Camel AI documentation](https://docs.camel-ai.org/cookbooks/agents_tracking.html)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n\\n```bash\\npip install \\\"camel-ai[all]==0.2.11\\\"\\npip
-        install agentops\\n```\\n\\n```python\\nimport os\\nimport agentops\\nfrom
-        camel.agents import ChatAgent\\nfrom camel.messages import BaseMessage\\nfrom
-        camel.models import ModelFactory\\nfrom camel.types import ModelPlatformType,
-        ModelType\\n\\n# Initialize AgentOps\\nagentops.init(os.getenv(\\\"AGENTOPS_API_KEY\\\"),
-        default_tags=[\\\"CAMEL Example\\\"])\\n\\n# Import toolkits after AgentOps
-        init for tracking\\nfrom camel.toolkits import SearchToolkit\\n\\n# Set up
-        the agent with search tools\\nsys_msg = BaseMessage.make_assistant_message(\\n
-        \   role_name='Tools calling operator',\\n    content='You are a helpful assistant'\\n)\\n\\n#
-        Configure tools and model\\ntools = [*SearchToolkit().get_tools()]\\nmodel
-        = ModelFactory.create(\\n    model_platform=ModelPlatformType.OPENAI,\\n    model_type=ModelType.GPT_4O_MINI,\\n)\\n\\n#
-        Create and run the agent\\ncamel_agent = ChatAgent(\\n    system_message=sys_msg,\\n
-        \   model=model,\\n    tools=tools,\\n)\\n\\nresponse = camel_agent.step(\\\"What
-        is AgentOps?\\\")\\nprint(response)\\n\\nagentops.end_session(\\\"Success\\\")\\n```\\n\\nCheck
-        out our [Camel integration guide](https://docs.agentops.ai/v1/integrations/camel)
-        for more examples including multi-agent scenarios.\\n</details>\\n\\n### Langchain
-        \U0001F99C\U0001F517\\n\\nAgentOps works seamlessly with applications built
-        using Langchain. To use the handler, install Langchain as an optional dependency:\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```shell\\npip install agentops[langchain]\\n```\\n\\nTo
-        use the handler, import and set\\n\\n```python\\nimport os\\nfrom langchain.chat_models
-        import ChatOpenAI\\nfrom langchain.agents import initialize_agent, AgentType\\nfrom
-        agentops.partners.langchain_callback_handler import LangchainCallbackHandler\\n\\nAGENTOPS_API_KEY
-        = os.environ['AGENTOPS_API_KEY']\\nhandler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY,
-        tags=['Langchain Example'])\\n\\nllm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,\\n
-        \                callbacks=[handler],\\n                 model='gpt-3.5-turbo')\\n\\nagent
-        = initialize_agent(tools,\\n                         llm,\\n                         agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\\n
-        \                        verbose=True,\\n                         callbacks=[handler],
-        # You must pass in a callback handler to record your agent\\n                         handle_parsing_errors=True)\\n```\\n\\nCheck
-        out the [Langchain Examples Notebook](./examples/langchain_examples.ipynb)
-        for more details including Async handlers.\\n\\n</details>\\n\\n### Cohere
-        \u2328\uFE0F\\n\\nFirst class support for Cohere(>=5.4.0). This is a living
-        integration, should you need any added functionality please message us on
-        Discord!\\n\\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/cohere)\\n-
-        [Official Cohere documentation](https://docs.cohere.com/reference/about)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install cohere\\n```\\n\\n```python
-        python\\nimport cohere\\nimport agentops\\n\\n# Beginning of program's code
-        (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\nco
-        = cohere.Client()\\n\\nchat = co.chat(\\n    message=\\\"Is it pronounced
-        ceaux-hear or co-hehray?\\\"\\n)\\n\\nprint(chat)\\n\\nagentops.end_session('Success')\\n```\\n\\n```python
-        python\\nimport cohere\\nimport agentops\\n\\n# Beginning of program's code
-        (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nco
-        = cohere.Client()\\n\\nstream = co.chat_stream(\\n    message=\\\"Write me
-        a haiku about the synergies between Cohere and AgentOps\\\"\\n)\\n\\nfor event
-        in stream:\\n    if event.event_type == \\\"text-generation\\\":\\n        print(event.text,
-        end='')\\n\\nagentops.end_session('Success')\\n```\\n</details>\\n\\n\\n###
-        Anthropic \uFE68\\n\\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\\n\\n-
-        [AgentOps integration guide](https://docs.agentops.ai/v1/integrations/anthropic)\\n-
-        [Official Anthropic documentation](https://docs.anthropic.com/en/docs/welcome)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install anthropic\\n```\\n\\n```python
-        python\\nimport anthropic\\nimport agentops\\n\\n# Beginning of program's
-        code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nclient
-        = anthropic.Anthropic(\\n    # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"ANTHROPIC_API_KEY\\\"),\\n)\\n\\nmessage
-        = client.messages.create(\\n        max_tokens=1024,\\n        messages=[\\n
-        \           {\\n                \\\"role\\\": \\\"user\\\",\\n                \\\"content\\\":
-        \\\"Tell me a cool fact about AgentOps\\\",\\n            }\\n        ],\\n
-        \       model=\\\"claude-3-opus-20240229\\\",\\n    )\\nprint(message.content)\\n\\nagentops.end_session('Success')\\n```\\n\\nStreaming\\n```python
-        python\\nimport anthropic\\nimport agentops\\n\\n# Beginning of program's
-        code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nclient
-        = anthropic.Anthropic(\\n    # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"ANTHROPIC_API_KEY\\\"),\\n)\\n\\nstream
-        = client.messages.create(\\n    max_tokens=1024,\\n    model=\\\"claude-3-opus-20240229\\\",\\n
-        \   messages=[\\n        {\\n            \\\"role\\\": \\\"user\\\",\\n            \\\"content\\\":
-        \\\"Tell me something cool about streaming agents\\\",\\n        }\\n    ],\\n
-        \   stream=True,\\n)\\n\\nresponse = \\\"\\\"\\nfor event in stream:\\n    if
-        event.type == \\\"content_block_delta\\\":\\n        response += event.delta.text\\n
-        \   elif event.type == \\\"message_stop\\\":\\n        print(\\\"\\\\n\\\")\\n
-        \       print(response)\\n        print(\\\"\\\\n\\\")\\n```\\n\\nAsync\\n\\n```python
-        python\\nimport asyncio\\nfrom anthropic import AsyncAnthropic\\n\\nclient
-        = AsyncAnthropic(\\n    # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"ANTHROPIC_API_KEY\\\"),\\n)\\n\\n\\nasync
-        def main() -> None:\\n    message = await client.messages.create(\\n        max_tokens=1024,\\n
-        \       messages=[\\n            {\\n                \\\"role\\\": \\\"user\\\",\\n
-        \               \\\"content\\\": \\\"Tell me something interesting about async
-        agents\\\",\\n            }\\n        ],\\n        model=\\\"claude-3-opus-20240229\\\",\\n
-        \   )\\n    print(message.content)\\n\\n\\nawait main()\\n```\\n</details>\\n\\n###
-        Mistral \u303D\uFE0F\\n\\nTrack agents built with the Anthropic Python SDK
-        (>=0.32.0).\\n\\n- [AgentOps integration example](./examples/mistral//mistral_example.ipynb)\\n-
-        [Official Mistral documentation](https://docs.mistral.ai)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install mistralai\\n```\\n\\nSync\\n\\n```python
-        python\\nfrom mistralai import Mistral\\nimport agentops\\n\\n# Beginning
-        of program's code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR
-        API KEY HERE>)\\n\\nclient = Mistral(\\n    # This is the default and can
-        be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\nmessage
-        = client.chat.complete(\\n        messages=[\\n            {\\n                \\\"role\\\":
-        \\\"user\\\",\\n                \\\"content\\\": \\\"Tell me a cool fact about
-        AgentOps\\\",\\n            }\\n        ],\\n        model=\\\"open-mistral-nemo\\\",\\n
-        \   )\\nprint(message.choices[0].message.content)\\n\\nagentops.end_session('Success')\\n```\\n\\nStreaming\\n\\n```python
-        python\\nfrom mistralai import Mistral\\nimport agentops\\n\\n# Beginning
-        of program's code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR
-        API KEY HERE>)\\n\\nclient = Mistral(\\n    # This is the default and can
-        be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\nmessage
-        = client.chat.stream(\\n        messages=[\\n            {\\n                \\\"role\\\":
-        \\\"user\\\",\\n                \\\"content\\\": \\\"Tell me something cool
-        about streaming agents\\\",\\n            }\\n        ],\\n        model=\\\"open-mistral-nemo\\\",\\n
-        \   )\\n\\nresponse = \\\"\\\"\\nfor event in message:\\n    if event.data.choices[0].finish_reason
-        == \\\"stop\\\":\\n        print(\\\"\\\\n\\\")\\n        print(response)\\n
-        \       print(\\\"\\\\n\\\")\\n    else:\\n        response += event.text\\n\\nagentops.end_session('Success')\\n```\\n\\nAsync\\n\\n```python
-        python\\nimport asyncio\\nfrom mistralai import Mistral\\n\\nclient = Mistral(\\n
-        \   # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\n\\nasync
-        def main() -> None:\\n    message = await client.chat.complete_async(\\n        messages=[\\n
-        \           {\\n                \\\"role\\\": \\\"user\\\",\\n                \\\"content\\\":
-        \\\"Tell me something interesting about async agents\\\",\\n            }\\n
-        \       ],\\n        model=\\\"open-mistral-nemo\\\",\\n    )\\n    print(message.choices[0].message.content)\\n\\n\\nawait
-        main()\\n```\\n\\nAsync Streaming\\n\\n```python python\\nimport asyncio\\nfrom
-        mistralai import Mistral\\n\\nclient = Mistral(\\n    # This is the default
-        and can be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\n\\nasync
-        def main() -> None:\\n    message = await client.chat.stream_async(\\n        messages=[\\n
-        \           {\\n                \\\"role\\\": \\\"user\\\",\\n                \\\"content\\\":
-        \\\"Tell me something interesting about async streaming agents\\\",\\n            }\\n
-        \       ],\\n        model=\\\"open-mistral-nemo\\\",\\n    )\\n\\n    response
-        = \\\"\\\"\\n    async for event in message:\\n        if event.data.choices[0].finish_reason
-        == \\\"stop\\\":\\n            print(\\\"\\\\n\\\")\\n            print(response)\\n
-        \           print(\\\"\\\\n\\\")\\n        else:\\n            response +=
-        event.text\\n\\n\\nawait main()\\n```\\n</details>\\n\\n\\n\\n### CamelAI
-        \uFE68\\n\\nTrack agents built with the CamelAI Python SDK (>=0.32.0).\\n\\n-
-        [CamelAI integration guide](https://docs.camel-ai.org/cookbooks/agents_tracking.html#)\\n-
-        [Official CamelAI documentation](https://docs.camel-ai.org/index.html)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install camel-ai[all]\\npip
-        install agentops\\n```\\n\\n```python python\\n#Import Dependencies\\nimport
-        agentops\\nimport os\\nfrom getpass import getpass\\nfrom dotenv import load_dotenv\\n\\n#Set
-        Keys\\nload_dotenv()\\nopenai_api_key = os.getenv(\\\"OPENAI_API_KEY\\\")
-        or \\\"<your openai key here>\\\"\\nagentops_api_key = os.getenv(\\\"AGENTOPS_API_KEY\\\")
-        or \\\"<your agentops key here>\\\"\\n\\n\\n\\n```\\n</details>\\n\\n[You
-        can find usage examples here!](examples/camelai_examples/README.md).\\n\\n\\n\\n###
-        LiteLLM \U0001F685\\n\\nAgentOps provides support for LiteLLM(>=1.3.1), allowing
-        you to call 100+ LLMs using the same Input/Output Format. \\n\\n- [AgentOps
-        integration example](https://docs.agentops.ai/v1/integrations/litellm)\\n-
-        [Official LiteLLM documentation](https://docs.litellm.ai/docs/providers)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install litellm\\n```\\n\\n```python
-        python\\n# Do not use LiteLLM like this\\n# from litellm import completion\\n#
-        ...\\n# response = completion(model=\\\"claude-3\\\", messages=messages)\\n\\n#
-        Use LiteLLM like this\\nimport litellm\\n...\\nresponse = litellm.completion(model=\\\"claude-3\\\",
-        messages=messages)\\n# or\\nresponse = await litellm.acompletion(model=\\\"claude-3\\\",
-        messages=messages)\\n```\\n</details>\\n\\n### LlamaIndex \U0001F999\\n\\n\\nAgentOps
-        works seamlessly with applications built using LlamaIndex, a framework for
-        building context-augmented generative AI applications with LLMs.\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```shell\\npip install llama-index-instrumentation-agentops\\n```\\n\\nTo
-        use the handler, import and set\\n\\n```python\\nfrom llama_index.core import
-        set_global_handler\\n\\n# NOTE: Feel free to set your AgentOps environment
-        variables (e.g., 'AGENTOPS_API_KEY')\\n# as outlined in the AgentOps documentation,
-        or pass the equivalent keyword arguments\\n# anticipated by AgentOps' AOClient
-        as **eval_params in set_global_handler.\\n\\nset_global_handler(\\\"agentops\\\")\\n```\\n\\nCheck
-        out the [LlamaIndex docs](https://docs.llamaindex.ai/en/stable/module_guides/observability/?h=agentops#agentops)
-        for more details.\\n\\n</details>\\n\\n### Llama Stack \U0001F999\U0001F95E\\n\\nAgentOps
-        provides support for Llama Stack Python Client(>=0.0.53), allowing you to
-        monitor your Agentic applications. \\n\\n- [AgentOps integration example 1](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-fdddf65549f3714f8f007ce7dfd1cde720329fe54155d54389dd50fbd81813cb)\\n-
-        [AgentOps integration example 2](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-6688ff4fb7ab1ce7b1cc9b8362ca27264a3060c16737fb1d850305787a6e3699)\\n-
-        [Official Llama Stack Python Client](https://github.com/meta-llama/llama-stack-client-python)\\n\\n###
-        SwarmZero AI \U0001F41D\\n\\nTrack and analyze SwarmZero agents with full
-        observability. Set an `AGENTOPS_API_KEY` in your environment and initialize
-        AgentOps to get started.\\n\\n- [SwarmZero](https://swarmzero.ai) - Advanced
-        multi-agent framework\\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/swarmzero)\\n-
-        [SwarmZero AI integration example](https://docs.swarmzero.ai/examples/ai-agents/build-and-monitor-a-web-search-agent)\\n-
-        [SwarmZero AI - AgentOps documentation](https://docs.swarmzero.ai/sdk/observability/agentops)\\n-
-        [Official SwarmZero Python SDK](https://github.com/swarmzero/swarmzero)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n\\n```bash\\npip install swarmzero\\npip
-        install agentops\\n```\\n\\n```python\\nfrom dotenv import load_dotenv\\nload_dotenv()\\n\\nimport
-        agentops\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nfrom swarmzero import
-        Agent, Swarm\\n# ...\\n```\\n</details>\\n\\n## Time travel debugging \U0001F52E\\n\\n<div
-        style=\\\"justify-content: center\\\">\\n  <img src=\\\"docs/images/external/app_screenshots/time_travel_banner.png\\\"
-        alt=\\\"Time Travel Banner\\\">\\n</div>\\n\\n<br />\\n\\n[Try it out!](https://app.agentops.ai/timetravel)\\n\\n##
-        Agent Arena \U0001F94A\\n\\n(coming soon!)\\n\\n## Evaluations Roadmap \U0001F9ED\\n\\n|
-        Platform                                                                     |
-        Dashboard                                  | Evals                                  |\\n|
-        ----------------------------------------------------------------------------
-        | ------------------------------------------ | --------------------------------------
-        |\\n| \u2705 Python SDK                                                                |
-        \u2705 Multi-session and Cross-session metrics | \u2705 Custom eval metrics
-        \                |\\n| \U0001F6A7 Evaluation builder API                                                    |
-        \u2705 Custom event tag tracking\_              | \U0001F51C Agent scorecards
-        \                   |\\n| \u2705 [Javascript/Typescript SDK](https://github.com/AgentOps-AI/agentops-node)
-        | \u2705 Session replays                         | \U0001F51C Evaluation playground
-        + leaderboard |\\n\\n## Debugging Roadmap \U0001F9ED\\n\\n| Performance testing
-        \                      | Environments                                                                        |
-        LLM Testing                                 | Reasoning and execution testing
-        \                  |\\n| ----------------------------------------- | -----------------------------------------------------------------------------------
-        | ------------------------------------------- | -------------------------------------------------
-        |\\n| \u2705 Event latency analysis                 | \U0001F51C Non-stationary
-        environment testing                                               | \U0001F51C
-        LLM non-deterministic function detection | \U0001F6A7 Infinite loops and recursive
-        thought detection |\\n| \u2705 Agent workflow execution pricing       | \U0001F51C
-        Multi-modal environments                                                         |
-        \U0001F6A7 Token limit overflow flags               | \U0001F51C Faulty reasoning
-        detection                     |\\n| \U0001F6A7 Success validators (external)
-        \         | \U0001F51C Execution containers                                                             |
-        \U0001F51C Context limit overflow flags             | \U0001F51C Generative
-        code validators                     |\\n| \U0001F51C Agent controllers/skill
-        tests          | \u2705 Honeypot and prompt injection detection ([PromptArmor](https://promptarmor.com))
-        | \U0001F51C API bill tracking                        | \U0001F51C Error breakpoint
-        analysis                      |\\n| \U0001F51C Information context constraint
-        testing | \U0001F51C Anti-agent roadblocks (i.e. Captchas)                                            |
-        \U0001F51C CI/CD integration checks                 |                                                   |\\n|
-        \U0001F51C Regression testing                     | \U0001F51C Multi-agent
-        framework visualization                                              |                                             |
-        \                                                  |\\n\\n### Why AgentOps?
-        \U0001F914\\n\\nWithout the right tools, AI agents are slow, expensive, and
-        unreliable. Our mission is to bring your agent from prototype to production.
-        Here's why AgentOps stands out:\\n\\n- **Comprehensive Observability**: Track
-        your AI agents' performance, user interactions, and API usage.\\n- **Real-Time
-        Monitoring**: Get instant insights with session replays, metrics, and live
-        monitoring tools.\\n- **Cost Control**: Monitor and manage your spend on LLM
-        and API calls.\\n- **Failure Detection**: Quickly identify and respond to
-        agent failures and multi-agent interaction issues.\\n- **Tool Usage Statistics**:
-        Understand how your agents utilize external tools with detailed analytics.\\n-
-        **Session-Wide Metrics**: Gain a holistic view of your agents' sessions with
-        comprehensive statistics.\\n\\nAgentOps is designed to make agent observability,
-        testing, and monitoring easy.\\n\\n\\n## Star History\\n\\nCheck out our growth
-        in the community:\\n\\n<img src=\\\"https://api.star-history.com/svg?repos=AgentOps-AI/agentops&type=Date\\\"
-        style=\\\"max-width: 500px\\\" width=\\\"50%\\\" alt=\\\"Logo\\\">\\n\\n##
-        Popular projects using AgentOps\\n\\n\\n| Repository | Stars  |\\n| :--------
-        \ | -----: |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/2707039?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [geekan](https://github.com/geekan)
-        / [MetaGPT](https://github.com/geekan/MetaGPT) | 42787 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/130722866?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [run-llama](https://github.com/run-llama)
-        / [llama_index](https://github.com/run-llama/llama_index) | 34446 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/170677839?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [crewAIInc](https://github.com/crewAIInc)
-        / [crewAI](https://github.com/crewAIInc/crewAI) | 18287 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/134388954?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [camel-ai](https://github.com/camel-ai)
-        / [camel](https://github.com/camel-ai/camel) | 5166 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/152537519?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [superagent-ai](https://github.com/superagent-ai)
-        / [superagent](https://github.com/superagent-ai/superagent) | 5050 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/30197649?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [iyaja](https://github.com/iyaja)
-        / [llama-fs](https://github.com/iyaja/llama-fs) | 4713 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/162546372?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [BasedHardware](https://github.com/BasedHardware)
-        / [Omi](https://github.com/BasedHardware/Omi) | 2723 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/454862?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [MervinPraison](https://github.com/MervinPraison)
-        / [PraisonAI](https://github.com/MervinPraison/PraisonAI) | 2007 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/140554352?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [AgentOps-AI](https://github.com/AgentOps-AI)
-        / [Jaiqu](https://github.com/AgentOps-AI/Jaiqu) | 272 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/173542722?s=48&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [swarmzero](https://github.com/swarmzero)
-        / [swarmzero](https://github.com/swarmzero/swarmzero) | 195 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/3074263?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [strnad](https://github.com/strnad)
-        / [CrewAI-Studio](https://github.com/strnad/CrewAI-Studio) | 134 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/18406448?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [alejandro-ao](https://github.com/alejandro-ao)
-        / [exa-crewai](https://github.com/alejandro-ao/exa-crewai) | 55 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/64493665?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [tonykipkemboi](https://github.com/tonykipkemboi)
-        / [youtube_yapper_trapper](https://github.com/tonykipkemboi/youtube_yapper_trapper)
-        | 47 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/17598928?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [sethcoast](https://github.com/sethcoast)
-        / [cover-letter-builder](https://github.com/sethcoast/cover-letter-builder)
-        | 27 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/109994880?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [bhancockio](https://github.com/bhancockio)
-        / [chatgpt4o-analysis](https://github.com/bhancockio/chatgpt4o-analysis) |
-        19 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/14105911?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [breakstring](https://github.com/breakstring)
-        / [Agentic_Story_Book_Workflow](https://github.com/breakstring/Agentic_Story_Book_Workflow)
-        | 14 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/124134656?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [MULTI-ON](https://github.com/MULTI-ON)
-        / [multion-python](https://github.com/MULTI-ON/multion-python) | 13 |\\n\\n\\n_Generated
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+      string: '{"info":{"author":null,"author_email":"Alex Reibman <areibman@gmail.com>, Shawn Qiu <siyangqiu@gmail.com>, Braelyn Boynton <bboynton97@gmail.com>, Howard Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush Shukla <ps4534@nyu.edu>","bugtrack_url":null,"classifiers":["License :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming Language :: Python :: 3.13","Programming Language :: Python :: 3.9"],"description":"<div align=\"center\">\n  <a href=\"https://agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/logo/github-banner.png\" alt=\"Logo\">\n  </a>\n</div>\n\n<div align=\"center\">\n  <em>Observability and DevTool platform for AI Agents</em>\n</div>\n\n<br />\n\n<div align=\"center\">\n  <a href=\"https://pepy.tech/project/agentops\">\n    <img src=\"https://static.pepy.tech/badge/agentops/month\"
+        alt=\"Downloads\">\n  </a>\n  <a href=\"https://github.com/agentops-ai/agentops/issues\">\n  <img src=\"https://img.shields.io/github/commit-activity/m/agentops-ai/agentops\" alt=\"git commit activity\">\n  </a>\n  <img src=\"https://img.shields.io/pypi/v/agentops?&color=3670A0\" alt=\"PyPI - Version\">\n  <a href=\"https://opensource.org/licenses/MIT\">\n    <img src=\"https://img.shields.io/badge/License-MIT-yellow.svg?&color=3670A0\" alt=\"License: MIT\">\n  </a>\n</div>\n\n<p align=\"center\">\n  <a href=\"https://twitter.com/agentopsai/\">\n    <img src=\"https://img.shields.io/twitter/follow/agentopsai?style=social\" alt=\"Twitter\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://discord.gg/FagdcwwXRR\">\n    <img src=\"https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord\" alt=\"Discord\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://app.agentops.ai/?ref=gh\">\n    <img src=\"https://img.shields.io/badge/Dashboard-blue.svg?style=flat-square\"
+        alt=\"Dashboard\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://docs.agentops.ai/introduction\">\n    <img src=\"https://img.shields.io/badge/Documentation-orange.svg?style=flat-square\" alt=\"Documentation\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://entelligence.ai/AgentOps-AI&agentops\">\n    <img src=\"https://img.shields.io/badge/Chat%20with%20Docs-green.svg?style=flat-square\" alt=\"Chat with Docs\" style=\"height: 20px;\">\n  </a>\n</p>\n\n\n\n<div style=\"justify-content: center\">\n  <img src=\"docs/images/external/app_screenshots/dashboard-banner.png\" alt=\"Dashboard Banner\">\n</div>\n\n<br/>\n\n\nAgentOps helps developers build, evaluate, and monitor AI agents. From prototype to production.\n\n|                                       |                                                               |\n| ------------------------------------- | ------------------------------------------------------------- |\n| 📊 **Replay Analytics and Debugging** | Step-by-step
+        agent execution graphs                           |\n| 💸 **LLM Cost Management**            | Track spend with LLM foundation model providers               |\n| 🧪 **Agent Benchmarking**             | Test your agents against 1,000+ evals                         |\n| 🔐 **Compliance and Security**        | Detect common prompt injection and data exfiltration exploits |\n| 🤝 **Framework Integrations**         | Native Integrations with CrewAI, AG2(AutoGen), Camel AI, & LangChain         |\n\n## Quick Start ⌨️\n\n```bash\npip install agentops\n```\n\n\n#### Session replays in 2 lines of code\n\nInitialize the AgentOps client and automatically get analytics on all your LLM calls.\n\n[Get an API key](https://app.agentops.ai/settings/projects)\n\n```python\nimport agentops\n\n# Beginning of your program (i.e. main.py, __init__.py)\nagentops.init( < INSERT YOUR API KEY HERE >)\n\n...\n\n# End of program\nagentops.end_session(''Success'')\n```\n\nAll your sessions can be viewed on the [AgentOps
+        dashboard](https://app.agentops.ai?ref=gh)\n<br/>\n\n<details>\n  <summary>Agent Debugging</summary>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/session-drilldown-metadata.png\" style=\"width: 90%;\" alt=\"Agent Metadata\"/>\n  </a>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/chat-viewer.png\" style=\"width: 90%;\" alt=\"Chat Viewer\"/>\n  </a>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/session-drilldown-graphs.png\" style=\"width: 90%;\" alt=\"Event Graphs\"/>\n  </a>\n</details>\n\n<details>\n  <summary>Session Replays</summary>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/session-replay.png\" style=\"width: 90%;\" alt=\"Session Replays\"/>\n  </a>\n</details>\n\n<details open>\n  <summary>Summary Analytics</summary>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n   <img
+        src=\"docs/images/external/app_screenshots/overview.png\" style=\"width: 90%;\" alt=\"Summary Analytics\"/>\n  </a>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n   <img src=\"docs/images/external/app_screenshots/overview-charts.png\" style=\"width: 90%;\" alt=\"Summary Analytics Charts\"/>\n  </a>\n</details>\n\n\n### First class Developer Experience\nAdd powerful observability to your agents, tools, and functions with as little code as possible: one line at a time.\n<br/>\nRefer to our [documentation](http://docs.agentops.ai)\n\n```python\n# Automatically associate all Events with the agent that originated them\nfrom agentops import track_agent\n\n@track_agent(name=''SomeCustomName'')\nclass MyAgent:\n  ...\n```\n\n```python\n# Automatically create ToolEvents for tools that agents will use\nfrom agentops import record_tool\n\n@record_tool(''SampleToolName'')\ndef sample_tool(...):\n  ...\n```\n\n```python\n# Automatically create ActionEvents for other functions.\nfrom agentops
+        import record_action\n\n@agentops.record_action(''sample function being record'')\ndef sample_function(...):\n  ...\n```\n\n```python\n# Manually record any other Events\nfrom agentops import record, ActionEvent\n\nrecord(ActionEvent(\"received_user_input\"))\n```\n\n## Integrations 🦾\n\n### CrewAI 🛶\n\nBuild Crew agents with observability with only 2 lines of code. Simply set an `AGENTOPS_API_KEY` in your environment, and your crews will get automatic monitoring on the AgentOps dashboard.\n\n```bash\npip install ''crewai[agentops]''\n```\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/crewai)\n- [Official CrewAI documentation](https://docs.crewai.com/how-to/AgentOps-Observability)\n\n### AG2 🤖\nWith only two lines of code, add full observability and monitoring to AG2 (formerly AutoGen) agents. Set an `AGENTOPS_API_KEY` in your environment and call `agentops.init()`\n\n- [AG2 Observability Example](https://docs.ag2.ai/notebooks/agentchat_agentops)\n-
+        [AG2 - AgentOps Documentation](https://docs.ag2.ai/docs/ecosystem/agentops)\n\n### Camel AI 🐪\n\nTrack and analyze CAMEL agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [Camel AI](https://www.camel-ai.org/) - Advanced agent communication framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/camel)\n- [Official Camel AI documentation](https://docs.camel-ai.org/cookbooks/agents_tracking.html)\n\n<details>\n  <summary>Installation</summary>\n\n```bash\npip install \"camel-ai[all]==0.2.11\"\npip install agentops\n```\n\n```python\nimport os\nimport agentops\nfrom camel.agents import ChatAgent\nfrom camel.messages import BaseMessage\nfrom camel.models import ModelFactory\nfrom camel.types import ModelPlatformType, ModelType\n\n# Initialize AgentOps\nagentops.init(os.getenv(\"AGENTOPS_API_KEY\"), default_tags=[\"CAMEL Example\"])\n\n# Import toolkits after AgentOps init for tracking\nfrom
+        camel.toolkits import SearchToolkit\n\n# Set up the agent with search tools\nsys_msg = BaseMessage.make_assistant_message(\n    role_name=''Tools calling operator'',\n    content=''You are a helpful assistant''\n)\n\n# Configure tools and model\ntools = [*SearchToolkit().get_tools()]\nmodel = ModelFactory.create(\n    model_platform=ModelPlatformType.OPENAI,\n    model_type=ModelType.GPT_4O_MINI,\n)\n\n# Create and run the agent\ncamel_agent = ChatAgent(\n    system_message=sys_msg,\n    model=model,\n    tools=tools,\n)\n\nresponse = camel_agent.step(\"What is AgentOps?\")\nprint(response)\n\nagentops.end_session(\"Success\")\n```\n\nCheck out our [Camel integration guide](https://docs.agentops.ai/v1/integrations/camel) for more examples including multi-agent scenarios.\n</details>\n\n### Langchain 🦜🔗\n\nAgentOps works seamlessly with applications built using Langchain. To use the handler, install Langchain as an optional dependency:\n\n<details>\n  <summary>Installation</summary>\n  \n```shell\npip
+        install agentops[langchain]\n```\n\nTo use the handler, import and set\n\n```python\nimport os\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.agents import initialize_agent, AgentType\nfrom agentops.partners.langchain_callback_handler import LangchainCallbackHandler\n\nAGENTOPS_API_KEY = os.environ[''AGENTOPS_API_KEY'']\nhandler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=[''Langchain Example''])\n\nllm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,\n                 callbacks=[handler],\n                 model=''gpt-3.5-turbo'')\n\nagent = initialize_agent(tools,\n                         llm,\n                         agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n                         verbose=True,\n                         callbacks=[handler], # You must pass in a callback handler to record your agent\n                         handle_parsing_errors=True)\n```\n\nCheck out the [Langchain Examples Notebook](./examples/langchain_examples.ipynb) for
+        more details including Async handlers.\n\n</details>\n\n### Cohere ⌨️\n\nFirst class support for Cohere(>=5.4.0). This is a living integration, should you need any added functionality please message us on Discord!\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/cohere)\n- [Official Cohere documentation](https://docs.cohere.com/reference/about)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install cohere\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\nco = cohere.Client()\n\nchat = co.chat(\n    message=\"Is it pronounced ceaux-hear or co-hehray?\"\n)\n\nprint(chat)\n\nagentops.end_session(''Success'')\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nco = cohere.Client()\n\nstream = co.chat_stream(\n    message=\"Write
+        me a haiku about the synergies between Cohere and AgentOps\"\n)\n\nfor event in stream:\n    if event.event_type == \"text-generation\":\n        print(event.text, end='''')\n\nagentops.end_session(''Success'')\n```\n</details>\n\n\n### Anthropic ﹨\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration guide](https://docs.agentops.ai/v1/integrations/anthropic)\n- [Official Anthropic documentation](https://docs.anthropic.com/en/docs/welcome)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install anthropic\n```\n\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = anthropic.Anthropic(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nmessage = client.messages.create(\n        max_tokens=1024,\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\":
+        \"Tell me a cool fact about AgentOps\",\n            }\n        ],\n        model=\"claude-3-opus-20240229\",\n    )\nprint(message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = anthropic.Anthropic(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nstream = client.messages.create(\n    max_tokens=1024,\n    model=\"claude-3-opus-20240229\",\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"Tell me something cool about streaming agents\",\n        }\n    ],\n    stream=True,\n)\n\nresponse = \"\"\nfor event in stream:\n    if event.type == \"content_block_delta\":\n        response += event.delta.text\n    elif event.type == \"message_stop\":\n        print(\"\\n\")\n        print(response)\n        print(\"\\n\")\n```\n\nAsync\n\n```python
+        python\nimport asyncio\nfrom anthropic import AsyncAnthropic\n\nclient = AsyncAnthropic(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\n\nasync def main() -> None:\n    message = await client.messages.create(\n        max_tokens=1024,\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something interesting about async agents\",\n            }\n        ],\n        model=\"claude-3-opus-20240229\",\n    )\n    print(message.content)\n\n\nawait main()\n```\n</details>\n\n### Mistral 〽️\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration example](./examples/mistral//mistral_example.ipynb)\n- [Official Mistral documentation](https://docs.mistral.ai)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install mistralai\n```\n\nSync\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s
+        code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.complete(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me a cool fact about AgentOps\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\nprint(message.choices[0].message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.stream(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something cool
+        about streaming agents\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\n\nresponse = \"\"\nfor event in message:\n    if event.data.choices[0].finish_reason == \"stop\":\n        print(\"\\n\")\n        print(response)\n        print(\"\\n\")\n    else:\n        response += event.text\n\nagentops.end_session(''Success'')\n```\n\nAsync\n\n```python python\nimport asyncio\nfrom mistralai import Mistral\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n    message = await client.chat.complete_async(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something interesting about async agents\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\n    print(message.choices[0].message.content)\n\n\nawait main()\n```\n\nAsync Streaming\n\n```python python\nimport asyncio\nfrom mistralai
+        import Mistral\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n    message = await client.chat.stream_async(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something interesting about async streaming agents\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\n\n    response = \"\"\n    async for event in message:\n        if event.data.choices[0].finish_reason == \"stop\":\n            print(\"\\n\")\n            print(response)\n            print(\"\\n\")\n        else:\n            response += event.text\n\n\nawait main()\n```\n</details>\n\n\n\n### CamelAI ﹨\n\nTrack agents built with the CamelAI Python SDK (>=0.32.0).\n\n- [CamelAI integration guide](https://docs.camel-ai.org/cookbooks/agents_tracking.html#)\n- [Official CamelAI documentation](https://docs.camel-ai.org/index.html)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip
+        install camel-ai[all]\npip install agentops\n```\n\n```python python\n#Import Dependencies\nimport agentops\nimport os\nfrom getpass import getpass\nfrom dotenv import load_dotenv\n\n#Set Keys\nload_dotenv()\nopenai_api_key = os.getenv(\"OPENAI_API_KEY\") or \"<your openai key here>\"\nagentops_api_key = os.getenv(\"AGENTOPS_API_KEY\") or \"<your agentops key here>\"\n\n\n\n```\n</details>\n\n[You can find usage examples here!](examples/camelai_examples/README.md).\n\n\n\n### LiteLLM 🚅\n\nAgentOps provides support for LiteLLM(>=1.3.1), allowing you to call 100+ LLMs using the same Input/Output Format. \n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/litellm)\n- [Official LiteLLM documentation](https://docs.litellm.ai/docs/providers)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install litellm\n```\n\n```python python\n# Do not use LiteLLM like this\n# from litellm import completion\n# ...\n# response = completion(model=\"claude-3\",
+        messages=messages)\n\n# Use LiteLLM like this\nimport litellm\n...\nresponse = litellm.completion(model=\"claude-3\", messages=messages)\n# or\nresponse = await litellm.acompletion(model=\"claude-3\", messages=messages)\n```\n</details>\n\n### LlamaIndex 🦙\n\n\nAgentOps works seamlessly with applications built using LlamaIndex, a framework for building context-augmented generative AI applications with LLMs.\n\n<details>\n  <summary>Installation</summary>\n  \n```shell\npip install llama-index-instrumentation-agentops\n```\n\nTo use the handler, import and set\n\n```python\nfrom llama_index.core import set_global_handler\n\n# NOTE: Feel free to set your AgentOps environment variables (e.g., ''AGENTOPS_API_KEY'')\n# as outlined in the AgentOps documentation, or pass the equivalent keyword arguments\n# anticipated by AgentOps'' AOClient as **eval_params in set_global_handler.\n\nset_global_handler(\"agentops\")\n```\n\nCheck out the [LlamaIndex docs](https://docs.llamaindex.ai/en/stable/module_guides/observability/?h=agentops#agentops)
+        for more details.\n\n</details>\n\n### Llama Stack 🦙🥞\n\nAgentOps provides support for Llama Stack Python Client(>=0.0.53), allowing you to monitor your Agentic applications. \n\n- [AgentOps integration example 1](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-fdddf65549f3714f8f007ce7dfd1cde720329fe54155d54389dd50fbd81813cb)\n- [AgentOps integration example 2](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-6688ff4fb7ab1ce7b1cc9b8362ca27264a3060c16737fb1d850305787a6e3699)\n- [Official Llama Stack Python Client](https://github.com/meta-llama/llama-stack-client-python)\n\n### SwarmZero AI 🐝\n\nTrack and analyze SwarmZero agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [SwarmZero](https://swarmzero.ai) - Advanced multi-agent framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/swarmzero)\n-
+        [SwarmZero AI integration example](https://docs.swarmzero.ai/examples/ai-agents/build-and-monitor-a-web-search-agent)\n- [SwarmZero AI - AgentOps documentation](https://docs.swarmzero.ai/sdk/observability/agentops)\n- [Official SwarmZero Python SDK](https://github.com/swarmzero/swarmzero)\n\n<details>\n  <summary>Installation</summary>\n\n```bash\npip install swarmzero\npip install agentops\n```\n\n```python\nfrom dotenv import load_dotenv\nload_dotenv()\n\nimport agentops\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nfrom swarmzero import Agent, Swarm\n# ...\n```\n</details>\n\n## Time travel debugging 🔮\n\n<div style=\"justify-content: center\">\n  <img src=\"docs/images/external/app_screenshots/time_travel_banner.png\" alt=\"Time Travel Banner\">\n</div>\n\n<br />\n\n[Try it out!](https://app.agentops.ai/timetravel)\n\n## Agent Arena 🥊\n\n(coming soon!)\n\n## Evaluations Roadmap 🧭\n\n| Platform                                                                     | Dashboard                                  |
+        Evals                                  |\n| ---------------------------------------------------------------------------- | ------------------------------------------ | -------------------------------------- |\n| ✅ Python SDK                                                                | ✅ Multi-session and Cross-session metrics | ✅ Custom eval metrics                 |\n| 🚧 Evaluation builder API                                                    | ✅ Custom event tag tracking               | 🔜 Agent scorecards                    |\n| ✅ [Javascript/Typescript SDK](https://github.com/AgentOps-AI/agentops-node) | ✅ Session replays                         | 🔜 Evaluation playground + leaderboard |\n\n## Debugging Roadmap 🧭\n\n| Performance testing                       | Environments                                                                        | LLM Testing                                 | Reasoning and execution testing                   |\n| -----------------------------------------
+        | ----------------------------------------------------------------------------------- | ------------------------------------------- | ------------------------------------------------- |\n| ✅ Event latency analysis                 | 🔜 Non-stationary environment testing                                               | 🔜 LLM non-deterministic function detection | 🚧 Infinite loops and recursive thought detection |\n| ✅ Agent workflow execution pricing       | 🔜 Multi-modal environments                                                         | 🚧 Token limit overflow flags               | 🔜 Faulty reasoning detection                     |\n| 🚧 Success validators (external)          | 🔜 Execution containers                                                             | 🔜 Context limit overflow flags             | 🔜 Generative code validators                     |\n| 🔜 Agent controllers/skill tests          | ✅ Honeypot and prompt injection detection ([PromptArmor](https://promptarmor.com))
+        | 🔜 API bill tracking                        | 🔜 Error breakpoint analysis                      |\n| 🔜 Information context constraint testing | 🔜 Anti-agent roadblocks (i.e. Captchas)                                            | 🔜 CI/CD integration checks                 |                                                   |\n| 🔜 Regression testing                     | 🔜 Multi-agent framework visualization                                              |                                             |                                                   |\n\n### Why AgentOps? 🤔\n\nWithout the right tools, AI agents are slow, expensive, and unreliable. Our mission is to bring your agent from prototype to production. Here''s why AgentOps stands out:\n\n- **Comprehensive Observability**: Track your AI agents'' performance, user interactions, and API usage.\n- **Real-Time Monitoring**: Get instant insights with session replays, metrics, and live monitoring tools.\n- **Cost Control**: Monitor
+        and manage your spend on LLM and API calls.\n- **Failure Detection**: Quickly identify and respond to agent failures and multi-agent interaction issues.\n- **Tool Usage Statistics**: Understand how your agents utilize external tools with detailed analytics.\n- **Session-Wide Metrics**: Gain a holistic view of your agents'' sessions with comprehensive statistics.\n\nAgentOps is designed to make agent observability, testing, and monitoring easy.\n\n\n## Star History\n\nCheck out our growth in the community:\n\n<img src=\"https://api.star-history.com/svg?repos=AgentOps-AI/agentops&type=Date\" style=\"max-width: 500px\" width=\"50%\" alt=\"Logo\">\n\n## Popular projects using AgentOps\n\n\n| Repository | Stars  |\n| :--------  | -----: |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/2707039?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [geekan](https://github.com/geekan) / [MetaGPT](https://github.com/geekan/MetaGPT) | 42787 |\n|<img class=\"avatar
+        mr-2\" src=\"https://avatars.githubusercontent.com/u/130722866?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [run-llama](https://github.com/run-llama) / [llama_index](https://github.com/run-llama/llama_index) | 34446 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/170677839?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [crewAIInc](https://github.com/crewAIInc) / [crewAI](https://github.com/crewAIInc/crewAI) | 18287 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/134388954?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [camel-ai](https://github.com/camel-ai) / [camel](https://github.com/camel-ai/camel) | 5166 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/152537519?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [superagent-ai](https://github.com/superagent-ai) / [superagent](https://github.com/superagent-ai/superagent) | 5050 |\n|<img class=\"avatar mr-2\"
+        src=\"https://avatars.githubusercontent.com/u/30197649?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [iyaja](https://github.com/iyaja) / [llama-fs](https://github.com/iyaja/llama-fs) | 4713 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/162546372?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [BasedHardware](https://github.com/BasedHardware) / [Omi](https://github.com/BasedHardware/Omi) | 2723 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/454862?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [MervinPraison](https://github.com/MervinPraison) / [PraisonAI](https://github.com/MervinPraison/PraisonAI) | 2007 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/140554352?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [AgentOps-AI](https://github.com/AgentOps-AI) / [Jaiqu](https://github.com/AgentOps-AI/Jaiqu) | 272 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/173542722?s=48&v=4\"
+        width=\"20\" height=\"20\" alt=\"\">  &nbsp; [swarmzero](https://github.com/swarmzero) / [swarmzero](https://github.com/swarmzero/swarmzero) | 195 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/3074263?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [strnad](https://github.com/strnad) / [CrewAI-Studio](https://github.com/strnad/CrewAI-Studio) | 134 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/18406448?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [alejandro-ao](https://github.com/alejandro-ao) / [exa-crewai](https://github.com/alejandro-ao/exa-crewai) | 55 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/64493665?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [tonykipkemboi](https://github.com/tonykipkemboi) / [youtube_yapper_trapper](https://github.com/tonykipkemboi/youtube_yapper_trapper) | 47 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/17598928?s=40&v=4\"
+        width=\"20\" height=\"20\" alt=\"\">  &nbsp; [sethcoast](https://github.com/sethcoast) / [cover-letter-builder](https://github.com/sethcoast/cover-letter-builder) | 27 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/109994880?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [bhancockio](https://github.com/bhancockio) / [chatgpt4o-analysis](https://github.com/bhancockio/chatgpt4o-analysis) | 19 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/14105911?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [breakstring](https://github.com/breakstring) / [Agentic_Story_Book_Workflow](https://github.com/breakstring/Agentic_Story_Book_Workflow) | 14 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/124134656?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [MULTI-ON](https://github.com/MULTI-ON) / [multion-python](https://github.com/MULTI-ON/multion-python) | 13 |\n\n\n_Generated using [github-dependents-info](https://github.com/nvuillam/github-dependents-info),
+        by [Nicolas Vuillamy](https://github.com/nvuillam)_\n","description_content_type":"text/markdown","docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":null,"keywords":null,"license":null,"license_expression":null,"license_files":["LICENSE"],"maintainer":null,"maintainer_email":null,"name":"agentops","package_url":"https://pypi.org/project/agentops/","platform":null,"project_url":"https://pypi.org/project/agentops/","project_urls":{"Homepage":"https://github.com/AgentOps-AI/agentops","Issues":"https://github.com/AgentOps-AI/agentops/issues"},"provides_extra":null,"release_url":"https://pypi.org/project/agentops/0.3.26/","requires_dist":["opentelemetry-api==1.22.0; python_version < \"3.10\"","opentelemetry-api>=1.27.0; python_version >= \"3.10\"","opentelemetry-exporter-otlp-proto-http==1.22.0; python_version < \"3.10\"","opentelemetry-exporter-otlp-proto-http>=1.27.0; python_version >= \"3.10\"","opentelemetry-sdk==1.22.0;
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-        Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush
-        Shukla <ps4534@nyu.edu>\",\"bugtrack_url\":null,\"classifiers\":[\"License
-        :: OSI Approved :: MIT License\",\"Operating System :: OS Independent\",\"Programming
-        Language :: Python :: 3\",\"Programming Language :: Python :: 3.10\",\"Programming
-        Language :: Python :: 3.11\",\"Programming Language :: Python :: 3.12\",\"Programming
-        Language :: Python :: 3.13\",\"Programming Language :: Python :: 3.9\"],\"description\":\"<div
-        align=\\\"center\\\">\\n  <a href=\\\"https://agentops.ai?ref=gh\\\">\\n    <img
-        src=\\\"docs/images/external/logo/github-banner.png\\\" alt=\\\"Logo\\\">\\n
-        \ </a>\\n</div>\\n\\n<div align=\\\"center\\\">\\n  <em>Observability and
-        DevTool platform for AI Agents</em>\\n</div>\\n\\n<br />\\n\\n<div align=\\\"center\\\">\\n
-        \ <a href=\\\"https://pepy.tech/project/agentops\\\">\\n    <img src=\\\"https://static.pepy.tech/badge/agentops/month\\\"
-        alt=\\\"Downloads\\\">\\n  </a>\\n  <a href=\\\"https://github.com/agentops-ai/agentops/issues\\\">\\n
-        \ <img src=\\\"https://img.shields.io/github/commit-activity/m/agentops-ai/agentops\\\"
-        alt=\\\"git commit activity\\\">\\n  </a>\\n  <img src=\\\"https://img.shields.io/pypi/v/agentops?&color=3670A0\\\"
-        alt=\\\"PyPI - Version\\\">\\n  <a href=\\\"https://opensource.org/licenses/MIT\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/License-MIT-yellow.svg?&color=3670A0\\\"
-        alt=\\\"License: MIT\\\">\\n  </a>\\n</div>\\n\\n<p align=\\\"center\\\">\\n
-        \ <a href=\\\"https://twitter.com/agentopsai/\\\">\\n    <img src=\\\"https://img.shields.io/twitter/follow/agentopsai?style=social\\\"
-        alt=\\\"Twitter\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://discord.gg/FagdcwwXRR\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord\\\"
-        alt=\\\"Discord\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://app.agentops.ai/?ref=gh\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/Dashboard-blue.svg?style=flat-square\\\"
-        alt=\\\"Dashboard\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://docs.agentops.ai/introduction\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/Documentation-orange.svg?style=flat-square\\\"
-        alt=\\\"Documentation\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n  <a href=\\\"https://entelligence.ai/AgentOps-AI&agentops\\\">\\n
-        \   <img src=\\\"https://img.shields.io/badge/Chat%20with%20Docs-green.svg?style=flat-square\\\"
-        alt=\\\"Chat with Docs\\\" style=\\\"height: 20px;\\\">\\n  </a>\\n</p>\\n\\n\\n\\n<div
-        style=\\\"justify-content: center\\\">\\n  <img src=\\\"docs/images/external/app_screenshots/dashboard-banner.png\\\"
-        alt=\\\"Dashboard Banner\\\">\\n</div>\\n\\n<br/>\\n\\n\\nAgentOps helps developers
-        build, evaluate, and monitor AI agents. From prototype to production.\\n\\n|
-        \                                      |                                                               |\\n|
-        ------------------------------------- | -------------------------------------------------------------
-        |\\n| \U0001F4CA **Replay Analytics and Debugging** | Step-by-step agent execution
-        graphs                           |\\n| \U0001F4B8 **LLM Cost Management**
-        \           | Track spend with LLM foundation model providers               |\\n|
-        \U0001F9EA **Agent Benchmarking**             | Test your agents against 1,000+
-        evals                         |\\n| \U0001F510 **Compliance and Security**
-        \       | Detect common prompt injection and data exfiltration exploits |\\n|
-        \U0001F91D **Framework Integrations**         | Native Integrations with CrewAI,
-        AG2(AutoGen), Camel AI, & LangChain         |\\n\\n## Quick Start \u2328\uFE0F\\n\\n```bash\\npip
-        install agentops\\n```\\n\\n\\n#### Session replays in 2 lines of code\\n\\nInitialize
-        the AgentOps client and automatically get analytics on all your LLM calls.\\n\\n[Get
-        an API key](https://app.agentops.ai/settings/projects)\\n\\n```python\\nimport
-        agentops\\n\\n# Beginning of your program (i.e. main.py, __init__.py)\\nagentops.init(
-        < INSERT YOUR API KEY HERE >)\\n\\n...\\n\\n# End of program\\nagentops.end_session('Success')\\n```\\n\\nAll
-        your sessions can be viewed on the [AgentOps dashboard](https://app.agentops.ai?ref=gh)\\n<br/>\\n\\n<details>\\n
-        \ <summary>Agent Debugging</summary>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/session-drilldown-metadata.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Agent Metadata\\\"/>\\n  </a>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/chat-viewer.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Chat Viewer\\\"/>\\n  </a>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/session-drilldown-graphs.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Event Graphs\\\"/>\\n  </a>\\n</details>\\n\\n<details>\\n
-        \ <summary>Session Replays</summary>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \   <img src=\\\"docs/images/external/app_screenshots/session-replay.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Session Replays\\\"/>\\n  </a>\\n</details>\\n\\n<details
-        open>\\n  <summary>Summary Analytics</summary>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \  <img src=\\\"docs/images/external/app_screenshots/overview.png\\\" style=\\\"width:
-        90%;\\\" alt=\\\"Summary Analytics\\\"/>\\n  </a>\\n  <a href=\\\"https://app.agentops.ai?ref=gh\\\">\\n
-        \  <img src=\\\"docs/images/external/app_screenshots/overview-charts.png\\\"
-        style=\\\"width: 90%;\\\" alt=\\\"Summary Analytics Charts\\\"/>\\n  </a>\\n</details>\\n\\n\\n###
-        First class Developer Experience\\nAdd powerful observability to your agents,
-        tools, and functions with as little code as possible: one line at a time.\\n<br/>\\nRefer
-        to our [documentation](http://docs.agentops.ai)\\n\\n```python\\n# Automatically
-        associate all Events with the agent that originated them\\nfrom agentops import
-        track_agent\\n\\n@track_agent(name='SomeCustomName')\\nclass MyAgent:\\n  ...\\n```\\n\\n```python\\n#
-        Automatically create ToolEvents for tools that agents will use\\nfrom agentops
-        import record_tool\\n\\n@record_tool('SampleToolName')\\ndef sample_tool(...):\\n
-        \ ...\\n```\\n\\n```python\\n# Automatically create ActionEvents for other
-        functions.\\nfrom agentops import record_action\\n\\n@agentops.record_action('sample
-        function being record')\\ndef sample_function(...):\\n  ...\\n```\\n\\n```python\\n#
-        Manually record any other Events\\nfrom agentops import record, ActionEvent\\n\\nrecord(ActionEvent(\\\"received_user_input\\\"))\\n```\\n\\n##
-        Integrations \U0001F9BE\\n\\n### CrewAI \U0001F6F6\\n\\nBuild Crew agents
-        with observability with only 2 lines of code. Simply set an `AGENTOPS_API_KEY`
-        in your environment, and your crews will get automatic monitoring on the AgentOps
-        dashboard.\\n\\n```bash\\npip install 'crewai[agentops]'\\n```\\n\\n- [AgentOps
-        integration example](https://docs.agentops.ai/v1/integrations/crewai)\\n-
-        [Official CrewAI documentation](https://docs.crewai.com/how-to/AgentOps-Observability)\\n\\n###
-        AG2 \U0001F916\\nWith only two lines of code, add full observability and monitoring
-        to AG2 (formerly AutoGen) agents. Set an `AGENTOPS_API_KEY` in your environment
-        and call `agentops.init()`\\n\\n- [AG2 Observability Example](https://docs.ag2.ai/notebooks/agentchat_agentops)\\n-
-        [AG2 - AgentOps Documentation](https://docs.ag2.ai/docs/ecosystem/agentops)\\n\\n###
-        Camel AI \U0001F42A\\n\\nTrack and analyze CAMEL agents with full observability.
-        Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get
-        started.\\n\\n- [Camel AI](https://www.camel-ai.org/) - Advanced agent communication
-        framework\\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/camel)\\n-
-        [Official Camel AI documentation](https://docs.camel-ai.org/cookbooks/agents_tracking.html)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n\\n```bash\\npip install \\\"camel-ai[all]==0.2.11\\\"\\npip
-        install agentops\\n```\\n\\n```python\\nimport os\\nimport agentops\\nfrom
-        camel.agents import ChatAgent\\nfrom camel.messages import BaseMessage\\nfrom
-        camel.models import ModelFactory\\nfrom camel.types import ModelPlatformType,
-        ModelType\\n\\n# Initialize AgentOps\\nagentops.init(os.getenv(\\\"AGENTOPS_API_KEY\\\"),
-        default_tags=[\\\"CAMEL Example\\\"])\\n\\n# Import toolkits after AgentOps
-        init for tracking\\nfrom camel.toolkits import SearchToolkit\\n\\n# Set up
-        the agent with search tools\\nsys_msg = BaseMessage.make_assistant_message(\\n
-        \   role_name='Tools calling operator',\\n    content='You are a helpful assistant'\\n)\\n\\n#
-        Configure tools and model\\ntools = [*SearchToolkit().get_tools()]\\nmodel
-        = ModelFactory.create(\\n    model_platform=ModelPlatformType.OPENAI,\\n    model_type=ModelType.GPT_4O_MINI,\\n)\\n\\n#
-        Create and run the agent\\ncamel_agent = ChatAgent(\\n    system_message=sys_msg,\\n
-        \   model=model,\\n    tools=tools,\\n)\\n\\nresponse = camel_agent.step(\\\"What
-        is AgentOps?\\\")\\nprint(response)\\n\\nagentops.end_session(\\\"Success\\\")\\n```\\n\\nCheck
-        out our [Camel integration guide](https://docs.agentops.ai/v1/integrations/camel)
-        for more examples including multi-agent scenarios.\\n</details>\\n\\n### Langchain
-        \U0001F99C\U0001F517\\n\\nAgentOps works seamlessly with applications built
-        using Langchain. To use the handler, install Langchain as an optional dependency:\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```shell\\npip install agentops[langchain]\\n```\\n\\nTo
-        use the handler, import and set\\n\\n```python\\nimport os\\nfrom langchain.chat_models
-        import ChatOpenAI\\nfrom langchain.agents import initialize_agent, AgentType\\nfrom
-        agentops.partners.langchain_callback_handler import LangchainCallbackHandler\\n\\nAGENTOPS_API_KEY
-        = os.environ['AGENTOPS_API_KEY']\\nhandler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY,
-        tags=['Langchain Example'])\\n\\nllm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,\\n
-        \                callbacks=[handler],\\n                 model='gpt-3.5-turbo')\\n\\nagent
-        = initialize_agent(tools,\\n                         llm,\\n                         agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\\n
-        \                        verbose=True,\\n                         callbacks=[handler],
-        # You must pass in a callback handler to record your agent\\n                         handle_parsing_errors=True)\\n```\\n\\nCheck
-        out the [Langchain Examples Notebook](./examples/langchain_examples.ipynb)
-        for more details including Async handlers.\\n\\n</details>\\n\\n### Cohere
-        \u2328\uFE0F\\n\\nFirst class support for Cohere(>=5.4.0). This is a living
-        integration, should you need any added functionality please message us on
-        Discord!\\n\\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/cohere)\\n-
-        [Official Cohere documentation](https://docs.cohere.com/reference/about)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install cohere\\n```\\n\\n```python
-        python\\nimport cohere\\nimport agentops\\n\\n# Beginning of program's code
-        (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\nco
-        = cohere.Client()\\n\\nchat = co.chat(\\n    message=\\\"Is it pronounced
-        ceaux-hear or co-hehray?\\\"\\n)\\n\\nprint(chat)\\n\\nagentops.end_session('Success')\\n```\\n\\n```python
-        python\\nimport cohere\\nimport agentops\\n\\n# Beginning of program's code
-        (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nco
-        = cohere.Client()\\n\\nstream = co.chat_stream(\\n    message=\\\"Write me
-        a haiku about the synergies between Cohere and AgentOps\\\"\\n)\\n\\nfor event
-        in stream:\\n    if event.event_type == \\\"text-generation\\\":\\n        print(event.text,
-        end='')\\n\\nagentops.end_session('Success')\\n```\\n</details>\\n\\n\\n###
-        Anthropic \uFE68\\n\\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\\n\\n-
-        [AgentOps integration guide](https://docs.agentops.ai/v1/integrations/anthropic)\\n-
-        [Official Anthropic documentation](https://docs.anthropic.com/en/docs/welcome)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install anthropic\\n```\\n\\n```python
-        python\\nimport anthropic\\nimport agentops\\n\\n# Beginning of program's
-        code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nclient
-        = anthropic.Anthropic(\\n    # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"ANTHROPIC_API_KEY\\\"),\\n)\\n\\nmessage
-        = client.messages.create(\\n        max_tokens=1024,\\n        messages=[\\n
-        \           {\\n                \\\"role\\\": \\\"user\\\",\\n                \\\"content\\\":
-        \\\"Tell me a cool fact about AgentOps\\\",\\n            }\\n        ],\\n
-        \       model=\\\"claude-3-opus-20240229\\\",\\n    )\\nprint(message.content)\\n\\nagentops.end_session('Success')\\n```\\n\\nStreaming\\n```python
-        python\\nimport anthropic\\nimport agentops\\n\\n# Beginning of program's
-        code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nclient
-        = anthropic.Anthropic(\\n    # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"ANTHROPIC_API_KEY\\\"),\\n)\\n\\nstream
-        = client.messages.create(\\n    max_tokens=1024,\\n    model=\\\"claude-3-opus-20240229\\\",\\n
-        \   messages=[\\n        {\\n            \\\"role\\\": \\\"user\\\",\\n            \\\"content\\\":
-        \\\"Tell me something cool about streaming agents\\\",\\n        }\\n    ],\\n
-        \   stream=True,\\n)\\n\\nresponse = \\\"\\\"\\nfor event in stream:\\n    if
-        event.type == \\\"content_block_delta\\\":\\n        response += event.delta.text\\n
-        \   elif event.type == \\\"message_stop\\\":\\n        print(\\\"\\\\n\\\")\\n
-        \       print(response)\\n        print(\\\"\\\\n\\\")\\n```\\n\\nAsync\\n\\n```python
-        python\\nimport asyncio\\nfrom anthropic import AsyncAnthropic\\n\\nclient
-        = AsyncAnthropic(\\n    # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"ANTHROPIC_API_KEY\\\"),\\n)\\n\\n\\nasync
-        def main() -> None:\\n    message = await client.messages.create(\\n        max_tokens=1024,\\n
-        \       messages=[\\n            {\\n                \\\"role\\\": \\\"user\\\",\\n
-        \               \\\"content\\\": \\\"Tell me something interesting about async
-        agents\\\",\\n            }\\n        ],\\n        model=\\\"claude-3-opus-20240229\\\",\\n
-        \   )\\n    print(message.content)\\n\\n\\nawait main()\\n```\\n</details>\\n\\n###
-        Mistral \u303D\uFE0F\\n\\nTrack agents built with the Anthropic Python SDK
-        (>=0.32.0).\\n\\n- [AgentOps integration example](./examples/mistral//mistral_example.ipynb)\\n-
-        [Official Mistral documentation](https://docs.mistral.ai)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install mistralai\\n```\\n\\nSync\\n\\n```python
-        python\\nfrom mistralai import Mistral\\nimport agentops\\n\\n# Beginning
-        of program's code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR
-        API KEY HERE>)\\n\\nclient = Mistral(\\n    # This is the default and can
-        be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\nmessage
-        = client.chat.complete(\\n        messages=[\\n            {\\n                \\\"role\\\":
-        \\\"user\\\",\\n                \\\"content\\\": \\\"Tell me a cool fact about
-        AgentOps\\\",\\n            }\\n        ],\\n        model=\\\"open-mistral-nemo\\\",\\n
-        \   )\\nprint(message.choices[0].message.content)\\n\\nagentops.end_session('Success')\\n```\\n\\nStreaming\\n\\n```python
-        python\\nfrom mistralai import Mistral\\nimport agentops\\n\\n# Beginning
-        of program's code (i.e. main.py, __init__.py)\\nagentops.init(<INSERT YOUR
-        API KEY HERE>)\\n\\nclient = Mistral(\\n    # This is the default and can
-        be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\nmessage
-        = client.chat.stream(\\n        messages=[\\n            {\\n                \\\"role\\\":
-        \\\"user\\\",\\n                \\\"content\\\": \\\"Tell me something cool
-        about streaming agents\\\",\\n            }\\n        ],\\n        model=\\\"open-mistral-nemo\\\",\\n
-        \   )\\n\\nresponse = \\\"\\\"\\nfor event in message:\\n    if event.data.choices[0].finish_reason
-        == \\\"stop\\\":\\n        print(\\\"\\\\n\\\")\\n        print(response)\\n
-        \       print(\\\"\\\\n\\\")\\n    else:\\n        response += event.text\\n\\nagentops.end_session('Success')\\n```\\n\\nAsync\\n\\n```python
-        python\\nimport asyncio\\nfrom mistralai import Mistral\\n\\nclient = Mistral(\\n
-        \   # This is the default and can be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\n\\nasync
-        def main() -> None:\\n    message = await client.chat.complete_async(\\n        messages=[\\n
-        \           {\\n                \\\"role\\\": \\\"user\\\",\\n                \\\"content\\\":
-        \\\"Tell me something interesting about async agents\\\",\\n            }\\n
-        \       ],\\n        model=\\\"open-mistral-nemo\\\",\\n    )\\n    print(message.choices[0].message.content)\\n\\n\\nawait
-        main()\\n```\\n\\nAsync Streaming\\n\\n```python python\\nimport asyncio\\nfrom
-        mistralai import Mistral\\n\\nclient = Mistral(\\n    # This is the default
-        and can be omitted\\n    api_key=os.environ.get(\\\"MISTRAL_API_KEY\\\"),\\n)\\n\\n\\nasync
-        def main() -> None:\\n    message = await client.chat.stream_async(\\n        messages=[\\n
-        \           {\\n                \\\"role\\\": \\\"user\\\",\\n                \\\"content\\\":
-        \\\"Tell me something interesting about async streaming agents\\\",\\n            }\\n
-        \       ],\\n        model=\\\"open-mistral-nemo\\\",\\n    )\\n\\n    response
-        = \\\"\\\"\\n    async for event in message:\\n        if event.data.choices[0].finish_reason
-        == \\\"stop\\\":\\n            print(\\\"\\\\n\\\")\\n            print(response)\\n
-        \           print(\\\"\\\\n\\\")\\n        else:\\n            response +=
-        event.text\\n\\n\\nawait main()\\n```\\n</details>\\n\\n\\n\\n### CamelAI
-        \uFE68\\n\\nTrack agents built with the CamelAI Python SDK (>=0.32.0).\\n\\n-
-        [CamelAI integration guide](https://docs.camel-ai.org/cookbooks/agents_tracking.html#)\\n-
-        [Official CamelAI documentation](https://docs.camel-ai.org/index.html)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install camel-ai[all]\\npip
-        install agentops\\n```\\n\\n```python python\\n#Import Dependencies\\nimport
-        agentops\\nimport os\\nfrom getpass import getpass\\nfrom dotenv import load_dotenv\\n\\n#Set
-        Keys\\nload_dotenv()\\nopenai_api_key = os.getenv(\\\"OPENAI_API_KEY\\\")
-        or \\\"<your openai key here>\\\"\\nagentops_api_key = os.getenv(\\\"AGENTOPS_API_KEY\\\")
-        or \\\"<your agentops key here>\\\"\\n\\n\\n\\n```\\n</details>\\n\\n[You
-        can find usage examples here!](examples/camelai_examples/README.md).\\n\\n\\n\\n###
-        LiteLLM \U0001F685\\n\\nAgentOps provides support for LiteLLM(>=1.3.1), allowing
-        you to call 100+ LLMs using the same Input/Output Format. \\n\\n- [AgentOps
-        integration example](https://docs.agentops.ai/v1/integrations/litellm)\\n-
-        [Official LiteLLM documentation](https://docs.litellm.ai/docs/providers)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```bash\\npip install litellm\\n```\\n\\n```python
-        python\\n# Do not use LiteLLM like this\\n# from litellm import completion\\n#
-        ...\\n# response = completion(model=\\\"claude-3\\\", messages=messages)\\n\\n#
-        Use LiteLLM like this\\nimport litellm\\n...\\nresponse = litellm.completion(model=\\\"claude-3\\\",
-        messages=messages)\\n# or\\nresponse = await litellm.acompletion(model=\\\"claude-3\\\",
-        messages=messages)\\n```\\n</details>\\n\\n### LlamaIndex \U0001F999\\n\\n\\nAgentOps
-        works seamlessly with applications built using LlamaIndex, a framework for
-        building context-augmented generative AI applications with LLMs.\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n  \\n```shell\\npip install llama-index-instrumentation-agentops\\n```\\n\\nTo
-        use the handler, import and set\\n\\n```python\\nfrom llama_index.core import
-        set_global_handler\\n\\n# NOTE: Feel free to set your AgentOps environment
-        variables (e.g., 'AGENTOPS_API_KEY')\\n# as outlined in the AgentOps documentation,
-        or pass the equivalent keyword arguments\\n# anticipated by AgentOps' AOClient
-        as **eval_params in set_global_handler.\\n\\nset_global_handler(\\\"agentops\\\")\\n```\\n\\nCheck
-        out the [LlamaIndex docs](https://docs.llamaindex.ai/en/stable/module_guides/observability/?h=agentops#agentops)
-        for more details.\\n\\n</details>\\n\\n### Llama Stack \U0001F999\U0001F95E\\n\\nAgentOps
-        provides support for Llama Stack Python Client(>=0.0.53), allowing you to
-        monitor your Agentic applications. \\n\\n- [AgentOps integration example 1](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-fdddf65549f3714f8f007ce7dfd1cde720329fe54155d54389dd50fbd81813cb)\\n-
-        [AgentOps integration example 2](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-6688ff4fb7ab1ce7b1cc9b8362ca27264a3060c16737fb1d850305787a6e3699)\\n-
-        [Official Llama Stack Python Client](https://github.com/meta-llama/llama-stack-client-python)\\n\\n###
-        SwarmZero AI \U0001F41D\\n\\nTrack and analyze SwarmZero agents with full
-        observability. Set an `AGENTOPS_API_KEY` in your environment and initialize
-        AgentOps to get started.\\n\\n- [SwarmZero](https://swarmzero.ai) - Advanced
-        multi-agent framework\\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/swarmzero)\\n-
-        [SwarmZero AI integration example](https://docs.swarmzero.ai/examples/ai-agents/build-and-monitor-a-web-search-agent)\\n-
-        [SwarmZero AI - AgentOps documentation](https://docs.swarmzero.ai/sdk/observability/agentops)\\n-
-        [Official SwarmZero Python SDK](https://github.com/swarmzero/swarmzero)\\n\\n<details>\\n
-        \ <summary>Installation</summary>\\n\\n```bash\\npip install swarmzero\\npip
-        install agentops\\n```\\n\\n```python\\nfrom dotenv import load_dotenv\\nload_dotenv()\\n\\nimport
-        agentops\\nagentops.init(<INSERT YOUR API KEY HERE>)\\n\\nfrom swarmzero import
-        Agent, Swarm\\n# ...\\n```\\n</details>\\n\\n## Time travel debugging \U0001F52E\\n\\n<div
-        style=\\\"justify-content: center\\\">\\n  <img src=\\\"docs/images/external/app_screenshots/time_travel_banner.png\\\"
-        alt=\\\"Time Travel Banner\\\">\\n</div>\\n\\n<br />\\n\\n[Try it out!](https://app.agentops.ai/timetravel)\\n\\n##
-        Agent Arena \U0001F94A\\n\\n(coming soon!)\\n\\n## Evaluations Roadmap \U0001F9ED\\n\\n|
-        Platform                                                                     |
-        Dashboard                                  | Evals                                  |\\n|
-        ----------------------------------------------------------------------------
-        | ------------------------------------------ | --------------------------------------
-        |\\n| \u2705 Python SDK                                                                |
-        \u2705 Multi-session and Cross-session metrics | \u2705 Custom eval metrics
-        \                |\\n| \U0001F6A7 Evaluation builder API                                                    |
-        \u2705 Custom event tag tracking\_              | \U0001F51C Agent scorecards
-        \                   |\\n| \u2705 [Javascript/Typescript SDK](https://github.com/AgentOps-AI/agentops-node)
-        | \u2705 Session replays                         | \U0001F51C Evaluation playground
-        + leaderboard |\\n\\n## Debugging Roadmap \U0001F9ED\\n\\n| Performance testing
-        \                      | Environments                                                                        |
-        LLM Testing                                 | Reasoning and execution testing
-        \                  |\\n| ----------------------------------------- | -----------------------------------------------------------------------------------
-        | ------------------------------------------- | -------------------------------------------------
-        |\\n| \u2705 Event latency analysis                 | \U0001F51C Non-stationary
-        environment testing                                               | \U0001F51C
-        LLM non-deterministic function detection | \U0001F6A7 Infinite loops and recursive
-        thought detection |\\n| \u2705 Agent workflow execution pricing       | \U0001F51C
-        Multi-modal environments                                                         |
-        \U0001F6A7 Token limit overflow flags               | \U0001F51C Faulty reasoning
-        detection                     |\\n| \U0001F6A7 Success validators (external)
-        \         | \U0001F51C Execution containers                                                             |
-        \U0001F51C Context limit overflow flags             | \U0001F51C Generative
-        code validators                     |\\n| \U0001F51C Agent controllers/skill
-        tests          | \u2705 Honeypot and prompt injection detection ([PromptArmor](https://promptarmor.com))
-        | \U0001F51C API bill tracking                        | \U0001F51C Error breakpoint
-        analysis                      |\\n| \U0001F51C Information context constraint
-        testing | \U0001F51C Anti-agent roadblocks (i.e. Captchas)                                            |
-        \U0001F51C CI/CD integration checks                 |                                                   |\\n|
-        \U0001F51C Regression testing                     | \U0001F51C Multi-agent
-        framework visualization                                              |                                             |
-        \                                                  |\\n\\n### Why AgentOps?
-        \U0001F914\\n\\nWithout the right tools, AI agents are slow, expensive, and
-        unreliable. Our mission is to bring your agent from prototype to production.
-        Here's why AgentOps stands out:\\n\\n- **Comprehensive Observability**: Track
-        your AI agents' performance, user interactions, and API usage.\\n- **Real-Time
-        Monitoring**: Get instant insights with session replays, metrics, and live
-        monitoring tools.\\n- **Cost Control**: Monitor and manage your spend on LLM
-        and API calls.\\n- **Failure Detection**: Quickly identify and respond to
-        agent failures and multi-agent interaction issues.\\n- **Tool Usage Statistics**:
-        Understand how your agents utilize external tools with detailed analytics.\\n-
-        **Session-Wide Metrics**: Gain a holistic view of your agents' sessions with
-        comprehensive statistics.\\n\\nAgentOps is designed to make agent observability,
-        testing, and monitoring easy.\\n\\n\\n## Star History\\n\\nCheck out our growth
-        in the community:\\n\\n<img src=\\\"https://api.star-history.com/svg?repos=AgentOps-AI/agentops&type=Date\\\"
-        style=\\\"max-width: 500px\\\" width=\\\"50%\\\" alt=\\\"Logo\\\">\\n\\n##
-        Popular projects using AgentOps\\n\\n\\n| Repository | Stars  |\\n| :--------
-        \ | -----: |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/2707039?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [geekan](https://github.com/geekan)
-        / [MetaGPT](https://github.com/geekan/MetaGPT) | 42787 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/130722866?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [run-llama](https://github.com/run-llama)
-        / [llama_index](https://github.com/run-llama/llama_index) | 34446 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/170677839?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [crewAIInc](https://github.com/crewAIInc)
-        / [crewAI](https://github.com/crewAIInc/crewAI) | 18287 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/134388954?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [camel-ai](https://github.com/camel-ai)
-        / [camel](https://github.com/camel-ai/camel) | 5166 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/152537519?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [superagent-ai](https://github.com/superagent-ai)
-        / [superagent](https://github.com/superagent-ai/superagent) | 5050 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/30197649?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [iyaja](https://github.com/iyaja)
-        / [llama-fs](https://github.com/iyaja/llama-fs) | 4713 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/162546372?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [BasedHardware](https://github.com/BasedHardware)
-        / [Omi](https://github.com/BasedHardware/Omi) | 2723 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/454862?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [MervinPraison](https://github.com/MervinPraison)
-        / [PraisonAI](https://github.com/MervinPraison/PraisonAI) | 2007 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/140554352?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [AgentOps-AI](https://github.com/AgentOps-AI)
-        / [Jaiqu](https://github.com/AgentOps-AI/Jaiqu) | 272 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/173542722?s=48&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [swarmzero](https://github.com/swarmzero)
-        / [swarmzero](https://github.com/swarmzero/swarmzero) | 195 |\\n|<img class=\\\"avatar
-        mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/3074263?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [strnad](https://github.com/strnad)
-        / [CrewAI-Studio](https://github.com/strnad/CrewAI-Studio) | 134 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/18406448?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [alejandro-ao](https://github.com/alejandro-ao)
-        / [exa-crewai](https://github.com/alejandro-ao/exa-crewai) | 55 |\\n|<img
-        class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/64493665?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [tonykipkemboi](https://github.com/tonykipkemboi)
-        / [youtube_yapper_trapper](https://github.com/tonykipkemboi/youtube_yapper_trapper)
-        | 47 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/17598928?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [sethcoast](https://github.com/sethcoast)
-        / [cover-letter-builder](https://github.com/sethcoast/cover-letter-builder)
-        | 27 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/109994880?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [bhancockio](https://github.com/bhancockio)
-        / [chatgpt4o-analysis](https://github.com/bhancockio/chatgpt4o-analysis) |
-        19 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/14105911?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [breakstring](https://github.com/breakstring)
-        / [Agentic_Story_Book_Workflow](https://github.com/breakstring/Agentic_Story_Book_Workflow)
-        | 14 |\\n|<img class=\\\"avatar mr-2\\\" src=\\\"https://avatars.githubusercontent.com/u/124134656?s=40&v=4\\\"
-        width=\\\"20\\\" height=\\\"20\\\" alt=\\\"\\\">  &nbsp; [MULTI-ON](https://github.com/MULTI-ON)
-        / [multion-python](https://github.com/MULTI-ON/multion-python) | 13 |\\n\\n\\n_Generated
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+      string: '{"info":{"author":null,"author_email":"Alex Reibman <areibman@gmail.com>, Shawn Qiu <siyangqiu@gmail.com>, Braelyn Boynton <bboynton97@gmail.com>, Howard Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush Shukla <ps4534@nyu.edu>","bugtrack_url":null,"classifiers":["License :: OSI Approved :: MIT License","Operating System :: OS Independent","Programming Language :: Python :: 3","Programming Language :: Python :: 3.10","Programming Language :: Python :: 3.11","Programming Language :: Python :: 3.12","Programming Language :: Python :: 3.13","Programming Language :: Python :: 3.9"],"description":"<div align=\"center\">\n  <a href=\"https://agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/logo/github-banner.png\" alt=\"Logo\">\n  </a>\n</div>\n\n<div align=\"center\">\n  <em>Observability and DevTool platform for AI Agents</em>\n</div>\n\n<br />\n\n<div align=\"center\">\n  <a href=\"https://pepy.tech/project/agentops\">\n    <img src=\"https://static.pepy.tech/badge/agentops/month\"
+        alt=\"Downloads\">\n  </a>\n  <a href=\"https://github.com/agentops-ai/agentops/issues\">\n  <img src=\"https://img.shields.io/github/commit-activity/m/agentops-ai/agentops\" alt=\"git commit activity\">\n  </a>\n  <img src=\"https://img.shields.io/pypi/v/agentops?&color=3670A0\" alt=\"PyPI - Version\">\n  <a href=\"https://opensource.org/licenses/MIT\">\n    <img src=\"https://img.shields.io/badge/License-MIT-yellow.svg?&color=3670A0\" alt=\"License: MIT\">\n  </a>\n</div>\n\n<p align=\"center\">\n  <a href=\"https://twitter.com/agentopsai/\">\n    <img src=\"https://img.shields.io/twitter/follow/agentopsai?style=social\" alt=\"Twitter\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://discord.gg/FagdcwwXRR\">\n    <img src=\"https://img.shields.io/badge/discord-7289da.svg?style=flat-square&logo=discord\" alt=\"Discord\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://app.agentops.ai/?ref=gh\">\n    <img src=\"https://img.shields.io/badge/Dashboard-blue.svg?style=flat-square\"
+        alt=\"Dashboard\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://docs.agentops.ai/introduction\">\n    <img src=\"https://img.shields.io/badge/Documentation-orange.svg?style=flat-square\" alt=\"Documentation\" style=\"height: 20px;\">\n  </a>\n  <a href=\"https://entelligence.ai/AgentOps-AI&agentops\">\n    <img src=\"https://img.shields.io/badge/Chat%20with%20Docs-green.svg?style=flat-square\" alt=\"Chat with Docs\" style=\"height: 20px;\">\n  </a>\n</p>\n\n\n\n<div style=\"justify-content: center\">\n  <img src=\"docs/images/external/app_screenshots/dashboard-banner.png\" alt=\"Dashboard Banner\">\n</div>\n\n<br/>\n\n\nAgentOps helps developers build, evaluate, and monitor AI agents. From prototype to production.\n\n|                                       |                                                               |\n| ------------------------------------- | ------------------------------------------------------------- |\n| 📊 **Replay Analytics and Debugging** | Step-by-step
+        agent execution graphs                           |\n| 💸 **LLM Cost Management**            | Track spend with LLM foundation model providers               |\n| 🧪 **Agent Benchmarking**             | Test your agents against 1,000+ evals                         |\n| 🔐 **Compliance and Security**        | Detect common prompt injection and data exfiltration exploits |\n| 🤝 **Framework Integrations**         | Native Integrations with CrewAI, AG2(AutoGen), Camel AI, & LangChain         |\n\n## Quick Start ⌨️\n\n```bash\npip install agentops\n```\n\n\n#### Session replays in 2 lines of code\n\nInitialize the AgentOps client and automatically get analytics on all your LLM calls.\n\n[Get an API key](https://app.agentops.ai/settings/projects)\n\n```python\nimport agentops\n\n# Beginning of your program (i.e. main.py, __init__.py)\nagentops.init( < INSERT YOUR API KEY HERE >)\n\n...\n\n# End of program\nagentops.end_session(''Success'')\n```\n\nAll your sessions can be viewed on the [AgentOps
+        dashboard](https://app.agentops.ai?ref=gh)\n<br/>\n\n<details>\n  <summary>Agent Debugging</summary>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/session-drilldown-metadata.png\" style=\"width: 90%;\" alt=\"Agent Metadata\"/>\n  </a>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/chat-viewer.png\" style=\"width: 90%;\" alt=\"Chat Viewer\"/>\n  </a>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/session-drilldown-graphs.png\" style=\"width: 90%;\" alt=\"Event Graphs\"/>\n  </a>\n</details>\n\n<details>\n  <summary>Session Replays</summary>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n    <img src=\"docs/images/external/app_screenshots/session-replay.png\" style=\"width: 90%;\" alt=\"Session Replays\"/>\n  </a>\n</details>\n\n<details open>\n  <summary>Summary Analytics</summary>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n   <img
+        src=\"docs/images/external/app_screenshots/overview.png\" style=\"width: 90%;\" alt=\"Summary Analytics\"/>\n  </a>\n  <a href=\"https://app.agentops.ai?ref=gh\">\n   <img src=\"docs/images/external/app_screenshots/overview-charts.png\" style=\"width: 90%;\" alt=\"Summary Analytics Charts\"/>\n  </a>\n</details>\n\n\n### First class Developer Experience\nAdd powerful observability to your agents, tools, and functions with as little code as possible: one line at a time.\n<br/>\nRefer to our [documentation](http://docs.agentops.ai)\n\n```python\n# Automatically associate all Events with the agent that originated them\nfrom agentops import track_agent\n\n@track_agent(name=''SomeCustomName'')\nclass MyAgent:\n  ...\n```\n\n```python\n# Automatically create ToolEvents for tools that agents will use\nfrom agentops import record_tool\n\n@record_tool(''SampleToolName'')\ndef sample_tool(...):\n  ...\n```\n\n```python\n# Automatically create ActionEvents for other functions.\nfrom agentops
+        import record_action\n\n@agentops.record_action(''sample function being record'')\ndef sample_function(...):\n  ...\n```\n\n```python\n# Manually record any other Events\nfrom agentops import record, ActionEvent\n\nrecord(ActionEvent(\"received_user_input\"))\n```\n\n## Integrations 🦾\n\n### CrewAI 🛶\n\nBuild Crew agents with observability with only 2 lines of code. Simply set an `AGENTOPS_API_KEY` in your environment, and your crews will get automatic monitoring on the AgentOps dashboard.\n\n```bash\npip install ''crewai[agentops]''\n```\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/crewai)\n- [Official CrewAI documentation](https://docs.crewai.com/how-to/AgentOps-Observability)\n\n### AG2 🤖\nWith only two lines of code, add full observability and monitoring to AG2 (formerly AutoGen) agents. Set an `AGENTOPS_API_KEY` in your environment and call `agentops.init()`\n\n- [AG2 Observability Example](https://docs.ag2.ai/notebooks/agentchat_agentops)\n-
+        [AG2 - AgentOps Documentation](https://docs.ag2.ai/docs/ecosystem/agentops)\n\n### Camel AI 🐪\n\nTrack and analyze CAMEL agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [Camel AI](https://www.camel-ai.org/) - Advanced agent communication framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/camel)\n- [Official Camel AI documentation](https://docs.camel-ai.org/cookbooks/agents_tracking.html)\n\n<details>\n  <summary>Installation</summary>\n\n```bash\npip install \"camel-ai[all]==0.2.11\"\npip install agentops\n```\n\n```python\nimport os\nimport agentops\nfrom camel.agents import ChatAgent\nfrom camel.messages import BaseMessage\nfrom camel.models import ModelFactory\nfrom camel.types import ModelPlatformType, ModelType\n\n# Initialize AgentOps\nagentops.init(os.getenv(\"AGENTOPS_API_KEY\"), default_tags=[\"CAMEL Example\"])\n\n# Import toolkits after AgentOps init for tracking\nfrom
+        camel.toolkits import SearchToolkit\n\n# Set up the agent with search tools\nsys_msg = BaseMessage.make_assistant_message(\n    role_name=''Tools calling operator'',\n    content=''You are a helpful assistant''\n)\n\n# Configure tools and model\ntools = [*SearchToolkit().get_tools()]\nmodel = ModelFactory.create(\n    model_platform=ModelPlatformType.OPENAI,\n    model_type=ModelType.GPT_4O_MINI,\n)\n\n# Create and run the agent\ncamel_agent = ChatAgent(\n    system_message=sys_msg,\n    model=model,\n    tools=tools,\n)\n\nresponse = camel_agent.step(\"What is AgentOps?\")\nprint(response)\n\nagentops.end_session(\"Success\")\n```\n\nCheck out our [Camel integration guide](https://docs.agentops.ai/v1/integrations/camel) for more examples including multi-agent scenarios.\n</details>\n\n### Langchain 🦜🔗\n\nAgentOps works seamlessly with applications built using Langchain. To use the handler, install Langchain as an optional dependency:\n\n<details>\n  <summary>Installation</summary>\n  \n```shell\npip
+        install agentops[langchain]\n```\n\nTo use the handler, import and set\n\n```python\nimport os\nfrom langchain.chat_models import ChatOpenAI\nfrom langchain.agents import initialize_agent, AgentType\nfrom agentops.partners.langchain_callback_handler import LangchainCallbackHandler\n\nAGENTOPS_API_KEY = os.environ[''AGENTOPS_API_KEY'']\nhandler = LangchainCallbackHandler(api_key=AGENTOPS_API_KEY, tags=[''Langchain Example''])\n\nllm = ChatOpenAI(openai_api_key=OPENAI_API_KEY,\n                 callbacks=[handler],\n                 model=''gpt-3.5-turbo'')\n\nagent = initialize_agent(tools,\n                         llm,\n                         agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n                         verbose=True,\n                         callbacks=[handler], # You must pass in a callback handler to record your agent\n                         handle_parsing_errors=True)\n```\n\nCheck out the [Langchain Examples Notebook](./examples/langchain_examples.ipynb) for
+        more details including Async handlers.\n\n</details>\n\n### Cohere ⌨️\n\nFirst class support for Cohere(>=5.4.0). This is a living integration, should you need any added functionality please message us on Discord!\n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/cohere)\n- [Official Cohere documentation](https://docs.cohere.com/reference/about)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install cohere\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\nco = cohere.Client()\n\nchat = co.chat(\n    message=\"Is it pronounced ceaux-hear or co-hehray?\"\n)\n\nprint(chat)\n\nagentops.end_session(''Success'')\n```\n\n```python python\nimport cohere\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nco = cohere.Client()\n\nstream = co.chat_stream(\n    message=\"Write
+        me a haiku about the synergies between Cohere and AgentOps\"\n)\n\nfor event in stream:\n    if event.event_type == \"text-generation\":\n        print(event.text, end='''')\n\nagentops.end_session(''Success'')\n```\n</details>\n\n\n### Anthropic ﹨\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration guide](https://docs.agentops.ai/v1/integrations/anthropic)\n- [Official Anthropic documentation](https://docs.anthropic.com/en/docs/welcome)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install anthropic\n```\n\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = anthropic.Anthropic(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nmessage = client.messages.create(\n        max_tokens=1024,\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\":
+        \"Tell me a cool fact about AgentOps\",\n            }\n        ],\n        model=\"claude-3-opus-20240229\",\n    )\nprint(message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n```python python\nimport anthropic\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = anthropic.Anthropic(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\nstream = client.messages.create(\n    max_tokens=1024,\n    model=\"claude-3-opus-20240229\",\n    messages=[\n        {\n            \"role\": \"user\",\n            \"content\": \"Tell me something cool about streaming agents\",\n        }\n    ],\n    stream=True,\n)\n\nresponse = \"\"\nfor event in stream:\n    if event.type == \"content_block_delta\":\n        response += event.delta.text\n    elif event.type == \"message_stop\":\n        print(\"\\n\")\n        print(response)\n        print(\"\\n\")\n```\n\nAsync\n\n```python
+        python\nimport asyncio\nfrom anthropic import AsyncAnthropic\n\nclient = AsyncAnthropic(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"ANTHROPIC_API_KEY\"),\n)\n\n\nasync def main() -> None:\n    message = await client.messages.create(\n        max_tokens=1024,\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something interesting about async agents\",\n            }\n        ],\n        model=\"claude-3-opus-20240229\",\n    )\n    print(message.content)\n\n\nawait main()\n```\n</details>\n\n### Mistral 〽️\n\nTrack agents built with the Anthropic Python SDK (>=0.32.0).\n\n- [AgentOps integration example](./examples/mistral//mistral_example.ipynb)\n- [Official Mistral documentation](https://docs.mistral.ai)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install mistralai\n```\n\nSync\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s
+        code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.complete(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me a cool fact about AgentOps\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\nprint(message.choices[0].message.content)\n\nagentops.end_session(''Success'')\n```\n\nStreaming\n\n```python python\nfrom mistralai import Mistral\nimport agentops\n\n# Beginning of program''s code (i.e. main.py, __init__.py)\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\nmessage = client.chat.stream(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something cool
+        about streaming agents\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\n\nresponse = \"\"\nfor event in message:\n    if event.data.choices[0].finish_reason == \"stop\":\n        print(\"\\n\")\n        print(response)\n        print(\"\\n\")\n    else:\n        response += event.text\n\nagentops.end_session(''Success'')\n```\n\nAsync\n\n```python python\nimport asyncio\nfrom mistralai import Mistral\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n    message = await client.chat.complete_async(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something interesting about async agents\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\n    print(message.choices[0].message.content)\n\n\nawait main()\n```\n\nAsync Streaming\n\n```python python\nimport asyncio\nfrom mistralai
+        import Mistral\n\nclient = Mistral(\n    # This is the default and can be omitted\n    api_key=os.environ.get(\"MISTRAL_API_KEY\"),\n)\n\n\nasync def main() -> None:\n    message = await client.chat.stream_async(\n        messages=[\n            {\n                \"role\": \"user\",\n                \"content\": \"Tell me something interesting about async streaming agents\",\n            }\n        ],\n        model=\"open-mistral-nemo\",\n    )\n\n    response = \"\"\n    async for event in message:\n        if event.data.choices[0].finish_reason == \"stop\":\n            print(\"\\n\")\n            print(response)\n            print(\"\\n\")\n        else:\n            response += event.text\n\n\nawait main()\n```\n</details>\n\n\n\n### CamelAI ﹨\n\nTrack agents built with the CamelAI Python SDK (>=0.32.0).\n\n- [CamelAI integration guide](https://docs.camel-ai.org/cookbooks/agents_tracking.html#)\n- [Official CamelAI documentation](https://docs.camel-ai.org/index.html)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip
+        install camel-ai[all]\npip install agentops\n```\n\n```python python\n#Import Dependencies\nimport agentops\nimport os\nfrom getpass import getpass\nfrom dotenv import load_dotenv\n\n#Set Keys\nload_dotenv()\nopenai_api_key = os.getenv(\"OPENAI_API_KEY\") or \"<your openai key here>\"\nagentops_api_key = os.getenv(\"AGENTOPS_API_KEY\") or \"<your agentops key here>\"\n\n\n\n```\n</details>\n\n[You can find usage examples here!](examples/camelai_examples/README.md).\n\n\n\n### LiteLLM 🚅\n\nAgentOps provides support for LiteLLM(>=1.3.1), allowing you to call 100+ LLMs using the same Input/Output Format. \n\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/litellm)\n- [Official LiteLLM documentation](https://docs.litellm.ai/docs/providers)\n\n<details>\n  <summary>Installation</summary>\n  \n```bash\npip install litellm\n```\n\n```python python\n# Do not use LiteLLM like this\n# from litellm import completion\n# ...\n# response = completion(model=\"claude-3\",
+        messages=messages)\n\n# Use LiteLLM like this\nimport litellm\n...\nresponse = litellm.completion(model=\"claude-3\", messages=messages)\n# or\nresponse = await litellm.acompletion(model=\"claude-3\", messages=messages)\n```\n</details>\n\n### LlamaIndex 🦙\n\n\nAgentOps works seamlessly with applications built using LlamaIndex, a framework for building context-augmented generative AI applications with LLMs.\n\n<details>\n  <summary>Installation</summary>\n  \n```shell\npip install llama-index-instrumentation-agentops\n```\n\nTo use the handler, import and set\n\n```python\nfrom llama_index.core import set_global_handler\n\n# NOTE: Feel free to set your AgentOps environment variables (e.g., ''AGENTOPS_API_KEY'')\n# as outlined in the AgentOps documentation, or pass the equivalent keyword arguments\n# anticipated by AgentOps'' AOClient as **eval_params in set_global_handler.\n\nset_global_handler(\"agentops\")\n```\n\nCheck out the [LlamaIndex docs](https://docs.llamaindex.ai/en/stable/module_guides/observability/?h=agentops#agentops)
+        for more details.\n\n</details>\n\n### Llama Stack 🦙🥞\n\nAgentOps provides support for Llama Stack Python Client(>=0.0.53), allowing you to monitor your Agentic applications. \n\n- [AgentOps integration example 1](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-fdddf65549f3714f8f007ce7dfd1cde720329fe54155d54389dd50fbd81813cb)\n- [AgentOps integration example 2](https://github.com/AgentOps-AI/agentops/pull/530/files/65a5ab4fdcf310326f191d4b870d4f553591e3ea#diff-6688ff4fb7ab1ce7b1cc9b8362ca27264a3060c16737fb1d850305787a6e3699)\n- [Official Llama Stack Python Client](https://github.com/meta-llama/llama-stack-client-python)\n\n### SwarmZero AI 🐝\n\nTrack and analyze SwarmZero agents with full observability. Set an `AGENTOPS_API_KEY` in your environment and initialize AgentOps to get started.\n\n- [SwarmZero](https://swarmzero.ai) - Advanced multi-agent framework\n- [AgentOps integration example](https://docs.agentops.ai/v1/integrations/swarmzero)\n-
+        [SwarmZero AI integration example](https://docs.swarmzero.ai/examples/ai-agents/build-and-monitor-a-web-search-agent)\n- [SwarmZero AI - AgentOps documentation](https://docs.swarmzero.ai/sdk/observability/agentops)\n- [Official SwarmZero Python SDK](https://github.com/swarmzero/swarmzero)\n\n<details>\n  <summary>Installation</summary>\n\n```bash\npip install swarmzero\npip install agentops\n```\n\n```python\nfrom dotenv import load_dotenv\nload_dotenv()\n\nimport agentops\nagentops.init(<INSERT YOUR API KEY HERE>)\n\nfrom swarmzero import Agent, Swarm\n# ...\n```\n</details>\n\n## Time travel debugging 🔮\n\n<div style=\"justify-content: center\">\n  <img src=\"docs/images/external/app_screenshots/time_travel_banner.png\" alt=\"Time Travel Banner\">\n</div>\n\n<br />\n\n[Try it out!](https://app.agentops.ai/timetravel)\n\n## Agent Arena 🥊\n\n(coming soon!)\n\n## Evaluations Roadmap 🧭\n\n| Platform                                                                     | Dashboard                                  |
+        Evals                                  |\n| ---------------------------------------------------------------------------- | ------------------------------------------ | -------------------------------------- |\n| ✅ Python SDK                                                                | ✅ Multi-session and Cross-session metrics | ✅ Custom eval metrics                 |\n| 🚧 Evaluation builder API                                                    | ✅ Custom event tag tracking               | 🔜 Agent scorecards                    |\n| ✅ [Javascript/Typescript SDK](https://github.com/AgentOps-AI/agentops-node) | ✅ Session replays                         | 🔜 Evaluation playground + leaderboard |\n\n## Debugging Roadmap 🧭\n\n| Performance testing                       | Environments                                                                        | LLM Testing                                 | Reasoning and execution testing                   |\n| -----------------------------------------
+        | ----------------------------------------------------------------------------------- | ------------------------------------------- | ------------------------------------------------- |\n| ✅ Event latency analysis                 | 🔜 Non-stationary environment testing                                               | 🔜 LLM non-deterministic function detection | 🚧 Infinite loops and recursive thought detection |\n| ✅ Agent workflow execution pricing       | 🔜 Multi-modal environments                                                         | 🚧 Token limit overflow flags               | 🔜 Faulty reasoning detection                     |\n| 🚧 Success validators (external)          | 🔜 Execution containers                                                             | 🔜 Context limit overflow flags             | 🔜 Generative code validators                     |\n| 🔜 Agent controllers/skill tests          | ✅ Honeypot and prompt injection detection ([PromptArmor](https://promptarmor.com))
+        | 🔜 API bill tracking                        | 🔜 Error breakpoint analysis                      |\n| 🔜 Information context constraint testing | 🔜 Anti-agent roadblocks (i.e. Captchas)                                            | 🔜 CI/CD integration checks                 |                                                   |\n| 🔜 Regression testing                     | 🔜 Multi-agent framework visualization                                              |                                             |                                                   |\n\n### Why AgentOps? 🤔\n\nWithout the right tools, AI agents are slow, expensive, and unreliable. Our mission is to bring your agent from prototype to production. Here''s why AgentOps stands out:\n\n- **Comprehensive Observability**: Track your AI agents'' performance, user interactions, and API usage.\n- **Real-Time Monitoring**: Get instant insights with session replays, metrics, and live monitoring tools.\n- **Cost Control**: Monitor
+        and manage your spend on LLM and API calls.\n- **Failure Detection**: Quickly identify and respond to agent failures and multi-agent interaction issues.\n- **Tool Usage Statistics**: Understand how your agents utilize external tools with detailed analytics.\n- **Session-Wide Metrics**: Gain a holistic view of your agents'' sessions with comprehensive statistics.\n\nAgentOps is designed to make agent observability, testing, and monitoring easy.\n\n\n## Star History\n\nCheck out our growth in the community:\n\n<img src=\"https://api.star-history.com/svg?repos=AgentOps-AI/agentops&type=Date\" style=\"max-width: 500px\" width=\"50%\" alt=\"Logo\">\n\n## Popular projects using AgentOps\n\n\n| Repository | Stars  |\n| :--------  | -----: |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/2707039?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [geekan](https://github.com/geekan) / [MetaGPT](https://github.com/geekan/MetaGPT) | 42787 |\n|<img class=\"avatar
+        mr-2\" src=\"https://avatars.githubusercontent.com/u/130722866?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [run-llama](https://github.com/run-llama) / [llama_index](https://github.com/run-llama/llama_index) | 34446 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/170677839?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [crewAIInc](https://github.com/crewAIInc) / [crewAI](https://github.com/crewAIInc/crewAI) | 18287 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/134388954?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [camel-ai](https://github.com/camel-ai) / [camel](https://github.com/camel-ai/camel) | 5166 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/152537519?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [superagent-ai](https://github.com/superagent-ai) / [superagent](https://github.com/superagent-ai/superagent) | 5050 |\n|<img class=\"avatar mr-2\"
+        src=\"https://avatars.githubusercontent.com/u/30197649?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [iyaja](https://github.com/iyaja) / [llama-fs](https://github.com/iyaja/llama-fs) | 4713 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/162546372?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [BasedHardware](https://github.com/BasedHardware) / [Omi](https://github.com/BasedHardware/Omi) | 2723 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/454862?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [MervinPraison](https://github.com/MervinPraison) / [PraisonAI](https://github.com/MervinPraison/PraisonAI) | 2007 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/140554352?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [AgentOps-AI](https://github.com/AgentOps-AI) / [Jaiqu](https://github.com/AgentOps-AI/Jaiqu) | 272 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/173542722?s=48&v=4\"
+        width=\"20\" height=\"20\" alt=\"\">  &nbsp; [swarmzero](https://github.com/swarmzero) / [swarmzero](https://github.com/swarmzero/swarmzero) | 195 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/3074263?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [strnad](https://github.com/strnad) / [CrewAI-Studio](https://github.com/strnad/CrewAI-Studio) | 134 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/18406448?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [alejandro-ao](https://github.com/alejandro-ao) / [exa-crewai](https://github.com/alejandro-ao/exa-crewai) | 55 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/64493665?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [tonykipkemboi](https://github.com/tonykipkemboi) / [youtube_yapper_trapper](https://github.com/tonykipkemboi/youtube_yapper_trapper) | 47 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/17598928?s=40&v=4\"
+        width=\"20\" height=\"20\" alt=\"\">  &nbsp; [sethcoast](https://github.com/sethcoast) / [cover-letter-builder](https://github.com/sethcoast/cover-letter-builder) | 27 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/109994880?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [bhancockio](https://github.com/bhancockio) / [chatgpt4o-analysis](https://github.com/bhancockio/chatgpt4o-analysis) | 19 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/14105911?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [breakstring](https://github.com/breakstring) / [Agentic_Story_Book_Workflow](https://github.com/breakstring/Agentic_Story_Book_Workflow) | 14 |\n|<img class=\"avatar mr-2\" src=\"https://avatars.githubusercontent.com/u/124134656?s=40&v=4\" width=\"20\" height=\"20\" alt=\"\">  &nbsp; [MULTI-ON](https://github.com/MULTI-ON) / [multion-python](https://github.com/MULTI-ON/multion-python) | 13 |\n\n\n_Generated using [github-dependents-info](https://github.com/nvuillam/github-dependents-info),
+        by [Nicolas Vuillamy](https://github.com/nvuillam)_\n","description_content_type":"text/markdown","docs_url":null,"download_url":null,"downloads":{"last_day":-1,"last_month":-1,"last_week":-1},"dynamic":null,"home_page":null,"keywords":null,"license":null,"license_expression":null,"license_files":["LICENSE"],"maintainer":null,"maintainer_email":null,"name":"agentops","package_url":"https://pypi.org/project/agentops/","platform":null,"project_url":"https://pypi.org/project/agentops/","project_urls":{"Homepage":"https://github.com/AgentOps-AI/agentops","Issues":"https://github.com/AgentOps-AI/agentops/issues"},"provides_extra":null,"release_url":"https://pypi.org/project/agentops/0.3.26/","requires_dist":["opentelemetry-api==1.22.0; python_version < \"3.10\"","opentelemetry-api>=1.27.0; python_version >= \"3.10\"","opentelemetry-exporter-otlp-proto-http==1.22.0; python_version < \"3.10\"","opentelemetry-exporter-otlp-proto-http>=1.27.0; python_version >= \"3.10\"","opentelemetry-sdk==1.22.0;
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-      - **The Rise of AI Agents in Remote Work**  \nAs remote work continues to crystallize
-      in the corporate environment, AI agents are transforming how teams collaborate
-      and operate. An article could delve into case studies from companies like GitLab
-      and Buffer that have successfully integrated AI tools to facilitate communication,
-      project tracking, and workload distribution among distributed teams. By interviewing
-      team leaders and employees, we can uncover how AI agents are not merely assisting
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-      workers. This exploration would not only highlight the practical applications
-      of AI in remote settings but also provoke critical discussions about the evolving
-      nature of work, employee satisfaction, and balance between AI support and human
-      effort.\n\n- **AI as a Creative Collaborator**  \nThe boundaries of human creativity
-      are being challenged as AI becomes a key player in the creative process. An
-      example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s
-      Sensei, which are enabling artists, writers, and designers to push their creative
-      limits. The article could feature testimonials from creators who have embraced
-      AI as a partner in innovation, sharing stories of how these collaborations have
-      led to unexpected and inspiring outcomes. Additionally, engaging with experts
-      in the field of AI ethics can deepen the discussion about the implications of
-      AI in art, including authorship, originality, and the definition of creativity
-      itself. This exploration could stimulate a broader dialogue on the future of
-      the creative industry and the role of AI within it.\n\n- **Personalized Learning
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-      learning experiences are crafted for students. For instance, platforms like
-      DreamBox and Knewton use AI algorithms to tailor lessons based on individual
-      learning styles and paces. An article addressing this topic could synthesize
-      insights gathered from educators who have implemented AI in their classrooms,
-      sharing success stories and challenges faced when adjusting teaching methodologies.
-      By highlighting real user experiences, the discussion could extend to the ethical
-      implications of data privacy, algorithmic bias, and equity in education, thereby
-      painting a comprehensive picture of how AI is both a tool of progress and a
-      subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making**  \nAs
-      organizations increasingly rely on AI for decision-making\u2014be it through
-      predictive analytics in finance or recruiting algorithms in HR\u2014the ethical
-      implications of these technologies come into sharp focus. This article could
-      pull in case studies such as Amazon''s recruitment tool that inadvertently favored
-      male candidates, thereby unveiling the hidden biases entrenched in AI systems.
-      By interviewing ethicists, data scientists, and business leaders, the narrative
-      could explore how companies are navigating these ethical waters, balancing efficiency
-      and fairness, and ensuring that AI serves as an equitable decision-making assistant
-      rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier**  \nThe
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-      from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI
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-      "timestamp": "2025-10-21T14:21:26.581604+00:00", "type": "task_completed", "event_data":
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-      - **The Rise of AI Agents in Remote Work**  \nAs remote work continues to crystallize
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-      workers. This exploration would not only highlight the practical applications
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-      nature of work, employee satisfaction, and balance between AI support and human
-      effort.\n\n- **AI as a Creative Collaborator**  \nThe boundaries of human creativity
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-      the creative industry and the role of AI within it.\n\n- **Personalized Learning
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-      By highlighting real user experiences, the discussion could extend to the ethical
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+      stakeholders to add depth to discussions on ethical implications and collaboration in creativity.\n - Use more precise language when discussing topics, ensuring clarity and accessibility for readers.\n - Encourage exploration of user experiences and testimonials to provide more relatable content, especially in education and mental health contexts.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}], "tools": null, "callbacks": ["<crewai.utilities.token_counter_callback.TokenCalcHandler object at 0x12d80d6a0>"], "available_functions": null}}, {"event_id": "0ff635a9-0d57-4d4d-991a-71e2fc6e6b86", "timestamp": "2025-10-21T14:21:25.185353+00:00", "type": "llm_call_completed", "event_data": {"timestamp": "2025-10-21T14:21:25.185353+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, "task_id": "b6c0fa7b-c537-48d9-9456-914fd6dbc421", "task_name":
+      "Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.", "agent_id": "d2d12d9f-88f6-4955-bf69-f5f9868961c3", "agent_role": "Researcher", "from_task": null, "from_agent": null, "messages": [{"role": "system", "content": "You are Researcher. You''re an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.\nYour personal goal is: Make the best research and analysis on content about AI and AI agents\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats,
+      my job depends on it!"}, {"role": "user", "content": "\nCurrent Task: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.\n\nThis is the expected criteria for your final answer: 5 bullet points with a paragraph for each idea.\nyou MUST return the actual complete content as the final answer, not a summary.\n\nYou MUST follow these instructions: \n - Incorporate specific examples and case studies in initial outputs for clearer illustration of concepts.\n - Engage more with current events or trends to enhance relevance, especially in fields like remote work and decision-making.\n - Invite perspectives from experts and stakeholders to add depth to discussions on ethical implications and collaboration in creativity.\n - Use more precise language when discussing topics, ensuring clarity and accessibility
+      for readers.\n - Encourage exploration of user experiences and testimonials to provide more relatable content, especially in education and mental health contexts.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}], "response": "I now can give a great answer.  \nFinal Answer:\n\n- **The Rise of AI Agents in Remote Work**  \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical
+      applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator**  \nThe boundaries of human creativity are being challenged as AI becomes a key player in the creative process. An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the
+      role of AI within it.\n\n- **Personalized Learning through AI**  \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons based on individual learning styles and paces. An article addressing this topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making**  \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical
+      implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing ethicists, data scientists, and business leaders, the narrative could explore how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier**  \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI
+      in this sensitive field. Through this exploration, readers would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration, allowing for real-world applications and ethical considerations regarding AI. The proposed articles have the potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.", "call_type": "<LLMCallType.LLM_CALL: ''llm_call''>", "model": "gpt-4o-mini"}}, {"event_id": "4b0cdd26-0257-4e1e-95b5-f174793088f4", "timestamp": "2025-10-21T14:21:25.185503+00:00", "type": "agent_execution_completed", "event_data": {"agent_role": "Researcher", "agent_goal": "Make the best research and analysis on content about AI and AI agents", "agent_backstory": "You''re an expert
+      researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer."}}, {"event_id": "cf861e0b-7e38-4162-8c7f-64135c9b59f4", "timestamp": "2025-10-21T14:21:25.186875+00:00", "type": "task_started", "event_data": {"task_description": "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each idea. agent: Researcher agent_goal: Make the best research and analysis on content
+      about AI and AI agents Task Output: - **The Rise of AI Agents in Remote Work**  \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator**  \nThe boundaries of human creativity are being challenged as AI becomes a key player in the creative
+      process. An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI**  \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons based on individual learning styles and paces. An article addressing
+      this topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making**  \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing ethicists, data scientists, and business leaders, the narrative could
+      explore how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier**  \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground
+      for exploration, allowing for real-world applications and ethical considerations regarding AI. The proposed articles have the potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.", "expected_output": "Evaluation Score from 1 to 10 based on the performance of the agents on the tasks", "task_name": "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each idea. agent:
+      Researcher agent_goal: Make the best research and analysis on content about AI and AI agents Task Output: - **The Rise of AI Agents in Remote Work**  \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator**  \nThe boundaries of human creativity
+      are being challenged as AI becomes a key player in the creative process. An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI**  \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons
+      based on individual learning styles and paces. An article addressing this topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making**  \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing
+      ethicists, data scientists, and business leaders, the narrative could explore how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier**  \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying on technology
+      for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration, allowing for real-world applications and ethical considerations regarding AI. The proposed articles have the potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.", "context": null, "agent_role": "Task Execution Evaluator", "task_id": "73f03e42-b56a-4e3b-8015-2377ba04393b"}}, {"event_id": "2f874052-6753-4641-97a8-ae358ff85ac8", "timestamp": "2025-10-21T14:21:25.187449+00:00", "type": "agent_execution_started", "event_data": {"agent_role": "Task Execution Evaluator", "agent_goal": "Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.", "agent_backstory": "Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in
+      the crew based on the tasks they have performed"}}, {"event_id": "b60f05bd-529b-4041-bb8b-fd6a84e9c163", "timestamp": "2025-10-21T14:21:25.187556+00:00", "type": "llm_call_started", "event_data": {"timestamp": "2025-10-21T14:21:25.187556+00:00", "type": "llm_call_started", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, "task_id": "73f03e42-b56a-4e3b-8015-2377ba04393b", "task_name": "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each
+      idea. agent: Researcher agent_goal: Make the best research and analysis on content about AI and AI agents Task Output: - **The Rise of AI Agents in Remote Work**  \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator**  \nThe boundaries
+      of human creativity are being challenged as AI becomes a key player in the creative process. An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI**  \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms
+      to tailor lessons based on individual learning styles and paces. An article addressing this topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making**  \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems.
+      By interviewing ethicists, data scientists, and business leaders, the narrative could explore how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier**  \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying
+      on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration, allowing for real-world applications and ethical considerations regarding AI. The proposed articles have the potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.", "agent_id": "ddff2e62-4b2d-474d-a519-2acef5744e2b", "agent_role": "Task Execution Evaluator", "from_task": null, "from_agent": null, "model": "gpt-4o-mini", "messages": [{"role": "system", "content": "You are Task Execution Evaluator. Evaluator agent for crew evaluation with precise capabilities to evaluate the performance of the agents in the crew based on the tasks they have performed\nYour personal goal is: Your goal is to evaluate the performance of the agents in the crew based on the tasks they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.\nTo give my best complete
+      final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent Task: Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each idea. agent: Researcher agent_goal: Make the best research and analysis on content about AI
+      and AI agents Task Output: - **The Rise of AI Agents in Remote Work**  \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator**  \nThe boundaries of human creativity are being challenged as AI becomes a key player in the creative process.
+      An example can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI**  \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons based on individual learning styles and paces. An article addressing this
+      topic could synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making**  \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing ethicists, data scientists, and business leaders, the narrative could explore
+      how companies are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier**  \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration,
+      allowing for real-world applications and ethical considerations regarding AI. The proposed articles have the potential to engage a wide readership by blending current trends, expert insights, and relatable experiences\u2014making them both informative and meaningful.\n\nThis is the expected criteria for your final answer: Evaluation Score from 1 to 10 based on the performance of the agents on the tasks\nyou MUST return the actual complete content as the final answer, not a summary.\nEnsure your final answer contains only the content in the following format: {\n  \"quality\": float\n}\n\nEnsure the final output does not include any code block markers like ```json or ```python.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}], "tools": null, "callbacks": ["<crewai.utilities.token_counter_callback.TokenCalcHandler object at 0x12dce4e00>"], "available_functions": null}}, {"event_id": "1d66949b-ab73-4070-a772-77fbdcda1823",
+      "timestamp": "2025-10-21T14:21:26.580077+00:00", "type": "llm_call_completed", "event_data": {"timestamp": "2025-10-21T14:21:26.580077+00:00", "type": "llm_call_completed", "source_fingerprint": null, "source_type": null, "fingerprint_metadata": null, "task_id": "73f03e42-b56a-4e3b-8015-2377ba04393b", "task_name": "Based on the task description and the expected output, compare and evaluate the performance of the agents in the crew based on the Task Output they have performed using score from 1 to 10 evaluating on completion, quality, and overall performance.task_description: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each idea. agent: Researcher agent_goal: Make the best research and analysis on content about AI and AI
+      agents Task Output: - **The Rise of AI Agents in Remote Work**  \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the evolving nature of work, employee satisfaction, and balance between AI support and human effort.\n\n- **AI as a Creative Collaborator**  \nThe boundaries of human creativity are being challenged as AI becomes a key player in the creative process. An example
+      can be drawn from collaborative tools like OpenAI''s DALL-E or Adobe''s Sensei, which are enabling artists, writers, and designers to push their creative limits. The article could feature testimonials from creators who have embraced AI as a partner in innovation, sharing stories of how these collaborations have led to unexpected and inspiring outcomes. Additionally, engaging with experts in the field of AI ethics can deepen the discussion about the implications of AI in art, including authorship, originality, and the definition of creativity itself. This exploration could stimulate a broader dialogue on the future of the creative industry and the role of AI within it.\n\n- **Personalized Learning through AI**  \nIn the realm of education, AI is reshaping how personalized learning experiences are crafted for students. For instance, platforms like DreamBox and Knewton use AI algorithms to tailor lessons based on individual learning styles and paces. An article addressing this topic could
+      synthesize insights gathered from educators who have implemented AI in their classrooms, sharing success stories and challenges faced when adjusting teaching methodologies. By highlighting real user experiences, the discussion could extend to the ethical implications of data privacy, algorithmic bias, and equity in education, thereby painting a comprehensive picture of how AI is both a tool of progress and a subject for scrutiny.\n\n- **The Ethical Implications of AI in Decision Making**  \nAs organizations increasingly rely on AI for decision-making\u2014be it through predictive analytics in finance or recruiting algorithms in HR\u2014the ethical implications of these technologies come into sharp focus. This article could pull in case studies such as Amazon''s recruitment tool that inadvertently favored male candidates, thereby unveiling the hidden biases entrenched in AI systems. By interviewing ethicists, data scientists, and business leaders, the narrative could explore how companies
+      are navigating these ethical waters, balancing efficiency and fairness, and ensuring that AI serves as an equitable decision-making assistant rather than a perpetuator of injustice. \n\n- **AI in Mental Health: A New Frontier**  \nThe integration of AI in mental health care presents a myriad of possibilities, from chatbots like Woebot providing cognitive behavioral therapy (CBT) to AI tools that analyze speech for emotional cues. An inspiring article could chronicle the journeys of users who have benefited from AI-driven mental health services, juxtaposed with expert opinions from psychologists discussing the potential and limitations of AI in this sensitive field. Through this exploration, readers would gain a nuanced understanding of how AI is shaping therapeutic practices, the importance of human empathy in mental health support, and the ethical concerns that arise from relying on technology for emotional well-being.\n\nNotes: Each idea provides a rich ground for exploration, allowing
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+      about this topic could be. Return the list of ideas with their paragraph and your notes. task_expected_output: 5 bullet points with a paragraph for each idea. agent: Researcher agent_goal: Make the best research and analysis on content about AI and AI agents Task Output: - **The Rise of AI Agents in Remote Work**  \nAs remote work continues to crystallize in the corporate environment, AI agents are transforming how teams collaborate and operate. An article could delve into case studies from companies like GitLab and Buffer that have successfully integrated AI tools to facilitate communication, project tracking, and workload distribution among distributed teams. By interviewing team leaders and employees, we can uncover how AI agents are not merely assisting but actively enhancing productivity and reducing the cognitive load on human workers. This exploration would not only highlight the practical applications of AI in remote settings but also provoke critical discussions about the
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-    body: '{"messages": [{"role": "user", "content": "Assess the quality of the training
-      data based on the llm output, human feedback , and llm output improved result.\n\nIteration:
-      0\nInitial Output:\n- **The Evolution of AI Agents in Customer Service**\n  The
-      landscape of customer service has radically transformed with the advent of AI
-      agents. This article could delve into how businesses are employing AI to enhance
-      customer experiences, reduce wait times, and streamline operations. Highlighting
-      real-world case studies from leading companies, it would explore the measures
-      taken to train these AI agents, the challenges of human emotional intelligence
-      versus AI, and the future implications of AI agents in understanding and predicting
-      customer needs. This combination would provide a comprehensive look at the efficiencies
-      gained, while also addressing ethical considerations in AI communication.\n\n-
-      **AI Agents as Companions: The Future of Personal Relationships**\n  As technology
-      blurs the lines between human and machine interaction, the concept of AI agents
-      as companions is becoming increasingly popular. This article could explore the
-      psychological and social implications of forming bonds with AI, looking at current
-      AI companion projects such as virtual pets and virtual therapists. It would
-      question if these AI relationships can indeed fulfill emotional needs, and what
-      it means for human connection in an increasingly digital world. This exploration
-      would not only gather diverse viewpoints but also touch upon ethical considerations
-      surrounding companionship and loneliness.\n\n- **AI Agents in Creative Arts:
-      Redefining Creativity**\n  The infusion of AI into creative arts poses unique
-      questions about authorship and originality. This topic could lead to a compelling
-      article that examines how AI agents are being used to create paintings, compose
-      music, and write literature. By analyzing specific tools such as OpenAI\u2019s
-      Muse and Google\u2019s DeepDream, the article would aim to untangle the perceived
-      boundaries of creativity, discussing whether AI can ever genuinely create or
-      merely mimic human artists. This perspective would bring readers into the fascinating
-      intersection of technology and art, raising questions about the future of creative
-      expression.\n\n- **Ethical Considerations of AI Agents in Decision Making**\n  With
-      AI increasingly taking on decision-making roles in various sectors\u2014from
-      finance to healthcare and even law\u2014it\u2019s critical to scrutinize the
-      ethical ramifications of these technologies. An article focused on this topic
-      could explore how AI agents analyze vast datasets to inform decisions, the potential
-      biases encoded in their algorithms, and the accountability measures necessary
-      to monitor their outputs. It would invite a discussion on what role human oversight
-      should play, making the case for a balanced integration of AI agents that respects
-      both efficiency and ethical standards.\n\n- **The Financial Impacts of AI Agents
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-      the need for agile approaches to market challenges. This article could investigate
-      how AI agents are reshaping financial strategies in startups, from automating
-      mundane tasks to offering insights through data analysis. By highlighting successful
-      startup case studies and quantifying improvements brought about by integrating
-      AI agents, readers would grasp the potential cost savings and increased efficiency
-      that AI can offer. It would further explore how these startups leverage their
-      AI capabilities for competitive advantages, marking a crucial study for entrepreneurs
-      and investors alike.\n\nEach idea presents an opportunity to deeply analyze
-      the impacts of AI agents across various facets of modern life, emphasizing not
-      only their technological advancements but also the accompanying ethical and
-      social implications.\n\nHuman Feedback:\nGreat work!\n\nImproved Output:\n-
-      **The Evolution of AI Agents in Customer Service**\n  The landscape of customer
-      service has radically transformed with the advent of AI agents. This article
-      could delve into how businesses are employing AI to enhance customer experiences,
-      reduce wait times, and streamline operations. By featuring in-depth case studies
-      from leading companies such as Amazon and Zappos, it would explore the measures
-      taken to train these AI agents while addressing the challenges of human emotional
-      intelligence versus AI. The article would further investigate the future implications
-      of AI agents in understanding and predicting customer needs, ensuring a comprehensive
-      look at the efficiencies gained, and addressing the ethical considerations surrounding
-      AI communication.\n\n- **AI Agents as Companions: The Future of Personal Relationships**\n  As
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-      It would challenge readers to consider whether AI can genuinely create or merely
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-      and art. The article would include perspectives from artists collaborating with
-      AI, offering insights on the evolving landscape of creativity and raising important
-      questions about the future of artistic expression in an AI-enhanced world.\n\n-
-      **Ethical Considerations of AI Agents in Decision Making**\n  With AI increasingly
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-      to law\u2014scrutinizing the ethical ramifications of these technologies is
-      imperative. This investigative article could explore how AI agents analyze vast
-      datasets to inform crucial decisions while uncovering potential biases embedded
-      in their algorithms. It would articulate necessary accountability measures for
-      monitoring AI outputs and invite thoughtful discussion on the importance of
-      human oversight. By spotlighting thought leaders in ethics and technology, the
-      article would ensure a balanced perspective on integrating AI agents that respects
-      both efficiency and ethical standards.\n\n- **The Financial Impacts of AI Agents
-      on Startups**\n  Startups, characterized by limited resources and the need for
-      agile strategies, are leveraging AI agents to reshape financial decision-making.
-      This article could investigate the ways in which startups are harnessing AI
-      to automate administrative tasks, gain insights through predictive analytics,
-      and improve customer engagement. By presenting in-depth case studies, the article
-      would quantify the improvements brought about by integrating AI agents, demonstrating
-      the potential cost savings and increased efficiency they offer. Furthermore,
-      the article could explore how startups leverage their AI capabilities for competitive
-      advantages, ultimately serving as a crucial resource for entrepreneurs and investors
-      looking to navigate the evolving business landscape.\n\nThese ideas not only
-      highlight the transformative impact of AI agents across various domains but
-      also address the underlying ethical, social, and financial dynamics that are
-      essential for a thorough understanding of their role in society today.\n\n------------------------------------------------\n\nIteration:
-      1\nInitial Output:\n- **The Rise of AI Agents in Remote Work Environments**  \nIn
-      the wake of the pandemic, remote work has become a part of our daily lives.
-      AI agents are stepping into this new workspace, facilitating communication,
-      task management, and team collaboration like never before. By exploring the
-      impact of AI agents in virtual settings, the article could delve into how these
-      intelligent systems optimize productivity, reduce operational costs, and enhance
-      employee satisfaction. The journey of these agents\u2014from simple chatbots
-      to sophisticated virtual assistants\u2014presents a fascinating evolution that
-      reshapes not just how we work but also how we connect.\n\n- **Ethical Implications
-      of AI Decision-Making**  \nAs AI systems gain prominence in decision-making
-      processes across various sectors\u2014from healthcare to finance\u2014the ethical
-      implications become more pressing. An insightful article could dissect the moral
-      dilemmas posed by AI, such as biases embedded in algorithms, data privacy concerns,
-      and the accountability of AI''s actions. By interrogating the complexities of
-      trust in AI systems, the piece could highlight the critical need for transparent
-      frameworks and governance, ensuring that AI serves humanity fairly and justly
-      in an increasingly automated world.\n\n- **AI Agents as Creative Collaborators**  \nAI''s
-      foray into creative domains is revolutionizing fields such as art, music, and
-      writing. An engaging article could showcase how AI agents function alongside
-      human creators, pushing the boundaries of creativity and innovation. This exploration
-      could highlight notable collaborations between humans and AI, celebrating the
-      intriguing ways in which technology enhances artistic expression. By illustrating
-      the symbiotic relationship between human intuition and AI''s analytical prowess,
-      the article could underscore that creativity is no longer solely a human domain
-      but a collective endeavor involving intelligent machines.\n\n- **Personalized
-      Learning Experiences through AI Agents**  \nEducation is undergoing a transformation
-      with the integration of AI agents into personalized learning environments. An
-      article could examine how these agents tailor educational content to individual
-      student''s needs, moving away from one-size-fits-all approaches toward truly
-      customized learning experiences. By interviewing educators and students, the
-      piece can illustrate the profound impact of AI on student engagement, academic
-      performance, and emotional support, making a strong case for the necessity of
-      AI-driven methods in modern education.\n\n- **The Future of AI in Mental Health
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-      AI agents are emerging as supplementary tools for psychological support. An
-      article could explore the role of AI in providing real-time assistance, stigma
-      reduction, and accessibility to mental health resources. By sharing success
-      stories and evidence from trials, the piece can encapsulate the potential for
-      AI agents to act as companions that offer kindness and empathy, alongside professional
-      support. This discussion may ultimately lead to a greater appreciation of AI''s
-      capacity to enhance mental well-being in an era where mental health challenges
-      are more prevalent than ever.\n\nNotes: Each of these ideas addresses a timely
-      and relevant intersection between technology and human experience, making them
-      compelling subjects for in-depth articles. The potential for engaging readers
-      with real-world applications, ethical considerations, and innovative collaborations
-      ensures that these topics will resonate widely and inspire thoughtful discussion.\n\nHuman
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-      Work Environments**  \nIn the wake of the pandemic, remote work has become a
-      staple in our daily routines, reshaping how businesses operate globally. AI
-      agents are uniquely positioned to enhance this dynamic, stepping into virtual
-      offices to facilitate seamless communication, task management, and collaborative
-      projects. An article exploring the impact of AI agents in remote settings could
-      investigate how these intelligent assistants optimize productivity, reduce operational
-      costs, and improve employee morale by mitigating the isolation often felt in
-      remote work. The journey of AI\u2014from basic scheduling tools to multifunctional
-      virtual colleagues\u2014offers a captivating narrative on technology''s role
-      in redefining our professional landscape and fostering human connections in
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-      systems increasingly influence critical decision-making in various sectors such
-      as healthcare, finance, and criminal justice, the ethical implications become
-      a hotbed for discussion. A thorough investigation into the moral dilemmas surrounding
-      AI could scrutinize issues such as system biases, data privacy, and the ambiguity
-      of accountability for AI-driven outcomes. This article could engage with thought
-      leaders and ethicists to illuminate the pressing need for ethical frameworks
-      and governance models that ensure AI technologies promote equity and are designed
-      to serve human interests, fostering a society where trust in AI can flourish.\n\n-
-      **AI Agents as Creative Collaborators**  \nThe canvas of creativity is expanding
-      with the emergence of AI agents as collaborators in artistic domains like visual
-      arts, music production, and literary composition. An engaging article could
-      celebrate the symbiotic relationship between human creators and AI, illustrating
-      how these intelligent systems are not mere tools but creative partners that
-      inspire innovation. By profiling groundbreaking collaborations where AI and
-      human artists co-create, this piece would delve into the possibilities that
-      arise when technology enhances human expression, evolving the narrative around
-      creativity as a shared endeavor rather than a solitary pursuit.\n\n- **Personalized
-      Learning Experiences through AI Agents**  \nEducation is at a transformational
-      crossroads, with AI agents revolutionizing how students learn through personalized
-      educational experiences. A compelling article could explore how these intelligent
-      systems adapt learning materials to meet each student''s unique needs, thereby
-      moving away from traditional, uniform teaching methods. Interviews with educators
-      and students could reveal powerful testimonials that attest to the positive
-      effects of AI-driven personalized learning, including improved engagement and
-      academic success, establishing a strong argument for the integration of intelligent
-      technologies in classrooms everywhere.\n\n- **The Future of AI in Mental Health
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-      expert interviews, and ethical considerations enriches these topics, ensuring
-      they resonate widely, inspire curiosity, and engage readers in meaningful dialogue.
-      This comprehensive approach will enhance the overall quality and relevance of
-      the articles while appealing to a diverse audience.\n\n------------------------------------------------\n\nPlease
-      provide:\n- Provide a list of clear, actionable instructions derived from the
-      Human Feedbacks to enhance the Agent''s performance. Analyze the differences
-      between Initial Outputs and Improved Outputs to generate specific action items
-      for future tasks. Ensure all key and specificpoints from the human feedback
-      are incorporated into these instructions.\n- A score from 0 to 10 evaluating
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-      the initial output based on the human feedback\n"}], "model": "gpt-4o-mini",
-      "tool_choice": {"type": "function", "function": {"name": "TrainingTaskEvaluation"}},
-      "tools": [{"type": "function", "function": {"name": "TrainingTaskEvaluation",
-      "description": "Correctly extracted `TrainingTaskEvaluation` with all the required
-      parameters with correct types", "parameters": {"properties": {"suggestions":
-      {"description": "List of clear, actionable instructions derived from the Human
-      Feedbacks to enhance the Agent''s performance. Analyze the differences between
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-      tasks. Ensure all key and specific points from the human feedback are incorporated
-      into these instructions.", "items": {"type": "string"}, "title": "Suggestions",
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-      "Final Summary", "type": "string"}}, "required": ["final_summary", "quality",
-      "suggestions"], "type": "object"}}}]}'
+    body: '{"messages": [{"role": "user", "content": "Assess the quality of the training data based on the llm output, human feedback , and llm output improved result.\n\nIteration: 0\nInitial Output:\n- **The Evolution of AI Agents in Customer Service**\n  The landscape of customer service has radically transformed with the advent of AI agents. This article could delve into how businesses are employing AI to enhance customer experiences, reduce wait times, and streamline operations. Highlighting real-world case studies from leading companies, it would explore the measures taken to train these AI agents, the challenges of human emotional intelligence versus AI, and the future implications of AI agents in understanding and predicting customer needs. This combination would provide a comprehensive look at the efficiencies gained, while also addressing ethical considerations in AI communication.\n\n- **AI Agents as Companions: The Future of Personal Relationships**\n  As technology blurs the
+      lines between human and machine interaction, the concept of AI agents as companions is becoming increasingly popular. This article could explore the psychological and social implications of forming bonds with AI, looking at current AI companion projects such as virtual pets and virtual therapists. It would question if these AI relationships can indeed fulfill emotional needs, and what it means for human connection in an increasingly digital world. This exploration would not only gather diverse viewpoints but also touch upon ethical considerations surrounding companionship and loneliness.\n\n- **AI Agents in Creative Arts: Redefining Creativity**\n  The infusion of AI into creative arts poses unique questions about authorship and originality. This topic could lead to a compelling article that examines how AI agents are being used to create paintings, compose music, and write literature. By analyzing specific tools such as OpenAI\u2019s Muse and Google\u2019s DeepDream, the article would
+      aim to untangle the perceived boundaries of creativity, discussing whether AI can ever genuinely create or merely mimic human artists. This perspective would bring readers into the fascinating intersection of technology and art, raising questions about the future of creative expression.\n\n- **Ethical Considerations of AI Agents in Decision Making**\n  With AI increasingly taking on decision-making roles in various sectors\u2014from finance to healthcare and even law\u2014it\u2019s critical to scrutinize the ethical ramifications of these technologies. An article focused on this topic could explore how AI agents analyze vast datasets to inform decisions, the potential biases encoded in their algorithms, and the accountability measures necessary to monitor their outputs. It would invite a discussion on what role human oversight should play, making the case for a balanced integration of AI agents that respects both efficiency and ethical standards.\n\n- **The Financial Impacts of AI
+      Agents on Startups**\n  Startups are typically characterized by limited resources and the need for agile approaches to market challenges. This article could investigate how AI agents are reshaping financial strategies in startups, from automating mundane tasks to offering insights through data analysis. By highlighting successful startup case studies and quantifying improvements brought about by integrating AI agents, readers would grasp the potential cost savings and increased efficiency that AI can offer. It would further explore how these startups leverage their AI capabilities for competitive advantages, marking a crucial study for entrepreneurs and investors alike.\n\nEach idea presents an opportunity to deeply analyze the impacts of AI agents across various facets of modern life, emphasizing not only their technological advancements but also the accompanying ethical and social implications.\n\nHuman Feedback:\nGreat work!\n\nImproved Output:\n- **The Evolution of AI Agents in
+      Customer Service**\n  The landscape of customer service has radically transformed with the advent of AI agents. This article could delve into how businesses are employing AI to enhance customer experiences, reduce wait times, and streamline operations. By featuring in-depth case studies from leading companies such as Amazon and Zappos, it would explore the measures taken to train these AI agents while addressing the challenges of human emotional intelligence versus AI. The article would further investigate the future implications of AI agents in understanding and predicting customer needs, ensuring a comprehensive look at the efficiencies gained, and addressing the ethical considerations surrounding AI communication.\n\n- **AI Agents as Companions: The Future of Personal Relationships**\n  As technology blurs the lines between human and machine interaction, the concept of AI agents as companions is becoming increasingly popular. This article could explore the psychological and social
+      implications of forming bonds with AI, spotlighting current AI companion projects like Replika and virtual pets such as Aibo. It would examine whether these AI relationships can genuinely fulfill emotional needs and consider what this trend means for human connection in an increasingly digital world. Through interviews with users and specialists, the article would offer diverse viewpoints and address the ethical considerations of companionship and loneliness, ultimately provoking thought on our future interactions with technology.\n\n- **AI Agents in Creative Arts: Redefining Creativity**\n  The infusion of AI into creative arts poses unique questions about authorship and originality. This compelling article could investigate how AI agents are actively creating paintings, composing music, and even writing prose, utilizing tools such as OpenAI\u2019s Muse and Google\u2019s DeepDream. It would challenge readers to consider whether AI can genuinely create or merely mimic human artistry,
+      delving into the fascinating intersection of technology and art. The article would include perspectives from artists collaborating with AI, offering insights on the evolving landscape of creativity and raising important questions about the future of artistic expression in an AI-enhanced world.\n\n- **Ethical Considerations of AI Agents in Decision Making**\n  With AI increasingly taking on decision-making roles in various sectors\u2014from finance and healthcare to law\u2014scrutinizing the ethical ramifications of these technologies is imperative. This investigative article could explore how AI agents analyze vast datasets to inform crucial decisions while uncovering potential biases embedded in their algorithms. It would articulate necessary accountability measures for monitoring AI outputs and invite thoughtful discussion on the importance of human oversight. By spotlighting thought leaders in ethics and technology, the article would ensure a balanced perspective on integrating
+      AI agents that respects both efficiency and ethical standards.\n\n- **The Financial Impacts of AI Agents on Startups**\n  Startups, characterized by limited resources and the need for agile strategies, are leveraging AI agents to reshape financial decision-making. This article could investigate the ways in which startups are harnessing AI to automate administrative tasks, gain insights through predictive analytics, and improve customer engagement. By presenting in-depth case studies, the article would quantify the improvements brought about by integrating AI agents, demonstrating the potential cost savings and increased efficiency they offer. Furthermore, the article could explore how startups leverage their AI capabilities for competitive advantages, ultimately serving as a crucial resource for entrepreneurs and investors looking to navigate the evolving business landscape.\n\nThese ideas not only highlight the transformative impact of AI agents across various domains but also address
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+      human intuition and AI''s analytical prowess, the article could underscore that creativity is no longer solely a human domain but a collective endeavor involving intelligent machines.\n\n- **Personalized Learning Experiences through AI Agents**  \nEducation is undergoing a transformation with the integration of AI agents into personalized learning environments. An article could examine how these agents tailor educational content to individual student''s needs, moving away from one-size-fits-all approaches toward truly customized learning experiences. By interviewing educators and students, the piece can illustrate the profound impact of AI on student engagement, academic performance, and emotional support, making a strong case for the necessity of AI-driven methods in modern education.\n\n- **The Future of AI in Mental Health Support**  \nAs mental health becomes an increasingly crucial area of focus, AI agents are emerging as supplementary tools for psychological support. An article
+      could explore the role of AI in providing real-time assistance, stigma reduction, and accessibility to mental health resources. By sharing success stories and evidence from trials, the piece can encapsulate the potential for AI agents to act as companions that offer kindness and empathy, alongside professional support. This discussion may ultimately lead to a greater appreciation of AI''s capacity to enhance mental well-being in an era where mental health challenges are more prevalent than ever.\n\nNotes: Each of these ideas addresses a timely and relevant intersection between technology and human experience, making them compelling subjects for in-depth articles. The potential for engaging readers with real-world applications, ethical considerations, and innovative collaborations ensures that these topics will resonate widely and inspire thoughtful discussion.\n\nHuman Feedback:\nGreat work!\n\nImproved Output:\n- **The Rise of AI Agents in Remote Work Environments**  \nIn the wake
+      of the pandemic, remote work has become a staple in our daily routines, reshaping how businesses operate globally. AI agents are uniquely positioned to enhance this dynamic, stepping into virtual offices to facilitate seamless communication, task management, and collaborative projects. An article exploring the impact of AI agents in remote settings could investigate how these intelligent assistants optimize productivity, reduce operational costs, and improve employee morale by mitigating the isolation often felt in remote work. The journey of AI\u2014from basic scheduling tools to multifunctional virtual colleagues\u2014offers a captivating narrative on technology''s role in redefining our professional landscape and fostering human connections in digital spaces.\n\n- **Ethical Implications of AI Decision-Making**  \nAs AI systems increasingly influence critical decision-making in various sectors such as healthcare, finance, and criminal justice, the ethical implications become a hotbed
+      for discussion. A thorough investigation into the moral dilemmas surrounding AI could scrutinize issues such as system biases, data privacy, and the ambiguity of accountability for AI-driven outcomes. This article could engage with thought leaders and ethicists to illuminate the pressing need for ethical frameworks and governance models that ensure AI technologies promote equity and are designed to serve human interests, fostering a society where trust in AI can flourish.\n\n- **AI Agents as Creative Collaborators**  \nThe canvas of creativity is expanding with the emergence of AI agents as collaborators in artistic domains like visual arts, music production, and literary composition. An engaging article could celebrate the symbiotic relationship between human creators and AI, illustrating how these intelligent systems are not mere tools but creative partners that inspire innovation. By profiling groundbreaking collaborations where AI and human artists co-create, this piece would delve
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-      can indeed fulfill emotional needs, and what it means for human connection in
-      an increasingly digital world. This exploration would not only gather diverse
-      viewpoints but also touch upon ethical considerations surrounding companionship
-      and loneliness.\\n\\n- **AI Agents in Creative Arts: Redefining Creativity**\\n
-      \ The infusion of AI into creative arts poses unique questions about authorship
-      and originality. This topic could lead to a compelling article that examines
-      how AI agents are being used to create paintings, compose music, and write literature.
-      By analyzing specific tools such as OpenAI\u2019s Muse and Google\u2019s DeepDream,
-      the article would aim to untangle the perceived boundaries of creativity, discussing
-      whether AI can ever genuinely create or merely mimic human artists. This perspective
-      would bring readers into the fascinating intersection of technology and art,
-      raising questions about the future of creative expression.\\n\\n- **Ethical
-      Considerations of AI Agents in Decision Making**\\n  With AI increasingly taking
-      on decision-making roles in various sectors\u2014from finance to healthcare
-      and even law\u2014it\u2019s critical to scrutinize the ethical ramifications
-      of these technologies. An article focused on this topic could explore how AI
-      agents analyze vast datasets to inform decisions, the potential biases encoded
-      in their algorithms, and the accountability measures necessary to monitor their
-      outputs. It would invite a discussion on what role human oversight should play,
-      making the case for a balanced integration of AI agents that respects both efficiency
-      and ethical standards.\\n\\n- **The Financial Impacts of AI Agents on Startups**\\n
-      \ Startups are typically characterized by limited resources and the need for
-      agile approaches to market challenges. This article could investigate how AI
-      agents are reshaping financial strategies in startups, from automating mundane
-      tasks to offering insights through data analysis. By highlighting successful
-      startup case studies and quantifying improvements brought about by integrating
-      AI agents, readers would grasp the potential cost savings and increased efficiency
-      that AI can offer. It would further explore how these startups leverage their
-      AI capabilities for competitive advantages, marking a crucial study for entrepreneurs
-      and investors alike.\\n\\nEach idea presents an opportunity to deeply analyze
-      the impacts of AI agents across various facets of modern life, emphasizing not
-      only their technological advancements but also the accompanying ethical and
-      social implications.\\n\\nHuman Feedback:\\nGreat work!\\n\\nImproved Output:\\n-
-      **The Evolution of AI Agents in Customer Service**\\n  The landscape of customer
-      service has radically transformed with the advent of AI agents. This article
-      could delve into how businesses are employing AI to enhance customer experiences,
-      reduce wait times, and streamline operations. By featuring in-depth case studies
-      from leading companies such as Amazon and Zappos, it would explore the measures
-      taken to train these AI agents while addressing the challenges of human emotional
-      intelligence versus AI. The article would further investigate the future implications
-      of AI agents in understanding and predicting customer needs, ensuring a comprehensive
-      look at the efficiencies gained, and addressing the ethical considerations surrounding
-      AI communication.\\n\\n- **AI Agents as Companions: The Future of Personal Relationships**\\n
-      \ As technology blurs the lines between human and machine interaction, the concept
-      of AI agents as companions is becoming increasingly popular. This article could
-      explore the psychological and social implications of forming bonds with AI,
-      spotlighting current AI companion projects like Replika and virtual pets such
-      as Aibo. It would examine whether these AI relationships can genuinely fulfill
-      emotional needs and consider what this trend means for human connection in an
-      increasingly digital world. Through interviews with users and specialists, the
-      article would offer diverse viewpoints and address the ethical considerations
-      of companionship and loneliness, ultimately provoking thought on our future
-      interactions with technology.\\n\\n- **AI Agents in Creative Arts: Redefining
-      Creativity**\\n  The infusion of AI into creative arts poses unique questions
-      about authorship and originality. This compelling article could investigate
-      how AI agents are actively creating paintings, composing music, and even writing
-      prose, utilizing tools such as OpenAI\u2019s Muse and Google\u2019s DeepDream.
-      It would challenge readers to consider whether AI can genuinely create or merely
-      mimic human artistry, delving into the fascinating intersection of technology
-      and art. The article would include perspectives from artists collaborating with
-      AI, offering insights on the evolving landscape of creativity and raising important
-      questions about the future of artistic expression in an AI-enhanced world.\\n\\n-
-      **Ethical Considerations of AI Agents in Decision Making**\\n  With AI increasingly
-      taking on decision-making roles in various sectors\u2014from finance and healthcare
-      to law\u2014scrutinizing the ethical ramifications of these technologies is
-      imperative. This investigative article could explore how AI agents analyze vast
-      datasets to inform crucial decisions while uncovering potential biases embedded
-      in their algorithms. It would articulate necessary accountability measures for
-      monitoring AI outputs and invite thoughtful discussion on the importance of
-      human oversight. By spotlighting thought leaders in ethics and technology, the
-      article would ensure a balanced perspective on integrating AI agents that respects
-      both efficiency and ethical standards.\\n\\n- **The Financial Impacts of AI
-      Agents on Startups**\\n  Startups, characterized by limited resources and the
-      need for agile strategies, are leveraging AI agents to reshape financial decision-making.
-      This article could investigate the ways in which startups are harnessing AI
-      to automate administrative tasks, gain insights through predictive analytics,
-      and improve customer engagement. By presenting in-depth case studies, the article
-      would quantify the improvements brought about by integrating AI agents, demonstrating
-      the potential cost savings and increased efficiency they offer. Furthermore,
-      the article could explore how startups leverage their AI capabilities for competitive
-      advantages, ultimately serving as a crucial resource for entrepreneurs and investors
-      looking to navigate the evolving business landscape.\\n\\nThese ideas not only
-      highlight the transformative impact of AI agents across various domains but
-      also address the underlying ethical, social, and financial dynamics that are
-      essential for a thorough understanding of their role in society today.\\n\\n------------------------------------------------\\n\\nIteration:
-      1\\nInitial Output:\\n- **The Rise of AI Agents in Remote Work Environments**
-      \ \\nIn the wake of the pandemic, remote work has become a part of our daily
-      lives. AI agents are stepping into this new workspace, facilitating communication,
-      task management, and team collaboration like never before. By exploring the
-      impact of AI agents in virtual settings, the article could delve into how these
-      intelligent systems optimize productivity, reduce operational costs, and enhance
-      employee satisfaction. The journey of these agents\u2014from simple chatbots
-      to sophisticated virtual assistants\u2014presents a fascinating evolution that
-      reshapes not just how we work but also how we connect.\\n\\n- **Ethical Implications
-      of AI Decision-Making**  \\nAs AI systems gain prominence in decision-making
-      processes across various sectors\u2014from healthcare to finance\u2014the ethical
-      implications become more pressing. An insightful article could dissect the moral
-      dilemmas posed by AI, such as biases embedded in algorithms, data privacy concerns,
-      and the accountability of AI's actions. By interrogating the complexities of
-      trust in AI systems, the piece could highlight the critical need for transparent
-      frameworks and governance, ensuring that AI serves humanity fairly and justly
-      in an increasingly automated world.\\n\\n- **AI Agents as Creative Collaborators**
-      \ \\nAI's foray into creative domains is revolutionizing fields such as art,
-      music, and writing. An engaging article could showcase how AI agents function
-      alongside human creators, pushing the boundaries of creativity and innovation.
-      This exploration could highlight notable collaborations between humans and AI,
-      celebrating the intriguing ways in which technology enhances artistic expression.
-      By illustrating the symbiotic relationship between human intuition and AI's
-      analytical prowess, the article could underscore that creativity is no longer
-      solely a human domain but a collective endeavor involving intelligent machines.\\n\\n-
-      **Personalized Learning Experiences through AI Agents**  \\nEducation is undergoing
-      a transformation with the integration of AI agents into personalized learning
-      environments. An article could examine how these agents tailor educational content
-      to individual student's needs, moving away from one-size-fits-all approaches
-      toward truly customized learning experiences. By interviewing educators and
-      students, the piece can illustrate the profound impact of AI on student engagement,
-      academic performance, and emotional support, making a strong case for the necessity
-      of AI-driven methods in modern education.\\n\\n- **The Future of AI in Mental
-      Health Support**  \\nAs mental health becomes an increasingly crucial area of
-      focus, AI agents are emerging as supplementary tools for psychological support.
-      An article could explore the role of AI in providing real-time assistance, stigma
-      reduction, and accessibility to mental health resources. By sharing success
-      stories and evidence from trials, the piece can encapsulate the potential for
-      AI agents to act as companions that offer kindness and empathy, alongside professional
-      support. This discussion may ultimately lead to a greater appreciation of AI's
-      capacity to enhance mental well-being in an era where mental health challenges
-      are more prevalent than ever.\\n\\nNotes: Each of these ideas addresses a timely
-      and relevant intersection between technology and human experience, making them
-      compelling subjects for in-depth articles. The potential for engaging readers
-      with real-world applications, ethical considerations, and innovative collaborations
-      ensures that these topics will resonate widely and inspire thoughtful discussion.\\n\\nHuman
-      Feedback:\\nGreat work!\\n\\nImproved Output:\\n- **The Rise of AI Agents in
-      Remote Work Environments**  \\nIn the wake of the pandemic, remote work has
-      become a staple in our daily routines, reshaping how businesses operate globally.
-      AI agents are uniquely positioned to enhance this dynamic, stepping into virtual
-      offices to facilitate seamless communication, task management, and collaborative
-      projects. An article exploring the impact of AI agents in remote settings could
-      investigate how these intelligent assistants optimize productivity, reduce operational
-      costs, and improve employee morale by mitigating the isolation often felt in
-      remote work. The journey of AI\u2014from basic scheduling tools to multifunctional
-      virtual colleagues\u2014offers a captivating narrative on technology's role
-      in redefining our professional landscape and fostering human connections in
-      digital spaces.\\n\\n- **Ethical Implications of AI Decision-Making**  \\nAs
-      AI systems increasingly influence critical decision-making in various sectors
-      such as healthcare, finance, and criminal justice, the ethical implications
-      become a hotbed for discussion. A thorough investigation into the moral dilemmas
-      surrounding AI could scrutinize issues such as system biases, data privacy,
-      and the ambiguity of accountability for AI-driven outcomes. This article could
-      engage with thought leaders and ethicists to illuminate the pressing need for
-      ethical frameworks and governance models that ensure AI technologies promote
-      equity and are designed to serve human interests, fostering a society where
-      trust in AI can flourish.\\n\\n- **AI Agents as Creative Collaborators**  \\nThe
-      canvas of creativity is expanding with the emergence of AI agents as collaborators
-      in artistic domains like visual arts, music production, and literary composition.
-      An engaging article could celebrate the symbiotic relationship between human
-      creators and AI, illustrating how these intelligent systems are not mere tools
-      but creative partners that inspire innovation. By profiling groundbreaking collaborations
-      where AI and human artists co-create, this piece would delve into the possibilities
-      that arise when technology enhances human expression, evolving the narrative
-      around creativity as a shared endeavor rather than a solitary pursuit.\\n\\n-
-      **Personalized Learning Experiences through AI Agents**  \\nEducation is at
-      a transformational crossroads, with AI agents revolutionizing how students learn
-      through personalized educational experiences. A compelling article could explore
-      how these intelligent systems adapt learning materials to meet each student's
-      unique needs, thereby moving away from traditional, uniform teaching methods.
-      Interviews with educators and students could reveal powerful testimonials that
-      attest to the positive effects of AI-driven personalized learning, including
-      improved engagement and academic success, establishing a strong argument for
-      the integration of intelligent technologies in classrooms everywhere.\\n\\n-
-      **The Future of AI in Mental Health Support**  \\nAs society increasingly acknowledges
-      mental health issues, AI agents are emerging as vital tools in providing mental
-      health support and resources. An insightful article could examine the potential
-      of AI as a supplemental resource for individuals seeking emotional assistance,
-      highlighting innovations in real-time supportive interactions and stigma reduction.
-      By showcasing case studies of successful AI applications in therapy, the piece
-      would underscore the transformative role these technologies can play in making
-      mental health care accessible and efficient, positioning AI not only as a technological
-      advancement but as a crucial ally in promoting overall well-being in modern
-      life.\\n\\nNotes: Each idea serves as a portal into crucial conversations about
-      technology's role in human experiences, categorized within a contemporary context.
-      The integration of personal stories, expert interviews, and ethical considerations
-      enriches these topics, ensuring they resonate widely, inspire curiosity, and
-      engage readers in meaningful dialogue. This comprehensive approach will enhance
-      the overall quality and relevance of the articles while appealing to a diverse
-      audience.\\n\\n------------------------------------------------\\n\\nPlease
-      provide:\\n- Provide a list of clear, actionable instructions derived from the
-      Human Feedbacks to enhance the Agent's performance. Analyze the differences
-      between Initial Outputs and Improved Outputs to generate specific action items
-      for future tasks. Ensure all key and specificpoints from the human feedback
-      are incorporated into these instructions.\\n- A score from 0 to 10 evaluating
-      on completion, quality, and overall performance from the improved output to
-      the initial output based on the human feedback\\n\"}],\"model\":\"gpt-4.1-mini\"}"
+    body: '{"messages":[{"role":"system","content":"I''m gonna convert this raw text into valid JSON."},{"role":"user","content":"Assess the quality of the training data based on the llm output, human feedback , and llm output improved result.\n\nIteration: 0\nInitial Output:\n- **The Evolution of AI Agents in Customer Service**\n  The landscape of customer service has radically transformed with the advent of AI agents. This article could delve into how businesses are employing AI to enhance customer experiences, reduce wait times, and streamline operations. Highlighting real-world case studies from leading companies, it would explore the measures taken to train these AI agents, the challenges of human emotional intelligence versus AI, and the future implications of AI agents in understanding and predicting customer needs. This combination would provide a comprehensive look at the efficiencies gained, while also addressing ethical considerations in AI communication.\n\n- **AI Agents as Companions:
+      The Future of Personal Relationships**\n  As technology blurs the lines between human and machine interaction, the concept of AI agents as companions is becoming increasingly popular. This article could explore the psychological and social implications of forming bonds with AI, looking at current AI companion projects such as virtual pets and virtual therapists. It would question if these AI relationships can indeed fulfill emotional needs, and what it means for human connection in an increasingly digital world. This exploration would not only gather diverse viewpoints but also touch upon ethical considerations surrounding companionship and loneliness.\n\n- **AI Agents in Creative Arts: Redefining Creativity**\n  The infusion of AI into creative arts poses unique questions about authorship and originality. This topic could lead to a compelling article that examines how AI agents are being used to create paintings, compose music, and write literature. By analyzing specific tools such
+      as OpenAI’s Muse and Google’s DeepDream, the article would aim to untangle the perceived boundaries of creativity, discussing whether AI can ever genuinely create or merely mimic human artists. This perspective would bring readers into the fascinating intersection of technology and art, raising questions about the future of creative expression.\n\n- **Ethical Considerations of AI Agents in Decision Making**\n  With AI increasingly taking on decision-making roles in various sectors—from finance to healthcare and even law—it’s critical to scrutinize the ethical ramifications of these technologies. An article focused on this topic could explore how AI agents analyze vast datasets to inform decisions, the potential biases encoded in their algorithms, and the accountability measures necessary to monitor their outputs. It would invite a discussion on what role human oversight should play, making the case for a balanced integration of AI agents that respects both efficiency and ethical standards.\n\n-
+      **The Financial Impacts of AI Agents on Startups**\n  Startups are typically characterized by limited resources and the need for agile approaches to market challenges. This article could investigate how AI agents are reshaping financial strategies in startups, from automating mundane tasks to offering insights through data analysis. By highlighting successful startup case studies and quantifying improvements brought about by integrating AI agents, readers would grasp the potential cost savings and increased efficiency that AI can offer. It would further explore how these startups leverage their AI capabilities for competitive advantages, marking a crucial study for entrepreneurs and investors alike.\n\nEach idea presents an opportunity to deeply analyze the impacts of AI agents across various facets of modern life, emphasizing not only their technological advancements but also the accompanying ethical and social implications.\n\nHuman Feedback:\nGreat work!\n\nImproved Output:\n- **The
+      Evolution of AI Agents in Customer Service**\n  The landscape of customer service has radically transformed with the advent of AI agents. This article could delve into how businesses are employing AI to enhance customer experiences, reduce wait times, and streamline operations. By featuring in-depth case studies from leading companies such as Amazon and Zappos, it would explore the measures taken to train these AI agents while addressing the challenges of human emotional intelligence versus AI. The article would further investigate the future implications of AI agents in understanding and predicting customer needs, ensuring a comprehensive look at the efficiencies gained, and addressing the ethical considerations surrounding AI communication.\n\n- **AI Agents as Companions: The Future of Personal Relationships**\n  As technology blurs the lines between human and machine interaction, the concept of AI agents as companions is becoming increasingly popular. This article could explore
+      the psychological and social implications of forming bonds with AI, spotlighting current AI companion projects like Replika and virtual pets such as Aibo. It would examine whether these AI relationships can genuinely fulfill emotional needs and consider what this trend means for human connection in an increasingly digital world. Through interviews with users and specialists, the article would offer diverse viewpoints and address the ethical considerations of companionship and loneliness, ultimately provoking thought on our future interactions with technology.\n\n- **AI Agents in Creative Arts: Redefining Creativity**\n  The infusion of AI into creative arts poses unique questions about authorship and originality. This compelling article could investigate how AI agents are actively creating paintings, composing music, and even writing prose, utilizing tools such as OpenAI’s Muse and Google’s DeepDream. It would challenge readers to consider whether AI can genuinely create or merely
+      mimic human artistry, delving into the fascinating intersection of technology and art. The article would include perspectives from artists collaborating with AI, offering insights on the evolving landscape of creativity and raising important questions about the future of artistic expression in an AI-enhanced world.\n\n- **Ethical Considerations of AI Agents in Decision Making**\n  With AI increasingly taking on decision-making roles in various sectors—from finance and healthcare to law—scrutinizing the ethical ramifications of these technologies is imperative. This investigative article could explore how AI agents analyze vast datasets to inform crucial decisions while uncovering potential biases embedded in their algorithms. It would articulate necessary accountability measures for monitoring AI outputs and invite thoughtful discussion on the importance of human oversight. By spotlighting thought leaders in ethics and technology, the article would ensure a balanced perspective on
+      integrating AI agents that respects both efficiency and ethical standards.\n\n- **The Financial Impacts of AI Agents on Startups**\n  Startups, characterized by limited resources and the need for agile strategies, are leveraging AI agents to reshape financial decision-making. This article could investigate the ways in which startups are harnessing AI to automate administrative tasks, gain insights through predictive analytics, and improve customer engagement. By presenting in-depth case studies, the article would quantify the improvements brought about by integrating AI agents, demonstrating the potential cost savings and increased efficiency they offer. Furthermore, the article could explore how startups leverage their AI capabilities for competitive advantages, ultimately serving as a crucial resource for entrepreneurs and investors looking to navigate the evolving business landscape.\n\nThese ideas not only highlight the transformative impact of AI agents across various domains
+      but also address the underlying ethical, social, and financial dynamics that are essential for a thorough understanding of their role in society today.\n\n------------------------------------------------\n\nIteration: 1\nInitial Output:\n- **The Rise of AI Agents in Remote Work Environments**  \nIn the wake of the pandemic, remote work has become a part of our daily lives. AI agents are stepping into this new workspace, facilitating communication, task management, and team collaboration like never before. By exploring the impact of AI agents in virtual settings, the article could delve into how these intelligent systems optimize productivity, reduce operational costs, and enhance employee satisfaction. The journey of these agents—from simple chatbots to sophisticated virtual assistants—presents a fascinating evolution that reshapes not just how we work but also how we connect.\n\n- **Ethical Implications of AI Decision-Making**  \nAs AI systems gain prominence in decision-making processes
+      across various sectors—from healthcare to finance—the ethical implications become more pressing. An insightful article could dissect the moral dilemmas posed by AI, such as biases embedded in algorithms, data privacy concerns, and the accountability of AI''s actions. By interrogating the complexities of trust in AI systems, the piece could highlight the critical need for transparent frameworks and governance, ensuring that AI serves humanity fairly and justly in an increasingly automated world.\n\n- **AI Agents as Creative Collaborators**  \nAI''s foray into creative domains is revolutionizing fields such as art, music, and writing. An engaging article could showcase how AI agents function alongside human creators, pushing the boundaries of creativity and innovation. This exploration could highlight notable collaborations between humans and AI, celebrating the intriguing ways in which technology enhances artistic expression. By illustrating the symbiotic relationship between human
+      intuition and AI''s analytical prowess, the article could underscore that creativity is no longer solely a human domain but a collective endeavor involving intelligent machines.\n\n- **Personalized Learning Experiences through AI Agents**  \nEducation is undergoing a transformation with the integration of AI agents into personalized learning environments. An article could examine how these agents tailor educational content to individual student''s needs, moving away from one-size-fits-all approaches toward truly customized learning experiences. By interviewing educators and students, the piece can illustrate the profound impact of AI on student engagement, academic performance, and emotional support, making a strong case for the necessity of AI-driven methods in modern education.\n\n- **The Future of AI in Mental Health Support**  \nAs mental health becomes an increasingly crucial area of focus, AI agents are emerging as supplementary tools for psychological support. An article could
+      explore the role of AI in providing real-time assistance, stigma reduction, and accessibility to mental health resources. By sharing success stories and evidence from trials, the piece can encapsulate the potential for AI agents to act as companions that offer kindness and empathy, alongside professional support. This discussion may ultimately lead to a greater appreciation of AI''s capacity to enhance mental well-being in an era where mental health challenges are more prevalent than ever.\n\nNotes: Each of these ideas addresses a timely and relevant intersection between technology and human experience, making them compelling subjects for in-depth articles. The potential for engaging readers with real-world applications, ethical considerations, and innovative collaborations ensures that these topics will resonate widely and inspire thoughtful discussion.\n\nHuman Feedback:\nGreat work!\n\nImproved Output:\n- **The Rise of AI Agents in Remote Work Environments**  \nIn the wake of the
+      pandemic, remote work has become a staple in our daily routines, reshaping how businesses operate globally. AI agents are uniquely positioned to enhance this dynamic, stepping into virtual offices to facilitate seamless communication, task management, and collaborative projects. An article exploring the impact of AI agents in remote settings could investigate how these intelligent assistants optimize productivity, reduce operational costs, and improve employee morale by mitigating the isolation often felt in remote work. The journey of AI—from basic scheduling tools to multifunctional virtual colleagues—offers a captivating narrative on technology''s role in redefining our professional landscape and fostering human connections in digital spaces.\n\n- **Ethical Implications of AI Decision-Making**  \nAs AI systems increasingly influence critical decision-making in various sectors such as healthcare, finance, and criminal justice, the ethical implications become a hotbed for discussion.
+      A thorough investigation into the moral dilemmas surrounding AI could scrutinize issues such as system biases, data privacy, and the ambiguity of accountability for AI-driven outcomes. This article could engage with thought leaders and ethicists to illuminate the pressing need for ethical frameworks and governance models that ensure AI technologies promote equity and are designed to serve human interests, fostering a society where trust in AI can flourish.\n\n- **AI Agents as Creative Collaborators**  \nThe canvas of creativity is expanding with the emergence of AI agents as collaborators in artistic domains like visual arts, music production, and literary composition. An engaging article could celebrate the symbiotic relationship between human creators and AI, illustrating how these intelligent systems are not mere tools but creative partners that inspire innovation. By profiling groundbreaking collaborations where AI and human artists co-create, this piece would delve into the possibilities
+      that arise when technology enhances human expression, evolving the narrative around creativity as a shared endeavor rather than a solitary pursuit.\n\n- **Personalized Learning Experiences through AI Agents**  \nEducation is at a transformational crossroads, with AI agents revolutionizing how students learn through personalized educational experiences. A compelling article could explore how these intelligent systems adapt learning materials to meet each student''s unique needs, thereby moving away from traditional, uniform teaching methods. Interviews with educators and students could reveal powerful testimonials that attest to the positive effects of AI-driven personalized learning, including improved engagement and academic success, establishing a strong argument for the integration of intelligent technologies in classrooms everywhere.\n\n- **The Future of AI in Mental Health Support**  \nAs society increasingly acknowledges mental health issues, AI agents are emerging as vital tools
+      in providing mental health support and resources. An insightful article could examine the potential of AI as a supplemental resource for individuals seeking emotional assistance, highlighting innovations in real-time supportive interactions and stigma reduction. By showcasing case studies of successful AI applications in therapy, the piece would underscore the transformative role these technologies can play in making mental health care accessible and efficient, positioning AI not only as a technological advancement but as a crucial ally in promoting overall well-being in modern life.\n\nNotes: Each idea serves as a portal into crucial conversations about technology''s role in human experiences, categorized within a contemporary context. The integration of personal stories, expert interviews, and ethical considerations enriches these topics, ensuring they resonate widely, inspire curiosity, and engage readers in meaningful dialogue. This comprehensive approach will enhance the overall
+      quality and relevance of the articles while appealing to a diverse audience.\n\n------------------------------------------------\n\nPlease provide:\n- Provide a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent''s performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific action items for future tasks. Ensure all key and specificpoints from the human feedback are incorporated into these instructions.\n- A score from 0 to 10 evaluating on completion, quality, and overall performance from the improved output to the initial output based on the human feedback\n"}],"model":"gpt-4.1-mini"}'
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-      raw text into valid JSON.\"},{\"role\":\"user\",\"content\":\"Assess the quality
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-      improved result.\\n\\nIteration: 0\\nInitial Output:\\n- **The Evolution of
-      AI Agents in Customer Service**\\n  The landscape of customer service has radically
-      transformed with the advent of AI agents. This article could delve into how
-      businesses are employing AI to enhance customer experiences, reduce wait times,
-      and streamline operations. Highlighting real-world case studies from leading
-      companies, it would explore the measures taken to train these AI agents, the
-      challenges of human emotional intelligence versus AI, and the future implications
-      of AI agents in understanding and predicting customer needs. This combination
-      would provide a comprehensive look at the efficiencies gained, while also addressing
-      ethical considerations in AI communication.\\n\\n- **AI Agents as Companions:
-      The Future of Personal Relationships**\\n  As technology blurs the lines between
-      human and machine interaction, the concept of AI agents as companions is becoming
-      increasingly popular. This article could explore the psychological and social
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-      such as virtual pets and virtual therapists. It would question if these AI relationships
-      can indeed fulfill emotional needs, and what it means for human connection in
-      an increasingly digital world. This exploration would not only gather diverse
-      viewpoints but also touch upon ethical considerations surrounding companionship
-      and loneliness.\\n\\n- **AI Agents in Creative Arts: Redefining Creativity**\\n
-      \ The infusion of AI into creative arts poses unique questions about authorship
-      and originality. This topic could lead to a compelling article that examines
-      how AI agents are being used to create paintings, compose music, and write literature.
-      By analyzing specific tools such as OpenAI\u2019s Muse and Google\u2019s DeepDream,
-      the article would aim to untangle the perceived boundaries of creativity, discussing
-      whether AI can ever genuinely create or merely mimic human artists. This perspective
-      would bring readers into the fascinating intersection of technology and art,
-      raising questions about the future of creative expression.\\n\\n- **Ethical
-      Considerations of AI Agents in Decision Making**\\n  With AI increasingly taking
-      on decision-making roles in various sectors\u2014from finance to healthcare
-      and even law\u2014it\u2019s critical to scrutinize the ethical ramifications
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-      outputs. It would invite a discussion on what role human oversight should play,
-      making the case for a balanced integration of AI agents that respects both efficiency
-      and ethical standards.\\n\\n- **The Financial Impacts of AI Agents on Startups**\\n
-      \ Startups are typically characterized by limited resources and the need for
-      agile approaches to market challenges. This article could investigate how AI
-      agents are reshaping financial strategies in startups, from automating mundane
-      tasks to offering insights through data analysis. By highlighting successful
-      startup case studies and quantifying improvements brought about by integrating
-      AI agents, readers would grasp the potential cost savings and increased efficiency
-      that AI can offer. It would further explore how these startups leverage their
-      AI capabilities for competitive advantages, marking a crucial study for entrepreneurs
-      and investors alike.\\n\\nEach idea presents an opportunity to deeply analyze
-      the impacts of AI agents across various facets of modern life, emphasizing not
-      only their technological advancements but also the accompanying ethical and
-      social implications.\\n\\nHuman Feedback:\\nGreat work!\\n\\nImproved Output:\\n-
-      **The Evolution of AI Agents in Customer Service**\\n  The landscape of customer
-      service has radically transformed with the advent of AI agents. This article
-      could delve into how businesses are employing AI to enhance customer experiences,
-      reduce wait times, and streamline operations. By featuring in-depth case studies
-      from leading companies such as Amazon and Zappos, it would explore the measures
-      taken to train these AI agents while addressing the challenges of human emotional
-      intelligence versus AI. The article would further investigate the future implications
-      of AI agents in understanding and predicting customer needs, ensuring a comprehensive
-      look at the efficiencies gained, and addressing the ethical considerations surrounding
-      AI communication.\\n\\n- **AI Agents as Companions: The Future of Personal Relationships**\\n
-      \ As technology blurs the lines between human and machine interaction, the concept
-      of AI agents as companions is becoming increasingly popular. This article could
-      explore the psychological and social implications of forming bonds with AI,
-      spotlighting current AI companion projects like Replika and virtual pets such
-      as Aibo. It would examine whether these AI relationships can genuinely fulfill
-      emotional needs and consider what this trend means for human connection in an
-      increasingly digital world. Through interviews with users and specialists, the
-      article would offer diverse viewpoints and address the ethical considerations
-      of companionship and loneliness, ultimately provoking thought on our future
-      interactions with technology.\\n\\n- **AI Agents in Creative Arts: Redefining
-      Creativity**\\n  The infusion of AI into creative arts poses unique questions
-      about authorship and originality. This compelling article could investigate
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-      prose, utilizing tools such as OpenAI\u2019s Muse and Google\u2019s DeepDream.
-      It would challenge readers to consider whether AI can genuinely create or merely
-      mimic human artistry, delving into the fascinating intersection of technology
-      and art. The article would include perspectives from artists collaborating with
-      AI, offering insights on the evolving landscape of creativity and raising important
-      questions about the future of artistic expression in an AI-enhanced world.\\n\\n-
-      **Ethical Considerations of AI Agents in Decision Making**\\n  With AI increasingly
-      taking on decision-making roles in various sectors\u2014from finance and healthcare
-      to law\u2014scrutinizing the ethical ramifications of these technologies is
-      imperative. This investigative article could explore how AI agents analyze vast
-      datasets to inform crucial decisions while uncovering potential biases embedded
-      in their algorithms. It would articulate necessary accountability measures for
-      monitoring AI outputs and invite thoughtful discussion on the importance of
-      human oversight. By spotlighting thought leaders in ethics and technology, the
-      article would ensure a balanced perspective on integrating AI agents that respects
-      both efficiency and ethical standards.\\n\\n- **The Financial Impacts of AI
-      Agents on Startups**\\n  Startups, characterized by limited resources and the
-      need for agile strategies, are leveraging AI agents to reshape financial decision-making.
-      This article could investigate the ways in which startups are harnessing AI
-      to automate administrative tasks, gain insights through predictive analytics,
-      and improve customer engagement. By presenting in-depth case studies, the article
-      would quantify the improvements brought about by integrating AI agents, demonstrating
-      the potential cost savings and increased efficiency they offer. Furthermore,
-      the article could explore how startups leverage their AI capabilities for competitive
-      advantages, ultimately serving as a crucial resource for entrepreneurs and investors
-      looking to navigate the evolving business landscape.\\n\\nThese ideas not only
-      highlight the transformative impact of AI agents across various domains but
-      also address the underlying ethical, social, and financial dynamics that are
-      essential for a thorough understanding of their role in society today.\\n\\n------------------------------------------------\\n\\nIteration:
-      1\\nInitial Output:\\n- **The Rise of AI Agents in Remote Work Environments**
-      \ \\nIn the wake of the pandemic, remote work has become a part of our daily
-      lives. AI agents are stepping into this new workspace, facilitating communication,
-      task management, and team collaboration like never before. By exploring the
-      impact of AI agents in virtual settings, the article could delve into how these
-      intelligent systems optimize productivity, reduce operational costs, and enhance
-      employee satisfaction. The journey of these agents\u2014from simple chatbots
-      to sophisticated virtual assistants\u2014presents a fascinating evolution that
-      reshapes not just how we work but also how we connect.\\n\\n- **Ethical Implications
-      of AI Decision-Making**  \\nAs AI systems gain prominence in decision-making
-      processes across various sectors\u2014from healthcare to finance\u2014the ethical
-      implications become more pressing. An insightful article could dissect the moral
-      dilemmas posed by AI, such as biases embedded in algorithms, data privacy concerns,
-      and the accountability of AI's actions. By interrogating the complexities of
-      trust in AI systems, the piece could highlight the critical need for transparent
-      frameworks and governance, ensuring that AI serves humanity fairly and justly
-      in an increasingly automated world.\\n\\n- **AI Agents as Creative Collaborators**
-      \ \\nAI's foray into creative domains is revolutionizing fields such as art,
-      music, and writing. An engaging article could showcase how AI agents function
-      alongside human creators, pushing the boundaries of creativity and innovation.
-      This exploration could highlight notable collaborations between humans and AI,
-      celebrating the intriguing ways in which technology enhances artistic expression.
-      By illustrating the symbiotic relationship between human intuition and AI's
-      analytical prowess, the article could underscore that creativity is no longer
-      solely a human domain but a collective endeavor involving intelligent machines.\\n\\n-
-      **Personalized Learning Experiences through AI Agents**  \\nEducation is undergoing
-      a transformation with the integration of AI agents into personalized learning
-      environments. An article could examine how these agents tailor educational content
-      to individual student's needs, moving away from one-size-fits-all approaches
-      toward truly customized learning experiences. By interviewing educators and
-      students, the piece can illustrate the profound impact of AI on student engagement,
-      academic performance, and emotional support, making a strong case for the necessity
-      of AI-driven methods in modern education.\\n\\n- **The Future of AI in Mental
-      Health Support**  \\nAs mental health becomes an increasingly crucial area of
-      focus, AI agents are emerging as supplementary tools for psychological support.
-      An article could explore the role of AI in providing real-time assistance, stigma
-      reduction, and accessibility to mental health resources. By sharing success
-      stories and evidence from trials, the piece can encapsulate the potential for
-      AI agents to act as companions that offer kindness and empathy, alongside professional
-      support. This discussion may ultimately lead to a greater appreciation of AI's
-      capacity to enhance mental well-being in an era where mental health challenges
-      are more prevalent than ever.\\n\\nNotes: Each of these ideas addresses a timely
-      and relevant intersection between technology and human experience, making them
-      compelling subjects for in-depth articles. The potential for engaging readers
-      with real-world applications, ethical considerations, and innovative collaborations
-      ensures that these topics will resonate widely and inspire thoughtful discussion.\\n\\nHuman
-      Feedback:\\nGreat work!\\n\\nImproved Output:\\n- **The Rise of AI Agents in
-      Remote Work Environments**  \\nIn the wake of the pandemic, remote work has
-      become a staple in our daily routines, reshaping how businesses operate globally.
-      AI agents are uniquely positioned to enhance this dynamic, stepping into virtual
-      offices to facilitate seamless communication, task management, and collaborative
-      projects. An article exploring the impact of AI agents in remote settings could
-      investigate how these intelligent assistants optimize productivity, reduce operational
-      costs, and improve employee morale by mitigating the isolation often felt in
-      remote work. The journey of AI\u2014from basic scheduling tools to multifunctional
-      virtual colleagues\u2014offers a captivating narrative on technology's role
-      in redefining our professional landscape and fostering human connections in
-      digital spaces.\\n\\n- **Ethical Implications of AI Decision-Making**  \\nAs
-      AI systems increasingly influence critical decision-making in various sectors
-      such as healthcare, finance, and criminal justice, the ethical implications
-      become a hotbed for discussion. A thorough investigation into the moral dilemmas
-      surrounding AI could scrutinize issues such as system biases, data privacy,
-      and the ambiguity of accountability for AI-driven outcomes. This article could
-      engage with thought leaders and ethicists to illuminate the pressing need for
-      ethical frameworks and governance models that ensure AI technologies promote
-      equity and are designed to serve human interests, fostering a society where
-      trust in AI can flourish.\\n\\n- **AI Agents as Creative Collaborators**  \\nThe
-      canvas of creativity is expanding with the emergence of AI agents as collaborators
-      in artistic domains like visual arts, music production, and literary composition.
-      An engaging article could celebrate the symbiotic relationship between human
-      creators and AI, illustrating how these intelligent systems are not mere tools
-      but creative partners that inspire innovation. By profiling groundbreaking collaborations
-      where AI and human artists co-create, this piece would delve into the possibilities
-      that arise when technology enhances human expression, evolving the narrative
-      around creativity as a shared endeavor rather than a solitary pursuit.\\n\\n-
-      **Personalized Learning Experiences through AI Agents**  \\nEducation is at
-      a transformational crossroads, with AI agents revolutionizing how students learn
-      through personalized educational experiences. A compelling article could explore
-      how these intelligent systems adapt learning materials to meet each student's
-      unique needs, thereby moving away from traditional, uniform teaching methods.
-      Interviews with educators and students could reveal powerful testimonials that
-      attest to the positive effects of AI-driven personalized learning, including
-      improved engagement and academic success, establishing a strong argument for
-      the integration of intelligent technologies in classrooms everywhere.\\n\\n-
-      **The Future of AI in Mental Health Support**  \\nAs society increasingly acknowledges
-      mental health issues, AI agents are emerging as vital tools in providing mental
-      health support and resources. An insightful article could examine the potential
-      of AI as a supplemental resource for individuals seeking emotional assistance,
-      highlighting innovations in real-time supportive interactions and stigma reduction.
-      By showcasing case studies of successful AI applications in therapy, the piece
-      would underscore the transformative role these technologies can play in making
-      mental health care accessible and efficient, positioning AI not only as a technological
-      advancement but as a crucial ally in promoting overall well-being in modern
-      life.\\n\\nNotes: Each idea serves as a portal into crucial conversations about
-      technology's role in human experiences, categorized within a contemporary context.
-      The integration of personal stories, expert interviews, and ethical considerations
-      enriches these topics, ensuring they resonate widely, inspire curiosity, and
-      engage readers in meaningful dialogue. This comprehensive approach will enhance
-      the overall quality and relevance of the articles while appealing to a diverse
-      audience.\\n\\n------------------------------------------------\\n\\nPlease
-      provide:\\n- Provide a list of clear, actionable instructions derived from the
-      Human Feedbacks to enhance the Agent's performance. Analyze the differences
-      between Initial Outputs and Improved Outputs to generate specific action items
-      for future tasks. Ensure all key and specificpoints from the human feedback
-      are incorporated into these instructions.\\n- A score from 0 to 10 evaluating
-      on completion, quality, and overall performance from the improved output to
-      the initial output based on the human feedback\\n\"}],\"model\":\"gpt-4.1-mini\",\"response_format\":{\"type\":\"json_schema\",\"json_schema\":{\"schema\":{\"properties\":{\"suggestions\":{\"description\":\"List
-      of clear, actionable instructions derived from the Human Feedbacks to enhance
-      the Agent's performance. Analyze the differences between Initial Outputs and
-      Improved Outputs to generate specific action items for future tasks. Ensure
-      all key and specific points from the human feedback are incorporated into these
-      instructions.\",\"items\":{\"type\":\"string\"},\"title\":\"Suggestions\",\"type\":\"array\"},\"quality\":{\"description\":\"A
-      score from 0 to 10 evaluating on completion, quality, and overall performance
-      from the improved output to the initial output based on the human feedback.\",\"title\":\"Quality\",\"type\":\"number\"},\"final_summary\":{\"description\":\"A
-      step by step action items to improve the next Agent based on the human-feedback
-      and improved output.\",\"title\":\"Final Summary\",\"type\":\"string\"}},\"required\":[\"suggestions\",\"quality\",\"final_summary\"],\"title\":\"TrainingTaskEvaluation\",\"type\":\"object\",\"additionalProperties\":false},\"name\":\"TrainingTaskEvaluation\",\"strict\":true}},\"stream\":false}"
+    body: '{"messages":[{"role":"system","content":"I''m gonna convert this raw text into valid JSON."},{"role":"user","content":"Assess the quality of the training data based on the llm output, human feedback , and llm output improved result.\n\nIteration: 0\nInitial Output:\n- **The Evolution of AI Agents in Customer Service**\n  The landscape of customer service has radically transformed with the advent of AI agents. This article could delve into how businesses are employing AI to enhance customer experiences, reduce wait times, and streamline operations. Highlighting real-world case studies from leading companies, it would explore the measures taken to train these AI agents, the challenges of human emotional intelligence versus AI, and the future implications of AI agents in understanding and predicting customer needs. This combination would provide a comprehensive look at the efficiencies gained, while also addressing ethical considerations in AI communication.\n\n- **AI Agents as Companions:
+      The Future of Personal Relationships**\n  As technology blurs the lines between human and machine interaction, the concept of AI agents as companions is becoming increasingly popular. This article could explore the psychological and social implications of forming bonds with AI, looking at current AI companion projects such as virtual pets and virtual therapists. It would question if these AI relationships can indeed fulfill emotional needs, and what it means for human connection in an increasingly digital world. This exploration would not only gather diverse viewpoints but also touch upon ethical considerations surrounding companionship and loneliness.\n\n- **AI Agents in Creative Arts: Redefining Creativity**\n  The infusion of AI into creative arts poses unique questions about authorship and originality. This topic could lead to a compelling article that examines how AI agents are being used to create paintings, compose music, and write literature. By analyzing specific tools such
+      as OpenAI’s Muse and Google’s DeepDream, the article would aim to untangle the perceived boundaries of creativity, discussing whether AI can ever genuinely create or merely mimic human artists. This perspective would bring readers into the fascinating intersection of technology and art, raising questions about the future of creative expression.\n\n- **Ethical Considerations of AI Agents in Decision Making**\n  With AI increasingly taking on decision-making roles in various sectors—from finance to healthcare and even law—it’s critical to scrutinize the ethical ramifications of these technologies. An article focused on this topic could explore how AI agents analyze vast datasets to inform decisions, the potential biases encoded in their algorithms, and the accountability measures necessary to monitor their outputs. It would invite a discussion on what role human oversight should play, making the case for a balanced integration of AI agents that respects both efficiency and ethical standards.\n\n-
+      **The Financial Impacts of AI Agents on Startups**\n  Startups are typically characterized by limited resources and the need for agile approaches to market challenges. This article could investigate how AI agents are reshaping financial strategies in startups, from automating mundane tasks to offering insights through data analysis. By highlighting successful startup case studies and quantifying improvements brought about by integrating AI agents, readers would grasp the potential cost savings and increased efficiency that AI can offer. It would further explore how these startups leverage their AI capabilities for competitive advantages, marking a crucial study for entrepreneurs and investors alike.\n\nEach idea presents an opportunity to deeply analyze the impacts of AI agents across various facets of modern life, emphasizing not only their technological advancements but also the accompanying ethical and social implications.\n\nHuman Feedback:\nGreat work!\n\nImproved Output:\n- **The
+      Evolution of AI Agents in Customer Service**\n  The landscape of customer service has radically transformed with the advent of AI agents. This article could delve into how businesses are employing AI to enhance customer experiences, reduce wait times, and streamline operations. By featuring in-depth case studies from leading companies such as Amazon and Zappos, it would explore the measures taken to train these AI agents while addressing the challenges of human emotional intelligence versus AI. The article would further investigate the future implications of AI agents in understanding and predicting customer needs, ensuring a comprehensive look at the efficiencies gained, and addressing the ethical considerations surrounding AI communication.\n\n- **AI Agents as Companions: The Future of Personal Relationships**\n  As technology blurs the lines between human and machine interaction, the concept of AI agents as companions is becoming increasingly popular. This article could explore
+      the psychological and social implications of forming bonds with AI, spotlighting current AI companion projects like Replika and virtual pets such as Aibo. It would examine whether these AI relationships can genuinely fulfill emotional needs and consider what this trend means for human connection in an increasingly digital world. Through interviews with users and specialists, the article would offer diverse viewpoints and address the ethical considerations of companionship and loneliness, ultimately provoking thought on our future interactions with technology.\n\n- **AI Agents in Creative Arts: Redefining Creativity**\n  The infusion of AI into creative arts poses unique questions about authorship and originality. This compelling article could investigate how AI agents are actively creating paintings, composing music, and even writing prose, utilizing tools such as OpenAI’s Muse and Google’s DeepDream. It would challenge readers to consider whether AI can genuinely create or merely
+      mimic human artistry, delving into the fascinating intersection of technology and art. The article would include perspectives from artists collaborating with AI, offering insights on the evolving landscape of creativity and raising important questions about the future of artistic expression in an AI-enhanced world.\n\n- **Ethical Considerations of AI Agents in Decision Making**\n  With AI increasingly taking on decision-making roles in various sectors—from finance and healthcare to law—scrutinizing the ethical ramifications of these technologies is imperative. This investigative article could explore how AI agents analyze vast datasets to inform crucial decisions while uncovering potential biases embedded in their algorithms. It would articulate necessary accountability measures for monitoring AI outputs and invite thoughtful discussion on the importance of human oversight. By spotlighting thought leaders in ethics and technology, the article would ensure a balanced perspective on
+      integrating AI agents that respects both efficiency and ethical standards.\n\n- **The Financial Impacts of AI Agents on Startups**\n  Startups, characterized by limited resources and the need for agile strategies, are leveraging AI agents to reshape financial decision-making. This article could investigate the ways in which startups are harnessing AI to automate administrative tasks, gain insights through predictive analytics, and improve customer engagement. By presenting in-depth case studies, the article would quantify the improvements brought about by integrating AI agents, demonstrating the potential cost savings and increased efficiency they offer. Furthermore, the article could explore how startups leverage their AI capabilities for competitive advantages, ultimately serving as a crucial resource for entrepreneurs and investors looking to navigate the evolving business landscape.\n\nThese ideas not only highlight the transformative impact of AI agents across various domains
+      but also address the underlying ethical, social, and financial dynamics that are essential for a thorough understanding of their role in society today.\n\n------------------------------------------------\n\nIteration: 1\nInitial Output:\n- **The Rise of AI Agents in Remote Work Environments**  \nIn the wake of the pandemic, remote work has become a part of our daily lives. AI agents are stepping into this new workspace, facilitating communication, task management, and team collaboration like never before. By exploring the impact of AI agents in virtual settings, the article could delve into how these intelligent systems optimize productivity, reduce operational costs, and enhance employee satisfaction. The journey of these agents—from simple chatbots to sophisticated virtual assistants—presents a fascinating evolution that reshapes not just how we work but also how we connect.\n\n- **Ethical Implications of AI Decision-Making**  \nAs AI systems gain prominence in decision-making processes
+      across various sectors—from healthcare to finance—the ethical implications become more pressing. An insightful article could dissect the moral dilemmas posed by AI, such as biases embedded in algorithms, data privacy concerns, and the accountability of AI''s actions. By interrogating the complexities of trust in AI systems, the piece could highlight the critical need for transparent frameworks and governance, ensuring that AI serves humanity fairly and justly in an increasingly automated world.\n\n- **AI Agents as Creative Collaborators**  \nAI''s foray into creative domains is revolutionizing fields such as art, music, and writing. An engaging article could showcase how AI agents function alongside human creators, pushing the boundaries of creativity and innovation. This exploration could highlight notable collaborations between humans and AI, celebrating the intriguing ways in which technology enhances artistic expression. By illustrating the symbiotic relationship between human
+      intuition and AI''s analytical prowess, the article could underscore that creativity is no longer solely a human domain but a collective endeavor involving intelligent machines.\n\n- **Personalized Learning Experiences through AI Agents**  \nEducation is undergoing a transformation with the integration of AI agents into personalized learning environments. An article could examine how these agents tailor educational content to individual student''s needs, moving away from one-size-fits-all approaches toward truly customized learning experiences. By interviewing educators and students, the piece can illustrate the profound impact of AI on student engagement, academic performance, and emotional support, making a strong case for the necessity of AI-driven methods in modern education.\n\n- **The Future of AI in Mental Health Support**  \nAs mental health becomes an increasingly crucial area of focus, AI agents are emerging as supplementary tools for psychological support. An article could
+      explore the role of AI in providing real-time assistance, stigma reduction, and accessibility to mental health resources. By sharing success stories and evidence from trials, the piece can encapsulate the potential for AI agents to act as companions that offer kindness and empathy, alongside professional support. This discussion may ultimately lead to a greater appreciation of AI''s capacity to enhance mental well-being in an era where mental health challenges are more prevalent than ever.\n\nNotes: Each of these ideas addresses a timely and relevant intersection between technology and human experience, making them compelling subjects for in-depth articles. The potential for engaging readers with real-world applications, ethical considerations, and innovative collaborations ensures that these topics will resonate widely and inspire thoughtful discussion.\n\nHuman Feedback:\nGreat work!\n\nImproved Output:\n- **The Rise of AI Agents in Remote Work Environments**  \nIn the wake of the
+      pandemic, remote work has become a staple in our daily routines, reshaping how businesses operate globally. AI agents are uniquely positioned to enhance this dynamic, stepping into virtual offices to facilitate seamless communication, task management, and collaborative projects. An article exploring the impact of AI agents in remote settings could investigate how these intelligent assistants optimize productivity, reduce operational costs, and improve employee morale by mitigating the isolation often felt in remote work. The journey of AI—from basic scheduling tools to multifunctional virtual colleagues—offers a captivating narrative on technology''s role in redefining our professional landscape and fostering human connections in digital spaces.\n\n- **Ethical Implications of AI Decision-Making**  \nAs AI systems increasingly influence critical decision-making in various sectors such as healthcare, finance, and criminal justice, the ethical implications become a hotbed for discussion.
+      A thorough investigation into the moral dilemmas surrounding AI could scrutinize issues such as system biases, data privacy, and the ambiguity of accountability for AI-driven outcomes. This article could engage with thought leaders and ethicists to illuminate the pressing need for ethical frameworks and governance models that ensure AI technologies promote equity and are designed to serve human interests, fostering a society where trust in AI can flourish.\n\n- **AI Agents as Creative Collaborators**  \nThe canvas of creativity is expanding with the emergence of AI agents as collaborators in artistic domains like visual arts, music production, and literary composition. An engaging article could celebrate the symbiotic relationship between human creators and AI, illustrating how these intelligent systems are not mere tools but creative partners that inspire innovation. By profiling groundbreaking collaborations where AI and human artists co-create, this piece would delve into the possibilities
+      that arise when technology enhances human expression, evolving the narrative around creativity as a shared endeavor rather than a solitary pursuit.\n\n- **Personalized Learning Experiences through AI Agents**  \nEducation is at a transformational crossroads, with AI agents revolutionizing how students learn through personalized educational experiences. A compelling article could explore how these intelligent systems adapt learning materials to meet each student''s unique needs, thereby moving away from traditional, uniform teaching methods. Interviews with educators and students could reveal powerful testimonials that attest to the positive effects of AI-driven personalized learning, including improved engagement and academic success, establishing a strong argument for the integration of intelligent technologies in classrooms everywhere.\n\n- **The Future of AI in Mental Health Support**  \nAs society increasingly acknowledges mental health issues, AI agents are emerging as vital tools
+      in providing mental health support and resources. An insightful article could examine the potential of AI as a supplemental resource for individuals seeking emotional assistance, highlighting innovations in real-time supportive interactions and stigma reduction. By showcasing case studies of successful AI applications in therapy, the piece would underscore the transformative role these technologies can play in making mental health care accessible and efficient, positioning AI not only as a technological advancement but as a crucial ally in promoting overall well-being in modern life.\n\nNotes: Each idea serves as a portal into crucial conversations about technology''s role in human experiences, categorized within a contemporary context. The integration of personal stories, expert interviews, and ethical considerations enriches these topics, ensuring they resonate widely, inspire curiosity, and engage readers in meaningful dialogue. This comprehensive approach will enhance the overall
+      quality and relevance of the articles while appealing to a diverse audience.\n\n------------------------------------------------\n\nPlease provide:\n- Provide a list of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent''s performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific action items for future tasks. Ensure all key and specificpoints from the human feedback are incorporated into these instructions.\n- A score from 0 to 10 evaluating on completion, quality, and overall performance from the improved output to the initial output based on the human feedback\n"}],"model":"gpt-4.1-mini","response_format":{"type":"json_schema","json_schema":{"schema":{"properties":{"suggestions":{"description":"List of clear, actionable instructions derived from the Human Feedbacks to enhance the Agent''s performance. Analyze the differences between Initial Outputs and Improved Outputs to generate specific action items
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index 108398421..e5830d03c 100644
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diff --git a/lib/crewai/tests/cassettes/test_guardrail_emits_events.yaml b/lib/crewai/tests/cassettes/test_guardrail_emits_events.yaml
index 22ef98655..530c495a0 100644
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+++ b/lib/crewai/tests/cassettes/test_guardrail_emits_events.yaml
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 interactions:
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-      theater of war for Italian forces, combining military history with human stories.\\n\\nThese
-      titles emphasize the Italian experience of the First World War, authored by
-      prominent Italian historians and writers, thus fulfilling the requirement for
-      Italian authorship and perspective.\\n\\n        Guardrail:\\n        Ensure
-      the authors are from Italy\\n\\n        Your task:\\n        - Confirm if the
-      Task result complies with the guardrail.\\n        - If not, provide clear feedback
-      explaining what is wrong (e.g., by how much it violates the rule, or what specific
-      part fails).\\n        - Focus only on identifying issues \u2014 do not propose
-      corrections.\\n        - If the Task result complies with the guardrail, saying
-      that is valid\\n        \"}],\"model\":\"gpt-4.1-mini\"}"
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+      with a focus on Italy’s involvement and the impact on Italian society and politics.\n\n2. \"La guerra bianca: Come gli Italiani hanno combattuto la Prima guerra mondiale\" by Alessandro Barbero  \n   - Explores the harsh conditions of mountain warfare endured by Italian soldiers, with vivid descriptions of battles and daily life on the Alpine front.\n\n3. \"La Prima Guerra Mondiale\" by Paolo Mieli  \n   - Provides a broad overview of the war, including Italy''s role and the broader European context, combining historical facts with cultural insights.\n\n4. \"1915-1918: La guerra italiana sul fronte interno\" by Giovanni De Luna  \n   - Focuses on the social and political effects of the war within Italy, examining public opinion, propaganda, and the home front.\n\n5. \"Il Giorno della Vittoria: 4 Novembre 1918\" by Paolo Gaspari  \n   - Detailed analysis of Italy’s final victories and the aftermath of the war, including the political and social changes that followed.\n\n6. \"La Guerra
+      prima della Grande Guerra\" by Alessandro Barbero  \n   - Discusses the international tensions preceding the First World War with an Italian perspective on diplomatic and military developments.\n\n7. \"La guerra dimenticata: Il fronte orientale, 1915-1917\" by Luca Micheletti  \n   - Covers Italy’s campaigns on the Eastern Front, less frequently discussed in general histories of the war, providing new insights into Italy’s military efforts.\n\n8. \"Tornare a casa. Grande Guerra 1914-1918\" by Marco Balzano  \n   - A novelized account based on testimonies and letters from Italian soldiers, blending historical documentation with personal narrative.\n\n9. \"I cento giorni: La battaglia finale della Grande Guerra\" by Giuseppe Cultrera  \n   - Concentrates on the decisive Italian military operations in the war’s final phase, highlighting strategy and key personalities.\n\n10. \"La Grande Guerra sul Carso\" by Mario Isnenghi  \n    - Focuses specifically on the Carso front, a crucial and
+      brutal theater of war for Italian forces, combining military history with human stories.\n\nThese titles emphasize the Italian experience of the First World War, authored by prominent Italian historians and writers, thus fulfilling the requirement for Italian authorship and perspective.\n\n        Guardrail:\n        Ensure the authors are from Italy\n\n        Your task:\n        - Confirm if the Task result complies with the guardrail.\n        - If not, provide clear feedback explaining what is wrong (e.g., by how much it violates the rule, or what specific part fails).\n        - Focus only on identifying issues — do not propose corrections.\n        - If the Task result complies with the guardrail, saying that is valid\n        "}],"model":"gpt-4.1-mini"}'
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-      VERY important to you, use the tools available and give your best Final Answer,
-      your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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diff --git a/lib/crewai/tests/cassettes/test_guardrail_when_an_error_occurs.yaml b/lib/crewai/tests/cassettes/test_guardrail_when_an_error_occurs.yaml
index 718805fbe..1ac34dfe5 100644
--- a/lib/crewai/tests/cassettes/test_guardrail_when_an_error_occurs.yaml
+++ b/lib/crewai/tests/cassettes/test_guardrail_when_an_error_occurs.yaml
@@ -1,17 +1,6 @@
 interactions:
 - request:
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-      personal goal is: Test Goal\nTo give my best complete final answer to the task
-      respond using the exact following format:\n\nThought: I now can give a great
-      answer\nFinal Answer: Your final answer must be the great and the most complete
-      as possible, it must be outcome described.\n\nI MUST use these formats, my job
-      depends on it!"}, {"role": "user", "content": "\nCurrent Task: Gather information
-      about available books on the First World War\n\nThis is the expected criteria
-      for your final answer: A list of available books on the First World War\nyou
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-      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop":
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+      advancement, utilizing machine learning, neural networks, and natural language processing to replicate human cognitive functions. The transformative power of AI spans across industries, driving revolutionary changes from predictive diagnostics in healthcare to algorithmic trading in finance. By rapidly analyzing large datasets, AI reveals hidden patterns, enhances efficiency, and sparks unprecedented innovation. As we embrace this technology, it is crucial to prioritize ethical considerations and responsible development to fully leverage its potential while addressing associated risks.\"\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: To craft an amazing paragraph about AI as described in the context, I should delegate this task to the Senior Writer and include all the detailed context provided.\n\nAction: Delegate work to coworker\nAction Input: {\"task\":
+      \"Write one amazing paragraph about AI\", \"context\": \"Artificial Intelligence (AI) is a transformative technology that leverages machine learning, neural networks, and natural language processing to perform tasks that traditionally require human intelligence, such as visual perception, speech recognition, and decision-making. Its potential to revolutionize industries\u2014from healthcare with predictive diagnostics to finance with algorithmic trading\u2014underscores its profound impact on our daily lives and the global economy. With the ability to analyze vast amounts of data quickly and accurately, AI can unveil hidden patterns, drive efficiencies, and foster innovation at an unprecedented scale. As we move forward, the ethical and responsible development of AI will be paramount to harness its benefits while mitigating risks.\", \"coworker\": \"Senior Writer\"}\n\nObservation: The Senior Writer has received the task and context.\nObservation: Artificial Intelligence (AI) is a
+      transformative technology that leverages machine learning, neural networks, and natural language processing to perform tasks that traditionally require human intelligence, such as visual perception, speech recognition, and decision-making. Its potential to revolutionize industries\u2014from healthcare with predictive diagnostics to finance with algorithmic trading\u2014underscores its profound impact on our daily lives and the global economy. With the ability to analyze vast amounts of data quickly and accurately, AI can unveil hidden patterns, drive efficiencies, and foster innovation at an unprecedented scale. As we move forward, the ethical and responsible development of AI will be paramount to harness its benefits while mitigating risks."}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
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index e5fdd5325..2b7fd6468 100644
--- a/lib/crewai/tests/cassettes/test_hierarchical_process.yaml
+++ b/lib/crewai/tests/cassettes/test_hierarchical_process.yaml
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+      algorithms that improve carbon capture methods, AI''s potential in environmental science is vast. This topic invites an exploration of existing AI applications in climate initiatives, with a focus on groundbreaking research and initiatives aimed at reducing humanity''s carbon footprint. Highlighting successful projects and technology partnerships can illustrate the positive impact AI can have on global climate efforts, inspiring further exploration and investment in this area.\n\n**4. The Future of Work: How AI is Reshaping Employment Landscapes**  \nThe discussions around AI''s impact on the workforce are both urgent and complex, as advances in automation and machine learning continue to transform the job market. This article could delve into the current trends of AI-driven job displacement alongside opportunities for upskilling and the creation of new job roles. By examining case studies of companies that integrate AI effectively and the resulting workforce adaptations, readers can
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+      but also probe deeper into ethical and practical considerations, making them timely and relevant for contemporary audiences."}, {"role": "assistant", "content": "```\nThought: To come up with interesting article ideas and write compelling highlights, I should first generate a list of potential topics. The Researcher can help with gathering current trends or interesting topics that might be engaging. Then, I can delegate writing the paragraph highlights to the Senior Writer.\n\nAction: Ask question to coworker\nAction Input: {\"question\": \"What are some current trending topics or subjects that are interesting to explore for an article?\", \"context\": \"I need to generate a list of 5 interesting ideas to explore for an article, each paired with an amazing paragraph highlight. Current trends or unusual insights could be a good source of inspiration for these ideas.\", \"coworker\": \"Researcher\"}\nObservation: **1. The Rise of Autonomous AI Agents in Daily Life**  \nAs artificial
+      intelligence technology progresses, the integration of autonomous AI agents into everyday life becomes increasingly prominent. These agents, capable of making decisions without human intervention, are reshaping industries from healthcare to finance. Exploring case studies where autonomous AI has successfully decreased operational costs or improved efficiency can reveal not only the benefits but also the ethical implications of delegating decision-making to machines. This topic offers an exciting opportunity to dive into the AI landscape, showcasing current developments such as AI assistants and autonomous vehicles.\n\n**2. Ethical Implications of Generative AI in Creative Industries**  \nThe surge of generative AI tools in creative fields, such as art, music, and writing, has sparked a heated debate about authorship and originality. This article could investigate how these tools are being used by artists and creators, examining both the potential for innovation and the risk of devaluing
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+      its application in AI development presents a fascinating topic worth examining. Decentralized AI could address issues of data privacy, security, and democratization in AI models by allowing users to retain ownership of data while benefiting from AI''s capabilities. This article could analyze how decentralized networks are disrupting traditional AI development models, featuring innovative projects that harness the synergy between blockchain and AI. Highlighting potential pitfalls and the future landscape of decentralized AI could stimulate discussion among technologists, entrepreneurs, and policymakers alike.\n\nThese topics not only reflect current trends but also probe deeper into ethical and practical considerations, making them timely and relevant for contemporary audiences."}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
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+    body: '{"messages": [{"role": "system", "content": "You are Crew Manager. You are a seasoned manager with a knack for getting the best out of your team.\nYou are also known for your ability to delegate work to the right people, and to ask the right questions to get the best out of your team.\nEven though you don''t perform tasks by yourself, you have a lot of experience in the field, which allows you to properly evaluate the work of your team members.\nYour personal goal is: Manage the team to complete the task in the best way possible.\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate work to coworker\nTool Arguments: {''task'': {''description'': ''The task to delegate'', ''type'': ''str''}, ''context'': {''description'': ''The context for the task'', ''type'': ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to delegate to'', ''type'': ''str''}}\nTool Description: Delegate a specific
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+      **1. The Rise of Autonomous AI Agents in Daily Life**  \nAs artificial intelligence technology progresses, the integration of autonomous AI agents into everyday life becomes increasingly prominent. These agents, capable of making decisions without human intervention, are reshaping industries from healthcare to finance. Exploring case studies where autonomous AI has successfully decreased operational costs or improved efficiency can reveal not only the benefits but also the ethical implications of delegating decision-making to machines. This topic offers an exciting opportunity to dive into the AI landscape, showcasing current developments such as AI assistants and autonomous vehicles.\n\n**2. Ethical Implications of Generative AI in Creative Industries**  \nThe surge of generative AI tools in creative fields, such as art, music, and writing, has sparked a heated debate about authorship and originality. This article could investigate how these tools are being used by artists and creators,
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index 14e4d68e3..2e2001e04 100644
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+      5 bullet points with a paragraph for each idea.\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: The first step is to brainstorm and research interesting ideas for the article. Since the task involves both research and writing, I will need to delegate the brainstorming of ideas to the Researcher and the writing of the paragraphs to the Senior Writer. \n\nAction: Delegate work to coworker\nAction Input: {\"task\": \"Come up with a list of 5 interesting ideas to explore for an article.\", \"context\": \"We need a list of 5 interesting ideas that would make compelling articles. The topics should be relevant, timely, and engaging for a broad audience.\", \"coworker\": \"Researcher\"}\nObservation: 1. **The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics** -
+      Explore how AI is revolutionizing healthcare through enhanced diagnostic tools, personalized treatment plans, and post-operative care. Discuss current achievements, potential future applications, and ethical considerations.\n\n2. **AI Agents in the Workplace: Boosting Productivity and Shaping the Future of Jobs** - Examine the integration of AI agents in various industries. Highlight how these intelligent agents are improving efficiency, automating mundane tasks, and assisting in decision-making processes. Address potential job displacement and the need for upskilling the workforce.\n\n3. **Ethical AI: Ensuring Fairness, Accountability, and Transparency** - Delve into the ethical implications of AI deployment. Analyze the frameworks and guidelines being proposed to ensure AI systems are fair, transparent, and accountable. Include case studies of ethical dilemmas faced by companies using AI.\n\n4. **AI-Powered Startups: Innovators Changing the Tech Landscape** - Provide an overview
+      of how AI-driven startups are disrupting traditional business models and industries. Profile some of the most innovative startups, their business models, and the unique challenges they face. Discuss the role of venture capital in fostering AI innovation.\n\n5. **The Future of AI in Entertainment: From Movies to Video Games** - Investigate how AI is transforming the entertainment industry, from scriptwriting and special effects in movies to creating more immersive and intelligent experiences in video games. Offer predictions about future trends and possible impacts on content creation and consumption.\n\nThese topics are designed to be timely, relevant, and engaging, ensuring a broad audience will find them compelling."}, {"role": "user", "content": "I did it wrong. Tried to both perform Action and give a Final Answer at the same time, I must do one or the other"}, {"role": "assistant", "content": "Thought: I''ll proceed to delegate the writing of one amazing paragraph highlight for
+      each of the identified ideas to the Senior Writer, ensuring each topic is adequately detailed to showcase its potential.\n\nAction: Delegate work to coworker\nAction Input: {\"task\": \"Write one amazing paragraph highlight for each of these 5 ideas that showcases how good an article about these topics could be.\", \"context\": \"Here are the 5 ideas:\\n1. The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics - Explore how AI is revolutionizing healthcare through enhanced diagnostic tools, personalized treatment plans, and post-operative care. Discuss current achievements, potential future applications, and ethical considerations.\\n2. AI Agents in the Workplace: Boosting Productivity and Shaping the Future of Jobs - Examine the integration of AI agents in various industries. Highlight how these intelligent agents are improving efficiency, automating mundane tasks, and assisting in decision-making processes. Address potential job displacement and the need for upskilling
+      the workforce.\\n3. Ethical AI: Ensuring Fairness, Accountability, and Transparency - Delve into the ethical implications of AI deployment. Analyze the frameworks and guidelines being proposed to ensure AI systems are fair, transparent, and accountable. Include case studies of ethical dilemmas faced by companies using AI.\\n4. AI-Powered Startups: Innovators Changing the Tech Landscape - Provide an overview of how AI-driven startups are disrupting traditional business models and industries. Profile some of the most innovative startups, their business models, and the unique challenges they face. Discuss the role of venture capital in fostering AI innovation.\\n5. The Future of AI in Entertainment: From Movies to Video Games - Investigate how AI is transforming the entertainment industry, from scriptwriting and special effects in movies to creating more immersive and intelligent experiences in video games. Offer predictions about future trends and possible impacts on content creation
+      and consumption.\", \"coworker\": \"Senior Writer\"}\nObservation:\nObservation: 1. **The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**\n   AI is undeniably revolutionizing the healthcare sector, introducing transformative changes in patient care and diagnostics. With the advent of advanced diagnostic tools powered by AI, healthcare professionals can now detect and monitor diseases with unprecedented accuracy and speed. Personalized treatment plans, which were once a distant dream, are becoming a reality as AI algorithms analyze vast amounts of patient data, tailoring treatments that are specific to individual needs. Post-operative care has also seen significant improvements, with AI-driven monitoring systems ensuring that patients receive timely and effective care. Current achievements like IBM Watson''s oncology platform or Google''s DeepMind in eye care are just the beginning, with future applications promising to further enhance surgical robotics, predictive
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-      showcases how good an article about this topic could be. Return the list of
-      ideas with their paragraph and your notes.\n\nThis is the expected criteria
-      for your final answer: 5 bullet points with a paragraph for each idea.\nyou
-      MUST return the actual complete content as the final answer, not a summary.\n\nBegin!
-      This is VERY important to you, use the tools available and give your best Final
-      Answer, your job depends on it!\n\nThought:"}, {"role": "assistant", "content":
-      "Thought: The first step is to brainstorm and research interesting ideas for
-      the article. Since the task involves both research and writing, I will need
-      to delegate the brainstorming of ideas to the Researcher and the writing of
-      the paragraphs to the Senior Writer. \n\nAction: Delegate work to coworker\nAction
-      Input: {\"task\": \"Come up with a list of 5 interesting ideas to explore for
-      an article.\", \"context\": \"We need a list of 5 interesting ideas that would
-      make compelling articles. The topics should be relevant, timely, and engaging
-      for a broad audience.\", \"coworker\": \"Researcher\"}\nObservation: 1. **The
-      Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**\n   \"Imagine
-      a world where routine check-ups and complex diagnostics are seamlessly conducted
-      by intelligent systems. AI in healthcare is rapidly transforming this vision
-      into reality, enhancing patient care through early and accurate disease detection,
-      personalized treatment plans, and continuous health monitoring. This revolution
-      is not just a futuristic concept; it''s already happening with AI-driven technologies
-      enabling doctors to make more informed decisions and offering patients a more
-      proactive approach to their health. The ethical and privacy concerns, however,
-      pose new challenges that the industry must navigate to ensure responsible advancement.\"\n\n2.
-      **AI Agents in the Workplace: Boosting Productivity and Shaping the Future of
-      Jobs**\n   \"The modern workplace is being redefined by the integration of AI
-      agents, intelligent systems designed to handle repetitive tasks, analyze vast
-      amounts of data, and assist in complex decision-making processes. These innovations
-      are boosting productivity, allowing employees to focus on creativity and strategic
-      thinking. The rise of AI in the workspace heralds a future where human and machine
-      collaboration leads to unparalleled efficiencies and novel job opportunities.
-      However, this shift also brings with it challenges related to job displacement
-      and the urgent need for reskilling the workforce to thrive in an AI-augmented
-      environment.\"\n\n3. **Ethical AI: Ensuring Fairness, Accountability, and Transparency**\n   \"As
-      AI technology becomes more pervasive, the quest for ethical AI has become critical.
-      Ensuring fairness, accountability, and transparency in AI systems is essential
-      to prevent biases and promote trust. This involves creating robust frameworks
-      and guidelines that govern AI development and deployment. Real-world case studies
-      highlight both the triumphs and the tribulations of tech companies as they navigate
-      the complex moral landscape that AI presents. As society grapples with these
-      challenges, the push for ethical AI continues to drive innovation and policy-making
-      in the tech industry.\"\n\n4. **AI-Powered Startups: Innovators Changing the
-      Tech Landscape**\n   \"In the bustling world of tech startups, AI-powered companies
-      are emerging as the vanguards of innovation, disrupting traditional business
-      models and reshaping entire industries. From revolutionizing financial services
-      to pioneering advances in healthcare and beyond, these startups are at the forefront
-      of technological progress. By leveraging machine learning and AI, they are solving
-      complex problems and bringing novel solutions to market at an unprecedented
-      pace. Investors are taking notice, pouring capital into these ventures, and
-      fueling a new wave of growth and competition in the AI ecosystem.\"\n\n5. **The
-      Future of AI in Entertainment: From Movies to Video Games**\n   \"The entertainment
-      industry is undergoing a seismic shift driven by AI technology. In movies, AI
-      is revolutionizing special effects, enabling filmmakers to create stunning,
-      hyper-realistic worlds. In video games, AI is enhancing player experiences by
-      creating adaptive, intelligent characters and dynamic storylines that respond
-      to player choices. This transformative power of AI promises to push the boundaries
-      of creativity, offering audiences more immersive and interactive entertainment
-      experiences. As the technology continues to evolve, the future of entertainment
-      looks set to be more exciting and unpredictable than ever before.\""}, {"role":
-      "assistant", "content": "Thought: I''ll proceed to delegate the writing of one
-      amazing paragraph highlight for each of the identified ideas to the Senior Writer,
-      ensuring each topic is adequately detailed to showcase its potential.\n\nAction:
-      Delegate work to coworker\nAction Input: {\"task\": \"Write one amazing paragraph
-      highlight for each of these 5 ideas that showcases how good an article about
-      these topics could be.\", \"context\": \"Here are the 5 ideas:\\n1. The Rise
-      of AI in Healthcare: Transforming Patient Care and Diagnostics - Explore how
-      AI is revolutionizing healthcare through enhanced diagnostic tools, personalized
-      treatment plans, and post-operative care. Discuss current achievements, potential
-      future applications, and ethical considerations.\\n2. AI Agents in the Workplace:
-      Boosting Productivity and Shaping the Future of Jobs - Examine the integration
-      of AI agents in various industries. Highlight how these intelligent agents are
-      improving efficiency, automating mundane tasks, and assisting in decision-making
-      processes. Address potential job displacement and the need for upskilling the
-      workforce.\\n3. Ethical AI: Ensuring Fairness, Accountability, and Transparency
-      - Delve into the ethical implications of AI deployment. Analyze the frameworks
-      and guidelines being proposed to ensure AI systems are fair, transparent, and
-      accountable. Include case studies of ethical dilemmas faced by companies using
-      AI.\\n4. AI-Powered Startups: Innovators Changing the Tech Landscape - Provide
-      an overview of how AI-driven startups are disrupting traditional business models
-      and industries. Profile some of the most innovative startups, their business
-      models, and the unique challenges they face. Discuss the role of venture capital
-      in fostering AI innovation.\\n5. The Future of AI in Entertainment: From Movies
-      to Video Games - Investigate how AI is transforming the entertainment industry,
-      from scriptwriting and special effects in movies to creating more immersive
-      and intelligent experiences in video games. Offer predictions about future trends
-      and possible impacts on content creation and consumption.\", \"coworker\": \"Senior
-      Writer\"}\nObservation:\nObservation: 1. **The Rise of AI in Healthcare: Transforming
-      Patient Care and Diagnostics**\n   AI is undeniably revolutionizing the healthcare
-      sector, introducing transformative changes in patient care and diagnostics.
-      With the advent of advanced diagnostic tools powered by AI, healthcare professionals
-      can now detect and monitor diseases with unprecedented accuracy and speed. Personalized
-      treatment plans, which were once a distant dream, are becoming a reality as
-      AI algorithms analyze vast amounts of patient data, tailoring treatments that
-      are specific to individual needs. Post-operative care has also seen significant
-      improvements, with AI-driven monitoring systems ensuring that patients receive
-      timely and effective care. Current achievements like IBM Watson''s oncology
-      platform or Google''s DeepMind in eye care are just the beginning, with future
-      applications promising to further enhance surgical robotics, predictive analytics,
-      and remote patient monitoring. However, alongside these advancements, ethical
-      considerations such as patient privacy, data security, and decision-making transparency
-      must be rigorously addressed to fully harness AI\u2019s potential in healthcare.\n\n2.
-      **AI Agents in the Workplace: Boosting Productivity and Shaping the Future of
-      Jobs**\n   The integration of AI agents in the workplace marks a pivotal shift
-      in how businesses operate and compete. These intelligent agents are revolutionizing
-      various industries by automating mundane tasks, optimizing workflows, and providing
-      actionable insights from data analysis. For instance, AI chatbots in customer
-      service can handle routine inquiries, freeing human agents to tackle more complex
-      issues. In manufacturing, AI systems predict equipment failures, reducing downtime
-      and maintaining peak productivity. By enhancing operational efficiency, AI agents
-      not only boost productivity but also reshape the future of jobs. While the risk
-      of job displacement is a valid concern, it concurrently heralds opportunities
-      for upskilling and creating new roles that harness human creativity and strategic
-      thinking. Preparing the workforce for these changes is crucial, fostering a
-      symbiotic relationship between AI and human labor to drive innovation and economic
-      growth.\n\n3. **Ethical AI: Ensuring Fairness, Accountability, and Transparency**\n   As
-      AI technologies become increasingly pervasive, ensuring their ethical deployment
-      is paramount. Ethical AI demands the formulation and implementation of robust
-      frameworks that enshrine fairness, accountability, and transparency. Such frameworks
-      are essential in mitigating biases embedded within AI systems, which can perpetuate
-      and even exacerbate inequality. Case studies, such as the controversy surrounding
-      facial recognition systems and their racial biases, highlight the pressing need
-      for ethical oversight. Transparency in how AI models make decisions, coupled
-      with accountability measures for their outcomes, establishes trust and integrity
-      in AI applications. Legislative frameworks from the EU\u2019s GDPR to the IEEE\u2019s
-      global initiatives for ethical AI provide the scaffolding for these principles.
-      Companies are now faced with the challenge of integrating these ethical considerations
-      into their development processes, ensuring that AI advancements benefit all
-      of society equitably.\n\n4. **AI-Powered Startups: Innovators Changing the Tech
-      Landscape**\n   AI-powered startups are at the forefront of a technological
-      renaissance, disrupting traditional business models and reshaping industries.
-      These startups leverage AI to offer innovative solutions, whether it''s automating
-      financial trading with precision algorithms or enhancing cybersecurity through
-      predictive analytics. High-profile examples like OpenAI and UiPath illustrate
-      the transformative potential of AI-driven innovation. These companies face unique
-      challenges, from securing sufficient funding to navigating the complexities
-      of large-scale implementation. Venture capital plays a critical role in this
-      ecosystem, providing the necessary resources and support to turn bold ideas
-      into impactful realities. The synergy between AI innovation and entrepreneurial
-      spirit is fostering a dynamic landscape where the boundaries of technology and
-      commerce are continually expanding.\n\n5. **The Future of AI in Entertainment:
-      From Movies to Video Games**\n   The entertainment industry is undergoing a
-      metamorphic change with the integration of AI technologies, paving the way for
-      more immersive and engaging experiences. In movies, AI streamlines the scriptwriting
-      process, enhances special effects, and even predicts box office success. Machine
-      learning models analyze vast datasets to create compelling narratives and optimize
-      marketing strategies. Video games have also been revolutionized by AI, with
-      non-player characters (NPCs) displaying more lifelike behaviors and adaptive
-      learning algorithms personalizing gaming experiences. Future trends indicate
-      a seamless blend of AI with augmented reality (AR) and virtual reality (VR),
-      creating interactive environments where the line between virtual and real worlds
-      blur. These advancements are not only transforming content creation but also
-      revolutionizing how audiences consume and interact with entertainment, predicting
-      a future where AI is an indispensable tool in the creative arsenal."}], "model":
-      "gpt-4o", "stop": ["\nObservation:"]}'
+    body: '{"messages": [{"role": "system", "content": "You are Crew Manager. You are a seasoned manager with a knack for getting the best out of your team.\nYou are also known for your ability to delegate work to the right people, and to ask the right questions to get the best out of your team.\nEven though you don''t perform tasks by yourself, you have a lot of experience in the field, which allows you to properly evaluate the work of your team members.\nYour personal goal is: Manage the team to complete the task in the best way possible.\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate work to coworker\nTool Arguments: {''task'': {''description'': ''The task to delegate'', ''type'': ''str''}, ''context'': {''description'': ''The context for the task'', ''type'': ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to delegate to'', ''type'': ''str''}}\nTool Description: Delegate a specific
+      task to one of the following coworkers: Researcher, Senior Writer\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolutely everything you know, don''t reference things but instead explain them.\nTool Name: Ask question to coworker\nTool Arguments: {''question'': {''description'': ''The question to ask'', ''type'': ''str''}, ''context'': {''description'': ''The context for the question'', ''type'': ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to ask'', ''type'': ''str''}}\nTool Description: Ask a specific question to one of the following coworkers: Researcher, Senior Writer\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolutely everything you know, don''t reference things but instead explain them.\n\nIMPORTANT:
+      Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [Delegate work to coworker, Ask question to coworker], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```"}, {"role": "user", "content": "\nCurrent Task: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.\n\nThis is the expected criteria for your final answer: 5 bullet points with a paragraph for each
+      idea.\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: The first step is to brainstorm and research interesting ideas for the article. Since the task involves both research and writing, I will need to delegate the brainstorming of ideas to the Researcher and the writing of the paragraphs to the Senior Writer. \n\nAction: Delegate work to coworker\nAction Input: {\"task\": \"Come up with a list of 5 interesting ideas to explore for an article.\", \"context\": \"We need a list of 5 interesting ideas that would make compelling articles. The topics should be relevant, timely, and engaging for a broad audience.\", \"coworker\": \"Researcher\"}\nObservation: 1. **The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**\n   \"Imagine a world where routine check-ups
+      and complex diagnostics are seamlessly conducted by intelligent systems. AI in healthcare is rapidly transforming this vision into reality, enhancing patient care through early and accurate disease detection, personalized treatment plans, and continuous health monitoring. This revolution is not just a futuristic concept; it''s already happening with AI-driven technologies enabling doctors to make more informed decisions and offering patients a more proactive approach to their health. The ethical and privacy concerns, however, pose new challenges that the industry must navigate to ensure responsible advancement.\"\n\n2. **AI Agents in the Workplace: Boosting Productivity and Shaping the Future of Jobs**\n   \"The modern workplace is being redefined by the integration of AI agents, intelligent systems designed to handle repetitive tasks, analyze vast amounts of data, and assist in complex decision-making processes. These innovations are boosting productivity, allowing employees to focus
+      on creativity and strategic thinking. The rise of AI in the workspace heralds a future where human and machine collaboration leads to unparalleled efficiencies and novel job opportunities. However, this shift also brings with it challenges related to job displacement and the urgent need for reskilling the workforce to thrive in an AI-augmented environment.\"\n\n3. **Ethical AI: Ensuring Fairness, Accountability, and Transparency**\n   \"As AI technology becomes more pervasive, the quest for ethical AI has become critical. Ensuring fairness, accountability, and transparency in AI systems is essential to prevent biases and promote trust. This involves creating robust frameworks and guidelines that govern AI development and deployment. Real-world case studies highlight both the triumphs and the tribulations of tech companies as they navigate the complex moral landscape that AI presents. As society grapples with these challenges, the push for ethical AI continues to drive innovation and
+      policy-making in the tech industry.\"\n\n4. **AI-Powered Startups: Innovators Changing the Tech Landscape**\n   \"In the bustling world of tech startups, AI-powered companies are emerging as the vanguards of innovation, disrupting traditional business models and reshaping entire industries. From revolutionizing financial services to pioneering advances in healthcare and beyond, these startups are at the forefront of technological progress. By leveraging machine learning and AI, they are solving complex problems and bringing novel solutions to market at an unprecedented pace. Investors are taking notice, pouring capital into these ventures, and fueling a new wave of growth and competition in the AI ecosystem.\"\n\n5. **The Future of AI in Entertainment: From Movies to Video Games**\n   \"The entertainment industry is undergoing a seismic shift driven by AI technology. In movies, AI is revolutionizing special effects, enabling filmmakers to create stunning, hyper-realistic worlds. In
+      video games, AI is enhancing player experiences by creating adaptive, intelligent characters and dynamic storylines that respond to player choices. This transformative power of AI promises to push the boundaries of creativity, offering audiences more immersive and interactive entertainment experiences. As the technology continues to evolve, the future of entertainment looks set to be more exciting and unpredictable than ever before.\""}, {"role": "assistant", "content": "Thought: I''ll proceed to delegate the writing of one amazing paragraph highlight for each of the identified ideas to the Senior Writer, ensuring each topic is adequately detailed to showcase its potential.\n\nAction: Delegate work to coworker\nAction Input: {\"task\": \"Write one amazing paragraph highlight for each of these 5 ideas that showcases how good an article about these topics could be.\", \"context\": \"Here are the 5 ideas:\\n1. The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics - Explore
+      how AI is revolutionizing healthcare through enhanced diagnostic tools, personalized treatment plans, and post-operative care. Discuss current achievements, potential future applications, and ethical considerations.\\n2. AI Agents in the Workplace: Boosting Productivity and Shaping the Future of Jobs - Examine the integration of AI agents in various industries. Highlight how these intelligent agents are improving efficiency, automating mundane tasks, and assisting in decision-making processes. Address potential job displacement and the need for upskilling the workforce.\\n3. Ethical AI: Ensuring Fairness, Accountability, and Transparency - Delve into the ethical implications of AI deployment. Analyze the frameworks and guidelines being proposed to ensure AI systems are fair, transparent, and accountable. Include case studies of ethical dilemmas faced by companies using AI.\\n4. AI-Powered Startups: Innovators Changing the Tech Landscape - Provide an overview of how AI-driven startups
+      are disrupting traditional business models and industries. Profile some of the most innovative startups, their business models, and the unique challenges they face. Discuss the role of venture capital in fostering AI innovation.\\n5. The Future of AI in Entertainment: From Movies to Video Games - Investigate how AI is transforming the entertainment industry, from scriptwriting and special effects in movies to creating more immersive and intelligent experiences in video games. Offer predictions about future trends and possible impacts on content creation and consumption.\", \"coworker\": \"Senior Writer\"}\nObservation:\nObservation: 1. **The Rise of AI in Healthcare: Transforming Patient Care and Diagnostics**\n   AI is undeniably revolutionizing the healthcare sector, introducing transformative changes in patient care and diagnostics. With the advent of advanced diagnostic tools powered by AI, healthcare professionals can now detect and monitor diseases with unprecedented accuracy
+      and speed. Personalized treatment plans, which were once a distant dream, are becoming a reality as AI algorithms analyze vast amounts of patient data, tailoring treatments that are specific to individual needs. Post-operative care has also seen significant improvements, with AI-driven monitoring systems ensuring that patients receive timely and effective care. Current achievements like IBM Watson''s oncology platform or Google''s DeepMind in eye care are just the beginning, with future applications promising to further enhance surgical robotics, predictive analytics, and remote patient monitoring. However, alongside these advancements, ethical considerations such as patient privacy, data security, and decision-making transparency must be rigorously addressed to fully harness AI\u2019s potential in healthcare.\n\n2. **AI Agents in the Workplace: Boosting Productivity and Shaping the Future of Jobs**\n   The integration of AI agents in the workplace marks a pivotal shift in how businesses
+      operate and compete. These intelligent agents are revolutionizing various industries by automating mundane tasks, optimizing workflows, and providing actionable insights from data analysis. For instance, AI chatbots in customer service can handle routine inquiries, freeing human agents to tackle more complex issues. In manufacturing, AI systems predict equipment failures, reducing downtime and maintaining peak productivity. By enhancing operational efficiency, AI agents not only boost productivity but also reshape the future of jobs. While the risk of job displacement is a valid concern, it concurrently heralds opportunities for upskilling and creating new roles that harness human creativity and strategic thinking. Preparing the workforce for these changes is crucial, fostering a symbiotic relationship between AI and human labor to drive innovation and economic growth.\n\n3. **Ethical AI: Ensuring Fairness, Accountability, and Transparency**\n   As AI technologies become increasingly
+      pervasive, ensuring their ethical deployment is paramount. Ethical AI demands the formulation and implementation of robust frameworks that enshrine fairness, accountability, and transparency. Such frameworks are essential in mitigating biases embedded within AI systems, which can perpetuate and even exacerbate inequality. Case studies, such as the controversy surrounding facial recognition systems and their racial biases, highlight the pressing need for ethical oversight. Transparency in how AI models make decisions, coupled with accountability measures for their outcomes, establishes trust and integrity in AI applications. Legislative frameworks from the EU\u2019s GDPR to the IEEE\u2019s global initiatives for ethical AI provide the scaffolding for these principles. Companies are now faced with the challenge of integrating these ethical considerations into their development processes, ensuring that AI advancements benefit all of society equitably.\n\n4. **AI-Powered Startups: Innovators
+      Changing the Tech Landscape**\n   AI-powered startups are at the forefront of a technological renaissance, disrupting traditional business models and reshaping industries. These startups leverage AI to offer innovative solutions, whether it''s automating financial trading with precision algorithms or enhancing cybersecurity through predictive analytics. High-profile examples like OpenAI and UiPath illustrate the transformative potential of AI-driven innovation. These companies face unique challenges, from securing sufficient funding to navigating the complexities of large-scale implementation. Venture capital plays a critical role in this ecosystem, providing the necessary resources and support to turn bold ideas into impactful realities. The synergy between AI innovation and entrepreneurial spirit is fostering a dynamic landscape where the boundaries of technology and commerce are continually expanding.\n\n5. **The Future of AI in Entertainment: From Movies to Video Games**\n   The
+      entertainment industry is undergoing a metamorphic change with the integration of AI technologies, paving the way for more immersive and engaging experiences. In movies, AI streamlines the scriptwriting process, enhances special effects, and even predicts box office success. Machine learning models analyze vast datasets to create compelling narratives and optimize marketing strategies. Video games have also been revolutionized by AI, with non-player characters (NPCs) displaying more lifelike behaviors and adaptive learning algorithms personalizing gaming experiences. Future trends indicate a seamless blend of AI with augmented reality (AR) and virtual reality (VR), creating interactive environments where the line between virtual and real worlds blur. These advancements are not only transforming content creation but also revolutionizing how audiences consume and interact with entertainment, predicting a future where AI is an indispensable tool in the creative arsenal."}], "model": "gpt-4o",
+      "stop": ["\nObservation:"]}'
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index 708acb688..0093cc841 100644
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-      a trend, but a paradigm shift in how we interact with technology in our daily
-      lives, promising a future where every experience can be uniquely tailored to
-      individual needs."}], "model": "gpt-4o"}'
+    body: '{"messages": [{"role": "system", "content": "You are Crew Manager. You are a seasoned manager with a knack for getting the best out of your team.\nYou are also known for your ability to delegate work to the right people, and to ask the right questions to get the best out of your team.\nEven though you don''t perform tasks by yourself, you have a lot of experience in the field, which allows you to properly evaluate the work of your team members.\nYour personal goal is: Manage the team to complete the task in the best way possible.\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate work to coworker(task: str, context: str, coworker: Optional[str] = None, **kwargs)\nTool Description: Delegate a specific task to one of the following coworkers: Researcher, Senior Writer\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing
+      about the task, so share absolute everything you know, don''t reference things but instead explain them.\nTool Arguments: {''task'': {''title'': ''Task'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'': ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\nTool Name: Ask question to coworker(question: str, context: str, coworker: Optional[str] = None, **kwargs)\nTool Description: Ask a specific question to one of the following coworkers: Researcher, Senior Writer\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolute everything you know, don''t reference things but instead explain them.\nTool Arguments: {''question'': {''title'': ''Question'', ''type'': ''string''}, ''context'': {''title'': ''Context'', ''type'': ''string''}, ''coworker'': {''title'':
+      ''Coworker'', ''type'': ''string''}, ''kwargs'': {''title'': ''Kwargs'', ''type'': ''object''}}\n\nUse the following format:\n\nThought: you should always think about what to do\nAction: the action to take, only one name of [Delegate work to coworker, Ask question to coworker], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple python dictionary, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n\nOnce all necessary information is gathered:\n\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n"}, {"role": "user", "content": "\nCurrent Task: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.\n\nThis is the expect criteria for your final answer:
+      5 bullet points with a paragraph for each idea.\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: To come up with a list of 5 interesting article ideas and write a compelling paragraph for each one, I should first generate the ideas. For this, I will delegate the task to the Researcher and then use the Senior Writer to create the paragraphs.\n\nAction: Delegate work to coworker\nAction Input: {\"task\": \"Generate a list of 5 interesting ideas to explore for an article.\", \"context\": \"We need to come up with 5 interesting and unique article ideas that will captivate readers.\", \"coworker\": \"Researcher\"}\nObservation: 1. **The Rise of AI in Healthcare: Revolutionizing Diagnosis and Treatment**\n   - This article could explore how AI is making strides in the healthcare
+      industry. From AI-driven diagnostic tools to personalized treatment plans, the piece could highlight real-world applications and the potential future impact. Interviews with healthcare professionals and case studies could add depth and credibility.\n\n2. **AI and Ethics: Navigating the Moral Complexity of Machine Intelligence**\n   - Delve into the ethical considerations surrounding AI development and deployment. The article could examine issues such as bias in AI algorithms, privacy concerns, and the ethical responsibility of AI developers. Expert opinions from ethicists, technologists, and policymakers can provide a well-rounded perspective.\n\n3. **AI Agents in the Workplace: Enhancing Productivity or Eroding Jobs?**\n   - This piece can analyze the dual impact of AI agents in the professional environment. While some argue that AI can significantly boost productivity by automating mundane tasks, others fear job displacement. The article can present both sides, backed by data and
+      forecasts about labor market trends.\n\n4. **From Science Fiction to Reality: The Evolution of AI Agents in Popular Culture**\n   - An engaging piece that traces the depiction of AI agents in movies, books, and video games, and compares these fictional portrayals to the current state of AI technology. This can include interviews with creators from the entertainment industry and AI experts to discuss how close we are to realizing these sci-fi visions.\n\n5. **The Future of AI in Personalization: Creating Tailored Experiences in Everyday Life**\n   - Discuss how AI is enhancing personalization in various domains such as shopping, entertainment, education, and personal finance. The focus can be on current technologies and future possibilities, featuring insights from industry leaders and technologists who are driving these innovations.\n\nThese five article ideas are designed to draw in readers by addressing contemporary issues and trends in AI, while also providing expert insights and
+      balanced viewpoints."}, {"role": "user", "content": "I did it wrong. Tried to both perform Action and give a Final Answer at the same time, I must do one or the other"}, {"role": "assistant", "content": "Thought: Now that I have the list of 5 interesting article ideas from the Researcher, the next step is to ask the Senior Writer to create a compelling paragraph for each idea. This will ensure that each topic is highlighted effectively.\n\nAction: Delegate work to coworker\nAction Input: {\"task\": \"Write one amazing paragraph highlight for each of these 5 article ideas: \\n\\n1. **The Rise of AI in Healthcare: Revolutionizing Diagnosis and Treatment**\\n- This article could explore how AI is making strides in the healthcare industry. From AI-driven diagnostic tools to personalized treatment plans, the piece could highlight real-world applications and the potential future impact. Interviews with healthcare professionals and case studies could add depth and credibility.\\n\\n2. **AI
+      and Ethics: Navigating the Moral Complexity of Machine Intelligence**\\n- Delve into the ethical considerations surrounding AI development and deployment. The article could examine issues such as bias in AI algorithms, privacy concerns, and the ethical responsibility of AI developers. Expert opinions from ethicists, technologists, and policymakers can provide a well-rounded perspective.\\n\\n3. **AI Agents in the Workplace: Enhancing Productivity or Eroding Jobs?**\\n- This piece can analyze the dual impact of AI agents in the professional environment. While some argue that AI can significantly boost productivity by automating mundane tasks, others fear job displacement. The article can present both sides, backed by data and forecasts about labor market trends.\\n\\n4. **From Science Fiction to Reality: The Evolution of AI Agents in Popular Culture**\\n- An engaging piece that traces the depiction of AI agents in movies, books, and video games, and compares these fictional portrayals
+      to the current state of AI technology. This can include interviews with creators from the entertainment industry and AI experts to discuss how close we are to realizing these sci-fi visions.\\n\\n5. **The Future of AI in Personalization: Creating Tailored Experiences in Everyday Life**\\n- Discuss how AI is enhancing personalization in various domains such as shopping, entertainment, education, and personal finance. The focus can be on current technologies and future possibilities, featuring insights from industry leaders and technologists who are driving these innovations.\", \"context\": \"Create compelling paragraphs that highlight the potential and intrigue of each article idea. These should be engaging and showcase how fascinating each article could be.\", \"coworker\": \"Senior Writer\"}\n\nObservation: The Senior Writer provided the following compelling paragraphs for each article idea:\n\n1. **The Rise of AI in Healthcare: Revolutionizing Diagnosis and Treatment**\n   - Imagine
+      a world where a doctor can diagnose a condition with pinpoint accuracy, thanks to the help of artificial intelligence. AI is already transforming healthcare with advanced diagnostic tools and personalized treatment plans. This article would delve into real-world cases where AI has made a significant impact, providing insights from healthcare professionals who witness these technological advancements firsthand. The future of medicine is not only in the hands of doctors and nurses but also in the code and algorithms of AI systems.\n\n2. **AI and Ethics: Navigating the Moral Complexity of Machine Intelligence**\n   - The rapid development of artificial intelligence brings with it not just technological advancements but also a slew of ethical dilemmas. This article will explore the complex moral landscape AI inhabits, questioning the bias that may be programmed into algorithms, the privacy concerns surrounding data usage, and the ethical responsibilities of those who develop AI technologies.
+      Insightful perspectives from ethicists, technologists, and policymakers will highlight how society can navigate these challenges to ensure that AI benefits humanity as a whole.\n\n3. **AI Agents in the Workplace: Enhancing Productivity or Eroding Jobs?**\n   - As AI agents become more integrated into the workforce, the debate over their impact intensifies. This article will examine how AI can boost productivity by automating routine tasks, thereby freeing up human workers for more creative and strategic roles. However, it will also address the fear of job displacement and present data and forecasts about the future labor market. Balanced viewpoints from industry analysts and workers affected by AI implementation will offer a comprehensive understanding of the pros and cons of AI in the workplace.\n\n4. **From Science Fiction to Reality: The Evolution of AI Agents in Popular Culture**\n   - From HAL 9000 to Jarvis, AI agents have long captivated our imaginations in books, movies, and
+      video games. This article will take readers on a journey through time, highlighting how AI has been portrayed in popular culture and comparing these fictional depictions to the current state of AI technology. Conversations with creators from the entertainment industry and AI experts will reveal whether these sci-fi visions are becoming our reality and what the future holds for AI in culture.\n\n5. **The Future of AI in Personalization: Creating Tailored Experiences in Everyday Life**\n   - In an increasingly digital world, AI is enhancing personalization across various domains, crafting tailored experiences that cater to individual needs. This article will explore how AI is revolutionizing everything from shopping and entertainment to education and personal finance. Featuring insights from industry leaders and technologists, the piece will showcase the current state of personalization technologies and speculate on future advancements, painting a vivid picture of a world where AI knows
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+      continues to evolve, it brings with it a host of ethical dilemmas that society must grapple with. This article will delve into the moral intricacies of AI, focusing on pressing issues such as algorithmic bias, the preservation of privacy, and the ethical duties of AI developers. By examining high-profile cases of biased AI and exploring the perspectives of ethicists, technologists, and policymakers, the piece will offer a comprehensive look at the ethical landscape of machine intelligence. It aims to provoke thought and spark dialogue on how we can responsibly harness AI''s power while safeguarding human values and rights.\n\n3. **AI Agents in the Workplace: Enhancing Productivity or Eroding Jobs?**\nThe integration of AI agents in the workplace presents a paradox of progress and peril. On one hand, AI can drastically enhance productivity by automating repetitive tasks and offering intelligent insights that drive innovation. On the other, there is a growing concern about job displacement
+      and the socioeconomic implications of a heavily automated workforce. This article will explore this dual impact, presenting data and forecasts on labor market trends and featuring opinions from industry experts, economists, and affected workers. By striking a balance between optimism and caution, it will provide a nuanced view of AI''s role in reshaping the future of work.\n\n4. **From Science Fiction to Reality: The Evolution of AI Agents in Popular Culture**\nThe portrayal of AI agents in popular culture has long fascinated and terrified us, from the benevolent, human-like robots of movies and books to the dystopian scenarios of video games. This article will take readers on a journey through the evolution of AI in fiction, contrasting these imaginative narratives with the current capabilities and limitations of real-world AI. Interviews with filmmakers, authors, and game developers, alongside insights from AI researchers, will shed light on how closely today''s technology mirrors
+      these sci-fi visions. The piece aims to captivate readers by exploring the interplay between creativity and technological advancement, revealing the thin line between fantasy and reality.\n\n5. **The Future of AI in Personalization: Creating Tailored Experiences in Everyday Life**\nAI is revolutionizing the concept of personalization, offering users tailored experiences in areas as diverse as shopping, entertainment, education, and personal finance. This article will explore how AI algorithms are learning from user behaviors and preferences to deliver highly customized content and recommendations, enhancing user satisfaction and engagement. By highlighting current technologies and featuring interviews with industry leaders and technologists, it will forecast future possibilities and innovations. The piece will demonstrate how AI personalization is not just a trend, but a paradigm shift in how we interact with technology in our daily lives, promising a future where every experience
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-      should be the coworker, the question you have for them, and ALL necessary context
-      to ask the question properly, they know nothing about the question, so share
-      absolutely everything you know, don''t reference things but instead explain
-      them.\n\nIMPORTANT: Use the following format in your response:\n\n```\nThought:
-      you should always think about what to do\nAction: the action to take, only one
-      name of [Delegate work to coworker, Ask question to coworker], just the name,
-      exactly as it''s written.\nAction Input: the input to the action, just a simple
-      JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation:
-      the result of the action\n```\n\nOnce all necessary information is gathered,
-      return the following format:\n\n```\nThought: I now know the final answer\nFinal
-      Answer: the final answer to the original input question\n```"}, {"role": "user",
-      "content": "\nCurrent Task: Come up with a list of 5 interesting ideas to explore
-      for an article, then write one amazing paragraph highlight for each idea that
-      showcases how good an article about this topic could be. Return the list of
-      ideas with their paragraph and your notes.\n\nThis is the expected criteria
-      for your final answer: 5 bullet points with a paragraph for each idea.\nyou
-      MUST return the actual complete content as the final answer, not a summary.\n\nBegin!
-      This is VERY important to you, use the tools available and give your best Final
-      Answer, your job depends on it!\n\nThought:"}, {"role": "assistant", "content":
-      "Thought: To come up with a list of 5 interesting article ideas and write a
-      compelling paragraph for each one, I should first generate the ideas. For this,
-      I will delegate the task to the Researcher and then use the Senior Writer to
-      create the paragraphs.\n\nAction: Delegate work to coworker\nAction Input: {\"task\":
-      \"Generate a list of 5 interesting ideas to explore for an article.\", \"context\":
-      \"We need to come up with 5 interesting and unique article ideas that will captivate
-      readers.\", \"coworker\": \"Researcher\"}\nObservation: 1. **The Rise of AI
-      in Healthcare: Revolutionizing Diagnosis and Treatment**\n   - Artificial intelligence
-      is transforming the healthcare industry in unprecedented ways. From enhancing
-      diagnostic accuracy with AI-driven tools to creating personalized treatment
-      plans, AI is bridging gaps in medical care and bringing about a revolution.
-      Imagine a world where diseases are diagnosed at lightning speed, and treatments
-      are tailored to individual genetic makeups. This article will explore these
-      groundbreaking advancements, featuring real-world applications and expert insights,
-      revealing the future of healthcare driven by AI.\n\n2. **AI and Ethics: Navigating
-      the Moral Complexity of Machine Intelligence**\n   - As AI technology advances,
-      ethical dilemmas emerge, challenging our understanding of morality and responsibility.
-      This article delves into the complex world of AI ethics, examining pressing
-      issues like algorithmic bias, privacy concerns, and the ethical responsibilities
-      of developers. With input from ethicists, technologists, and policymakers, it
-      offers a comprehensive look at the moral landscape of AI, guiding readers through
-      the critical questions that shape the future of machine intelligence and its
-      impact on society.\n\n3. **AI Agents in the Workplace: Enhancing Productivity
-      or Eroding Jobs?**\n   - The introduction of AI agents in the workplace presents
-      a double-edged sword; while these technologies can vastly enhance productivity
-      by automating routine tasks, they also raise concerns about job displacement.
-      This article paints a balanced picture, drawing on data and expert forecasts
-      to explore both sides of the argument. Are we heading towards a future where
-      AI aids human creativity and efficiency, or are we on the brink of a job crisis?
-      The answer may be a nuanced blend of both.\n\n4. **From Science Fiction to Reality:
-      The Evolution of AI Agents in Popular Culture**\n   - AI has long been a staple
-      of science fiction, captivating audiences with visions of intelligent machines.
-      This article traces the portrayal of AI agents in movies, books, and video games,
-      juxtaposing these fictional narratives against today\u2019s technological realities.
-      Interviews with creators from the entertainment industry and AI experts illuminate
-      how close\u2014or far\u2014we are from making these sci-fi dreams a reality,
-      providing a fascinating journey through the evolution of AI in popular culture.\n\n5.
-      **The Future of AI in Personalization: Creating Tailored Experiences in Everyday
-      Life**\n   - Imagine a world where every experience, from shopping to entertainment,
-      is uniquely tailored to your preferences by intelligent systems. This article
-      explores the cutting-edge of AI-driven personalization, discussing current technologies
-      and future potentials. Featuring insights from industry leaders and technologists,
-      readers will discover how AI is revolutionizing the way we live, offering bespoke
-      experiences that were once the realm of fantasy but are swiftly becoming a part
-      of our daily lives."}, {"role": "assistant", "content": "Thought: Now that I
-      have the list of 5 interesting article ideas from the Researcher, the next step
-      is to ask the Senior Writer to create a compelling paragraph for each idea.
-      This will ensure that each topic is highlighted effectively.\n\nAction: Delegate
-      work to coworker\nAction Input: {\"task\": \"Write one amazing paragraph highlight
-      for each of these 5 article ideas: \\n\\n1. **The Rise of AI in Healthcare:
-      Revolutionizing Diagnosis and Treatment**\\n- This article could explore how
-      AI is making strides in the healthcare industry. From AI-driven diagnostic tools
-      to personalized treatment plans, the piece could highlight real-world applications
-      and the potential future impact. Interviews with healthcare professionals and
-      case studies could add depth and credibility.\\n\\n2. **AI and Ethics: Navigating
-      the Moral Complexity of Machine Intelligence**\\n- Delve into the ethical considerations
-      surrounding AI development and deployment. The article could examine issues
-      such as bias in AI algorithms, privacy concerns, and the ethical responsibility
-      of AI developers. Expert opinions from ethicists, technologists, and policymakers
-      can provide a well-rounded perspective.\\n\\n3. **AI Agents in the Workplace:
-      Enhancing Productivity or Eroding Jobs?**\\n- This piece can analyze the dual
-      impact of AI agents in the professional environment. While some argue that AI
-      can significantly boost productivity by automating mundane tasks, others fear
-      job displacement. The article can present both sides, backed by data and forecasts
-      about labor market trends.\\n\\n4. **From Science Fiction to Reality: The Evolution
-      of AI Agents in Popular Culture**\\n- An engaging piece that traces the depiction
-      of AI agents in movies, books, and video games, and compares these fictional
-      portrayals to the current state of AI technology. This can include interviews
-      with creators from the entertainment industry and AI experts to discuss how
-      close we are to realizing these sci-fi visions.\\n\\n5. **The Future of AI in
-      Personalization: Creating Tailored Experiences in Everyday Life**\\n- Discuss
-      how AI is enhancing personalization in various domains such as shopping, entertainment,
-      education, and personal finance. The focus can be on current technologies and
-      future possibilities, featuring insights from industry leaders and technologists
-      who are driving these innovations.\", \"context\": \"Create compelling paragraphs
-      that highlight the potential and intrigue of each article idea. These should
-      be engaging and showcase how fascinating each article could be.\", \"coworker\":
-      \"Senior Writer\"}\n\nObservation: The Senior Writer provided the following
-      compelling paragraphs for each article idea:\n\n1. **The Rise of AI in Healthcare:
-      Revolutionizing Diagnosis and Treatment**\n   - Imagine a world where a doctor
-      can diagnose a condition with pinpoint accuracy, thanks to the help of artificial
-      intelligence. AI is already transforming healthcare with advanced diagnostic
-      tools and personalized treatment plans. This article would delve into real-world
-      cases where AI has made a significant impact, providing insights from healthcare
-      professionals who witness these technological advancements firsthand. The future
-      of medicine is not only in the hands of doctors and nurses but also in the code
-      and algorithms of AI systems.\n\n2. **AI and Ethics: Navigating the Moral Complexity
-      of Machine Intelligence**\n   - The rapid development of artificial intelligence
-      brings with it not just technological advancements but also a slew of ethical
-      dilemmas. This article will explore the complex moral landscape AI inhabits,
-      questioning the bias that may be programmed into algorithms, the privacy concerns
-      surrounding data usage, and the ethical responsibilities of those who develop
-      AI technologies. Insightful perspectives from ethicists, technologists, and
-      policymakers will highlight how society can navigate these challenges to ensure
-      that AI benefits humanity as a whole.\n\n3. **AI Agents in the Workplace: Enhancing
-      Productivity or Eroding Jobs?**\n   - As AI agents become more integrated into
-      the workforce, the debate over their impact intensifies. This article will examine
-      how AI can boost productivity by automating routine tasks, thereby freeing up
-      human workers for more creative and strategic roles. However, it will also address
-      the fear of job displacement and present data and forecasts about the future
-      labor market. Balanced viewpoints from industry analysts and workers affected
-      by AI implementation will offer a comprehensive understanding of the pros and
-      cons of AI in the workplace.\n\n4. **From Science Fiction to Reality: The Evolution
-      of AI Agents in Popular Culture**\n   - From HAL 9000 to Jarvis, AI agents have
-      long captivated our imaginations in books, movies, and video games. This article
-      will take readers on a journey through time, highlighting how AI has been portrayed
-      in popular culture and comparing these fictional depictions to the current state
-      of AI technology. Conversations with creators from the entertainment industry
-      and AI experts will reveal whether these sci-fi visions are becoming our reality
-      and what the future holds for AI in culture.\n\n5. **The Future of AI in Personalization:
-      Creating Tailored Experiences in Everyday Life**\n   - In an increasingly digital
-      world, AI is enhancing personalization across various domains, crafting tailored
-      experiences that cater to individual needs. This article will explore how AI
-      is revolutionizing everything from shopping and entertainment to education and
-      personal finance. Featuring insights from industry leaders and technologists,
-      the piece will showcase the current state of personalization technologies and
-      speculate on future advancements, painting a vivid picture of a world where
-      AI knows us better than we know ourselves.\nObservation: 1. **The Rise of AI
-      in Healthcare: Revolutionizing Diagnosis and Treatment**\n   - Artificial Intelligence
-      is spearheading a transformative era in healthcare, fundamentally altering how
-      diseases are diagnosed and treated. AI-driven diagnostic tools are already outperforming
-      human experts in identifying ailments from medical images, while personalized
-      treatment plans are becoming a reality through predictive analytics that cater
-      to individual patient needs. This article will dive into real-world applications,
-      such as AI algorithms detecting early-stage cancers or predicting patient outcomes
-      with unprecedented accuracy. Featuring interviews with healthcare professionals
-      and insightful case studies, it will paint a vivid picture of how AI is not
-      only enhancing medical outcomes but also reshaping the entire healthcare landscape
-      for the future.\n\n2. **AI and Ethics: Navigating the Moral Complexity of Machine
-      Intelligence**\n   - As Artificial Intelligence continues to evolve, it brings
-      with it a host of ethical dilemmas that society must grapple with. This article
-      will delve into the moral intricacies of AI, focusing on pressing issues such
-      as algorithmic bias, the preservation of privacy, and the ethical duties of
-      AI developers. By examining high-profile cases of biased AI and exploring the
-      perspectives of ethicists, technologists, and policymakers, the piece will offer
-      a comprehensive look at the ethical landscape of machine intelligence. It aims
-      to provoke thought and spark dialogue on how we can responsibly harness AI''s
-      power while safeguarding human values and rights.\n\n3. **AI Agents in the Workplace:
-      Enhancing Productivity or Eroding Jobs?**\n   - The integration of AI agents
-      in the workplace presents a paradox of progress and peril. On one hand, AI can
-      drastically enhance productivity by automating repetitive tasks and offering
-      intelligent insights that drive innovation. On the other, there is a growing
-      concern about job displacement and the socioeconomic implications of a heavily
-      automated workforce. This article will explore this dual impact, presenting
-      data and forecasts on labor market trends and featuring opinions from industry
-      experts, economists, and affected workers. By striking a balance between optimism
-      and caution, it will provide a nuanced view of AI''s role in reshaping the future
-      of work.\n\n4. **From Science Fiction to Reality: The Evolution of AI Agents
-      in Popular Culture**\n   - The portrayal of AI agents in popular culture has
-      long fascinated and terrified us, from the benevolent, human-like robots of
-      movies and books to the dystopian scenarios of video games. This article will
-      take readers on a journey through the evolution of AI in fiction, contrasting
-      these imaginative narratives with the current capabilities and limitations of
-      real-world AI. Interviews with filmmakers, authors, and game developers, alongside
-      insights from AI researchers, will shed light on how closely today''s technology
-      mirrors these sci-fi visions. The piece aims to captivate readers by exploring
-      the interplay between creativity and technological advancement, revealing the
-      thin line between fantasy and reality.\n\n5. **The Future of AI in Personalization:
-      Creating Tailored Experiences in Everyday Life**\n   - AI is revolutionizing
-      the concept of personalization, offering users tailored experiences in areas
-      as diverse as shopping, entertainment, education, and personal finance. This
-      article will explore how AI algorithms are learning from user behaviors and
-      preferences to deliver highly customized content and recommendations, enhancing
-      user satisfaction and engagement. By highlighting current technologies and featuring
-      interviews with industry leaders and technologists, it will forecast future
-      possibilities and innovations. The piece will demonstrate how AI personalization
-      is not just a trend, but a paradigm shift in how we interact with technology
-      in our daily lives, promising a future where every experience can be uniquely
-      tailored to individual needs. \n\nThese paragraphs showcase the potential and
-      intrigue of each topic, demonstrating how engaging and insightful the articles
-      could be."}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
+    body: '{"messages": [{"role": "system", "content": "You are Crew Manager. You are a seasoned manager with a knack for getting the best out of your team.\nYou are also known for your ability to delegate work to the right people, and to ask the right questions to get the best out of your team.\nEven though you don''t perform tasks by yourself, you have a lot of experience in the field, which allows you to properly evaluate the work of your team members.\nYour personal goal is: Manage the team to complete the task in the best way possible.\nYou ONLY have access to the following tools, and should NEVER make up tools that are not listed here:\n\nTool Name: Delegate work to coworker\nTool Arguments: {''task'': {''description'': ''The task to delegate'', ''type'': ''str''}, ''context'': {''description'': ''The context for the task'', ''type'': ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to delegate to'', ''type'': ''str''}}\nTool Description: Delegate a specific
+      task to one of the following coworkers: Researcher, Senior Writer\nThe input to this tool should be the coworker, the task you want them to do, and ALL necessary context to execute the task, they know nothing about the task, so share absolutely everything you know, don''t reference things but instead explain them.\nTool Name: Ask question to coworker\nTool Arguments: {''question'': {''description'': ''The question to ask'', ''type'': ''str''}, ''context'': {''description'': ''The context for the question'', ''type'': ''str''}, ''coworker'': {''description'': ''The role/name of the coworker to ask'', ''type'': ''str''}}\nTool Description: Ask a specific question to one of the following coworkers: Researcher, Senior Writer\nThe input to this tool should be the coworker, the question you have for them, and ALL necessary context to ask the question properly, they know nothing about the question, so share absolutely everything you know, don''t reference things but instead explain them.\n\nIMPORTANT:
+      Use the following format in your response:\n\n```\nThought: you should always think about what to do\nAction: the action to take, only one name of [Delegate work to coworker, Ask question to coworker], just the name, exactly as it''s written.\nAction Input: the input to the action, just a simple JSON object, enclosed in curly braces, using \" to wrap keys and values.\nObservation: the result of the action\n```\n\nOnce all necessary information is gathered, return the following format:\n\n```\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n```"}, {"role": "user", "content": "\nCurrent Task: Come up with a list of 5 interesting ideas to explore for an article, then write one amazing paragraph highlight for each idea that showcases how good an article about this topic could be. Return the list of ideas with their paragraph and your notes.\n\nThis is the expected criteria for your final answer: 5 bullet points with a paragraph for each
+      idea.\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}, {"role": "assistant", "content": "Thought: To come up with a list of 5 interesting article ideas and write a compelling paragraph for each one, I should first generate the ideas. For this, I will delegate the task to the Researcher and then use the Senior Writer to create the paragraphs.\n\nAction: Delegate work to coworker\nAction Input: {\"task\": \"Generate a list of 5 interesting ideas to explore for an article.\", \"context\": \"We need to come up with 5 interesting and unique article ideas that will captivate readers.\", \"coworker\": \"Researcher\"}\nObservation: 1. **The Rise of AI in Healthcare: Revolutionizing Diagnosis and Treatment**\n   - Artificial intelligence is transforming the healthcare industry in unprecedented ways. From enhancing diagnostic
+      accuracy with AI-driven tools to creating personalized treatment plans, AI is bridging gaps in medical care and bringing about a revolution. Imagine a world where diseases are diagnosed at lightning speed, and treatments are tailored to individual genetic makeups. This article will explore these groundbreaking advancements, featuring real-world applications and expert insights, revealing the future of healthcare driven by AI.\n\n2. **AI and Ethics: Navigating the Moral Complexity of Machine Intelligence**\n   - As AI technology advances, ethical dilemmas emerge, challenging our understanding of morality and responsibility. This article delves into the complex world of AI ethics, examining pressing issues like algorithmic bias, privacy concerns, and the ethical responsibilities of developers. With input from ethicists, technologists, and policymakers, it offers a comprehensive look at the moral landscape of AI, guiding readers through the critical questions that shape the future of
+      machine intelligence and its impact on society.\n\n3. **AI Agents in the Workplace: Enhancing Productivity or Eroding Jobs?**\n   - The introduction of AI agents in the workplace presents a double-edged sword; while these technologies can vastly enhance productivity by automating routine tasks, they also raise concerns about job displacement. This article paints a balanced picture, drawing on data and expert forecasts to explore both sides of the argument. Are we heading towards a future where AI aids human creativity and efficiency, or are we on the brink of a job crisis? The answer may be a nuanced blend of both.\n\n4. **From Science Fiction to Reality: The Evolution of AI Agents in Popular Culture**\n   - AI has long been a staple of science fiction, captivating audiences with visions of intelligent machines. This article traces the portrayal of AI agents in movies, books, and video games, juxtaposing these fictional narratives against today\u2019s technological realities. Interviews
+      with creators from the entertainment industry and AI experts illuminate how close\u2014or far\u2014we are from making these sci-fi dreams a reality, providing a fascinating journey through the evolution of AI in popular culture.\n\n5. **The Future of AI in Personalization: Creating Tailored Experiences in Everyday Life**\n   - Imagine a world where every experience, from shopping to entertainment, is uniquely tailored to your preferences by intelligent systems. This article explores the cutting-edge of AI-driven personalization, discussing current technologies and future potentials. Featuring insights from industry leaders and technologists, readers will discover how AI is revolutionizing the way we live, offering bespoke experiences that were once the realm of fantasy but are swiftly becoming a part of our daily lives."}, {"role": "assistant", "content": "Thought: Now that I have the list of 5 interesting article ideas from the Researcher, the next step is to ask the Senior Writer
+      to create a compelling paragraph for each idea. This will ensure that each topic is highlighted effectively.\n\nAction: Delegate work to coworker\nAction Input: {\"task\": \"Write one amazing paragraph highlight for each of these 5 article ideas: \\n\\n1. **The Rise of AI in Healthcare: Revolutionizing Diagnosis and Treatment**\\n- This article could explore how AI is making strides in the healthcare industry. From AI-driven diagnostic tools to personalized treatment plans, the piece could highlight real-world applications and the potential future impact. Interviews with healthcare professionals and case studies could add depth and credibility.\\n\\n2. **AI and Ethics: Navigating the Moral Complexity of Machine Intelligence**\\n- Delve into the ethical considerations surrounding AI development and deployment. The article could examine issues such as bias in AI algorithms, privacy concerns, and the ethical responsibility of AI developers. Expert opinions from ethicists, technologists,
+      and policymakers can provide a well-rounded perspective.\\n\\n3. **AI Agents in the Workplace: Enhancing Productivity or Eroding Jobs?**\\n- This piece can analyze the dual impact of AI agents in the professional environment. While some argue that AI can significantly boost productivity by automating mundane tasks, others fear job displacement. The article can present both sides, backed by data and forecasts about labor market trends.\\n\\n4. **From Science Fiction to Reality: The Evolution of AI Agents in Popular Culture**\\n- An engaging piece that traces the depiction of AI agents in movies, books, and video games, and compares these fictional portrayals to the current state of AI technology. This can include interviews with creators from the entertainment industry and AI experts to discuss how close we are to realizing these sci-fi visions.\\n\\n5. **The Future of AI in Personalization: Creating Tailored Experiences in Everyday Life**\\n- Discuss how AI is enhancing personalization
+      in various domains such as shopping, entertainment, education, and personal finance. The focus can be on current technologies and future possibilities, featuring insights from industry leaders and technologists who are driving these innovations.\", \"context\": \"Create compelling paragraphs that highlight the potential and intrigue of each article idea. These should be engaging and showcase how fascinating each article could be.\", \"coworker\": \"Senior Writer\"}\n\nObservation: The Senior Writer provided the following compelling paragraphs for each article idea:\n\n1. **The Rise of AI in Healthcare: Revolutionizing Diagnosis and Treatment**\n   - Imagine a world where a doctor can diagnose a condition with pinpoint accuracy, thanks to the help of artificial intelligence. AI is already transforming healthcare with advanced diagnostic tools and personalized treatment plans. This article would delve into real-world cases where AI has made a significant impact, providing insights from
+      healthcare professionals who witness these technological advancements firsthand. The future of medicine is not only in the hands of doctors and nurses but also in the code and algorithms of AI systems.\n\n2. **AI and Ethics: Navigating the Moral Complexity of Machine Intelligence**\n   - The rapid development of artificial intelligence brings with it not just technological advancements but also a slew of ethical dilemmas. This article will explore the complex moral landscape AI inhabits, questioning the bias that may be programmed into algorithms, the privacy concerns surrounding data usage, and the ethical responsibilities of those who develop AI technologies. Insightful perspectives from ethicists, technologists, and policymakers will highlight how society can navigate these challenges to ensure that AI benefits humanity as a whole.\n\n3. **AI Agents in the Workplace: Enhancing Productivity or Eroding Jobs?**\n   - As AI agents become more integrated into the workforce, the debate
+      over their impact intensifies. This article will examine how AI can boost productivity by automating routine tasks, thereby freeing up human workers for more creative and strategic roles. However, it will also address the fear of job displacement and present data and forecasts about the future labor market. Balanced viewpoints from industry analysts and workers affected by AI implementation will offer a comprehensive understanding of the pros and cons of AI in the workplace.\n\n4. **From Science Fiction to Reality: The Evolution of AI Agents in Popular Culture**\n   - From HAL 9000 to Jarvis, AI agents have long captivated our imaginations in books, movies, and video games. This article will take readers on a journey through time, highlighting how AI has been portrayed in popular culture and comparing these fictional depictions to the current state of AI technology. Conversations with creators from the entertainment industry and AI experts will reveal whether these sci-fi visions are
+      becoming our reality and what the future holds for AI in culture.\n\n5. **The Future of AI in Personalization: Creating Tailored Experiences in Everyday Life**\n   - In an increasingly digital world, AI is enhancing personalization across various domains, crafting tailored experiences that cater to individual needs. This article will explore how AI is revolutionizing everything from shopping and entertainment to education and personal finance. Featuring insights from industry leaders and technologists, the piece will showcase the current state of personalization technologies and speculate on future advancements, painting a vivid picture of a world where AI knows us better than we know ourselves.\nObservation: 1. **The Rise of AI in Healthcare: Revolutionizing Diagnosis and Treatment**\n   - Artificial Intelligence is spearheading a transformative era in healthcare, fundamentally altering how diseases are diagnosed and treated. AI-driven diagnostic tools are already outperforming human
+      experts in identifying ailments from medical images, while personalized treatment plans are becoming a reality through predictive analytics that cater to individual patient needs. This article will dive into real-world applications, such as AI algorithms detecting early-stage cancers or predicting patient outcomes with unprecedented accuracy. Featuring interviews with healthcare professionals and insightful case studies, it will paint a vivid picture of how AI is not only enhancing medical outcomes but also reshaping the entire healthcare landscape for the future.\n\n2. **AI and Ethics: Navigating the Moral Complexity of Machine Intelligence**\n   - As Artificial Intelligence continues to evolve, it brings with it a host of ethical dilemmas that society must grapple with. This article will delve into the moral intricacies of AI, focusing on pressing issues such as algorithmic bias, the preservation of privacy, and the ethical duties of AI developers. By examining high-profile cases
+      of biased AI and exploring the perspectives of ethicists, technologists, and policymakers, the piece will offer a comprehensive look at the ethical landscape of machine intelligence. It aims to provoke thought and spark dialogue on how we can responsibly harness AI''s power while safeguarding human values and rights.\n\n3. **AI Agents in the Workplace: Enhancing Productivity or Eroding Jobs?**\n   - The integration of AI agents in the workplace presents a paradox of progress and peril. On one hand, AI can drastically enhance productivity by automating repetitive tasks and offering intelligent insights that drive innovation. On the other, there is a growing concern about job displacement and the socioeconomic implications of a heavily automated workforce. This article will explore this dual impact, presenting data and forecasts on labor market trends and featuring opinions from industry experts, economists, and affected workers. By striking a balance between optimism and caution, it
+      will provide a nuanced view of AI''s role in reshaping the future of work.\n\n4. **From Science Fiction to Reality: The Evolution of AI Agents in Popular Culture**\n   - The portrayal of AI agents in popular culture has long fascinated and terrified us, from the benevolent, human-like robots of movies and books to the dystopian scenarios of video games. This article will take readers on a journey through the evolution of AI in fiction, contrasting these imaginative narratives with the current capabilities and limitations of real-world AI. Interviews with filmmakers, authors, and game developers, alongside insights from AI researchers, will shed light on how closely today''s technology mirrors these sci-fi visions. The piece aims to captivate readers by exploring the interplay between creativity and technological advancement, revealing the thin line between fantasy and reality.\n\n5. **The Future of AI in Personalization: Creating Tailored Experiences in Everyday Life**\n   - AI is
+      revolutionizing the concept of personalization, offering users tailored experiences in areas as diverse as shopping, entertainment, education, and personal finance. This article will explore how AI algorithms are learning from user behaviors and preferences to deliver highly customized content and recommendations, enhancing user satisfaction and engagement. By highlighting current technologies and featuring interviews with industry leaders and technologists, it will forecast future possibilities and innovations. The piece will demonstrate how AI personalization is not just a trend, but a paradigm shift in how we interact with technology in our daily lives, promising a future where every experience can be uniquely tailored to individual needs. \n\nThese paragraphs showcase the potential and intrigue of each topic, demonstrating how engaging and insightful the articles could be."}], "model": "gpt-4o", "stop": ["\nObservation:"]}'
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index fb720006a..bdf199462 100644
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diff --git a/lib/crewai/tests/cassettes/test_long_term_memory_with_memory_flag.yaml b/lib/crewai/tests/cassettes/test_long_term_memory_with_memory_flag.yaml
index d98c83924..9c53a815c 100644
--- a/lib/crewai/tests/cassettes/test_long_term_memory_with_memory_flag.yaml
+++ b/lib/crewai/tests/cassettes/test_long_term_memory_with_memory_flag.yaml
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-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
-      13,\n    \"total_tokens\": 13\n  }\n}\n"
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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-      completed based on the description, expected output, and actual results.\n\nTask
-      Description:\nResearch a topic to teach a kid aged 6 about math.\n\nExpected
-      Output:\nA topic, explanation, angle, and examples.\n\nActual Output:\nI now
-      can give a great answer  \nFinal Answer: \n\n**Topic: Introduction to Basic
-      Addition**\n\n**Explanation:**\nBasic addition is about combining two or more
-      groups of things together to find out how many there are in total. It''s one
-      of the most fundamental concepts in math and is a building block for all other
-      math skills. Teaching addition to a 6-year-old involves using simple numbers
-      and relatable examples that help them visualize and understand the concept of
-      adding together.\n\n**Angle:**\nTo make the concept of addition fun and engaging,
-      we can use everyday objects that a child is familiar with, such as toys, fruits,
-      or drawing items. Incorporating visuals and interactive elements will keep their
-      attention and help reinforce the idea of combining numbers.\n\n**Examples:**\n\n1.
-      **Using Objects:**\n   - **Scenario:** Let\u2019s say you have 2 apples and
-      your friend gives you 3 more apples.\n   - **Visual**: Arrange the apples in
-      front of the child.\n   - **Question:** \"How many apples do you have now?\"\n   -
-      **Calculation:** 2 apples (your apples) + 3 apples (friend''s apples) = 5 apples.  \n   -
-      **Conclusion:** \"You now have 5 apples!\"\n\n2. **Drawing Pictures:**\n   -
-      **Scenario:** Draw 4 stars on one side of the paper and 2 stars on the other
-      side.\n   - **Activity:** Ask the child to count the stars in the first group
-      and then the second group.\n   - **Question:** \"If we put them together, how
-      many stars do we have?\"\n   - **Calculation:** 4 stars + 2 stars = 6 stars.  \n   -
-      **Conclusion:** \"You drew 6 stars all together!\"\n\n3. **Story Problems:**\n   -
-      **Scenario:** \"You have 5 toy cars, and you buy 3 more from the store. How
-      many cars do you have?\"\n   - **Interaction:** Create a fun story around the
-      toy cars (perhaps the cars are going on an adventure).\n   - **Calculation:**
-      5 toy cars + 3 toy cars = 8 toy cars.  \n   - **Conclusion:** \"You now have
-      a total of 8 toy cars for your adventure!\"\n\n4. **Games:**\n   - **Activity:**
-      Play a simple game where you roll a pair of dice. Each die shows a number.\n   -
-      **Task:** Ask the child to add the numbers on the dice together.\n   - **Example:**
-      If one die shows 2 and the other shows 4, the child will say \u201c2 + 4 = 6!\u201d\n   -
-      **Conclusion:** \u201cWhoever gets the highest number wins a point!\u201d\n\nIn
-      summary, when teaching a 6-year-old about basic addition, it is essential to
-      use simple numbers, real-life examples, visual aids, and engaging activities.
-      This ensures the child can grasp the concept while having a fun learning experience.
-      Making math relatable to their world helps build a strong foundation for their
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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\"\n
-      \   }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\":
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+      null, "source_type": "short_term_memory", "fingerprint_metadata": null, "task_id": "3283d0f7-7159-47a9-abf0-a1bfe4dafb13", "task_name": "Research a topic to teach a kid aged 6 about math.", "agent_id": "5b1ba567-c4c3-4327-9c2e-4215c53bffb6", "agent_role": "Researcher", "from_task": null, "from_agent": null, "value": "I now can give a great answer  \nFinal Answer: \n\n**Topic: Introduction to Basic Addition**\n\n**Explanation:**\nBasic addition is about combining two or more groups of things together to find out how many there are in total. It''s one of the most fundamental concepts in math and is a building block for all other math skills. Teaching addition to a 6-year-old involves using simple numbers and relatable examples that help them visualize and understand the concept of adding together.\n\n**Angle:**\nTo make the concept of addition fun and engaging, we can use everyday objects that a child is familiar with, such as toys, fruits, or drawing items. Incorporating visuals and
+      interactive elements will keep their attention and help reinforce the idea of combining numbers.\n\n**Examples:**\n\n1. **Using Objects:**\n   - **Scenario:** Let\u2019s say you have 2 apples and your friend gives you 3 more apples.\n   - **Visual**: Arrange the apples in front of the child.\n   - **Question:** \"How many apples do you have now?\"\n   - **Calculation:** 2 apples (your apples) + 3 apples (friend''s apples) = 5 apples.  \n   - **Conclusion:** \"You now have 5 apples!\"\n\n2. **Drawing Pictures:**\n   - **Scenario:** Draw 4 stars on one side of the paper and 2 stars on the other side.\n   - **Activity:** Ask the child to count the stars in the first group and then the second group.\n   - **Question:** \"If we put them together, how many stars do we have?\"\n   - **Calculation:** 4 stars + 2 stars = 6 stars.  \n   - **Conclusion:** \"You drew 6 stars all together!\"\n\n3. **Story Problems:**\n   - **Scenario:** \"You have 5 toy cars, and you buy 3 more from the store.
+      How many cars do you have?\"\n   - **Interaction:** Create a fun story around the toy cars (perhaps the cars are going on an adventure).\n   - **Calculation:** 5 toy cars + 3 toy cars = 8 toy cars.  \n   - **Conclusion:** \"You now have a total of 8 toy cars for your adventure!\"\n\n4. **Games:**\n   - **Activity:** Play a simple game where you roll a pair of dice. Each die shows a number.\n   - **Task:** Ask the child to add the numbers on the dice together.\n   - **Example:** If one die shows 2 and the other shows 4, the child will say \u201c2 + 4 = 6!\u201d\n   - **Conclusion:** \u201cWhoever gets the highest number wins a point!\u201d\n\nIn summary, when teaching a 6-year-old about basic addition, it is essential to use simple numbers, real-life examples, visual aids, and engaging activities. This ensures the child can grasp the concept while having a fun learning experience. Making math relatable to their world helps build a strong foundation for their future learning!", "metadata":
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\"\
+        \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 27,\n    \"total_tokens\": 27\n  }\n}\n"
     headers:
       CF-RAY:
       - 996fcf368d2ced1a-MXP
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/test_manager_agent_delegating_to_all_agents.yaml b/lib/crewai/tests/cassettes/test_manager_agent_delegating_to_all_agents.yaml
index 9f010961f..b8684772f 100644
--- a/lib/crewai/tests/cassettes/test_manager_agent_delegating_to_all_agents.yaml
+++ b/lib/crewai/tests/cassettes/test_manager_agent_delegating_to_all_agents.yaml
@@ -103,8 +103,6 @@ interactions:
       - 8c85efda98b91cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -301,8 +299,6 @@ interactions:
       - 8c85efe80b481cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -515,8 +511,6 @@ interactions:
       - 8c85f0066de41cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -683,8 +677,6 @@ interactions:
       - 8c85f01b2a9d1cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -1010,8 +1002,6 @@ interactions:
       - 8c85f05ddcc51cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/test_manager_agent_delegating_to_assigned_task_agent.yaml b/lib/crewai/tests/cassettes/test_manager_agent_delegating_to_assigned_task_agent.yaml
index a20987426..e778c67db 100644
--- a/lib/crewai/tests/cassettes/test_manager_agent_delegating_to_assigned_task_agent.yaml
+++ b/lib/crewai/tests/cassettes/test_manager_agent_delegating_to_assigned_task_agent.yaml
@@ -166,8 +166,6 @@ interactions:
       - 8c85ee46ce271cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -305,8 +303,6 @@ interactions:
       - 8c85ee5809ea1cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -614,8 +610,6 @@ interactions:
       - 8c85ee784bd11cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -795,8 +789,6 @@ interactions:
       - 8c85eeee3f7a1cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -990,8 +982,6 @@ interactions:
       - 8c85eefafac11cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -1161,8 +1151,6 @@ interactions:
       - 8c85ef09ef831cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -1418,8 +1406,6 @@ interactions:
       - 8c85ef325d6b1cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -1617,8 +1603,6 @@ interactions:
       - 8c85ef4b4e7a1cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -2012,8 +1996,6 @@ interactions:
       - 8c85ef926b591cf3-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/test_memory_events_are_emitted.yaml b/lib/crewai/tests/cassettes/test_memory_events_are_emitted.yaml
index de4056ebf..cc320c3fc 100644
--- a/lib/crewai/tests/cassettes/test_memory_events_are_emitted.yaml
+++ b/lib/crewai/tests/cassettes/test_memory_events_are_emitted.yaml
@@ -6,39 +6,16 @@ interactions:
     uri: https://pypi.org/pypi/agentops/json
   response:
     body:
-      string: '{"info":{"author":null,"author_email":"Alex Reibman <areibman@gmail.com>,
-        Shawn Qiu <siyangqiu@gmail.com>, Braelyn Boynton <bboynton97@gmail.com>, Howard
-        Gil <howardbgil@gmail.com>, Constantin Teodorescu <teocns@gmail.com>, Pratyush
-        Shukla <ps4534@nyu.edu>, Travis Dent <tcdent@gmail.com>, Dwij Patel <dwijpatel1704@gmail.com>,
-        Fenil Faldu <fenilfaldu143@gmail.com>","bugtrack_url":null,"classifiers":["License
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+        \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 487,\n    \"total_tokens\": 487\n  }\n}\n"
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       CF-RAY:
       - 9587aa302e706294-GRU
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
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       Date:
@@ -962,57 +525,11 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"messages": [{"role": "user", "content": "Assess the quality of the task
-      completed based on the description, expected output, and actual results.\n\nTask
-      Description:\nResearch a topic to teach a kid aged 6 about math.\n\nExpected
-      Output:\nA topic, explanation, angle, and examples.\n\nActual Output:\nI now
-      can give a great answer  \nFinal Answer: \n\n**Topic:** Introduction to Addition\n\n**Explanation:**
-      \nAddition is one of the simplest operations in math. It''s all about putting
-      things together. When we add, we combine two or more numbers to find out how
-      many we have in total. For a 6-year-old, it can be visualized as combining different
-      groups of objects. Here''s how we can teach it:\n\n1. **Basic Concept**: Explain
-      that addition means bringing two amounts together to get a new total. Use simple
-      language like, \"If you have 2 apples and I give you 3 more apples, how many
-      apples do you have now?\"\n\n2. **Visual Aids**: Use physical objects like blocks,
-      beads, or fruit. Show the child one group with 2 blocks and another group with
-      3 blocks. Next, combine them and count the total together.\n\n3. **Symbols of
-      Addition**: Introduce the plus sign (+) and the equals sign (=). For instance,
-      you can explain that \"2 + 3 = 5\" means that when adding 2 and 3 together,
-      they make 5.\n\n**Angle:** \nMake it fun and interactive! Use games and stories
-      to keep the child engaged. For example, you could create a story about a little
-      monster who collects candies. Every time he meets a friend, he adds more candies
-      to his pile. \n\n**Examples:**\n- **Using Blocks**: Start with 4 blocks. If
-      you add 2 more, how many blocks do you have? (4 + 2 = 6)\n- **Finger Counting**:
-      Have the child count fingers. Hold up 3 fingers on one hand and 2 on the other
-      hand. Ask, \"How many fingers are up?\" and help them see that when they count
-      all the fingers together, they get 5.\n- **Story Problem**: \"You have 1 toy
-      car, and your friend gives you 3 more toy cars. How many do you have now?\"
-      Write it down for them: 1 + 3 = ? And help them count to find the answer is
-      4.\n\nMake sure to encourage the child as they explore addition! Celebrate their
-      successes and provide help as needed. This will help foster a positive attitude
-      towards math, making it not just an academic subject, but an enjoyable activity.\n\nPlease
-      provide:\n- Bullet points suggestions to improve future similar tasks\n- A score
-      from 0 to 10 evaluating on completion, quality, and overall performance- Entities
-      extracted from the task output, if any, their type, description, and relationships"}],
-      "model": "gpt-4o-mini", "tool_choice": {"type": "function", "function": {"name":
-      "TaskEvaluation"}}, "tools": [{"type": "function", "function": {"name": "TaskEvaluation",
-      "description": "Correctly extracted `TaskEvaluation` with all the required parameters
-      with correct types", "parameters": {"$defs": {"Entity": {"properties": {"name":
-      {"description": "The name of the entity.", "title": "Name", "type": "string"},
-      "type": {"description": "The type of the entity.", "title": "Type", "type":
-      "string"}, "description": {"description": "Description of the entity.", "title":
-      "Description", "type": "string"}, "relationships": {"description": "Relationships
-      of the entity.", "items": {"type": "string"}, "title": "Relationships", "type":
-      "array"}}, "required": ["name", "type", "description", "relationships"], "title":
-      "Entity", "type": "object"}}, "properties": {"suggestions": {"description":
-      "Suggestions to improve future similar tasks.", "items": {"type": "string"},
-      "title": "Suggestions", "type": "array"}, "quality": {"description": "A score
-      from 0 to 10 evaluating on completion, quality, and overall performance, all
-      taking into account the task description, expected output, and the result of
-      the task.", "title": "Quality", "type": "number"}, "entities": {"description":
-      "Entities extracted from the task output.", "items": {"$ref": "#/$defs/Entity"},
-      "title": "Entities", "type": "array"}}, "required": ["entities", "quality",
-      "suggestions"], "type": "object"}}}]}'
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@@ -1967,39 +1011,9 @@ interactions:
       code: 200
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 - request:
-    body: '{"messages":[{"role":"system","content":"Convert all responses into valid
-      JSON output."},{"role":"user","content":"Assess the quality of the task completed
-      based on the description, expected output, and actual results.\n\nTask Description:\nResearch
-      a topic to teach a kid aged 6 about math.\n\nExpected Output:\nA topic, explanation,
-      angle, and examples.\n\nActual Output:\nI now can give a great answer  \nFinal
-      Answer: \n\n**Topic:** Introduction to Addition\n\n**Explanation:** \nAddition
-      is one of the simplest operations in math. It''s all about putting things together.
-      When we add, we combine two or more numbers to find out how many we have in
-      total. For a 6-year-old, it can be visualized as combining different groups
-      of objects. Here''s how we can teach it:\n\n1. **Basic Concept**: Explain that
-      addition means bringing two amounts together to get a new total. Use simple
-      language like, \"If you have 2 apples and I give you 3 more apples, how many
-      apples do you have now?\"\n\n2. **Visual Aids**: Use physical objects like blocks,
-      beads, or fruit. Show the child one group with 2 blocks and another group with
-      3 blocks. Next, combine them and count the total together.\n\n3. **Symbols of
-      Addition**: Introduce the plus sign (+) and the equals sign (=). For instance,
-      you can explain that \"2 + 3 = 5\" means that when adding 2 and 3 together,
-      they make 5.\n\n**Angle:** \nMake it fun and interactive! Use games and stories
-      to keep the child engaged. For example, you could create a story about a little
-      monster who collects candies. Every time he meets a friend, he adds more candies
-      to his pile. \n\n**Examples:**\n- **Using Blocks**: Start with 4 blocks. If
-      you add 2 more, how many blocks do you have? (4 + 2 = 6)\n- **Finger Counting**:
-      Have the child count fingers. Hold up 3 fingers on one hand and 2 on the other
-      hand. Ask, \"How many fingers are up?\" and help them see that when they count
-      all the fingers together, they get 5.\n- **Story Problem**: \"You have 1 toy
-      car, and your friend gives you 3 more toy cars. How many do you have now?\"
-      Write it down for them: 1 + 3 = ? And help them count to find the answer is
-      4.\n\nMake sure to encourage the child as they explore addition! Celebrate their
-      successes and provide help as needed. This will help foster a positive attitude
-      towards math, making it not just an academic subject, but an enjoyable activity.\n\nPlease
-      provide:\n- Bullet points suggestions to improve future similar tasks\n- A score
-      from 0 to 10 evaluating on completion, quality, and overall performance- Entities
-      extracted from the task output, if any, their type, description, and relationships"}],"model":"gpt-4.1-mini"}'
+    body: '{"messages":[{"role":"system","content":"Convert all responses into valid JSON output."},{"role":"user","content":"Assess the quality of the task completed based on the description, expected output, and actual results.\n\nTask Description:\nResearch a topic to teach a kid aged 6 about math.\n\nExpected Output:\nA topic, explanation, angle, and examples.\n\nActual Output:\nI now can give a great answer  \nFinal Answer: \n\n**Topic:** Introduction to Addition\n\n**Explanation:** \nAddition is one of the simplest operations in math. It''s all about putting things together. When we add, we combine two or more numbers to find out how many we have in total. For a 6-year-old, it can be visualized as combining different groups of objects. Here''s how we can teach it:\n\n1. **Basic Concept**: Explain that addition means bringing two amounts together to get a new total. Use simple language like, \"If you have 2 apples and I give you 3 more apples, how many apples do you have now?\"\n\n2.
+      **Visual Aids**: Use physical objects like blocks, beads, or fruit. Show the child one group with 2 blocks and another group with 3 blocks. Next, combine them and count the total together.\n\n3. **Symbols of Addition**: Introduce the plus sign (+) and the equals sign (=). For instance, you can explain that \"2 + 3 = 5\" means that when adding 2 and 3 together, they make 5.\n\n**Angle:** \nMake it fun and interactive! Use games and stories to keep the child engaged. For example, you could create a story about a little monster who collects candies. Every time he meets a friend, he adds more candies to his pile. \n\n**Examples:**\n- **Using Blocks**: Start with 4 blocks. If you add 2 more, how many blocks do you have? (4 + 2 = 6)\n- **Finger Counting**: Have the child count fingers. Hold up 3 fingers on one hand and 2 on the other hand. Ask, \"How many fingers are up?\" and help them see that when they count all the fingers together, they get 5.\n- **Story Problem**: \"You have 1 toy
+      car, and your friend gives you 3 more toy cars. How many do you have now?\" Write it down for them: 1 + 3 = ? And help them count to find the answer is 4.\n\nMake sure to encourage the child as they explore addition! Celebrate their successes and provide help as needed. This will help foster a positive attitude towards math, making it not just an academic subject, but an enjoyable activity.\n\nPlease provide:\n- Bullet points suggestions to improve future similar tasks\n- A score from 0 to 10 evaluating on completion, quality, and overall performance- Entities extracted from the task output, if any, their type, description, and relationships"}],"model":"gpt-4.1-mini"}'
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       Date:
@@ -2301,8 +1227,7 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"input":["Examples(Illustrative Examples): Specific instances used to
-      explain addition including using blocks, finger counting, and story problems."],"model":"text-embedding-3-small","encoding_format":"base64"}'
+    body: '{"input":["Examples(Illustrative Examples): Specific instances used to explain addition including using blocks, finger counting, and story problems."],"model":"text-embedding-3-small","encoding_format":"base64"}'
     headers:
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diff --git a/lib/crewai/tests/cassettes/test_multimodal_agent_describing_image_successfully.yaml b/lib/crewai/tests/cassettes/test_multimodal_agent_describing_image_successfully.yaml
index c9371c243..3e4e7fbba 100644
--- a/lib/crewai/tests/cassettes/test_multimodal_agent_describing_image_successfully.yaml
+++ b/lib/crewai/tests/cassettes/test_multimodal_agent_describing_image_successfully.yaml
@@ -137,8 +137,6 @@ interactions:
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diff --git a/lib/crewai/tests/cassettes/test_multimodal_agent_live_image_analysis.yaml b/lib/crewai/tests/cassettes/test_multimodal_agent_live_image_analysis.yaml
index 76417ce27..12742ec0c 100644
--- a/lib/crewai/tests/cassettes/test_multimodal_agent_live_image_analysis.yaml
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diff --git a/lib/crewai/tests/cassettes/test_multiple_before_after_crew.yaml b/lib/crewai/tests/cassettes/test_multiple_before_after_crew.yaml
index 590ee1d37..437e26c4e 100644
--- a/lib/crewai/tests/cassettes/test_multiple_before_after_crew.yaml
+++ b/lib/crewai/tests/cassettes/test_multiple_before_after_crew.yaml
@@ -174,22 +174,8 @@ interactions:
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+      with the mains topics, each with a full section of information. Formatted as markdown without ''```''\n\nyou MUST return the actual complete content as the final answer, not a summary.\n\nThis is the context you''re working with:\n1. **Genetically Modified Plants for Climate Resilience**: In 2025, significant progress has been made in developing genetically engineered crops that are more resilient to extreme weather conditions such as drought, heat, and flooding. Using advanced gene editing techniques like CRISPR-Cas9, plants such as maize and soybeans have been tailored to survive and produce stable yields despite climate stress.\n\n2. **Enhanced Carbon Capture through Plant Engineering**: Researchers have identified and bioengineered certain plant species to increase their carbon sequestration capacity. Innovations include modifying root systems and photosynthetic pathways to absorb and store more atmospheric carbon dioxide, contributing to climate change mitigation strategies.\n\n3.
+      **Vertical Farming Integration with AI and Robotics**: Modern vertical farms combine hydroponics and aeroponics with artificial intelligence and robotics to optimize plant growth cycles. AI monitors environmental conditions and nutrient levels in real-time, while robots manage planting and harvesting, significantly increasing efficiency and reducing labor costs.\n\n4. **Discovery of Plant Immune System Pathways**: Cutting-edge research has unveiled new molecular pathways in plants that govern their immune responses to pathogens. This knowledge is being applied to develop crop varieties with enhanced natural resistance, reducing the dependence on chemical pesticides.\n\n5. **Microbiome Engineering for Soil and Plant Health**: Advances in understanding the plant microbiome have led to the creation of customized microbial inoculants that improve nutrient uptake, boost growth, and enhance stress tolerance. These soil and root microbiome treatments are now widely used to promote sustainable
+      agriculture.\n\n6. **Smart Plant Sensors for Real-Time Monitoring**: Biotechnological breakthroughs have resulted in the development of nanosensors that can be integrated into plants to provide real-time data on plant health, water status, and nutrient deficiencies. This technology enables precision agriculture by allowing farmers to make timely, data-driven decisions.\n\n7. **Phytoremediation with Genetically Enhanced Species**: New genetically modified plants have been developed with an increased ability to absorb heavy metals and pollutants from contaminated soils and water bodies. These plants serve as eco-friendly, cost-effective solutions for environmental cleanup.\n\n8. **Urban Greening for Climate Resilience**: Cities in 2025 are increasingly adopting urban forestry projects that use specially selected plant species to combat the urban heat island effect, improve air quality, and support urban biodiversity. These initiatives also play a vital role in enhancing mental health
+      and wellbeing for residents.\n\n9. **Plant-Based Bioplastics and Sustainable Materials**: Innovations in plant biotechnology have enabled the production of biodegradable plastics and other sustainable materials derived from plant biomass. This technology offers alternatives to petroleum-based products, helping reduce plastic pollution and carbon emissions.\n\n10. **Advancements in Photosynthesis Efficiency**: Scientists have successfully introduced and optimized synthetic pathways to enhance photosynthesis in crops, resulting in a 30-50% increase in growth rates and yields. This breakthrough addresses the global food security challenge by enabling more efficient energy conversion in plants.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}],"model":"gpt-4.1-mini"}'
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+        n\\nThis genetic modification process involves targeting genes responsible for stress response and water-use efficiency, enabling these plants to maintain stable physiological functions and yields even under environmental stress. Field trials have demonstrated that these resilient crops outperform conventional varieties by maintaining productivity during critical growth phases when weather conditions are unfavorable.\\n\\nThe impact of these genetically modified plants goes beyond yield stability; they contribute to food security by offering farmers dependable harvests amidst erratic climatic events. Moreover, the reduction in crop failure risk underscores their role in mitigating economic losses in agriculture dependent communities.\\n\\n---\\n\\n## 2. Enhanced Carbon Capture through Plant Engineering\\n\\nMitigation of climate change through carbon sequestration has seen groundbreaking advancements through the bioengineering of plants with improved capacity to capture and store\
+        \ atmospheric CO2. Researchers have manipulated specific attributes of plant physiology, such as root architecture and photosynthetic pathways, to optimize carbon uptake.\\n\\nModifications include enhancing the depth and mass of root systems that can sequester carbon into soil more effectively and engineering photosynthetic processes to increase the rate and efficiency of CO2 fixation. These bioengineered plants act as living carbon sinks, helping curb greenhouse gas concentrations in the atmosphere.\\n\\nImportantly, these strategies are being incorporated into climate action frameworks, offering nature-based solutions that complement emissions reduction efforts. Cultivation of such modified species on a large scale could substantially contribute to global carbon management and environmental sustainability.\\n\\n---\\n\\n## 3. Vertical Farming Integration with AI and Robotics\\n\\nThe integration of artificial intelligence (AI) and robotics into vertical farming has revolutionized\
+        \ indoor agriculture. Modern vertical farms employ soilless cultivation methods like hydroponics and aeroponics to optimize resource use. These technologies are now augmented with AI systems that monitor and analyze critical parameters such as temperature, humidity, light intensity, and nutrient concentration in real-time.\\n\\nAI algorithms adjust environmental controls dynamically to optimize plant growth cycles, maximizing yield and minimizing waste. Concurrently, robotic systems execute tasks including seed planting, maintenance, and harvesting with precision and speed, significantly reducing manual labor needs and human error.\\n\\nThis synergy not only boosts production efficiency but also facilitates urban farming solutions that conserve space and resources, reduce transportation emissions, and provide fresh produce locally, helping to meet food demand in densely populated areas sustainably.\\n\\n---\\n\\n## 4. Discovery of Plant Immune System Pathways\\n\\nRecent research\
+        \ has uncovered previously unknown molecular pathways responsible for activating and regulating plant immune responses against pathogens. Through sophisticated molecular biology techniques, scientists have identified key signaling cascades and receptor proteins that recognize pathogenic threats and initiate defense mechanisms.\\n\\nThis deeper understanding enables the development of crop varieties that naturally possess enhanced disease resistance by either upregulating immune responses or blocking pathogen entry points, thereby reducing vulnerability to infections without relying heavily on chemical pesticides.\\n\\nThe implications for agriculture include lower pesticide use, diminished environmental pollution, decreased production costs, and improved crop health, ultimately contributing to more sustainable and eco-friendly farming systems.\\n\\n---\\n\\n## 5. Microbiome Engineering for Soil and Plant Health\\n\\nThe plant microbiome—the community of microorganisms associated\
+        \ with plant roots and surrounding soil—has come into focus as a pivotal factor in plant growth and resilience. Technological advances have led to the design of tailored microbial inoculants that can be introduced into the soil to improve nutrient availability, stimulate growth, and enhance stress tolerance.\\n\\nThese customized microbial consortia promote nutrient uptake efficiency, particularly nitrogen and phosphorus, and help plants better withstand drought, salinity, and pathogen attacks. The application of such microbiome engineering supports sustainable agriculture by reducing reliance on synthetic fertilizers and pesticides, improving soil health, and enhancing crop yield.\\n\\nConsequently, microbiome treatments are becoming standard practice worldwide for farmers seeking environmentally friendly methods to optimize productivity and soil sustainability.\\n\\n---\\n\\n## 6. Smart Plant Sensors for Real-Time Monitoring\\n\\nBiotechnological innovations have produced nanoscale\
+        \ sensors that can be embedded directly into plant tissues to monitor vital physiological parameters continuously. These smart sensors detect indicators such as plant hydration levels, nutrient deficiencies, and early stress signals caused by pests or environmental fluctuations.\\n\\nBy transmitting data wirelessly to farm management systems, these tools enable precision agriculture applications, where farmers can make informed, timely decisions regarding irrigation, fertilization, and protective measures. This leads to more efficient resource utilization, reduced waste, and enhanced crop health.\\n\\nAdoption of smart sensor technology is revolutionizing crop monitoring by facilitating proactive management strategies, increasing yields, and promoting sustainable practices through data-driven inputs.\\n\\n---\\n\\n## 7. Phytoremediation with Genetically Enhanced Species\\n\\nEnvironmental remediation efforts have benefited from genetically modified plants specifically engineered\
+        \ to absorb and detoxify heavy metals and pollutants from contaminated environments. These enhanced species possess increased uptake capabilities for harmful substances such as lead, mercury, arsenic, and certain organic pollutants.\\n\\nBy planting these phytoremediation agents in soils and water bodies affected by industrial waste or agricultural runoff, ecosystems can be cleaned in an eco-friendly, cost-effective manner without resorting to chemical or mechanical removal methods that are often expensive and disruptive.\\n\\nThis approach not only rehabilitates polluted sites but also reduces health risks for surrounding communities and restores land for productive use, making it a vital tool in environmental management.\\n\\n---\\n\\n## 8. Urban Greening for Climate Resilience\\n\\nUrban areas worldwide have accelerated adoption of greening initiatives aimed at mitigating climate change impacts and improving quality of life. Cities in 2025 implement large-scale urban forestry\
+        \ projects utilizing carefully selected plant species that are resilient to urban stressors and capable of providing critical ecosystem services.\\n\\nThese urban forests help reduce the urban heat island effect by providing shade and evapotranspiration cooling, improve air quality by filtering pollutants, and increase biodiversity by creating habitats for various species. Moreover, green spaces contribute significantly to the mental and physical health of residents, offering recreational areas and reducing stress levels.\\n\\nUrban greening is an integral component of climate action plans, fostering resilience, sustainability, and livability in growing metropolitan environments.\\n\\n---\\n\\n## 9. Plant-Based Bioplastics and Sustainable Materials\\n\\nA surge of innovation in plant biotechnology has enabled the production of biodegradable plastics and other sustainable materials derived directly from plant biomass. Advances in metabolic engineering allow plants and associated microorganisms\
+        \ to biosynthesize polymers like polylactic acid (PLA) and polyhydroxyalkanoates (PHA), which serve as eco-friendly alternatives to conventional petroleum-based plastics.\\n\\nThese bioplastics degrade more rapidly in natural environments, decreasing plastic pollution and lowering carbon footprints associated with manufacturing and disposal. Additionally, the use of agricultural by-products as feedstock for bioplastic production promotes circular economy principles.\\n\\nSuch technological progress offers industries a pathway toward sustainable packaging and material solutions, addressing environmental challenges posed by traditional plastic use.\\n\\n---\\n\\n## 10. Advancements in Photosynthesis Efficiency\\n\\nIn a landmark scientific breakthrough, researchers have successfully introduced synthetic pathways into crops that significantly enhance photosynthesis efficiency. By integrating optimized biochemical routes that reduce energy losses and increase carbon fixation rates, growth\
+        \ rates and crop yields have increased by 30-50%.\\n\\nThis improvement enhances the conversion of sunlight into biomass, enabling plants to generate more food energy from the same amount of light and CO2, thereby addressing pressing food security concerns globally.\\n\\nThese engineered pathways are being fine-tuned for deployment across major food crops, promising a transformative impact on agricultural productivity, food supply stability, and sustainability in the face of a growing global population and changing climate.\\n\\n---\\n\\n# Summary\\n\\nThe convergence of genetic engineering, biotechnology, artificial intelligence, and sustainable practices in 2025 has propelled plant science into a new era of innovation. From climate-resilient genetically modified crops to smart sensing technologies, and environmentally restorative phytoremediation to urban greening, these advances collectively contribute to resilient agriculture, ecological sustainability, and improved human wellbeing.\
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--- a/lib/crewai/tests/cassettes/test_output_json_to_another_task.yaml
+++ b/lib/crewai/tests/cassettes/test_output_json_to_another_task.yaml
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 interactions:
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index 477426b9d..616a8022e 100644
--- a/lib/crewai/tests/cassettes/test_output_pydantic_hierarchical.yaml
+++ b/lib/crewai/tests/cassettes/test_output_pydantic_hierarchical.yaml
@@ -1,49 +1,9 @@
 interactions:
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diff --git a/lib/crewai/tests/cassettes/test_task_interpolation_with_hyphens.yaml b/lib/crewai/tests/cassettes/test_task_interpolation_with_hyphens.yaml
index f28de41e7..639136be3 100644
--- a/lib/crewai/tests/cassettes/test_task_interpolation_with_hyphens.yaml
+++ b/lib/crewai/tests/cassettes/test_task_interpolation_with_hyphens.yaml
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     headers:
       CF-RAY:
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       Connection:
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-      Content-Encoding:
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       Content-Type:
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       Date:
@@ -1029,9 +550,7 @@ interactions:
       code: 200
       message: OK
 - request:
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-      addition by counting toy cars."], "model": "text-embedding-3-small", "encoding_format":
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+    body: '{"input": ["Example: Toy Cars(Math Example): An example used to illustrate addition by counting toy cars."], "model": "text-embedding-3-small", "encoding_format": "base64"}'
     headers:
       accept:
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+        \n    }\n  ],\n  \"model\": \"text-embedding-3-small\",\n  \"usage\": {\n    \"prompt_tokens\": 20,\n    \"total_tokens\": 20\n  }\n}\n"
     headers:
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-      - gzip
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       Date:
@@ -1668,40 +848,9 @@ interactions:
       code: 200
       message: OK
 - request:
-    body: '{"messages":[{"role":"system","content":"Convert all responses into valid
-      JSON output."},{"role":"user","content":"Assess the quality of the task completed
-      based on the description, expected output, and actual results.\n\nTask Description:\nResearch
-      a topic to teach a kid aged 6 about math.\n\nExpected Output:\nA topic, explanation,
-      angle, and examples.\n\nActual Output:\nI now can give a great answer.\nFinal
-      Answer: \n**Topic**: Basic Addition\n\n**Explanation**:\nAddition is a fundamental
-      concept in math that means combining two or more numbers to get a new total.
-      It''s like putting together pieces of a puzzle to see the whole picture. When
-      we add, we take two or more groups of things and count them all together.\n\n**Angle**:\nUse
-      relatable and engaging real-life scenarios to illustrate addition, making it
-      fun and easier for a 6-year-old to understand and apply.\n\n**Examples**:\n\n1.
-      **Counting Apples**:\n   Let''s say you have 2 apples and your friend gives
-      you 3 more apples. How many apples do you have in total?\n   - You start with
-      2 apples.\n   - Your friend gives you 3 more apples.\n   - Now, you count all
-      the apples together: 2 + 3 = 5.\n   - So, you have 5 apples in total.\n\n2.
-      **Toy Cars**:\n   Imagine you have 4 toy cars and you find 2 more toy cars in
-      your room. How many toy cars do you have now?\n   - You start with 4 toy cars.\n   -
-      You find 2 more toy cars.\n   - You count them all together: 4 + 2 = 6.\n   -
-      So, you have 6 toy cars in total.\n\n3. **Drawing Pictures**:\n   If you draw
-      3 pictures today and 2 pictures tomorrow, how many pictures will you have drawn
-      in total?\n   - You draw 3 pictures today.\n   - You draw 2 pictures tomorrow.\n   -
-      You add them together: 3 + 2 = 5.\n   - So, you will have drawn 5 pictures in
-      total.\n\n4. **Using Fingers**:\n   Let''s use your fingers to practice addition.
-      Show 3 fingers on one hand and 1 finger on the other hand. How many fingers
-      are you holding up?\n   - 3 fingers on one hand.\n   - 1 finger on the other
-      hand.\n   - Put them together and count: 3 + 1 = 4.\n   - So, you are holding
-      up 4 fingers.\n\nBy using objects that kids are familiar with, such as apples,
-      toy cars, drawings, and even their own fingers, we can make the concept of addition
-      relatable and enjoyable. Practicing with real items helps children visualize
-      the math and understand that addition is simply combining groups to find out
-      how many there are altogether.\n\nPlease provide:\n- Bullet points suggestions
-      to improve future similar tasks\n- A score from 0 to 10 evaluating on completion,
-      quality, and overall performance- Entities extracted from the task output, if
-      any, their type, description, and relationships"}],"model":"gpt-4.1-mini"}'
+    body: '{"messages":[{"role":"system","content":"Convert all responses into valid JSON output."},{"role":"user","content":"Assess the quality of the task completed based on the description, expected output, and actual results.\n\nTask Description:\nResearch a topic to teach a kid aged 6 about math.\n\nExpected Output:\nA topic, explanation, angle, and examples.\n\nActual Output:\nI now can give a great answer.\nFinal Answer: \n**Topic**: Basic Addition\n\n**Explanation**:\nAddition is a fundamental concept in math that means combining two or more numbers to get a new total. It''s like putting together pieces of a puzzle to see the whole picture. When we add, we take two or more groups of things and count them all together.\n\n**Angle**:\nUse relatable and engaging real-life scenarios to illustrate addition, making it fun and easier for a 6-year-old to understand and apply.\n\n**Examples**:\n\n1. **Counting Apples**:\n   Let''s say you have 2 apples and your friend gives you 3 more apples.
+      How many apples do you have in total?\n   - You start with 2 apples.\n   - Your friend gives you 3 more apples.\n   - Now, you count all the apples together: 2 + 3 = 5.\n   - So, you have 5 apples in total.\n\n2. **Toy Cars**:\n   Imagine you have 4 toy cars and you find 2 more toy cars in your room. How many toy cars do you have now?\n   - You start with 4 toy cars.\n   - You find 2 more toy cars.\n   - You count them all together: 4 + 2 = 6.\n   - So, you have 6 toy cars in total.\n\n3. **Drawing Pictures**:\n   If you draw 3 pictures today and 2 pictures tomorrow, how many pictures will you have drawn in total?\n   - You draw 3 pictures today.\n   - You draw 2 pictures tomorrow.\n   - You add them together: 3 + 2 = 5.\n   - So, you will have drawn 5 pictures in total.\n\n4. **Using Fingers**:\n   Let''s use your fingers to practice addition. Show 3 fingers on one hand and 1 finger on the other hand. How many fingers are you holding up?\n   - 3 fingers on one hand.\n   - 1 finger
+      on the other hand.\n   - Put them together and count: 3 + 1 = 4.\n   - So, you are holding up 4 fingers.\n\nBy using objects that kids are familiar with, such as apples, toy cars, drawings, and even their own fingers, we can make the concept of addition relatable and enjoyable. Practicing with real items helps children visualize the math and understand that addition is simply combining groups to find out how many there are altogether.\n\nPlease provide:\n- Bullet points suggestions to improve future similar tasks\n- A score from 0 to 10 evaluating on completion, quality, and overall performance- Entities extracted from the task output, if any, their type, description, and relationships"}],"model":"gpt-4.1-mini"}'
     headers:
       accept:
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index 21a2a802c..0deea8701 100644
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index f6726847b..99180c2e9 100644
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     headers:
       Accept:
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@@ -427,28 +273,8 @@ interactions:
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   response:
     body:
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-        \ .rails-default-error-page div.dialog > p {\n    margin: 0 0 1em;\n    padding:
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-        the address or the page may have moved.</p>\n    </div>\n    <p>If you are
-        the application owner check the logs for more information.</p>\n  </div>\n</body>\n</html>\n"
+      string: "<!DOCTYPE html>\n<html>\n<head>\n  <title>The page you were looking for doesn't exist (404)</title>\n  <meta name=\"viewport\" content=\"width=device-width,initial-scale=1\">\n  <style>\n  .rails-default-error-page {\n    background-color: #EFEFEF;\n    color: #2E2F30;\n    text-align: center;\n    font-family: arial, sans-serif;\n    margin: 0;\n  }\n\n  .rails-default-error-page div.dialog {\n    width: 95%;\n    max-width: 33em;\n    margin: 4em auto 0;\n  }\n\n  .rails-default-error-page div.dialog > div {\n    border: 1px solid #CCC;\n    border-right-color: #999;\n    border-left-color: #999;\n    border-bottom-color: #BBB;\n    border-top: #B00100 solid 4px;\n    border-top-left-radius: 9px;\n    border-top-right-radius: 9px;\n    background-color: white;\n    padding: 7px 12% 0;\n    box-shadow: 0 3px 8px rgba(50, 50, 50, 0.17);\n  }\n\n  .rails-default-error-page h1 {\n    font-size: 100%;\n    color: #730E15;\n    line-height: 1.5em;\n  }\n\n  .rails-default-error-page\
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     headers:
       Connection:
       - keep-alive
diff --git a/lib/crewai/tests/cassettes/tracing/TestTraceListenerSetup.test_events_collection_batch_manager.yaml b/lib/crewai/tests/cassettes/tracing/TestTraceListenerSetup.test_events_collection_batch_manager.yaml
index 1b1c78ffe..68945530e 100644
--- a/lib/crewai/tests/cassettes/tracing/TestTraceListenerSetup.test_events_collection_batch_manager.yaml
+++ b/lib/crewai/tests/cassettes/tracing/TestTraceListenerSetup.test_events_collection_batch_manager.yaml
@@ -1,16 +1,6 @@
 interactions:
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-      the world\n\nThis is the expected criteria for your final answer: hello world\nyou
-      MUST return the actual complete content as the final answer, not a summary.\n\nBegin!
-      This is VERY important to you, use the tools available and give your best Final
-      Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop":
-      ["\nObservation:"]}'
+    body: '{"messages": [{"role": "system", "content": "You are Test Agent. Test backstory\nYour personal goal is: Test goal\nTo give my best complete final answer to the task respond using the exact following format:\n\nThought: I now can give a great answer\nFinal Answer: Your final answer must be the great and the most complete as possible, it must be outcome described.\n\nI MUST use these formats, my job depends on it!"}, {"role": "user", "content": "\nCurrent Task: Say hello to the world\n\nThis is the expected criteria for your final answer: hello world\nyou MUST return the actual complete content as the final answer, not a summary.\n\nBegin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!\n\nThought:"}], "model": "gpt-4o-mini", "stop": ["\nObservation:"]}'
     headers:
       accept:
       - application/json
@@ -23,8 +13,7 @@ interactions:
       content-type:
       - application/json
       cookie:
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index 13c4fcc25..ffd427b8e 100644
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diff --git a/lib/crewai/tests/cassettes/tracing/TestTraceListenerSetup.test_trace_listener_setup_correctly_with_tracing_flag.yaml b/lib/crewai/tests/cassettes/tracing/TestTraceListenerSetup.test_trace_listener_setup_correctly_with_tracing_flag.yaml
index 84f8dfa2a..d40d8bf47 100644
--- a/lib/crewai/tests/cassettes/tracing/TestTraceListenerSetup.test_trace_listener_setup_correctly_with_tracing_flag.yaml
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index e468d8e11..1fb25ec6d 100644
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diff --git a/lib/crewai/tests/cassettes/utilities/test_converter_with_nested_model.yaml b/lib/crewai/tests/cassettes/utilities/test_converter_with_nested_model.yaml
index cd9f192c3..7ba2939d1 100644
--- a/lib/crewai/tests/cassettes/utilities/test_converter_with_nested_model.yaml
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index c20dc4d92..9c346c538 100644
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+++ b/lib/crewai/tests/cassettes/utilities/test_crew_emits_end_kickoff_event.yaml
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--- a/lib/crewai/tests/cassettes/utilities/test_crew_emits_end_task_event.yaml
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diff --git a/lib/crewai/tests/cassettes/utilities/test_crew_emits_kickoff_events.yaml b/lib/crewai/tests/cassettes/utilities/test_crew_emits_kickoff_events.yaml
index 2233bde21..cd579d43e 100644
--- a/lib/crewai/tests/cassettes/utilities/test_crew_emits_kickoff_events.yaml
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diff --git a/lib/crewai/tests/cassettes/utilities/test_crew_emits_start_kickoff_event.yaml b/lib/crewai/tests/cassettes/utilities/test_crew_emits_start_kickoff_event.yaml
index 82333fe7d..492b81a5d 100644
--- a/lib/crewai/tests/cassettes/utilities/test_crew_emits_start_kickoff_event.yaml
+++ b/lib/crewai/tests/cassettes/utilities/test_crew_emits_start_kickoff_event.yaml
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index e470049a7..c98fea8a8 100644
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index db824bb3d..f5c9041a4 100644
--- a/lib/crewai/tests/cassettes/utilities/test_crew_emits_task_failed_event.yaml
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diff --git a/lib/crewai/tests/cassettes/utilities/test_crew_emits_test_kickoff_type_event.yaml b/lib/crewai/tests/cassettes/utilities/test_crew_emits_test_kickoff_type_event.yaml
index aa6c77ad1..89b172a9e 100644
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+++ b/lib/crewai/tests/cassettes/utilities/test_crew_emits_test_kickoff_type_event.yaml
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       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -871,8 +863,6 @@ interactions:
       - 92933965e97cf96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -1117,8 +1107,6 @@ interactions:
       - 9293396b98c9f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -1388,8 +1376,6 @@ interactions:
       - 92933970ef9df96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -1684,8 +1670,6 @@ interactions:
       - 92933978f9f4f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -2047,8 +2031,6 @@ interactions:
       - 9293397e3850f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -2401,8 +2383,6 @@ interactions:
       - 929339872d3ff96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -2780,8 +2760,6 @@ interactions:
       - 929339942fb0f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -3224,8 +3202,6 @@ interactions:
       - 9293399bd9d3f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -3659,8 +3635,6 @@ interactions:
       - 929339a45d75f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -4122,8 +4096,6 @@ interactions:
       - 929339b3ef9cf96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -4651,8 +4623,6 @@ interactions:
       - 929339b9ae78f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -5169,8 +5139,6 @@ interactions:
       - 929339c919f9f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -5754,8 +5722,6 @@ interactions:
       - 929339ce889cf96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -6330,8 +6296,6 @@ interactions:
       - 929339e3c99ff96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -6937,8 +6901,6 @@ interactions:
       - 929339ea18dff96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -7613,8 +7575,6 @@ interactions:
       - 929339f6f9c9f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -8278,8 +8238,6 @@ interactions:
       - 92933a0dda9cf96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -9008,8 +8966,6 @@ interactions:
       - 92933a19fb8ef96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -9736,8 +9692,6 @@ interactions:
       - 92933a2c3cc8f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -10523,8 +10477,6 @@ interactions:
       - 92933a39ed31f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -11307,8 +11259,6 @@ interactions:
       - 92933a42893ef96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -12141,8 +12091,6 @@ interactions:
       - 92933a528fedf96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -13011,8 +12959,6 @@ interactions:
       - 92933a5e6819f96b-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/cassettes/utilities/test_tools_emits_finished_events.yaml b/lib/crewai/tests/cassettes/utilities/test_tools_emits_finished_events.yaml
index 548ac2b0a..91c2734b9 100644
--- a/lib/crewai/tests/cassettes/utilities/test_tools_emits_finished_events.yaml
+++ b/lib/crewai/tests/cassettes/utilities/test_tools_emits_finished_events.yaml
@@ -71,8 +71,6 @@ interactions:
       - 90fea7d78e1fceb9-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -191,8 +189,6 @@ interactions:
       - 90fea7dc5ba6ceb9-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
@@ -467,8 +463,6 @@ interactions:
       - 90fea7dfef41ceb9-SJC
       Connection:
       - keep-alive
-      Content-Encoding:
-      - gzip
       Content-Type:
       - application/json
       Date:
diff --git a/lib/crewai/tests/llms/azure/test_azure.py b/lib/crewai/tests/llms/azure/test_azure.py
index ec881363d..6c6ee5271 100644
--- a/lib/crewai/tests/llms/azure/test_azure.py
+++ b/lib/crewai/tests/llms/azure/test_azure.py
@@ -10,9 +10,9 @@ from crewai.agent import Agent
 from crewai.task import Task
 
 
-@pytest.fixture(autouse=True)
+@pytest.fixture
 def mock_azure_credentials():
-    """Automatically mock Azure credentials for all tests in this module."""
+    """Mock Azure credentials for tests that need them."""
     with patch.dict(os.environ, {
         "AZURE_API_KEY": "test-key",
         "AZURE_ENDPOINT": "https://test.openai.azure.com"
@@ -20,6 +20,7 @@ def mock_azure_credentials():
         yield
 
 
+@pytest.mark.usefixtures("mock_azure_credentials")
 def test_azure_completion_is_used_when_azure_provider():
     """
     Test that AzureCompletion from completion.py is used when LLM uses provider 'azure'
@@ -31,6 +32,7 @@ def test_azure_completion_is_used_when_azure_provider():
     assert llm.model == "gpt-4"
 
 
+@pytest.mark.usefixtures("mock_azure_credentials")
 def test_azure_completion_is_used_when_azure_openai_provider():
     """
     Test that AzureCompletion is used when provider is 'azure_openai'
@@ -101,6 +103,7 @@ def test_azure_tool_use_conversation_flow():
         assert mock_complete.called
 
 
+@pytest.mark.usefixtures("mock_azure_credentials")
 def test_azure_completion_module_is_imported():
     """
     Test that the completion module is properly imported when using Azure provider
@@ -189,6 +192,7 @@ def test_azure_specific_parameters():
     assert llm.api_version == "2024-02-01"
 
 
+@pytest.mark.usefixtures("mock_azure_credentials")
 def test_azure_completion_call():
     """
     Test that AzureCompletion call method works
@@ -203,6 +207,7 @@ def test_azure_completion_call():
         mock_call.assert_called_once_with("Hello, how are you?")
 
 
+@pytest.mark.usefixtures("mock_azure_credentials")
 def test_azure_completion_called_during_crew_execution():
     """
     Test that AzureCompletion.call is actually invoked when running a crew
@@ -235,6 +240,7 @@ def test_azure_completion_called_during_crew_execution():
         assert "14 million" in str(result)
 
 
+@pytest.mark.usefixtures("mock_azure_credentials")
 def test_azure_completion_call_arguments():
     """
     Test that AzureCompletion.call is invoked with correct arguments
@@ -661,38 +667,17 @@ def test_azure_http_error_handling():
             llm.call("Hello")
 
 
+@pytest.mark.vcr()
 def test_azure_streaming_completion():
     """
     Test that streaming completions work properly
     """
-    from crewai.llms.providers.azure.completion import AzureCompletion
-    from azure.ai.inference.models import StreamingChatCompletionsUpdate
+    llm = LLM(model="azure/gpt-4o-mini", stream=True)
+    result = llm.call("Say hello")
 
-    llm = LLM(model="azure/gpt-4", stream=True)
-
-    # Mock streaming response
-    with patch.object(llm.client, 'complete') as mock_complete:
-        # Create mock streaming updates with proper type
-        mock_updates = []
-        for chunk in ["Hello", " ", "world", "!"]:
-            mock_delta = MagicMock()
-            mock_delta.content = chunk
-            mock_delta.tool_calls = None
-
-            mock_choice = MagicMock()
-            mock_choice.delta = mock_delta
-
-            # Create mock update as StreamingChatCompletionsUpdate instance
-            mock_update = MagicMock(spec=StreamingChatCompletionsUpdate)
-            mock_update.choices = [mock_choice]
-            mock_updates.append(mock_update)
-
-        mock_complete.return_value = iter(mock_updates)
-
-        result = llm.call("Say hello")
-
-        # Verify the full response was assembled
-        assert result == "Hello world!"
+    assert result is not None
+    assert isinstance(result, str)
+    assert len(result) > 0
 
 
 def test_azure_api_version_default():
@@ -1112,4 +1097,33 @@ def test_azure_completion_params_preparation_with_drop_params():
         messages = [{"role": "user", "content": "Hello"}]
         params = llm._prepare_completion_params(messages)
 
-        assert params.get('stop') == None
\ No newline at end of file
+        assert params.get('stop') == None
+
+
+@pytest.mark.vcr()
+def test_azure_streaming_returns_usage_metrics():
+    """
+    Test that Azure streaming calls return proper token usage metrics.
+    """
+    agent = Agent(
+        role="Research Assistant",
+        goal="Find information about the capital of Spain",
+        backstory="You are a helpful research assistant.",
+        llm=LLM(model="azure/gpt-4o-mini", stream=True),
+        verbose=True,
+    )
+
+    task = Task(
+        description="What is the capital of Spain?",
+        expected_output="The capital of Spain",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+    result = crew.kickoff()
+
+    assert result.token_usage is not None
+    assert result.token_usage.total_tokens > 0
+    assert result.token_usage.prompt_tokens > 0
+    assert result.token_usage.completion_tokens > 0
+    assert result.token_usage.successful_requests >= 1
diff --git a/lib/crewai/tests/llms/azure/test_azure_async.py b/lib/crewai/tests/llms/azure/test_azure_async.py
index 90209d81a..1bbd9cf4c 100644
--- a/lib/crewai/tests/llms/azure/test_azure_async.py
+++ b/lib/crewai/tests/llms/azure/test_azure_async.py
@@ -3,6 +3,7 @@
 import pytest
 import tiktoken
 
+from crewai import Agent, Task, Crew
 from crewai.llm import LLM
 
 
@@ -114,3 +115,33 @@ async def test_azure_async_conversation():
 
     assert result is not None
     assert isinstance(result, str)
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio
+async def test_azure_async_streaming_returns_usage_metrics():
+    """
+    Test that Azure async streaming calls return proper token usage metrics.
+    """
+    agent = Agent(
+        role="Research Assistant",
+        goal="Find information about the capital of Germany",
+        backstory="You are a helpful research assistant.",
+        llm=LLM(model="azure/gpt-4o-mini", stream=True),
+        verbose=True,
+    )
+
+    task = Task(
+        description="What is the capital of Germany?",
+        expected_output="The capital of Germany",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+    result = await crew.kickoff_async()
+
+    assert result.token_usage is not None
+    assert result.token_usage.total_tokens > 0
+    assert result.token_usage.prompt_tokens > 0
+    assert result.token_usage.completion_tokens > 0
+    assert result.token_usage.successful_requests >= 1
diff --git a/lib/crewai/tests/llms/google/test_google.py b/lib/crewai/tests/llms/google/test_google.py
index 1dd585729..37f591de6 100644
--- a/lib/crewai/tests/llms/google/test_google.py
+++ b/lib/crewai/tests/llms/google/test_google.py
@@ -698,3 +698,33 @@ def test_gemini_stop_sequences_sent_to_api():
         assert hasattr(config, 'stop_sequences') or 'stop_sequences' in config.__dict__
         if hasattr(config, 'stop_sequences'):
             assert config.stop_sequences == ["\nObservation:", "\nThought:"]
+
+
+@pytest.mark.vcr()
+@pytest.mark.skip(reason="VCR cannot replay SSE streaming responses")
+def test_google_streaming_returns_usage_metrics():
+    """
+    Test that Google Gemini streaming calls return proper token usage metrics.
+    """
+    agent = Agent(
+        role="Research Assistant",
+        goal="Find information about the capital of Japan",
+        backstory="You are a helpful research assistant.",
+        llm=LLM(model="gemini/gemini-2.0-flash-exp", stream=True),
+        verbose=True,
+    )
+
+    task = Task(
+        description="What is the capital of Japan?",
+        expected_output="The capital of Japan",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+    result = crew.kickoff()
+
+    assert result.token_usage is not None
+    assert result.token_usage.total_tokens > 0
+    assert result.token_usage.prompt_tokens > 0
+    assert result.token_usage.completion_tokens > 0
+    assert result.token_usage.successful_requests >= 1
diff --git a/lib/crewai/tests/llms/google/test_google_async.py b/lib/crewai/tests/llms/google/test_google_async.py
index a68422dc4..1385ba74e 100644
--- a/lib/crewai/tests/llms/google/test_google_async.py
+++ b/lib/crewai/tests/llms/google/test_google_async.py
@@ -3,6 +3,7 @@
 import pytest
 import tiktoken
 
+from crewai import Agent, Task, Crew
 from crewai.llm import LLM
 from crewai.llms.providers.gemini.completion import GeminiCompletion
 
@@ -112,3 +113,34 @@ async def test_gemini_async_with_parameters():
 
     assert result is not None
     assert isinstance(result, str)
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio
+@pytest.mark.skip(reason="VCR cannot replay SSE streaming responses")
+async def test_google_async_streaming_returns_usage_metrics():
+    """
+    Test that Google Gemini async streaming calls return proper token usage metrics.
+    """
+    agent = Agent(
+        role="Research Assistant",
+        goal="Find information about the capital of Canada",
+        backstory="You are a helpful research assistant.",
+        llm=LLM(model="gemini/gemini-2.0-flash-exp", stream=True),
+        verbose=True,
+    )
+
+    task = Task(
+        description="What is the capital of Canada?",
+        expected_output="The capital of Canada",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+    result = await crew.kickoff_async()
+
+    assert result.token_usage is not None
+    assert result.token_usage.total_tokens > 0
+    assert result.token_usage.prompt_tokens > 0
+    assert result.token_usage.completion_tokens > 0
+    assert result.token_usage.successful_requests >= 1
diff --git a/lib/crewai/tests/llms/openai/test_openai.py b/lib/crewai/tests/llms/openai/test_openai.py
index 508b16d00..693bd3629 100644
--- a/lib/crewai/tests/llms/openai/test_openai.py
+++ b/lib/crewai/tests/llms/openai/test_openai.py
@@ -592,3 +592,32 @@ def test_openai_response_format_none():
 
     assert isinstance(result, str)
     assert len(result) > 0
+
+
+@pytest.mark.vcr()
+def test_openai_streaming_returns_usage_metrics():
+    """
+    Test that OpenAI streaming calls return proper token usage metrics.
+    """
+    agent = Agent(
+        role="Research Assistant",
+        goal="Find information about the capital of France",
+        backstory="You are a helpful research assistant.",
+        llm=LLM(model="gpt-4o-mini", stream=True),
+        verbose=True,
+    )
+
+    task = Task(
+        description="What is the capital of France?",
+        expected_output="The capital of France",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+    result = crew.kickoff()
+
+    assert result.token_usage is not None
+    assert result.token_usage.total_tokens > 0
+    assert result.token_usage.prompt_tokens > 0
+    assert result.token_usage.completion_tokens > 0
+    assert result.token_usage.successful_requests >= 1
diff --git a/lib/crewai/tests/llms/openai/test_openai_async.py b/lib/crewai/tests/llms/openai/test_openai_async.py
index 07efcac0f..e6bbf11d9 100644
--- a/lib/crewai/tests/llms/openai/test_openai_async.py
+++ b/lib/crewai/tests/llms/openai/test_openai_async.py
@@ -3,6 +3,7 @@
 import pytest
 import tiktoken
 
+from crewai import Agent, Task, Crew
 from crewai.llm import LLM
 
 
@@ -137,3 +138,33 @@ async def test_openai_async_with_parameters():
 
     assert result is not None
     assert isinstance(result, str)
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio
+async def test_openai_async_streaming_returns_usage_metrics():
+    """
+    Test that OpenAI async streaming calls return proper token usage metrics.
+    """
+    agent = Agent(
+        role="Research Assistant",
+        goal="Find information about the capital of Italy",
+        backstory="You are a helpful research assistant.",
+        llm=LLM(model="gpt-4o-mini", stream=True),
+        verbose=True,
+    )
+
+    task = Task(
+        description="What is the capital of Italy?",
+        expected_output="The capital of Italy",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task])
+    result = await crew.kickoff_async()
+
+    assert result.token_usage is not None
+    assert result.token_usage.total_tokens > 0
+    assert result.token_usage.prompt_tokens > 0
+    assert result.token_usage.completion_tokens > 0
+    assert result.token_usage.successful_requests >= 1
diff --git a/uv.lock b/uv.lock
index 08eafb1f4..42fb449de 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1239,7 +1239,7 @@ requires-dist = [
     { name = "click", specifier = "~=8.1.7" },
     { name = "crewai-tools", marker = "extra == 'tools'", editable = "lib/crewai-tools" },
     { name = "docling", marker = "extra == 'docling'", specifier = "~=2.63.0" },
-    { name = "google-genai", marker = "extra == 'google-genai'", specifier = "~=1.2.0" },
+    { name = "google-genai", marker = "extra == 'google-genai'", specifier = "~=1.49.0" },
     { name = "httpx-auth", marker = "extra == 'a2a'", specifier = "~=0.23.1" },
     { name = "httpx-sse", marker = "extra == 'a2a'", specifier = "~=0.4.0" },
     { name = "ibm-watsonx-ai", marker = "extra == 'watson'", specifier = "~=1.3.39" },
@@ -1890,7 +1890,7 @@ name = "exceptiongroup"
 version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
+    { name = "typing-extensions", marker = "python_full_version < '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/50/79/66800aadf48771f6b62f7eb014e352e5d06856655206165d775e675a02c9/exceptiongroup-1.3.1.tar.gz", hash = "sha256:8b412432c6055b0b7d14c310000ae93352ed6754f70fa8f7c34141f91c4e3219", size = 30371, upload-time = "2025-11-21T23:01:54.787Z" }
 wheels = [
@@ -2217,17 +2217,21 @@ wheels = [
 
 [[package]]
 name = "google-genai"
-version = "1.2.0"
+version = "1.49.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
+    { name = "anyio" },
     { name = "google-auth" },
+    { name = "httpx" },
     { name = "pydantic" },
     { name = "requests" },
+    { name = "tenacity" },
     { name = "typing-extensions" },
     { name = "websockets" },
 ]
+sdist = { url = "https://files.pythonhosted.org/packages/82/49/1a724ee3c3748fa50721d53a52d9fee88c67d0c43bb16eb2b10ee89ab239/google_genai-1.49.0.tar.gz", hash = "sha256:35eb16023b72e298571ae30e919c810694f258f2ba68fc77a2185c7c8829ad5a", size = 253493, upload-time = "2025-11-05T22:41:03.278Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/0d/ed/985f2d2e2b5fbd912ab0fdb11d6dc48c22553a6c4edffabb8146d53b974a/google_genai-1.2.0-py3-none-any.whl", hash = "sha256:609d61bee73f1a6ae5b47e9c7dd4b469d50318f050c5ceacf835b0f80f79d2d9", size = 130744, upload-time = "2025-02-12T16:40:03.601Z" },
+    { url = "https://files.pythonhosted.org/packages/d5/d3/84a152746dc7bdebb8ba0fd7d6157263044acd1d14b2a53e8df4a307b6b7/google_genai-1.49.0-py3-none-any.whl", hash = "sha256:ad49cd5be5b63397069e7aef9a4fe0a84cbdf25fcd93408e795292308db4ef32", size = 256098, upload-time = "2025-11-05T22:41:01.429Z" },
 ]
 
 [[package]]
@@ -4373,7 +4377,7 @@ name = "nvidia-cudnn-cu12"
 version = "9.10.2.21"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas-cu12" },
+    { name = "nvidia-cublas-cu12", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
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