diff --git a/docs/en/concepts/tasks.mdx b/docs/en/concepts/tasks.mdx index c9082dbc7..9eba77f19 100644 --- a/docs/en/concepts/tasks.mdx +++ b/docs/en/concepts/tasks.mdx @@ -224,6 +224,7 @@ By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput` | **JSON Dict** | `json_dict` | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task. | | **Agent** | `agent` | `str` | The agent that executed the task. | | **Output Format** | `output_format` | `OutputFormat` | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. | +| **Messages** | `messages` | `list[LLMMessage]` | The messages from the last task execution. | ### Task Methods and Properties diff --git a/lib/crewai/src/crewai/agent/core.py b/lib/crewai/src/crewai/agent/core.py index 1d94c4d19..b4f46a216 100644 --- a/lib/crewai/src/crewai/agent/core.py +++ b/lib/crewai/src/crewai/agent/core.py @@ -119,6 +119,7 @@ class Agent(BaseAgent): _times_executed: int = PrivateAttr(default=0) _mcp_clients: list[Any] = PrivateAttr(default_factory=list) + _last_messages: list[LLMMessage] = PrivateAttr(default_factory=list) max_execution_time: int | None = Field( default=None, description="Maximum execution time for an agent to execute a task", @@ -538,6 +539,12 @@ class Agent(BaseAgent): event=AgentExecutionCompletedEvent(agent=self, task=task, output=result), ) + self._last_messages = ( + self.agent_executor.messages.copy() + if self.agent_executor and hasattr(self.agent_executor, "messages") + else [] + ) + self._cleanup_mcp_clients() return result @@ -1341,6 +1348,15 @@ class Agent(BaseAgent): def set_fingerprint(self, fingerprint: Fingerprint) -> None: self.security_config.fingerprint = fingerprint + @property + def last_messages(self) -> list[LLMMessage]: + """Get messages from the last task execution. + + Returns: + List of LLM messages from the most recent task execution. + """ + return self._last_messages + def _get_knowledge_search_query(self, task_prompt: str, task: Task) -> str | None: """Generate a search query for the knowledge base based on the task description.""" crewai_event_bus.emit( diff --git a/lib/crewai/src/crewai/crew.py b/lib/crewai/src/crewai/crew.py index f5af4a426..cadd9d3b1 100644 --- a/lib/crewai/src/crewai/crew.py +++ b/lib/crewai/src/crewai/crew.py @@ -809,6 +809,7 @@ class Crew(FlowTrackable, BaseModel): "json_dict": output.json_dict, "output_format": output.output_format, "agent": output.agent, + "messages": output.messages, }, "task_index": task_index, "inputs": inputs, @@ -1236,6 +1237,7 @@ class Crew(FlowTrackable, BaseModel): pydantic=stored_output["pydantic"], json_dict=stored_output["json_dict"], output_format=stored_output["output_format"], + messages=stored_output.get("messages", []), ) self.tasks[i].output = task_output diff --git a/lib/crewai/src/crewai/lite_agent.py b/lib/crewai/src/crewai/lite_agent.py index 4314e900e..01b165638 100644 --- a/lib/crewai/src/crewai/lite_agent.py +++ b/lib/crewai/src/crewai/lite_agent.py @@ -358,6 +358,7 @@ class LiteAgent(FlowTrackable, BaseModel): pydantic=formatted_result, agent_role=self.role, usage_metrics=usage_metrics.model_dump() if usage_metrics else None, + messages=self._messages, ) # Process guardrail if set diff --git a/lib/crewai/src/crewai/lite_agent_output.py b/lib/crewai/src/crewai/lite_agent_output.py index 582f52cdd..4183dba1f 100644 --- a/lib/crewai/src/crewai/lite_agent_output.py +++ b/lib/crewai/src/crewai/lite_agent_output.py @@ -6,6 +6,8 @@ from typing import Any from pydantic import BaseModel, Field +from crewai.utilities.types import LLMMessage + class LiteAgentOutput(BaseModel): """Class that represents the result of a LiteAgent execution.""" @@ -20,6 +22,7 @@ class LiteAgentOutput(BaseModel): usage_metrics: dict[str, Any] | None = Field( description="Token usage metrics for this execution", default=None ) + messages: list[LLMMessage] = Field(description="Messages of the agent", default=[]) def to_dict(self) -> dict[str, Any]: """Convert pydantic_output to a dictionary.""" diff --git a/lib/crewai/src/crewai/task.py b/lib/crewai/src/crewai/task.py index 869419c25..dfb505d77 100644 --- a/lib/crewai/src/crewai/task.py +++ b/lib/crewai/src/crewai/task.py @@ -539,6 +539,7 @@ class Task(BaseModel): json_dict=json_output, agent=agent.role, output_format=self._get_output_format(), + messages=agent.last_messages, ) if self._guardrails: @@ -949,6 +950,7 @@ Follow these guidelines: json_dict=json_output, agent=agent.role, output_format=self._get_output_format(), + messages=agent.last_messages, ) return task_output diff --git a/lib/crewai/src/crewai/tasks/task_output.py b/lib/crewai/src/crewai/tasks/task_output.py index ba9f95c18..901604ac1 100644 --- a/lib/crewai/src/crewai/tasks/task_output.py +++ b/lib/crewai/src/crewai/tasks/task_output.py @@ -6,6 +6,7 @@ from typing import Any from pydantic import BaseModel, Field, model_validator from crewai.tasks.output_format import OutputFormat +from crewai.utilities.types import LLMMessage class TaskOutput(BaseModel): @@ -40,6 +41,7 @@ class TaskOutput(BaseModel): output_format: OutputFormat = Field( description="Output format of the task", default=OutputFormat.RAW ) + messages: list[LLMMessage] = Field(description="Messages of the task", default=[]) @model_validator(mode="after") def set_summary(self): diff --git a/lib/crewai/src/crewai/utilities/types.py b/lib/crewai/src/crewai/utilities/types.py index bc331a97e..a4627613d 100644 --- a/lib/crewai/src/crewai/utilities/types.py +++ b/lib/crewai/src/crewai/utilities/types.py @@ -1,6 +1,8 @@ """Types for CrewAI utilities.""" -from typing import Any, Literal, TypedDict +from typing import Any, Literal + +from typing_extensions import TypedDict class LLMMessage(TypedDict): diff --git a/lib/crewai/tests/agents/test_lite_agent.py b/lib/crewai/tests/agents/test_lite_agent.py index 0c6b00c23..f2fa4b2e6 100644 --- a/lib/crewai/tests/agents/test_lite_agent.py +++ b/lib/crewai/tests/agents/test_lite_agent.py @@ -238,6 +238,27 @@ def test_lite_agent_returns_usage_metrics(): assert result.usage_metrics["total_tokens"] > 0 +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_lite_agent_output_includes_messages(): + """Test that LiteAgentOutput includes messages from agent execution.""" + llm = LLM(model="gpt-4o-mini") + agent = Agent( + role="Research Assistant", + goal="Find information about the population of Tokyo", + backstory="You are a helpful research assistant who can search for information about the population of Tokyo.", + llm=llm, + tools=[WebSearchTool()], + verbose=True, + ) + + result = agent.kickoff("What is the population of Tokyo?") + + assert isinstance(result, LiteAgentOutput) + assert hasattr(result, "messages") + assert isinstance(result.messages, list) + assert len(result.messages) > 0 + + @pytest.mark.vcr(filter_headers=["authorization"]) @pytest.mark.asyncio async def test_lite_agent_returns_usage_metrics_async(): diff --git a/lib/crewai/tests/cassettes/test_lite_agent_output_includes_messages.yaml b/lib/crewai/tests/cassettes/test_lite_agent_output_includes_messages.yaml new file mode 100644 index 000000000..c71e22690 --- /dev/null +++ b/lib/crewai/tests/cassettes/test_lite_agent_output_includes_messages.yaml @@ -0,0 +1,261 @@ +interactions: +- request: + body: '{"messages":[{"role":"system","content":"You are Research Assistant. You + are a helpful research assistant who can search for information about the population + of Tokyo.\nYour personal goal is: Find information about the population of Tokyo\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: search_web\nTool Arguments: {''query'': {''description'': + None, ''type'': ''str''}}\nTool Description: Search the web for information + about a topic.\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 [search_web], 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":"What is the population of Tokyo?"}],"model":"gpt-4o-mini"}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - '1160' + content-type: + - application/json + host: + - api.openai.com + user-agent: + - OpenAI/Python 1.109.1 + x-stainless-arch: + - arm64 + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.109.1 + x-stainless-read-timeout: + - '600' + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: !!binary | + H4sIAAAAAAAAAwAAAP//jJM9b9swEIZ3/YoDZzvwd2xtRTI0HZolcIcqkGnqJLGheCx5Suoa/u+F + 5A/JTQp00XDPva/ui/sIQOhMxCBUKVlVzgzv5Lf7fDtbmcfRV1M9/l5/Wa/N6/r+7fNydycGjYK2 + P1DxWXWjqHIGWZM9YuVRMjau49vFZDleLVbzFlSUoWlkhePhjIaVtno4GU1mw9HtcLw8qUvSCoOI + 4XsEALBvv02dNsNfIobR4BypMARZoIgvSQDCk2kiQoagA0vLYtBBRZbRtqVvNpvEPpVUFyXH8AAW + MQMmCCi9KiEnD1wiGMkYGLTNyVeyaRI8FtJn2hZtgiNXmyOgHJ7oZUc3if2kmkh8ckvfcHuOwYN1 + NcewT8TPGv0uEXEiVO09Wv7IDCajyTQRh8RuNpt+Lx7zOshmnrY2pgektcStSTvF5xM5XOZmqHCe + tuEvqci11aFMPcpAtplRYHKipYcI4LndT301cuE8VY5TphdsfzeZnvYjurPo6HR5gkwsTU+1OIMr + vzRDltqE3oaFkqrErJN25yDrTFMPRL2u31fzkfexc22L/7HvgFLoGLPUecy0uu64S/PYvJp/pV2m + 3BYsAvpXrTBljb7ZRIa5rM3xlkXYBcYqzbUt0Duvjwedu3S+GMl8gfP5SkSH6A8AAAD//wMAJGbR + +94DAAA= + headers: + CF-RAY: + - 99c98dd3ddb9ce6c-SJC + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Tue, 11 Nov 2025 00:08:16 GMT + Server: + - cloudflare + Set-Cookie: + - __cf_bm=6maCeRS26vR_uzqYdtL7RXY7kzGdvLhWcE2RP2PnZS0-1762819696-1.0.1.1-72zCZZVBiGDdwPDvETKS_fUA4DYCLVyVHDYW2qpSxxAUuWKNPLxQQ1PpeI7YuB9v.y1e3oapeuV5mBjcP4c9_ZbH.ZI14TUNOexPUB6yCaQ; + path=/; expires=Tue, 11-Nov-25 00:38:16 GMT; domain=.api.openai.com; HttpOnly; + Secure; SameSite=None + - _cfuvid=a.XOUFuP.5IthR7ITJrIWIZSWWAkmHU._pM9.qhCnhM-1762819696364-0.0.1.1-604800000; + path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None + Strict-Transport-Security: + - max-age=31536000; includeSubDomains; preload + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + access-control-expose-headers: + - X-Request-ID + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + openai-organization: + - crewai-iuxna1 + openai-processing-ms: + - '1199' + openai-project: + - proj_xitITlrFeen7zjNSzML82h9x + openai-version: + - '2020-10-01' + x-envoy-upstream-service-time: + - '1351' + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-project-tokens: + - '150000000' + x-ratelimit-limit-requests: + - '30000' + x-ratelimit-limit-tokens: + - '150000000' + x-ratelimit-remaining-project-tokens: + - '149999735' + x-ratelimit-remaining-requests: + - '29999' + x-ratelimit-remaining-tokens: + - '149999735' + x-ratelimit-reset-project-tokens: + - 0s + x-ratelimit-reset-requests: + - 2ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_50a8251d98f748bb8e73304a2548b694 + status: + code: 200 + message: OK +- request: + body: '{"messages":[{"role":"system","content":"You are Research Assistant. You + are a helpful research assistant who can search for information about the population + of Tokyo.\nYour personal goal is: Find information about the population of Tokyo\n\nYou + ONLY have access to the following tools, and should NEVER make up tools that + are not listed here:\n\nTool Name: search_web\nTool Arguments: {''query'': {''description'': + None, ''type'': ''str''}}\nTool Description: Search the web for information + about a topic.\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 [search_web], 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":"What is the population of Tokyo?"},{"role":"assistant","content":"```\nThought: + I need to search for the latest information regarding the population of Tokyo.\nAction: + search_web\nAction Input: {\"query\":\"current population of Tokyo 2023\"}\n```\nObservation: + Tokyo''s population in 2023 was approximately 21 million people in the city + proper, and 37 million in the greater metropolitan area."}],"model":"gpt-4o-mini"}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - '1521' + content-type: + - application/json + cookie: + - __cf_bm=6maCeRS26vR_uzqYdtL7RXY7kzGdvLhWcE2RP2PnZS0-1762819696-1.0.1.1-72zCZZVBiGDdwPDvETKS_fUA4DYCLVyVHDYW2qpSxxAUuWKNPLxQQ1PpeI7YuB9v.y1e3oapeuV5mBjcP4c9_ZbH.ZI14TUNOexPUB6yCaQ; + _cfuvid=a.XOUFuP.5IthR7ITJrIWIZSWWAkmHU._pM9.qhCnhM-1762819696364-0.0.1.1-604800000 + host: + - api.openai.com + user-agent: + - OpenAI/Python 1.109.1 + x-stainless-arch: + - arm64 + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.109.1 + x-stainless-read-timeout: + - '600' + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: !!binary | + H4sIAAAAAAAAAwAAAP//jFPLbtswELz7KxY8W4Es+RHr1ifgQw8F3OZQBxJDrSTWFJcgqSRG4H8v + KD+kNCnQCwFyZpazs+TLBIDJkmXARMO9aI2KPvG7z81d8mWf4E/cxN9+PK5SvfkYf08Pe8+mQUEP + v1H4i+pGUGsUekn6BAuL3GOoOlstk9vZerle9UBLJaogq42P5hS1UssoiZN5FK+i2e1Z3ZAU6FgG + vyYAAC/9GnzqEp9ZBvH0ctKic7xGll1JAMySCieMOyed5/rk+QwK0h51b70oip3eNtTVjc9gA5qe + YB8W3yBUUnMFXLsntDv9td996HcZbBsEQ6ZTPLQMVMGW9gcCqSGJkxSkA26MpWfZco/qAMkMWqlU + IBskozBQwy1C+gMYSwYtcF1CuroSz4y6j9JCi96SISU918At8pudLopi3JrFqnM8xKs7pUYA15p8 + 77UP9f6MHK8xKqqNpQf3l5RVUkvX5Ba5Ix0ic54M69HjBOC+H1f3agLMWGqNzz3tsb8ujdNTPTa8 + kgGdX0BPnquRar6cvlMvL9Fzqdxo4Exw0WA5SIfXwbtS0giYjLp+6+a92qfOpa7/p/wACIHGY5kb + i6UUrzseaBbDJ/oX7Zpyb5g5tI9SYO4l2jCJEiveqfN3dAfnsc0rqWu0xsrT+65MvljGvFriYrFm + k+PkDwAAAP//AwDgLjwY7QMAAA== + headers: + CF-RAY: + - 99c98dde7fc9ce6c-SJC + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Tue, 11 Nov 2025 00:08:18 GMT + Server: + - cloudflare + Strict-Transport-Security: + - max-age=31536000; includeSubDomains; preload + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + access-control-expose-headers: + - X-Request-ID + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + openai-organization: + - crewai-iuxna1 + openai-processing-ms: + - '1339' + openai-project: + - proj_xitITlrFeen7zjNSzML82h9x + openai-version: + - '2020-10-01' + x-envoy-upstream-service-time: + - '1523' + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-project-tokens: + - '150000000' + x-ratelimit-limit-requests: + - '30000' + x-ratelimit-limit-tokens: + - '150000000' + x-ratelimit-remaining-project-tokens: + - '149999657' + x-ratelimit-remaining-requests: + - '29999' + x-ratelimit-remaining-tokens: + - '149999657' + x-ratelimit-reset-project-tokens: + - 0s + x-ratelimit-reset-requests: + - 2ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_ade054352f8c4dfdba50683755eba41d + status: + code: 200 + message: OK +version: 1 diff --git a/lib/crewai/tests/cassettes/test_task_output_includes_messages.yaml b/lib/crewai/tests/cassettes/test_task_output_includes_messages.yaml new file mode 100644 index 000000000..5f9f33fe8 --- /dev/null +++ b/lib/crewai/tests/cassettes/test_task_output_includes_messages.yaml @@ -0,0 +1,423 @@ +interactions: +- request: + body: '{"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: Give me a + list of 3 interesting ideas about AI.\n\nThis is the expected criteria for your + final answer: Bullet point list of 3 ideas.\nyou MUST return the actual complete + content as the final answer, not a summary.\n\nYou MUST follow these instructions: + \n - Include specific examples and real-world case studies to enhance the credibility + and depth of the article ideas.\n - Incorporate mentions of notable companies, + projects, or tools relevant to each topic to provide concrete context.\n - Add + diverse viewpoints such as interviews with experts, users, or thought leaders + to enrich the narrative and lend authority.\n - Address ethical, social, and + emotional considerations explicitly to reflect a balanced and comprehensive + analysis.\n - Enhance the descriptions by including implications for future + developments and the potential impact on society.\n - Use more engaging and + vivid language that draws the reader into each topic''s nuances and importance.\n + - Include notes or summaries that contextualize each set of ideas in terms of + relevance and potential reader engagement.\n - In future tasks, focus on elaborating + initial outlines into more detailed and nuanced article proposals with richer + content and insights.\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"}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - '2076' + content-type: + - application/json + host: + - api.openai.com + user-agent: + - OpenAI/Python 1.109.1 + x-stainless-arch: + - arm64 + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.109.1 + x-stainless-read-timeout: + - '600' + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: !!binary | + H4sIAAAAAAAAA1xXS44kxw3d6xREbwwMqgsaWWPJvSvNT21roIE0kAx5NswIViWnI4MpMiKra7TR + IbTxSbz3UXQSgxFZ1S1tGuiMH/n43iPrl08Arjhe3cBVGLGEaU7Xz/HHL3/YfTz+9GJ69sPXL8sr + e/bT9JMefv4Ql39dbfyEDB8olPOpbZBpTlRYcl8OSljIb336xd8++/Lp3//6xbO2MEmk5McOc7n+ + fPv0euLM1599+tmz608/v376+Xp8FA5kVzfw708AAH5pfz3QHOn+6gY+3Zy/TGSGB7q6uWwCuFJJ + /uUKzdgK5nK1eVgMkgvlFvu7UephLDdwC1mOEDDDgRcChIMnAJjtSPo+v+KMCXbtv5v3+X2+hidP + drfXL5QXyvCW1CRj4o8U4WvCVMaASjfwHS2SqsPCHzkf4C0Wplzg21qCTGTwblSPAN4qRQ7F395l + TKfCwZ48eZ8B3o1swJEQ6H5OomQwyhF2t8AGRTHbXnTyy8fLwzCcgDIOqX3mw5hOwDnywrGuURbP + b/JY5oTZYECjCJJhXkOMWBAwR5gfQmvFsy28EgXODmygDXjtMTMZJL4juP3qDfyIxSSvUMCIC0Gi + hRQPFD30IoCe5keCyW/HBEpBNNoGDpRl4mCb9npInNt6UcZkfrIgJ1EvViCFMpLi7K/XzD9XSiew + ysVTFCAM4zmjLbwgmt9wjr//+h/zKOxkhSYY0cBGOWaYVSY2As6XrPMB7jhmcgA/VD0BoabTBgwX + X0u8kEFZqzirYEeKcyFdKHvpt3Db/mM6GhzZAXmo1KyyJzN2+ljHu4droLQQJhikjEC5jNUYbYK9 + KOxuWw6zOJEZU4dKHJBsgIPUArPyguHUlloxjUJVLqdNpwQfxpYelbEBHDnRNKF59iPm2MhjlI3X + jJxn2BP6XgJjchyK09NG3hcIUlMEpVgDQWSbUbl4YfzCh4wxBDKDoRbAZALKdmdA9xhIB2whcaaf + najlBLzvfBFHRwlqpoVyOvkDRXmoheIWXk5SGoQe0gXAgTLtucBeZYJRZmpY8DSrLOTElkMW4x7L + 5Hj0iOh+JmXKwQ/cM5UTyEIKWItMbmsQKbDX7HrCO86HLbyqpSr53YlDA8nTDqlGgt3t9SxHUoqw + sJaKaYUDLvbUK6+E6brw5ELLXEQdi0aYI6HikAgGFi+JqG1WtvoeBJtQC+kGJlECJZslm5ftEfAU + ZGV8GfFcLrovlCMk3lPLO7ioO2nOJZB9Xz4kGRzhbfekIjMHNyGlRAvm0g5RPuChxWTABYrUMLpl + 1QkzHCml64HacgGEfc0R3YQwNX/oPOa8cCG/FyNpk3zwbGJXu2tLHWSCAZObAAxUjkQZCoUxS5JD + T6Axu9GvmQxn2l68uxbJMkltRrA7NL5whudui47bbY7VCUZ2Ay/vZ8zRo/5KPGL/6qjstGzgTTUO + 3au+L6KnQsmV82fzjpSWJoUi/iL2F5thfjtTXgX9YvfNN//778um8YXNqYL+yD/qHUUKd+2ZR/v9 + +yD3bf/kgTQzFpes5B5URtWe0oEyKRZRA6vB2eeRvKj5QL75D70knHGYVVyuZF51MnpQYxgxJcoH + 8pORu/4gSye/7F0uo6iNPHd76lc6o4YTBEkJB9Eu+O6KjSOoha0YtBYTaaYcKZd0OoffousdfAvf + uWKOoilCQCOwUmN3nC69H1Ezaa/RKmun2+XJ3e117C28ozerxBo8gxays+11RY2MGeY6JLbRz+5u + r9do3EaEinpHmFHdDPrGAJEGLOSwtRkD0krnVnOnQEsUvqM933nTj+IebKHapcNTHp3f9lCOUZQ/ + SrYNHEdOBHZHs88KcBTV0+r8HnbkPnd4ITqwdPZIB0J7z+kN59JFPsjQjDthoDYbOK/Wgmzh5aVR + tCBbmfcSqvnYEGQ+qVvoBuSYqZV9cwHRqeBvhNVP3BIb1BQ98jt73FIe5BFpkuA1/3hpG24GZzA6 + D5t546lbd5Bpksj7k4fdKPso7+jFb2lzXqTpEZ0vDvFffABxLCX//utvR8nuNh7+Hi1wbpYOC6lV + O4PONnWbefSABcmZQrl0hKKUo9u7H5jdtEzmka00B2vD0IMUXJrNm6s/750hQqipVMXUSX9fHj/p + TeBcvHTy1kt7zs1etQAXo7TfPjKhgHPhBR+baxtUyDwUzq1W+2orb/5kp77YqWQzK3ul93vS3oCU + w/jIaxrGWRa8aIldxlxOFws+02l3C6+9vWan+g18VTk1r33nfjSjUi4b2IUgNZfWA79vTcw6OeGd + VitH0TKeVswfWW/vURijNg9rKVR1ckEmiu2KveJEjYRA2ap2gUP0fiRzkwEmPmR77FILpkq2AZrm + Ea0P9+UScDhtnK89Yk5t5upcYs1ktoXnf5yZX4scEl0G1Lb5DQcVk33p8zOZZ882dtKsvW0QbENz + dwPOXLgV4MHie7dowmj9+DIcOJQtzznJafqznVqgjMry4KXOPx+c1Fr784Fo4ParYY8u3TbBH3Jr + BOtobVB9fThBwiOQT5DdWDZA9+Sz0p77uru3rbLsDv8HfJ4nwjZAw+52A4p97DEJTD47YEKdVvOz + qgtx6oNBm33ZgvK0anjr3Zz03Hvf8ZS5wGsatLbd/3SJPlc87kXbiLiw5+6TrJN1JjUfkhrEkhsC + 7ZwVraEL1X8ouL7degaKsUvr8nvDf9jERUJvZfvW50KqbVqLZHzI6zB4qOkccpfKZd7utJ5VhpXT + k2grwFpZPs9tzSrdVVbnquaaX8X8fwAAAP//jFjNbtwgEL7vUyBfcmkr7SZt95pjpLxCZBF7sFEw + EMCtcth3j74BL3jbSj2PjT3AfH+squh9ZfwrhN2Iok3jxr3cp0B3UQBLjLn5++k6xs1JjhrfBjXL + N5qd2SSdh72xgO4wafbOW7M7LZ+5NGHIBbg3b3sdtcQ3e7VFdXNvi056khv7KZKBRaospvDxSSw6 + rpGgMW4p75t4do5pXM4kR+64zh6jgVMZNfOFyghWTsuFD8GwjamsGhToTSFpdfUGIKxXQgYgvP7l + kjRfduhTrEv2PHGWMA+vwcnRZCzOpgnZyQI7v5PkMQVnJ+YDpBJAe0auDfKLT6SxkQsYJffVO1Pu + uV68HFKm6p0oL/6WmW7FuWLYZ+kzC6hMer9xS1r6oIUdUBRB4gaB5GwmuUXbzR6AHNqcJpBao0RY + ZFdjmoK01qW8kUiIXkrlcs2EjJswHPHm1Q7kGOc+kIzOIv+JyfmOq5eDEC+cPa27OKmDy/KpT+6N + +HP355I9dTXzqtWfD/elmnCotXA8nrbKbsV+JOQZscmvukEOM4313Rp2Qa64pnBo+v7zf/62du5d + 2+l/lq+FAdqIxn6LRdqe62OBEAr+67HrPvMPdxGRyEB90hRwFiMpuZqc1HUZKnuFiQ8+6BzXKd8/ + DKfz96M6/zh1h8vhEwAA//8DAJPMJFq9FAAA + headers: + CF-RAY: + - 99c98602dfefcf4d-SJC + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Tue, 11 Nov 2025 00:03:08 GMT + Server: + - cloudflare + Set-Cookie: + - __cf_bm=ObqPLq12_9tJ06.V1RkHCM6FH_YGcLoC2ykIFBEawa8-1762819388-1.0.1.1-l7PJTVbZ1vCcKdeOe8GQVuFL59SCk0xhO_dMFY2wuH5Ybd1hhM_Xcv_QivXVhZlBGlRgRAgG631P99JOs_IYAYcNFJReE.3NpPl34VfPVeQ; + path=/; expires=Tue, 11-Nov-25 00:33:08 GMT; domain=.api.openai.com; HttpOnly; + Secure; SameSite=None + - _cfuvid=kdn.HizdlSPG7cBu_zv1ZPcu0jMwDQIA4H9YvMXu6a0-1762819388587-0.0.1.1-604800000; + path=/; domain=.api.openai.com; HttpOnly; Secure; SameSite=None + Strict-Transport-Security: + - max-age=31536000; includeSubDomains; preload + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + access-control-expose-headers: + - X-Request-ID + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + openai-organization: + - crewai-iuxna1 + openai-processing-ms: + - '13504' + openai-project: + - proj_xitITlrFeen7zjNSzML82h9x + openai-version: + - '2020-10-01' + x-envoy-upstream-service-time: + - '13638' + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-project-tokens: + - '150000000' + x-ratelimit-limit-requests: + - '30000' + x-ratelimit-limit-tokens: + - '150000000' + x-ratelimit-remaining-project-tokens: + - '149999507' + x-ratelimit-remaining-requests: + - '29999' + x-ratelimit-remaining-tokens: + - '149999507' + x-ratelimit-reset-project-tokens: + - 0s + x-ratelimit-reset-requests: + - 2ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_2de40e1beb5f42ea896664df36e8ce8f + status: + code: 200 + message: OK +- request: + body: "{\"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: Summarize the ideas from the previous task.\\n\\nThis is the expected + criteria for your final answer: A summary of the ideas.\\nyou MUST return the + actual complete content as the final answer, not a summary.\\n\\nThis is the + context you're working with:\\n- **AI-Driven Personalized Healthcare: Revolutionizing + Patient Outcomes Through Predictive Analytics**\\n This idea explores how AI + is transforming healthcare by enabling highly individualized treatment plans + based on patient data and predictive models. For instance, companies like IBM + Watson Health have leveraged AI to analyze medical records, genomics, and clinical + trials to tailor cancer therapies uniquely suited to each patient. DeepMind\u2019s + AI system has shown promise in predicting kidney injury early, saving lives + through proactive intervention. Interviews with healthcare professionals and + patients reveal both enthusiasm for AI\u2019s potential and concerns about privacy + and data security, highlighting ethical dilemmas in handling sensitive information. + Socially, this shift could reduce disparities in healthcare access but also + risks exacerbating inequality if AI tools are unevenly distributed. Emotionally, + patients benefit from hope and improved prognosis but might also experience + anxiety over automated decision-making. Future implications include AI-powered + virtual health assistants and real-time monitoring with wearable biosensors, + promising a smarter, more responsive healthcare ecosystem that could extend + life expectancy and quality of life globally. This topic is relevant and engaging + as it touches human well-being at a fundamental level and invites readers to + consider the intricate balance between technology and ethics in medicine.\\n\\n- + **Autonomous AI Agents in Creative Industries: Expanding Boundaries of Art, + Music, and Storytelling**\\n This idea delves into AI agents like OpenAI\u2019s + DALL\xB7E for visual art, Jukedeck and OpenAI\u2019s Jukebox for music composition, + and narrative generators such as AI Dungeon, transforming creative processes. + These AI tools challenge traditional notions of authorship and creativity by + collaborating with human artists or independently generating content. Real-world + case studies include Warner Music experimenting with AI-driven music production + and the Guardian publishing AI-generated poetry, sparking public debate. Thought + leaders like AI artist Refik Anadol discuss how AI enhances creative horizons, + while skeptics worry about the dilution of human emotional expression and potential + job displacement for artists. Ethical discussions focus on copyright, ownership, + and the authenticity of AI-produced works. Socially, AI agents democratize access + to creative tools but may also commodify art. The emotional dimension involves + audiences' reception\u2014wonder and fascination versus skepticism and emotional + disconnect. Future trends anticipate sophisticated AI collaborators that understand + cultural context and emotions, potentially redefining art itself. This idea + captivates readers interested in the fusion of technology and the human spirit, + offering a rich narrative on innovation and identity.\\n\\n- **Ethical AI Governance: + Building Transparent, Accountable Systems for a Trustworthy Future**\\n This + topic addresses the urgent need for frameworks ensuring AI development aligns + with human values, emphasizing transparency, accountability, and fairness. Companies + like Google DeepMind and Microsoft have established AI ethics boards, while + initiatives such as OpenAI commit to responsible AI deployment. Real-world scenarios + include controversies over biased facial recognition systems used by law enforcement, + exemplified by cases involving companies like Clearview AI, raising societal + alarm about surveillance and discrimination. Experts like Timnit Gebru and Kate + Crawford provide critical perspectives on bias and structural injustice embedded + in AI systems, advocating for inclusive design and regulation. Ethically, this + topic probes the moral responsibility of creators versus users and the consequences + of autonomous AI decisions. Socially, there's a call for inclusive governance + involving diverse stakeholders to prevent marginalization. Emotionally, public + trust hinges on transparent communication and mitigation of fears related to + AI misuse or job displacement. Looking ahead, the establishment of international + AI regulatory standards and ethical certifications may become pivotal, ensuring + AI benefits are shared broadly and risks minimized. This topic strongly resonates + with readers concerned about the socio-political impact of AI and invites active + discourse on shaping a future where technology empowers rather than undermines + humanity.\\n\\nYou MUST follow these instructions: \\n - Include specific examples + and real-world case studies to enhance the credibility and depth of the article + ideas.\\n - Incorporate mentions of notable companies, projects, or tools relevant + to each topic to provide concrete context.\\n - Add diverse viewpoints such + as interviews with experts, users, or thought leaders to enrich the narrative + and lend authority.\\n - Address ethical, social, and emotional considerations + explicitly to reflect a balanced and comprehensive analysis.\\n - Enhance the + descriptions by including implications for future developments and the potential + impact on society.\\n - Use more engaging and vivid language that draws the + reader into each topic's nuances and importance.\\n - Include notes or summaries + that contextualize each set of ideas in terms of relevance and potential reader + engagement.\\n - In future tasks, focus on elaborating initial outlines into + more detailed and nuanced article proposals with richer content and insights.\\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\"}" + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - '6552' + content-type: + - application/json + cookie: + - __cf_bm=ObqPLq12_9tJ06.V1RkHCM6FH_YGcLoC2ykIFBEawa8-1762819388-1.0.1.1-l7PJTVbZ1vCcKdeOe8GQVuFL59SCk0xhO_dMFY2wuH5Ybd1hhM_Xcv_QivXVhZlBGlRgRAgG631P99JOs_IYAYcNFJReE.3NpPl34VfPVeQ; + _cfuvid=kdn.HizdlSPG7cBu_zv1ZPcu0jMwDQIA4H9YvMXu6a0-1762819388587-0.0.1.1-604800000 + host: + - api.openai.com + user-agent: + - OpenAI/Python 1.109.1 + x-stainless-arch: + - arm64 + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.109.1 + x-stainless-read-timeout: + - '600' + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.13.3 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + body: + string: !!binary | + H4sIAAAAAAAAA1xXzY4bxxG++ykKexRIQlKcRN7b2pIcBrItSwIUIL4Ue4ozJfZ0tau6yR35oofw + JU+Sex5FTxJUz3BJ6bLAcqa66+f7qfnjG4Ab7m5u4SYMWMKY4/oHfP/sw1v99WM3vvzX8x9//3mr + rx4f3te85WflZuURsvtAoZyjNkHGHKmwpPlxUMJCfuqTv//t6bMn3/3l2XftwSgdRQ/rc1l/u3my + Hjnx+unjp39dP/52/eTbJXwQDmQ3t/DvbwAA/mh/PdHU0f3NLTxenX8ZyQx7url9eAngRiX6Lzdo + xlYwzUkvD4OkQqnl/m6Q2g/lFraQ5AQBE/R8JEDovQDAZCdSgN/SS04Y4a79f+s//JYePbrbrp8r + HynBa1KThJE/Ugf/IIxlCKh0C2/oKLF6Y/gjpx5eY2FKBX6pJchIBu8G9RzgtVLHofjtdwnjVDjY + o0d+07uBDbgjhECpkBpIgjIQFMVke9ERW1gWz1X2cLcFTjA8ZLGC00BKkC9X4PkKoIS7SDBMmXSd + r8so3oLRk80Rk0FBjqL+QNr1nIpywELQkT8zv5tTx0fuKkbIS6kdFtzAa5ZEpN4DBwsmJoPIB4Lt + 9z/Beywmaekc1MKeghdSBIz3BcrSpiNagdHrwAhKQbSzFfSUZPRq/C6jYivA1EGInNqLRRkj7D25 + 1NsKguK+eCo7siwH8skHUi9LMXtmZfDxR+6Tty2wUZzgxGUAPBf2+dN/DGri36u3VvYcaQNveeSI + GqcVPCfKP3Hq2nvYHf2KzmuyyQqNBgMevXejJCvqdIHIe1obHluXMOOOI5cJdhNgCNXfiRPsRSmg + tfwx1EJw4C7RBJw+VPXkcSDsfBo2jbnIaIA5E3rzV4AxysljsxKNucHh3FB2fB0pOV43sE3G/VD2 + 9fyA6WRzEy7ggqwYCnuEQ9PbvvTHYOB+iH4EIDRIJFRtYP386c/TwJFgxDQBjTvF4PNuvcoqIxt5 + ocBjVjlSB3ImjN8wSKZVmxaB52DcJ95zwFQgiI8yGeBO6ow+yMpHDFOLpTK0Wq3QCbWzgXNrFSXj + r5qxcMt70Uh4Di3+riSoqSO1IOppwYgfRMEkMBWM0NEOC902qmRxxXEMzvQsAjvlrqfrRmIIZAY9 + ZoMjqVVrscp2mOFYE/3uXezYivKuqcrDgRINRpzgJGqUgO55RkjHllG5MNkGXoziQRgdnw9jcoXs + wPEOQcVMBTv7/OnPHSXac2nH7FVG2FEppD6fPokts/hCM2a5mahAr5hz9NAGmJoIjUCOpIC1yNjw + flV95yRjSXZpVpzAanbxaSkMdcQEH2rXuyht4JXIoaUm6oNseABKx3YMdbCvparLVIi1I4O77bpJ + JHVwZC3eyfl+eLCJuaIToTZR3LE4LEQNOop8nOVLCeO68EgwSuIiM61cOWLXOAk2ohbSFYyi5IBM + FL4ql4LMMrCBtzUMwCnJsWHtgv8iQGlw2YA+yg5j0weg+0yhYFrw7JBwkZB9e7yCEVtbiiO2SObQ + 2JuagilFOjpLGhMuaAAlH6I/MOACRWoYaAag49tPn/t/ohjXO2qFusRijNaYipDqLHE7jC3pHZUT + UYJQG4bW5IAvFIYkUfo5+zPZGrVss/hqLZJklOpDg7u+oZQT/OCO5BTdpq46B8hu4cV9xqbr8L3U + 1GFTQGeFlhX8VI3DbAZvi+hUKDomr4y1yUUu3tXYiDzIqSH0kgDOCfjYlGzA3OT5nMueKboHhQFj + pNSfO4O14bSNQrHjudWQZB6y7M9H+PDmgDKIuh5t4F3jszky0OCXTGnRxud3r179778v3PHI/aCZ + 6VwUHNkc1Khlpmuh+4Zyx1N2T1wk13vS4sRaVu7vxbVuMeR/1gN1FA4tq6u7/fed3F91J05zEQSi + 3LcVyST1toGfz0oPrSVnr7/bwvOaepIEerUZEdjVdJrfnV2qmxKOPsNmQTivLzlKgY6OFCXPG8pC + 7N0Ed9sNvHGKnkRjB02FwsIsjrE2r30giA2+XSwaAe9RE+mMmlayc03Zr8AI1eii31mlq4F87Fr8 + rjJMS2uLYjjMUvJuIPixonaMqR24k9hBrrtzTvOB6/M4O8hCRacVuGo3HrvuxGmJcTkPUtXI7Xkf + 62Irx7YvP/QYtbAVeEN7Pvg62UkEGvOA5r12hN9tvbbG4DOQB1H+KMlW8054kdz5NGt7n6+cgTxM + 0rIoNF0a+QKw4HYTDALOFtXMSylyUwVJfrlbFal0dFGhRgA/MpwZ4d4VfTF4yNH3LDJr77ufLYY8 + e61T1CUAguRJfe9YgZx8LRk4rx449nDFLIu8n5xjXwzhJHqwB0bTxU09JEYKZ15l0jLBXnGkFrOB + txJ49tcykNHFnH3RCw69j3TF/BVIptSALu4zrqQ7N2D3WW984DwDZVfLF9YYZByl4/3UBEcdxEVg + RLP1gs22NOVavjZ+rB1TcrwoenVNLGakUwrtuBP5PuDqn+RIcUnYKeQL+YGyJ2bj3NO2OzVqXM0y + MqVld3o5G/EVX50cS2nNoJXQOPVu+JIH73Voc7jbQpAYcSeKxdvTluJI85dGIc1KbS6hxlIVI7SP + u/uv/G1xptUX/bss0SfHJ/tnBc4FLpqGWpbFb7bRgL4tN6Ap+YRs/gbq66w83q59tYXVV0bXNiB/ + OvPJMiuXlS/gtjgEwh4tcJqV6WFNPg+h1WoUzopx2RZW/mWYSkOS1zzn0Pot+4W5HHy6OvNmsdgX + Zfg/AAAA//+MWctu5DYQvPsriDnLRmxsAiM3Z5E1jE1O8XUx4FAtDdcUKfMxGwfwvwfVTUmciQ85 + GQZHlNiP6qoid87Dk3oEKfMI0K/qt2KFxDyj0WcdyedOPRgTis8c+b+qeJGR/xxLyhX8JM3taGUc + AF8+0oRDnChlO3IA+VTTTPWc2C2GQ0m5aSZFPhWmXA9PZ2jP2ECzC2/yL8s0DjJzFYnySbtC2wzN + 64EMQrWciAWWhG7QNnpK6Ub9QZqDgBQqju4qVh9DGB2t2o4f/NOCNochi6KbRelK+QqvUYegWagK + QIY4am//qR3F+8qYbRQF97fN0i05gHnMwSeLHOCHmADNmEPdQyjFZCl1daDxLLU6gQxrwBIr5tHL + 1F9kqERSStjpH4qgewxJaEcgQmX6F7r9syPNmlA9PHU8WicUMHFueyBLZJqTSjyRdcIJ8YmRNHLi + +/oJdaxFy89zUY/anXS1TOrkq7oOHcmmjXK1B5cMP9vJ26we6RAL7/4VH/M56h9DiD3Q+mR7hhub + UHNcnq+okeAhQanvqVfajSHafMRXIXZrV6UcixGQgdBGW1GC+pkpF0YrJh8dpH4w0sisYJEKvLBT + 9FqsdFFPkKy8d6SxOKDbm5rIHLW3aWpGm0HSe+4TFAuzJgCDTDrIEk/ysrVCqmlQ2TcwFHgWqjon + 31+XtGj1C5mGt9FrkekAADkjwkvFVIVhp1kbtgdW8XYx/za60giFbazhzOOKPoq9wWq9WG9CnANT + 3B7KKyED+oWOwWE2VsLDRIxARNSkIzPQ2lf1rAlIiM5eDYQb9QXzTvtmPkDDQlRxliIdFhRkhaKt + z9r6phQzUA99Q74XN25DS+7b4iu96xQNg2ysJsvgt4n2yaaSqBKTvmeA9qP6Hg4r86lw97clEfCL + 5mWHpyrehJKy6ci/XQajNJIAfFNhLPUBRWdWiKGY2T6RGhOzKKnZngJ4bw5qLDpqn4kkO1UQVINA + pBGzFve2uRMk6Ih1eBhpC4V7U7B9J1gGZxO2J5pXMYoxIY7bylcqBmBnNnfqd6yeC/sRwdWxI/UJ + MDzZ6pYtikSPElrr1SLo9DI3xSxtxjdNLC+SDBb0VtTwnhCLagJNUh8283i9vr7Gn98Bc2zc2qQO + 2rwIRuAQkTKJkVDhW3N946Cpg0ZklFgBtxN6lhXgdg7WLw4nVCvI7CVMgItJcjuODv6eU6Ieqqb2 + LFQKJyAx3VqTVAmK0uoEU7dTU3HZDtoQm5Xa98nouYqixbobGJisiBMDWwue0pkd3dJfBqEVA5pk + NRQrqGjNcaNF3HMtBzqHPtm0ZQErB2XNIzaTCcXBJIqcSqokkyptDyU7lq3r61Ha7HOj+oBgjuXI + HJJm63sQdwglTEB99k6lzxZCb6dGiw6rWfh2015PRBpK0rgj8cW5ZkF71AU/jIuRb3Xlfb0KcWGc + Yziki0d3g/U2Hfcg2cHj2iPlMO949f1KqW985VLOblF24hnsc3ghft3t7e0n2XC33fU0yz+tyxmY + sa3c3d7ddx/sua+XBs3Fzc5oc6R+e3a75QEEhGbhqjn5fz/oo73l9NaP/2f7bcHAHKJ+v9ydtIfe + fhbpOzt8H/9sjTR/8C7BSTe0z5YistHToIur92oyYveDBX2ao5V7qmHefzJ39z/fDve/3O2u3q/+ + BQAA//8DAPcawNa2GwAA + headers: + CF-RAY: + - 99c9865b6af3cf4d-SJC + Connection: + - keep-alive + Content-Encoding: + - gzip + Content-Type: + - application/json + Date: + - Tue, 11 Nov 2025 00:03:32 GMT + Server: + - cloudflare + Strict-Transport-Security: + - max-age=31536000; includeSubDomains; preload + Transfer-Encoding: + - chunked + X-Content-Type-Options: + - nosniff + access-control-expose-headers: + - X-Request-ID + alt-svc: + - h3=":443"; ma=86400 + cf-cache-status: + - DYNAMIC + openai-organization: + - crewai-iuxna1 + openai-processing-ms: + - '22788' + openai-project: + - proj_xitITlrFeen7zjNSzML82h9x + openai-version: + - '2020-10-01' + x-envoy-upstream-service-time: + - '22942' + x-openai-proxy-wasm: + - v0.1 + x-ratelimit-limit-project-tokens: + - '150000000' + x-ratelimit-limit-requests: + - '30000' + x-ratelimit-limit-tokens: + - '150000000' + x-ratelimit-remaining-project-tokens: + - '149998392' + x-ratelimit-remaining-requests: + - '29999' + x-ratelimit-remaining-tokens: + - '149998392' + x-ratelimit-reset-project-tokens: + - 0s + x-ratelimit-reset-requests: + - 2ms + x-ratelimit-reset-tokens: + - 0s + x-request-id: + - req_48c359c72cdc47aeb89c6d6eeffdce7d + status: + code: 200 + message: OK +version: 1 diff --git a/lib/crewai/tests/test_crew.py b/lib/crewai/tests/test_crew.py index 1a1db50af..d4cf1acbf 100644 --- a/lib/crewai/tests/test_crew.py +++ b/lib/crewai/tests/test_crew.py @@ -340,7 +340,7 @@ def test_sync_task_execution(researcher, writer): ) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) # Because we are mocking execute_sync, we never hit the underlying _execute_core @@ -412,7 +412,7 @@ def test_manager_agent_delegating_to_assigned_task_agent(researcher, writer): ) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) # Because we are mocking execute_sync, we never hit the underlying _execute_core @@ -513,7 +513,7 @@ def test_manager_agent_delegates_with_varied_role_cases(): ) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) task.output = mock_task_output @@ -611,7 +611,7 @@ def test_crew_with_delegating_agents_should_not_override_task_tools(ceo, writer) ) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) # Because we are mocking execute_sync, we never hit the underlying _execute_core @@ -669,7 +669,7 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools(ceo, writer ) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) # Because we are mocking execute_sync, we never hit the underlying _execute_core @@ -788,7 +788,7 @@ def test_task_tools_override_agent_tools_with_allow_delegation(researcher, write ) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) # We mock execute_sync to verify which tools get used at runtime @@ -1225,7 +1225,7 @@ async def test_async_task_execution_call_count(researcher, writer): # Create a valid TaskOutput instance to mock the return value mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) # Create a MagicMock Future instance @@ -1784,7 +1784,7 @@ def test_hierarchical_kickoff_usage_metrics_include_manager(researcher): Task, "execute_sync", return_value=TaskOutput( - description="dummy", raw="Hello", agent=researcher.role + description="dummy", raw="Hello", agent=researcher.role, messages=[] ), ): crew.kickoff() @@ -1828,7 +1828,7 @@ def test_hierarchical_crew_creation_tasks_with_agents(researcher, writer): ) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) # Because we are mocking execute_sync, we never hit the underlying _execute_core @@ -1881,7 +1881,7 @@ def test_hierarchical_crew_creation_tasks_with_async_execution(researcher, write ) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) # Create a mock Future that returns our TaskOutput @@ -2246,11 +2246,13 @@ def test_conditional_task_uses_last_output(researcher, writer): description="First task output", raw="First success output", # Will be used by third task's condition agent=researcher.role, + messages=[], ) mock_third = TaskOutput( description="Third task output", raw="Third task executed", # Output when condition succeeds using first task output agent=writer.role, + messages=[], ) # Set up mocks for task execution and conditional logic @@ -2318,11 +2320,13 @@ def test_conditional_tasks_result_collection(researcher, writer): description="Success output", raw="Success output", # Triggers third task's condition agent=researcher.role, + messages=[], ) mock_conditional = TaskOutput( description="Conditional output", raw="Conditional task executed", agent=writer.role, + messages=[], ) # Set up mocks for task execution and conditional logic @@ -2399,6 +2403,7 @@ def test_multiple_conditional_tasks(researcher, writer): description="Mock success", raw="Success and proceed output", agent=researcher.role, + messages=[], ) # Set up mocks for task execution @@ -2806,7 +2811,7 @@ def test_manager_agent(researcher, writer): ) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) # Because we are mocking execute_sync, we never hit the underlying _execute_core @@ -3001,6 +3006,7 @@ def test_replay_feature(researcher, writer): output_format=OutputFormat.RAW, pydantic=None, summary="Mocked output for list of ideas", + messages=[], ) crew.kickoff() @@ -3052,6 +3058,7 @@ def test_crew_task_db_init(): output_format=OutputFormat.RAW, pydantic=None, summary="Write about AI in healthcare...", + messages=[], ) crew.kickoff() @@ -3114,6 +3121,7 @@ def test_replay_task_with_context(): output_format=OutputFormat.RAW, pydantic=None, summary="Detailed report on AI advancements...", + messages=[], ) mock_task_output2 = TaskOutput( description="Summarize the AI advancements report.", @@ -3123,6 +3131,7 @@ def test_replay_task_with_context(): output_format=OutputFormat.RAW, pydantic=None, summary="Summary of the AI advancements report...", + messages=[], ) mock_task_output3 = TaskOutput( description="Write an article based on the AI advancements summary.", @@ -3132,6 +3141,7 @@ def test_replay_task_with_context(): output_format=OutputFormat.RAW, pydantic=None, summary="Article on AI advancements...", + messages=[], ) mock_task_output4 = TaskOutput( description="Create a presentation based on the AI advancements article.", @@ -3141,6 +3151,7 @@ def test_replay_task_with_context(): output_format=OutputFormat.RAW, pydantic=None, summary="Presentation on AI advancements...", + messages=[], ) with patch.object(Task, "execute_sync") as mock_execute_task: @@ -3164,6 +3175,70 @@ def test_replay_task_with_context(): db_handler.reset() +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_replay_preserves_messages(): + """Test that replay preserves messages from stored task outputs.""" + from crewai.utilities.types import LLMMessage + + agent = Agent( + role="Test Agent", + goal="Test goal", + backstory="Test backstory", + allow_delegation=False, + ) + + task = Task( + description="Say hello", + expected_output="A greeting", + agent=agent, + ) + + crew = Crew(agents=[agent], tasks=[task], process=Process.sequential) + + mock_messages: list[LLMMessage] = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Say hello"}, + {"role": "assistant", "content": "Hello!"}, + ] + + mock_task_output = TaskOutput( + description="Say hello", + raw="Hello!", + agent="Test Agent", + messages=mock_messages, + ) + + with patch.object(Task, "execute_sync", return_value=mock_task_output): + crew.kickoff() + + # Verify the task output was stored with messages + db_handler = TaskOutputStorageHandler() + stored_outputs = db_handler.load() + assert stored_outputs is not None + assert len(stored_outputs) > 0 + + # Verify messages are in the stored output + stored_output = stored_outputs[0]["output"] + assert "messages" in stored_output + assert len(stored_output["messages"]) == 3 + assert stored_output["messages"][0]["role"] == "system" + assert stored_output["messages"][1]["role"] == "user" + assert stored_output["messages"][2]["role"] == "assistant" + + # Replay the task and verify messages are preserved + with patch.object(Task, "execute_sync", return_value=mock_task_output): + replayed_output = crew.replay(str(task.id)) + + # Verify the replayed task output has messages + assert len(replayed_output.tasks_output) > 0 + replayed_task_output = replayed_output.tasks_output[0] + assert hasattr(replayed_task_output, "messages") + assert isinstance(replayed_task_output.messages, list) + assert len(replayed_task_output.messages) == 3 + + db_handler.reset() + + @pytest.mark.vcr(filter_headers=["authorization"]) def test_replay_with_context(): agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal") @@ -3181,6 +3256,7 @@ def test_replay_with_context(): pydantic=None, json_dict={}, output_format=OutputFormat.RAW, + messages=[], ) task1.output = context_output @@ -3241,6 +3317,7 @@ def test_replay_with_context_set_to_nullable(): description="Test Task Output", raw="test raw output", agent="test_agent", + messages=[], ) crew.kickoff() @@ -3264,6 +3341,7 @@ def test_replay_with_invalid_task_id(): pydantic=None, json_dict={}, output_format=OutputFormat.RAW, + messages=[], ) task1.output = context_output @@ -3328,6 +3406,7 @@ def test_replay_interpolates_inputs_properly(mock_interpolate_inputs): pydantic=None, json_dict={}, output_format=OutputFormat.RAW, + messages=[], ) task1.output = context_output @@ -3386,6 +3465,7 @@ def test_replay_setup_context(): pydantic=None, json_dict={}, output_format=OutputFormat.RAW, + messages=[], ) task1.output = context_output crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential) @@ -3619,6 +3699,7 @@ def test_conditional_should_skip(researcher, writer): description="Task 1 description", raw="Task 1 output", agent="Researcher", + messages=[], ) result = crew_met.kickoff() @@ -3653,6 +3734,7 @@ def test_conditional_should_execute(researcher, writer): description="Task 1 description", raw="Task 1 output", agent="Researcher", + messages=[], ) crew_met.kickoff() @@ -3824,7 +3906,7 @@ def test_task_tools_preserve_code_execution_tools(): ) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) with patch.object( @@ -3878,7 +3960,7 @@ def test_multimodal_flag_adds_multimodal_tools(): crew = Crew(agents=[multimodal_agent], tasks=[task], process=Process.sequential) mock_task_output = TaskOutput( - description="Mock description", raw="mocked output", agent="mocked agent" + description="Mock description", raw="mocked output", agent="mocked agent", messages=[] ) # Mock execute_sync to verify the tools passed at runtime @@ -3942,6 +4024,7 @@ def test_multimodal_agent_image_tool_handling(): description="Mock description", raw="A detailed analysis of the image", agent="Image Analyst", + messages=[], ) with patch.object(Task, "execute_sync") as mock_execute_sync: diff --git a/lib/crewai/tests/test_task.py b/lib/crewai/tests/test_task.py index 73fedfc88..72fe23b4b 100644 --- a/lib/crewai/tests/test_task.py +++ b/lib/crewai/tests/test_task.py @@ -162,6 +162,7 @@ def test_task_callback_returns_task_output(): "name": task.name or task.description, "expected_output": "Bullet point list of 5 interesting ideas.", "output_format": OutputFormat.RAW, + "messages": [], } assert output_dict == expected_output @@ -1680,3 +1681,44 @@ def test_task_copy_with_list_context(): assert isinstance(copied_task2.context, list) assert len(copied_task2.context) == 1 assert copied_task2.context[0] is task1 + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_task_output_includes_messages(): + """Test that TaskOutput includes messages from agent execution.""" + researcher = Agent( + role="Researcher", + goal="Make the best research and analysis on content about AI and AI agents", + 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.", + allow_delegation=False, + ) + + task1 = Task( + description="Give me a list of 3 interesting ideas about AI.", + expected_output="Bullet point list of 3 ideas.", + agent=researcher, + ) + + task2 = Task( + description="Summarize the ideas from the previous task.", + expected_output="A summary of the ideas.", + agent=researcher, + ) + + crew = Crew(agents=[researcher], tasks=[task1, task2], process=Process.sequential) + result = crew.kickoff() + + # Verify both tasks have messages + assert len(result.tasks_output) == 2 + + # Check first task output has messages + task1_output = result.tasks_output[0] + assert hasattr(task1_output, "messages") + assert isinstance(task1_output.messages, list) + assert len(task1_output.messages) > 0 + + # Check second task output has messages + task2_output = result.tasks_output[1] + assert hasattr(task2_output, "messages") + assert isinstance(task2_output.messages, list) + assert len(task2_output.messages) > 0 diff --git a/lib/crewai/tests/test_task_guardrails.py b/lib/crewai/tests/test_task_guardrails.py index 22572bfd3..dd24458d3 100644 --- a/lib/crewai/tests/test_task_guardrails.py +++ b/lib/crewai/tests/test_task_guardrails.py @@ -38,6 +38,7 @@ def test_task_without_guardrail(): agent.role = "test_agent" agent.execute_task.return_value = "test result" agent.crew = None + agent.last_messages = [] task = create_smart_task(description="Test task", expected_output="Output") @@ -56,6 +57,7 @@ def test_task_with_successful_guardrail_func(): agent.role = "test_agent" agent.execute_task.return_value = "test result" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test task", expected_output="Output", guardrail=guardrail @@ -76,6 +78,7 @@ def test_task_with_failing_guardrail(): agent.role = "test_agent" agent.execute_task.side_effect = ["bad result", "good result"] agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test task", @@ -103,6 +106,7 @@ def test_task_with_guardrail_retries(): agent.role = "test_agent" agent.execute_task.return_value = "bad result" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test task", @@ -128,6 +132,7 @@ def test_guardrail_error_in_context(): agent = Mock() agent.role = "test_agent" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test task", @@ -295,6 +300,7 @@ def test_hallucination_guardrail_integration(): agent.role = "test_agent" agent.execute_task.return_value = "test result" agent.crew = None + agent.last_messages = [] mock_llm = Mock(spec=LLM) guardrail = HallucinationGuardrail( @@ -342,6 +348,7 @@ def test_multiple_guardrails_sequential_processing(): agent.role = "sequential_agent" agent.execute_task.return_value = "original text" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test sequential guardrails", @@ -391,6 +398,7 @@ def test_multiple_guardrails_with_validation_failure(): agent.role = "validation_agent" agent.execute_task = mock_execute_task agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test guardrails with validation", @@ -432,6 +440,7 @@ def test_multiple_guardrails_with_mixed_string_and_taskoutput(): agent.role = "mixed_agent" agent.execute_task.return_value = "original" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test mixed return types", @@ -469,6 +478,7 @@ def test_multiple_guardrails_with_retry_on_middle_guardrail(): agent.role = "retry_agent" agent.execute_task.return_value = "base" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test retry in middle guardrail", @@ -500,6 +510,7 @@ def test_multiple_guardrails_with_max_retries_exceeded(): agent.role = "failing_agent" agent.execute_task.return_value = "test" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test max retries with multiple guardrails", @@ -523,6 +534,7 @@ def test_multiple_guardrails_empty_list(): agent.role = "empty_agent" agent.execute_task.return_value = "no guardrails" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test empty guardrails list", @@ -582,6 +594,7 @@ def test_multiple_guardrails_processing_order(): agent.role = "order_agent" agent.execute_task.return_value = "base" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test processing order", @@ -625,6 +638,7 @@ def test_multiple_guardrails_with_pydantic_output(): agent.role = "pydantic_agent" agent.execute_task.return_value = "test content" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test guardrails with Pydantic", @@ -658,6 +672,7 @@ def test_guardrails_vs_single_guardrail_mutual_exclusion(): agent.role = "exclusion_agent" agent.execute_task.return_value = "test" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test mutual exclusion", @@ -700,6 +715,7 @@ def test_per_guardrail_independent_retry_tracking(): agent.role = "independent_retry_agent" agent.execute_task.return_value = "base" agent.crew = None + agent.last_messages = [] task = create_smart_task( description="Test independent retry tracking",