fix: allow failed pypi publish

Co-authored-by: Greyson LaLonde <greyson.r.lalonde@gmail.com>

.github/workflows/publish.yml

@@ -25,7 +25,7 @@ jobs:
 
       - name: Build packages
         run: |
-          uv build --all-packages
+          uv build --prerelease="allow" --all-packages
           rm dist/.gitignore
 
       - name: Upload artifacts
@@ -65,7 +65,19 @@ jobs:
         env:
           UV_PUBLISH_TOKEN: ${{ secrets.PYPI_API_TOKEN }}
         run: |
+          failed=0
           for package in dist/*; do
+            if [[ "$package" == *"crewai_devtools"* ]]; then
+              echo "Skipping private package: $package"
+              continue
+            fi
             echo "Publishing $package"
-            uv publish "$package"
+            if ! uv publish "$package"; then
+              echo "Failed to publish $package"
+              failed=1
+            fi
           done
+          if [ $failed -eq 1 ]; then
+            echo "Some packages failed to publish"
+            exit 1
+          fi

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube>=15.0.0",
     "requests>=2.32.5",
     "docker>=7.1.0",
-    "crewai==1.0.0a1",
+    "crewai==1.0.0a3",
     "lancedb>=0.5.4",
     "tiktoken>=0.8.0",
     "stagehand>=0.4.1",

lib/crewai-tools/src/crewai_tools/__init__.py

@@ -291,4 +291,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.0.0a2"
+__version__ = "1.0.0a3"

lib/crewai/pyproject.toml

@@ -11,7 +11,6 @@ dependencies = [
     # Core Dependencies
     "pydantic>=2.11.9",
     "openai>=1.13.3",
-    "litellm==1.74.9",
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber>=0.11.4",
@@ -50,7 +49,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.0.0a1",
+    "crewai-tools==1.0.0a3",
 ]
 embeddings = [
     "tiktoken~=0.8.0"
@@ -83,6 +82,9 @@ watson = [
 voyageai = [
     "voyageai>=0.3.5",
 ]
+litellm = [
+    "litellm>=1.74.9",
+]
 
 
 [project.scripts]

lib/crewai/src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.0.0a2"
+__version__ = "1.0.0a3"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/agents/crew_agent_executor.py

@@ -114,7 +114,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.messages: list[dict[str, str]] = []
         self.iterations = 0
         self.log_error_after = 3
-        existing_stop = self.llm.stop or []
+        existing_stop = getattr(self.llm, "stop", [])
         self.llm.stop = list(
             set(
                 existing_stop + self.stop
@@ -192,6 +192,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     callbacks=self.callbacks,
                     printer=self._printer,
                     from_task=self.task,
+                    from_agent=self.agent,
                 )
                 formatted_answer = process_llm_response(answer, self.use_stop_words)
 

lib/crewai/src/crewai/crew.py

@@ -1,16 +1,16 @@
 import asyncio
-import json
-import re
-import uuid
-import warnings
 from collections.abc import Callable
 from concurrent.futures import Future
 from copy import copy as shallow_copy
 from hashlib import md5
+import json
+import re
 from typing import (
     Any,
     cast,
 )
+import uuid
+import warnings
 
 from opentelemetry import baggage
 from opentelemetry.context import attach, detach
@@ -82,6 +82,7 @@ from crewai.utilities.planning_handler import CrewPlanner
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
 from crewai.utilities.training_handler import CrewTrainingHandler
 
+
 warnings.filterwarnings("ignore", category=SyntaxWarning, module="pysbd")
 
 
@@ -1353,13 +1354,34 @@ class Crew(FlowTrackable, BaseModel):
     def calculate_usage_metrics(self) -> UsageMetrics:
         """Calculates and returns the usage metrics."""
         total_usage_metrics = UsageMetrics()
+
         for agent in self.agents:
-            if hasattr(agent, "_token_process"):
-                token_sum = agent._token_process.get_summary()
-                total_usage_metrics.add_usage_metrics(token_sum)
+            if isinstance(agent.llm, BaseLLM):
+                llm_usage = agent.llm.get_token_usage_summary()
+
+                total_usage_metrics.add_usage_metrics(llm_usage)
+            else:
+                # fallback litellm
+                if hasattr(agent, "_token_process"):
+                    token_sum = agent._token_process.get_summary()
+                    total_usage_metrics.add_usage_metrics(token_sum)
+
         if self.manager_agent and hasattr(self.manager_agent, "_token_process"):
             token_sum = self.manager_agent._token_process.get_summary()
             total_usage_metrics.add_usage_metrics(token_sum)
+
+        if (
+            self.manager_agent
+            and hasattr(self.manager_agent, "llm")
+            and hasattr(self.manager_agent.llm, "get_token_usage_summary")
+        ):
+            if isinstance(self.manager_agent.llm, BaseLLM):
+                llm_usage = self.manager_agent.llm.get_token_usage_summary()
+            else:
+                llm_usage = self.manager_agent.llm._token_process.get_summary()
+
+            total_usage_metrics.add_usage_metrics(llm_usage)
+
         self.usage_metrics = total_usage_metrics
         return total_usage_metrics
 

lib/crewai/src/crewai/events/base_events.py

@@ -17,6 +17,11 @@ class BaseEvent(BaseModel):
     )
     fingerprint_metadata: dict[str, Any] | None = None  # Any relevant metadata
 
+    task_id: str | None = None
+    task_name: str | None = None
+    agent_id: str | None = None
+    agent_role: str | None = None
+
     def to_json(self, exclude: set[str] | None = None):
         """
         Converts the event to a JSON-serializable dictionary.
@@ -31,7 +36,7 @@ class BaseEvent(BaseModel):
 
     def _set_task_params(self, data: dict[str, Any]):
         if "from_task" in data and (task := data["from_task"]):
-            self.task_id = task.id
+            self.task_id = str(task.id)
             self.task_name = task.name or task.description
             self.from_task = None
 
@@ -42,6 +47,6 @@ class BaseEvent(BaseModel):
         if not agent:
             return
 
-        self.agent_id = agent.id
+        self.agent_id = str(agent.id)
         self.agent_role = agent.role
         self.from_agent = None

lib/crewai/src/crewai/events/types/llm_events.py

@@ -7,19 +7,23 @@ from crewai.events.base_events import BaseEvent
 
 
 class LLMEventBase(BaseEvent):
-    task_name: str | None = None
-    task_id: str | None = None
-
-    agent_id: str | None = None
-    agent_role: str | None = None
-
     from_task: Any | None = None
     from_agent: Any | None = None
 
     def __init__(self, **data):
+        if data.get("from_task"):
+            task = data["from_task"]
+            data["task_id"] = str(task.id)
+            data["task_name"] = task.name or task.description
+            data["from_task"] = None
+
+        if data.get("from_agent"):
+            agent = data["from_agent"]
+            data["agent_id"] = str(agent.id)
+            data["agent_role"] = agent.role
+            data["from_agent"] = None
+
         super().__init__(**data)
-        self._set_agent_params(data)
-        self._set_task_params(data)
 
 
 class LLMCallType(Enum):

lib/crewai/src/crewai/events/types/tool_usage_events.py

@@ -27,9 +27,20 @@ class ToolUsageEvent(BaseEvent):
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
     def __init__(self, **data):
+        if data.get("from_task"):
+            task = data["from_task"]
+            data["task_id"] = str(task.id)
+            data["task_name"] = task.name or task.description
+            data["from_task"] = None
+
+        if data.get("from_agent"):
+            agent = data["from_agent"]
+            data["agent_id"] = str(agent.id)
+            data["agent_role"] = agent.role
+            data["from_agent"] = None
+
         super().__init__(**data)
-        self._set_agent_params(data)
-        self._set_task_params(data)
+
         # Set fingerprint data from the agent
         if self.agent and hasattr(self.agent, "fingerprint") and self.agent.fingerprint:
             self.source_fingerprint = self.agent.fingerprint.uuid_str

lib/crewai/src/crewai/lite_agent.py

@@ -1,13 +1,13 @@
 import asyncio
-import inspect
-import uuid
 from collections.abc import Callable
+import inspect
 from typing import (
     Any,
     cast,
     get_args,
     get_origin,
 )
+import uuid
 
 from pydantic import (
     UUID4,
@@ -351,7 +351,10 @@ class LiteAgent(FlowTrackable, BaseModel):
                 )
 
         # Calculate token usage metrics
-        usage_metrics = self._token_process.get_summary()
+        if isinstance(self.llm, BaseLLM):
+            usage_metrics = self.llm.get_token_usage_summary()
+        else:
+            usage_metrics = self._token_process.get_summary()
 
         # Create output
         output = LiteAgentOutput(
@@ -400,7 +403,10 @@ class LiteAgent(FlowTrackable, BaseModel):
                 elif isinstance(guardrail_result.result, BaseModel):
                     output.pydantic = guardrail_result.result
 
-            usage_metrics = self._token_process.get_summary()
+            if isinstance(self.llm, BaseLLM):
+                usage_metrics = self.llm.get_token_usage_summary()
+            else:
+                usage_metrics = self._token_process.get_summary()
             output.usage_metrics = usage_metrics.model_dump() if usage_metrics else None
 
         # Emit completion event

lib/crewai/src/crewai/llm.py

@@ -1,13 +1,14 @@
+from collections import defaultdict
+from collections.abc import Callable
+from datetime import datetime
 import io
 import json
 import logging
 import os
 import sys
 import threading
-from collections import defaultdict
-from collections.abc import Callable
-from datetime import datetime
 from typing import (
+    TYPE_CHECKING,
     Any,
     Final,
     Literal,
@@ -17,7 +18,6 @@ from typing import (
 )
 
 from dotenv import load_dotenv
-from litellm.types.utils import ChatCompletionDeltaToolCall
 from pydantic import BaseModel, Field
 
 from crewai.events.event_bus import crewai_event_bus
@@ -39,19 +39,42 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 )
 from crewai.utilities.logger_utils import suppress_warnings
 
-with suppress_warnings():
+
+if TYPE_CHECKING:
+    from litellm import Choices
+    from litellm.exceptions import ContextWindowExceededError
+    from litellm.litellm_core_utils.get_supported_openai_params import (
+        get_supported_openai_params,
+    )
+    from litellm.types.utils import ChatCompletionDeltaToolCall, ModelResponse
+    from litellm.utils import supports_response_schema
+
+try:
     import litellm
     from litellm import Choices, CustomLogger
     from litellm.exceptions import ContextWindowExceededError
     from litellm.litellm_core_utils.get_supported_openai_params import (
         get_supported_openai_params,
     )
-    from litellm.types.utils import ModelResponse
+    from litellm.types.utils import ChatCompletionDeltaToolCall, ModelResponse
     from litellm.utils import supports_response_schema
 
+    LITELLM_AVAILABLE = True
+except ImportError:
+    LITELLM_AVAILABLE = False
+    litellm = None  # type: ignore
+    Choices = None  # type: ignore
+    ContextWindowExceededError = Exception  # type: ignore
+    get_supported_openai_params = None  # type: ignore
+    ChatCompletionDeltaToolCall = None  # type: ignore
+    ModelResponse = None  # type: ignore
+    supports_response_schema = None  # type: ignore
+
+
 load_dotenv()
 
-litellm.suppress_debug_info = True
+if LITELLM_AVAILABLE:
+    litellm.suppress_debug_info = True
 
 
 class FilteredStream(io.TextIOBase):
@@ -275,6 +298,77 @@ class AccumulatedToolArgs(BaseModel):
 class LLM(BaseLLM):
     completion_cost: float | None = None
 
+    def __new__(cls, model: str, is_litellm: bool = False, **kwargs) -> "LLM":
+        """Factory method that routes to native SDK or falls back to LiteLLM."""
+        if not model or not isinstance(model, str):
+            raise ValueError("Model must be a non-empty string")
+
+        provider = model.partition("/")[0] if "/" in model else "openai"
+
+        native_class = cls._get_native_provider(provider)
+        if native_class and not is_litellm:
+            try:
+                model_string = model.partition("/")[2] if "/" in model else model
+                return native_class(model=model_string, provider=provider, **kwargs)
+            except Exception as e:
+                import logging
+
+                logger = logging.getLogger(__name__)
+                logger.warning(
+                    f"Native SDK failed for {provider}: {e}, falling back to LiteLLM"
+                )
+
+        # FALLBACK to LiteLLM
+        if not LITELLM_AVAILABLE:
+            raise ImportError(
+                "Please install the required dependencies:\n"
+                "- For LiteLLM: uv add litellm"
+            )
+
+        instance = object.__new__(cls)
+        super(LLM, instance).__init__(model=model, is_litellm=True, **kwargs)
+        instance.is_litellm = True
+        return instance
+
+    @classmethod
+    def _get_native_provider(cls, provider: str) -> type | None:
+        """Get native provider class if available."""
+        if provider == "openai":
+            try:
+                from crewai.llms.providers.openai.completion import OpenAICompletion
+
+                return OpenAICompletion
+            except ImportError:
+                return None
+
+        elif provider == "anthropic" or provider == "claude":
+            try:
+                from crewai.llms.providers.anthropic.completion import (
+                    AnthropicCompletion,
+                )
+
+                return AnthropicCompletion
+            except ImportError:
+                return None
+
+        elif provider == "azure":
+            try:
+                from crewai.llms.providers.azure.completion import AzureCompletion
+
+                return AzureCompletion
+            except ImportError:
+                return None
+
+        elif provider == "google" or provider == "gemini":
+            try:
+                from crewai.llms.providers.gemini.completion import GeminiCompletion
+
+                return GeminiCompletion
+            except ImportError:
+                return None
+
+        return None
+
     def __init__(
         self,
         model: str,
@@ -284,7 +378,7 @@ class LLM(BaseLLM):
         n: int | None = None,
         stop: str | list[str] | None = None,
         max_completion_tokens: int | None = None,
-        max_tokens: int | None = None,
+        max_tokens: int | float | None = None,
         presence_penalty: float | None = None,
         frequency_penalty: float | None = None,
         logit_bias: dict[int, float] | None = None,
@@ -301,6 +395,11 @@ class LLM(BaseLLM):
         stream: bool = False,
         **kwargs,
     ):
+        """Initialize LLM instance.
+
+        Note: This __init__ method is only called for fallback instances.
+        Native provider instances handle their own initialization in their respective classes.
+        """
         self.model = model
         self.timeout = timeout
         self.temperature = temperature
@@ -328,7 +427,7 @@ class LLM(BaseLLM):
 
         litellm.drop_params = True
 
-        # Normalize self.stop to always be a List[str]
+        # Normalize self.stop to always be a list[str]
         if stop is None:
             self.stop: list[str] = []
         elif isinstance(stop, str):
@@ -349,7 +448,8 @@ class LLM(BaseLLM):
         Returns:
             bool: True if the model is from Anthropic, False otherwise.
         """
-        return any(prefix in model.lower() for prefix in ANTHROPIC_PREFIXES)
+        anthropic_prefixes = ("anthropic/", "claude-", "claude/")
+        return any(prefix in model.lower() for prefix in anthropic_prefixes)
 
     def _prepare_completion_params(
         self,
@@ -514,10 +614,6 @@ class LLM(BaseLLM):
                     # Add the chunk content to the full response
                     full_response += chunk_content
 
-                    # Emit the chunk event
-                    if not hasattr(crewai_event_bus, "emit"):
-                        raise Exception("crewai_event_bus must have an `emit` method")
-
                     crewai_event_bus.emit(
                         self,
                         event=LLMStreamChunkEvent(
@@ -623,7 +719,9 @@ class LLM(BaseLLM):
             # --- 8) If no tool calls or no available functions, return the text response directly
 
             if not tool_calls or not available_functions:
-                # Log token usage if available in streaming mode
+                # Track token usage and log callbacks if available in streaming mode
+                if usage_info:
+                    self._track_token_usage_internal(usage_info)
                 self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
                 # Emit completion event and return response
                 self._handle_emit_call_events(
@@ -640,7 +738,9 @@ class LLM(BaseLLM):
             if tool_result is not None:
                 return tool_result
 
-            # --- 10) Log token usage if available in streaming mode
+            # --- 10) Track token usage and log callbacks if available in streaming mode
+            if usage_info:
+                self._track_token_usage_internal(usage_info)
             self._handle_streaming_callbacks(callbacks, usage_info, last_chunk)
 
             # --- 11) Emit completion event and return response
@@ -671,11 +771,6 @@ class LLM(BaseLLM):
                 )
                 return full_response
 
-            # Emit failed event and re-raise the exception
-            if not hasattr(crewai_event_bus, "emit"):
-                raise AttributeError(
-                    "crewai_event_bus must have an 'emit' method"
-                ) from e
             crewai_event_bus.emit(
                 self,
                 event=LLMCallFailedEvent(
@@ -702,8 +797,7 @@ class LLM(BaseLLM):
                 current_tool_accumulator.function.arguments += (
                     tool_call.function.arguments
                 )
-            if not hasattr(crewai_event_bus, "emit"):
-                raise AttributeError("crewai_event_bus must have an 'emit' method")
+
             crewai_event_bus.emit(
                 self,
                 event=LLMStreamChunkEvent(
@@ -832,6 +926,7 @@ class LLM(BaseLLM):
                 messages=params["messages"],
             )
             return text_response
+
         # --- 6) If there is no text response, no available functions, but there are tool calls, return the tool calls
         if tool_calls and not available_functions and not text_response:
             return tool_calls
@@ -886,9 +981,6 @@ class LLM(BaseLLM):
                 function_args = json.loads(tool_call.function.arguments)
                 fn = available_functions[function_name]
 
-                # --- 3.2) Execute function
-                if not hasattr(crewai_event_bus, "emit"):
-                    raise AttributeError("crewai_event_bus must have an 'emit' method")
                 started_at = datetime.now()
                 crewai_event_bus.emit(
                     self,
@@ -928,10 +1020,6 @@ class LLM(BaseLLM):
                     function_name, lambda: None
                 )  # Ensure fn is always a callable
                 logging.error(f"Error executing function '{function_name}': {e}")
-                if not hasattr(crewai_event_bus, "emit"):
-                    raise AttributeError(
-                        "crewai_event_bus must have an 'emit' method"
-                    ) from e
                 crewai_event_bus.emit(
                     self,
                     event=LLMCallFailedEvent(error=f"Tool execution error: {e!s}"),
@@ -982,9 +1070,6 @@ class LLM(BaseLLM):
             ValueError: If response format is not supported
             LLMContextLengthExceededError: If input exceeds model's context limit
         """
-        # --- 1) Emit call started event
-        if not hasattr(crewai_event_bus, "emit"):
-            raise AttributeError("crewai_event_bus must have an 'emit' method")
         crewai_event_bus.emit(
             self,
             event=LLMCallStartedEvent(
@@ -1021,10 +1106,10 @@ class LLM(BaseLLM):
                     return self._handle_streaming_response(
                         params, callbacks, available_functions, from_task, from_agent
                     )
+
                 return self._handle_non_streaming_response(
                     params, callbacks, available_functions, from_task, from_agent
                 )
-
             except LLMContextLengthExceededError:
                 # Re-raise LLMContextLengthExceededError as it should be handled
                 # by the CrewAgentExecutor._invoke_loop method, which can then decide
@@ -1057,10 +1142,6 @@ class LLM(BaseLLM):
                         from_agent=from_agent,
                     )
 
-                if not hasattr(crewai_event_bus, "emit"):
-                    raise AttributeError(
-                        "crewai_event_bus must have an 'emit' method"
-                    ) from e
                 crewai_event_bus.emit(
                     self,
                     event=LLMCallFailedEvent(
@@ -1086,8 +1167,6 @@ class LLM(BaseLLM):
             from_agent: Optional agent object
             messages: Optional messages object
         """
-        if not hasattr(crewai_event_bus, "emit"):
-            raise AttributeError("crewai_event_bus must have an 'emit' method")
         crewai_event_bus.emit(
             self,
             event=LLMCallCompletedEvent(
@@ -1225,11 +1304,14 @@ class LLM(BaseLLM):
         if self.context_window_size != 0:
             return self.context_window_size
 
+        min_context = 1024
+        max_context = 2097152  # Current max from gemini-1.5-pro
+
         # Validate all context window sizes
         for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
-            if value < MIN_CONTEXT or value > MAX_CONTEXT:
+            if value < min_context or value > max_context:
                 raise ValueError(
-                    f"Context window for {key} must be between {MIN_CONTEXT} and {MAX_CONTEXT}"
+                    f"Context window for {key} must be between {min_context} and {max_context}"
                 )
 
         self.context_window_size = int(
@@ -1293,3 +1375,129 @@ class LLM(BaseLLM):
 
                 litellm.success_callback = success_callbacks
                 litellm.failure_callback = failure_callbacks
+
+    def __copy__(self):
+        """Create a shallow copy of the LLM instance."""
+        # Filter out parameters that are already explicitly passed to avoid conflicts
+        filtered_params = {
+            k: v
+            for k, v in self.additional_params.items()
+            if k
+            not in [
+                "model",
+                "is_litellm",
+                "temperature",
+                "top_p",
+                "n",
+                "max_completion_tokens",
+                "max_tokens",
+                "presence_penalty",
+                "frequency_penalty",
+                "logit_bias",
+                "response_format",
+                "seed",
+                "logprobs",
+                "top_logprobs",
+                "base_url",
+                "api_base",
+                "api_version",
+                "api_key",
+                "callbacks",
+                "reasoning_effort",
+                "stream",
+                "stop",
+            ]
+        }
+
+        # Create a new instance with the same parameters
+        return LLM(
+            model=self.model,
+            is_litellm=self.is_litellm,
+            temperature=self.temperature,
+            top_p=self.top_p,
+            n=self.n,
+            max_completion_tokens=self.max_completion_tokens,
+            max_tokens=self.max_tokens,
+            presence_penalty=self.presence_penalty,
+            frequency_penalty=self.frequency_penalty,
+            logit_bias=self.logit_bias,
+            response_format=self.response_format,
+            seed=self.seed,
+            logprobs=self.logprobs,
+            top_logprobs=self.top_logprobs,
+            base_url=self.base_url,
+            api_base=self.api_base,
+            api_version=self.api_version,
+            api_key=self.api_key,
+            callbacks=self.callbacks,
+            reasoning_effort=self.reasoning_effort,
+            stream=self.stream,
+            stop=self.stop,
+            **filtered_params,
+        )
+
+    def __deepcopy__(self, memo):
+        """Create a deep copy of the LLM instance."""
+        import copy
+
+        # Filter out parameters that are already explicitly passed to avoid conflicts
+        filtered_params = {
+            k: copy.deepcopy(v, memo)
+            for k, v in self.additional_params.items()
+            if k
+            not in [
+                "model",
+                "is_litellm",
+                "temperature",
+                "top_p",
+                "n",
+                "max_completion_tokens",
+                "max_tokens",
+                "presence_penalty",
+                "frequency_penalty",
+                "logit_bias",
+                "response_format",
+                "seed",
+                "logprobs",
+                "top_logprobs",
+                "base_url",
+                "api_base",
+                "api_version",
+                "api_key",
+                "callbacks",
+                "reasoning_effort",
+                "stream",
+                "stop",
+            ]
+        }
+
+        # Create a new instance with the same parameters
+        return LLM(
+            model=self.model,
+            is_litellm=self.is_litellm,
+            temperature=self.temperature,
+            top_p=self.top_p,
+            n=self.n,
+            max_completion_tokens=self.max_completion_tokens,
+            max_tokens=self.max_tokens,
+            presence_penalty=self.presence_penalty,
+            frequency_penalty=self.frequency_penalty,
+            logit_bias=copy.deepcopy(self.logit_bias, memo)
+            if self.logit_bias
+            else None,
+            response_format=copy.deepcopy(self.response_format, memo)
+            if self.response_format
+            else None,
+            seed=self.seed,
+            logprobs=self.logprobs,
+            top_logprobs=self.top_logprobs,
+            base_url=self.base_url,
+            api_base=self.api_base,
+            api_version=self.api_version,
+            api_key=self.api_key,
+            callbacks=copy.deepcopy(self.callbacks, memo) if self.callbacks else None,
+            reasoning_effort=self.reasoning_effort,
+            stream=self.stream,
+            stop=copy.deepcopy(self.stop, memo) if self.stop else None,
+            **filtered_params,
+        )

lib/crewai/src/crewai/llms/base_llm.py

@@ -1,12 +1,33 @@
 """Base LLM abstract class for CrewAI.
 
 This module provides the abstract base class for all LLM implementations
-in CrewAI.
+in CrewAI, including common functionality for native SDK implementations.
 """
 
 from abc import ABC, abstractmethod
+from datetime import datetime
+import json
+import logging
 from typing import Any, Final
 
+from pydantic import BaseModel
+
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.types.llm_events import (
+    LLMCallCompletedEvent,
+    LLMCallFailedEvent,
+    LLMCallStartedEvent,
+    LLMCallType,
+    LLMStreamChunkEvent,
+)
+from crewai.events.types.tool_usage_events import (
+    ToolUsageErrorEvent,
+    ToolUsageFinishedEvent,
+    ToolUsageStartedEvent,
+)
+from crewai.types.usage_metrics import UsageMetrics
+
+
 DEFAULT_CONTEXT_WINDOW_SIZE: Final[int] = 4096
 DEFAULT_SUPPORTS_STOP_WORDS: Final[bool] = True
 
@@ -27,13 +48,20 @@ class BaseLLM(ABC):
         model: The model identifier/name.
         temperature: Optional temperature setting for response generation.
         stop: A list of stop sequences that the LLM should use to stop generation.
+        additional_params: Additional provider-specific parameters.
     """
 
+    is_litellm: bool = False
+
     def __init__(
         self,
         model: str,
         temperature: float | None = None,
-        stop: list[str] | None = None,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        timeout: float | None = None,
+        provider: str | None = None,
+        **kwargs,
     ) -> None:
         """Initialize the BaseLLM with default attributes.
 
@@ -41,10 +69,44 @@ class BaseLLM(ABC):
             model: The model identifier/name.
             temperature: Optional temperature setting for response generation.
             stop: Optional list of stop sequences for generation.
+            **kwargs: Additional provider-specific parameters.
         """
+        if not model:
+            raise ValueError("Model name is required and cannot be empty")
+
         self.model = model
         self.temperature = temperature
-        self.stop: list[str] = stop or []
+        self.api_key = api_key
+        self.base_url = base_url
+        # Store additional parameters for provider-specific use
+        self.additional_params = kwargs
+        self._provider = provider or "openai"
+
+        stop = kwargs.pop("stop", None)
+        if stop is None:
+            self.stop: list[str] = []
+        elif isinstance(stop, str):
+            self.stop = [stop]
+        else:
+            self.stop = stop
+
+        self._token_usage = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+            "cached_prompt_tokens": 0,
+        }
+
+    @property
+    def provider(self) -> str:
+        """Get the provider of the LLM."""
+        return self._provider
+
+    @provider.setter
+    def provider(self, value: str) -> None:
+        """Set the provider of the LLM."""
+        self._provider = value
 
     @abstractmethod
     def call(
@@ -82,6 +144,17 @@ class BaseLLM(ABC):
             RuntimeError: If the LLM request fails for other reasons.
         """
 
+    def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]:
+        """Convert tools to a format that can be used for interference.
+
+        Args:
+            tools: List of tools to convert.
+
+        Returns:
+            List of converted tools (default implementation returns as-is)
+        """
+        return tools
+
     def supports_stop_words(self) -> bool:
         """Check if the LLM supports stop words.
 
@@ -90,6 +163,58 @@ class BaseLLM(ABC):
         """
         return DEFAULT_SUPPORTS_STOP_WORDS
 
+    def _supports_stop_words_implementation(self) -> bool:
+        """Check if stop words are configured for this LLM instance.
+
+        Native providers can override supports_stop_words() to return this value
+        to ensure consistent behavior based on whether stop words are actually configured.
+
+        Returns:
+            True if stop words are configured and can be applied
+        """
+        return bool(self.stop)
+
+    def _apply_stop_words(self, content: str) -> str:
+        """Apply stop words to truncate response content.
+
+        This method provides consistent stop word behavior across all native SDK providers.
+        Native providers should call this method to post-process their responses.
+
+        Args:
+            content: The raw response content from the LLM
+
+        Returns:
+            Content truncated at the first occurrence of any stop word
+
+        Example:
+            >>> llm = MyNativeLLM(stop=["Observation:", "Final Answer:"])
+            >>> response = "I need to search.\\n\\nAction: search\\nObservation: Found results"
+            >>> llm._apply_stop_words(response)
+            "I need to search.\\n\\nAction: search"
+        """
+        if not self.stop or not content:
+            return content
+
+        # Find the earliest occurrence of any stop word
+        earliest_stop_pos = len(content)
+        found_stop_word = None
+
+        for stop_word in self.stop:
+            stop_pos = content.find(stop_word)
+            if stop_pos != -1 and stop_pos < earliest_stop_pos:
+                earliest_stop_pos = stop_pos
+                found_stop_word = stop_word
+
+        # Truncate at the stop word if found
+        if found_stop_word is not None:
+            truncated = content[:earliest_stop_pos].strip()
+            logging.debug(
+                f"Applied stop word '{found_stop_word}' at position {earliest_stop_pos}"
+            )
+            return truncated
+
+        return content
+
     def get_context_window_size(self) -> int:
         """Get the context window size for the LLM.
 
@@ -98,3 +223,314 @@ class BaseLLM(ABC):
         """
         # Default implementation - subclasses should override with model-specific values
         return DEFAULT_CONTEXT_WINDOW_SIZE
+
+    # Common helper methods for native SDK implementations
+
+    def _emit_call_started_event(
+        self,
+        messages: str | list[dict[str, str]],
+        tools: list[dict] | None = None,
+        callbacks: list[Any] | None = None,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> None:
+        """Emit LLM call started event."""
+        if not hasattr(crewai_event_bus, "emit"):
+            raise ValueError("crewai_event_bus does not have an emit method") from None
+
+        crewai_event_bus.emit(
+            self,
+            event=LLMCallStartedEvent(
+                messages=messages,
+                tools=tools,
+                callbacks=callbacks,
+                available_functions=available_functions,
+                from_task=from_task,
+                from_agent=from_agent,
+                model=self.model,
+            ),
+        )
+
+    def _emit_call_completed_event(
+        self,
+        response: Any,
+        call_type: LLMCallType,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+        messages: str | list[dict[str, Any]] | None = None,
+    ) -> None:
+        """Emit LLM call completed event."""
+        crewai_event_bus.emit(
+            self,
+            event=LLMCallCompletedEvent(
+                messages=messages,
+                response=response,
+                call_type=call_type,
+                from_task=from_task,
+                from_agent=from_agent,
+                model=self.model,
+            ),
+        )
+
+    def _emit_call_failed_event(
+        self,
+        error: str,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> None:
+        """Emit LLM call failed event."""
+        if not hasattr(crewai_event_bus, "emit"):
+            raise ValueError("crewai_event_bus does not have an emit method") from None
+
+        crewai_event_bus.emit(
+            self,
+            event=LLMCallFailedEvent(
+                error=error,
+                from_task=from_task,
+                from_agent=from_agent,
+            ),
+        )
+
+    def _emit_stream_chunk_event(
+        self,
+        chunk: str,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+        tool_call: dict[str, Any] | None = None,
+    ) -> None:
+        """Emit stream chunk event."""
+        if not hasattr(crewai_event_bus, "emit"):
+            raise ValueError("crewai_event_bus does not have an emit method") from None
+
+        crewai_event_bus.emit(
+            self,
+            event=LLMStreamChunkEvent(
+                chunk=chunk,
+                tool_call=tool_call,
+                from_task=from_task,
+                from_agent=from_agent,
+            ),
+        )
+
+    def _handle_tool_execution(
+        self,
+        function_name: str,
+        function_args: dict[str, Any],
+        available_functions: dict[str, Any],
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str | None:
+        """Handle tool execution with proper event emission.
+
+        Args:
+            function_name: Name of the function to execute
+            function_args: Arguments to pass to the function
+            available_functions: Dict of available functions
+            from_task: Optional task object
+            from_agent: Optional agent object
+
+        Returns:
+            Result of function execution or None if function not found
+        """
+        if function_name not in available_functions:
+            logging.warning(
+                f"Function '{function_name}' not found in available functions"
+            )
+            return None
+
+        try:
+            # Emit tool usage started event
+            started_at = datetime.now()
+
+            crewai_event_bus.emit(
+                self,
+                event=ToolUsageStartedEvent(
+                    tool_name=function_name,
+                    tool_args=function_args,
+                    from_agent=from_agent,
+                    from_task=from_task,
+                ),
+            )
+
+            # Execute the function
+            fn = available_functions[function_name]
+            result = fn(**function_args)
+
+            # Emit tool usage finished event
+            crewai_event_bus.emit(
+                self,
+                event=ToolUsageFinishedEvent(
+                    output=result,
+                    tool_name=function_name,
+                    tool_args=function_args,
+                    started_at=started_at,
+                    finished_at=datetime.now(),
+                    from_task=from_task,
+                    from_agent=from_agent,
+                ),
+            )
+
+            # Emit LLM call completed event for tool call
+            self._emit_call_completed_event(
+                response=result,
+                call_type=LLMCallType.TOOL_CALL,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+
+            return str(result)
+
+        except Exception as e:
+            error_msg = f"Error executing function '{function_name}': {e!s}"
+            logging.error(error_msg)
+
+            # Emit tool usage error event
+            if not hasattr(crewai_event_bus, "emit"):
+                raise ValueError(
+                    "crewai_event_bus does not have an emit method"
+                ) from None
+
+            crewai_event_bus.emit(
+                self,
+                event=ToolUsageErrorEvent(
+                    tool_name=function_name,
+                    tool_args=function_args,
+                    error=error_msg,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                ),
+            )
+
+            # Emit LLM call failed event
+            self._emit_call_failed_event(
+                error=error_msg,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+
+            return None
+
+    def _format_messages(
+        self, messages: str | list[dict[str, str]]
+    ) -> list[dict[str, str]]:
+        """Convert messages to standard format.
+
+        Args:
+            messages: Input messages (string or list of message dicts)
+
+        Returns:
+            List of message dictionaries with 'role' and 'content' keys
+
+        Raises:
+            ValueError: If message format is invalid
+        """
+        if isinstance(messages, str):
+            return [{"role": "user", "content": messages}]
+
+        # Validate message format
+        for i, msg in enumerate(messages):
+            if not isinstance(msg, dict):
+                raise ValueError(f"Message at index {i} must be a dictionary")
+            if "role" not in msg or "content" not in msg:
+                raise ValueError(
+                    f"Message at index {i} must have 'role' and 'content' keys"
+                )
+
+        return messages
+
+    def _validate_structured_output(
+        self,
+        response: str,
+        response_format: type[BaseModel] | None,
+    ) -> str | BaseModel:
+        """Validate and parse structured output.
+
+        Args:
+            response: Raw response string
+            response_format: Optional Pydantic model for structured output
+
+        Returns:
+            Parsed response (BaseModel instance if response_format provided, otherwise string)
+
+        Raises:
+            ValueError: If structured output validation fails
+        """
+        if response_format is None:
+            return response
+
+        try:
+            # Try to parse as JSON first
+            if response.strip().startswith("{") or response.strip().startswith("["):
+                data = json.loads(response)
+                return response_format.model_validate(data)
+
+            # Try to extract JSON from response
+            import re
+
+            json_match = re.search(r"\{.*\}", response, re.DOTALL)
+            if json_match:
+                data = json.loads(json_match.group())
+                return response_format.model_validate(data)
+
+            raise ValueError("No JSON found in response")
+
+        except (json.JSONDecodeError, ValueError) as e:
+            logging.warning(f"Failed to parse structured output: {e}")
+            raise ValueError(
+                f"Failed to parse response into {response_format.__name__}: {e}"
+            ) from e
+
+    def _extract_provider(self, model: str) -> str:
+        """Extract provider from model string.
+
+        Args:
+            model: Model string (e.g., 'openai/gpt-4' or 'gpt-4')
+
+        Returns:
+            Provider name (e.g., 'openai')
+        """
+        if "/" in model:
+            return model.partition("/")[0]
+        return "openai"  # Default provider
+
+    def _track_token_usage_internal(self, usage_data: dict[str, Any]) -> None:
+        """Track token usage internally in the LLM instance.
+
+        Args:
+            usage_data: Token usage data from the API response
+        """
+        # Extract tokens in a provider-agnostic way
+        prompt_tokens = (
+            usage_data.get("prompt_tokens")
+            or usage_data.get("prompt_token_count")
+            or usage_data.get("input_tokens")
+            or 0
+        )
+
+        completion_tokens = (
+            usage_data.get("completion_tokens")
+            or usage_data.get("candidates_token_count")
+            or usage_data.get("output_tokens")
+            or 0
+        )
+
+        cached_tokens = (
+            usage_data.get("cached_tokens")
+            or usage_data.get("cached_prompt_tokens")
+            or 0
+        )
+
+        self._token_usage["prompt_tokens"] += prompt_tokens
+        self._token_usage["completion_tokens"] += completion_tokens
+        self._token_usage["total_tokens"] += prompt_tokens + completion_tokens
+        self._token_usage["successful_requests"] += 1
+        self._token_usage["cached_prompt_tokens"] += cached_tokens
+
+    def get_token_usage_summary(self) -> UsageMetrics:
+        """Get summary of token usage for this LLM instance.
+
+        Returns:
+            Dictionary with token usage totals
+        """
+        return UsageMetrics(**self._token_usage)

lib/crewai/src/crewai/llms/providers/anthropic/completion.py

@@ -0,0 +1,432 @@
+import json
+import logging
+import os
+from typing import Any
+
+from crewai.events.types.llm_events import LLMCallType
+from crewai.llms.base_llm import BaseLLM
+from crewai.utilities.agent_utils import is_context_length_exceeded
+from crewai.utilities.exceptions.context_window_exceeding_exception import (
+    LLMContextLengthExceededError,
+)
+
+
+try:
+    from anthropic import Anthropic
+    from anthropic.types import Message
+    from anthropic.types.tool_use_block import ToolUseBlock
+except ImportError:
+    raise ImportError(
+        "Anthropic native provider not available, to install: `uv add anthropic`"
+    ) from None
+
+
+class AnthropicCompletion(BaseLLM):
+    """Anthropic native completion implementation.
+
+    This class provides direct integration with the Anthropic Python SDK,
+    offering native tool use, streaming support, and proper message formatting.
+    """
+
+    def __init__(
+        self,
+        model: str = "claude-3-5-sonnet-20241022",
+        api_key: str | None = None,
+        base_url: str | None = None,
+        timeout: float | None = None,
+        max_retries: int = 2,
+        temperature: float | None = None,
+        max_tokens: int = 4096,  # Required for Anthropic
+        top_p: float | None = None,
+        stop_sequences: list[str] | None = None,
+        stream: bool = False,
+        **kwargs,
+    ):
+        """Initialize Anthropic chat completion client.
+
+        Args:
+            model: Anthropic model name (e.g., 'claude-3-5-sonnet-20241022')
+            api_key: Anthropic API key (defaults to ANTHROPIC_API_KEY env var)
+            base_url: Custom base URL for Anthropic API
+            timeout: Request timeout in seconds
+            max_retries: Maximum number of retries
+            temperature: Sampling temperature (0-1)
+            max_tokens: Maximum tokens in response (required for Anthropic)
+            top_p: Nucleus sampling parameter
+            stop_sequences: Stop sequences (Anthropic uses stop_sequences, not stop)
+            stream: Enable streaming responses
+            **kwargs: Additional parameters
+        """
+        super().__init__(
+            model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
+        )
+
+        # Initialize Anthropic client
+        self.client = Anthropic(
+            api_key=api_key or os.getenv("ANTHROPIC_API_KEY"),
+            base_url=base_url,
+            timeout=timeout,
+            max_retries=max_retries,
+        )
+
+        # Store completion parameters
+        self.max_tokens = max_tokens
+        self.top_p = top_p
+        self.stream = stream
+        self.stop_sequences = stop_sequences or []
+
+        # Model-specific settings
+        self.is_claude_3 = "claude-3" in model.lower()
+        self.supports_tools = self.is_claude_3  # Claude 3+ supports tool use
+
+    def call(
+        self,
+        messages: str | list[dict[str, str]],
+        tools: list[dict] | None = None,
+        callbacks: list[Any] | None = None,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str | Any:
+        """Call Anthropic messages API.
+
+        Args:
+            messages: Input messages for the chat completion
+            tools: List of tool/function definitions
+            callbacks: Callback functions (not used in native implementation)
+            available_functions: Available functions for tool calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            Chat completion response or tool call result
+        """
+        try:
+            # Emit call started event
+            self._emit_call_started_event(
+                messages=messages,
+                tools=tools,
+                callbacks=callbacks,
+                available_functions=available_functions,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+
+            # Format messages for Anthropic
+            formatted_messages, system_message = self._format_messages_for_anthropic(
+                messages
+            )
+
+            # Prepare completion parameters
+            completion_params = self._prepare_completion_params(
+                formatted_messages, system_message, tools
+            )
+
+            # Handle streaming vs non-streaming
+            if self.stream:
+                return self._handle_streaming_completion(
+                    completion_params, available_functions, from_task, from_agent
+                )
+
+            return self._handle_completion(
+                completion_params, available_functions, from_task, from_agent
+            )
+
+        except Exception as e:
+            error_msg = f"Anthropic 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
+
+    def _prepare_completion_params(
+        self,
+        messages: list[dict[str, str]],
+        system_message: str | None = None,
+        tools: list[dict] | None = None,
+    ) -> dict[str, Any]:
+        """Prepare parameters for Anthropic messages API.
+
+        Args:
+            messages: Formatted messages for Anthropic
+            system_message: Extracted system message
+            tools: Tool definitions
+
+        Returns:
+            Parameters dictionary for Anthropic API
+        """
+        params = {
+            "model": self.model,
+            "messages": messages,
+            "max_tokens": self.max_tokens,
+            "stream": self.stream,
+        }
+
+        # Add system message if present
+        if system_message:
+            params["system"] = system_message
+
+        # Add optional parameters if set
+        if self.temperature is not None:
+            params["temperature"] = self.temperature
+        if self.top_p is not None:
+            params["top_p"] = self.top_p
+        if self.stop_sequences:
+            params["stop_sequences"] = self.stop_sequences
+
+        # Handle tools for Claude 3+
+        if tools and self.supports_tools:
+            params["tools"] = self._convert_tools_for_interference(tools)
+
+        return params
+
+    def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]:
+        """Convert CrewAI tool format to Anthropic tool use format."""
+        from crewai.llms.providers.utils.common import safe_tool_conversion
+
+        anthropic_tools = []
+
+        for tool in tools:
+            name, description, parameters = safe_tool_conversion(tool, "Anthropic")
+
+            anthropic_tool = {
+                "name": name,
+                "description": description,
+            }
+
+            if parameters and isinstance(parameters, dict):
+                anthropic_tool["input_schema"] = parameters  # type: ignore
+
+            anthropic_tools.append(anthropic_tool)
+
+        return anthropic_tools
+
+    def _format_messages_for_anthropic(
+        self, messages: str | list[dict[str, str]]
+    ) -> tuple[list[dict[str, str]], str | None]:
+        """Format messages for Anthropic API.
+
+        Anthropic has specific requirements:
+        - System messages are separate from conversation messages
+        - Messages must alternate between user and assistant
+        - First message must be from user
+
+        Args:
+            messages: Input messages
+
+        Returns:
+            Tuple of (formatted_messages, system_message)
+        """
+        # Use base class formatting first
+        base_formatted = super()._format_messages(messages)
+
+        formatted_messages = []
+        system_message = None
+
+        for message in base_formatted:
+            role = message.get("role")
+            content = message.get("content", "")
+
+            if role == "system":
+                # Extract system message - Anthropic handles it separately
+                if system_message:
+                    system_message += f"\n\n{content}"
+                else:
+                    system_message = content
+            else:
+                # Add user/assistant messages - ensure both role and content are str, not None
+                role_str = role if role is not None else "user"
+                content_str = content if content is not None else ""
+                formatted_messages.append({"role": role_str, "content": content_str})
+
+        # Ensure first message is from user (Anthropic requirement)
+        if not formatted_messages:
+            # If no messages, add a default user message
+            formatted_messages.append({"role": "user", "content": "Hello"})
+        elif formatted_messages[0]["role"] != "user":
+            # If first message is not from user, insert a user message at the beginning
+            formatted_messages.insert(0, {"role": "user", "content": "Hello"})
+
+        return formatted_messages, system_message
+
+    def _handle_completion(
+        self,
+        params: dict[str, Any],
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str | Any:
+        """Handle non-streaming message completion."""
+        try:
+            response: Message = self.client.messages.create(**params)
+
+        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
+
+        usage = self._extract_anthropic_token_usage(response)
+        self._track_token_usage_internal(usage)
+
+        if response.content and available_functions:
+            for content_block in response.content:
+                if isinstance(content_block, ToolUseBlock):
+                    function_name = content_block.name
+                    function_args = content_block.input
+
+                    result = self._handle_tool_execution(
+                        function_name=function_name,
+                        function_args=function_args,  # type: ignore
+                        available_functions=available_functions,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                    )
+
+                    if result is not None:
+                        return result
+
+        # Extract text content
+        content = ""
+        if response.content:
+            for content_block in response.content:
+                if hasattr(content_block, "text"):
+                    content += content_block.text
+
+        content = self._apply_stop_words(content)
+
+        self._emit_call_completed_event(
+            response=content,
+            call_type=LLMCallType.LLM_CALL,
+            from_task=from_task,
+            from_agent=from_agent,
+            messages=params["messages"],
+        )
+
+        if usage.get("total_tokens", 0) > 0:
+            logging.info(f"Anthropic API usage: {usage}")
+
+        return content
+
+    def _handle_streaming_completion(
+        self,
+        params: dict[str, Any],
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str:
+        """Handle streaming message completion."""
+        full_response = ""
+        tool_uses = {}
+
+        # Make streaming API call
+        with self.client.messages.stream(**params) as stream:
+            for event in stream:
+                # Handle content delta events
+                if hasattr(event, "delta") and hasattr(event.delta, "text"):
+                    text_delta = event.delta.text
+                    full_response += text_delta
+                    self._emit_stream_chunk_event(
+                        chunk=text_delta,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                    )
+
+                # Handle tool use events
+                elif hasattr(event, "delta") and hasattr(event.delta, "partial_json"):
+                    # Tool use streaming - accumulate JSON
+                    tool_id = getattr(event, "index", "default")
+                    if tool_id not in tool_uses:
+                        tool_uses[tool_id] = {
+                            "name": "",
+                            "input": "",
+                        }
+
+                    if hasattr(event.delta, "name"):
+                        tool_uses[tool_id]["name"] = event.delta.name
+                    if hasattr(event.delta, "partial_json"):
+                        tool_uses[tool_id]["input"] += event.delta.partial_json
+
+        # Handle completed tool uses
+        if tool_uses and available_functions:
+            for tool_data in tool_uses.values():
+                function_name = tool_data["name"]
+
+                try:
+                    function_args = json.loads(tool_data["input"])
+                except json.JSONDecodeError as e:
+                    logging.error(f"Failed to parse streamed tool arguments: {e}")
+                    continue
+
+                # 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
+
+        # Apply stop words to full response
+        full_response = self._apply_stop_words(full_response)
+
+        # Emit completion event and return full response
+        self._emit_call_completed_event(
+            response=full_response,
+            call_type=LLMCallType.LLM_CALL,
+            from_task=from_task,
+            from_agent=from_agent,
+            messages=params["messages"],
+        )
+
+        return full_response
+
+    def supports_function_calling(self) -> bool:
+        """Check if the model supports function calling."""
+        return self.supports_tools
+
+    def supports_stop_words(self) -> bool:
+        """Check if the model supports stop words."""
+        return True  # All Claude models support stop sequences
+
+    def get_context_window_size(self) -> int:
+        """Get the context window size for the model."""
+        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO
+
+        # Context window sizes for Anthropic models
+        context_windows = {
+            "claude-3-5-sonnet": 200000,
+            "claude-3-5-haiku": 200000,
+            "claude-3-opus": 200000,
+            "claude-3-sonnet": 200000,
+            "claude-3-haiku": 200000,
+            "claude-3-7-sonnet": 200000,
+            "claude-2.1": 200000,
+            "claude-2": 100000,
+            "claude-instant": 100000,
+        }
+
+        # Find the best match for the model name
+        for model_prefix, size in context_windows.items():
+            if self.model.startswith(model_prefix):
+                return int(size * CONTEXT_WINDOW_USAGE_RATIO)
+
+        # 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]:
+        """Extract token usage from Anthropic response."""
+        if hasattr(response, "usage") and response.usage:
+            usage = response.usage
+            input_tokens = getattr(usage, "input_tokens", 0)
+            output_tokens = getattr(usage, "output_tokens", 0)
+            return {
+                "input_tokens": input_tokens,
+                "output_tokens": output_tokens,
+                "total_tokens": input_tokens + output_tokens,
+            }
+        return {"total_tokens": 0}

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -0,0 +1,473 @@
+import json
+import logging
+import os
+from typing import Any
+
+from crewai.utilities.agent_utils import is_context_length_exceeded
+from crewai.utilities.exceptions.context_window_exceeding_exception import (
+    LLMContextLengthExceededError,
+)
+
+
+try:
+    from azure.ai.inference import ChatCompletionsClient  # type: ignore
+    from azure.ai.inference.models import (  # type: ignore
+        ChatCompletions,
+        ChatCompletionsToolCall,
+        StreamingChatCompletionsUpdate,
+    )
+    from azure.core.credentials import AzureKeyCredential  # type: ignore
+    from azure.core.exceptions import HttpResponseError  # type: ignore
+    from crewai.events.types.llm_events import LLMCallType
+    from crewai.llms.base_llm import BaseLLM
+
+except ImportError:
+    raise ImportError(
+        "Azure AI Inference native provider not available, to install: `uv add azure-ai-inference`"
+    ) from None
+
+
+class AzureCompletion(BaseLLM):
+    """Azure AI Inference native completion implementation.
+
+    This class provides direct integration with the Azure AI Inference Python SDK,
+    offering native function calling, streaming support, and proper Azure authentication.
+    """
+
+    def __init__(
+        self,
+        model: str,
+        api_key: str | None = None,
+        endpoint: str | None = None,
+        api_version: str | None = None,
+        timeout: float | None = None,
+        max_retries: int = 2,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        frequency_penalty: float | None = None,
+        presence_penalty: float | None = None,
+        max_tokens: int | None = None,
+        stop: list[str] | None = None,
+        stream: bool = False,
+        **kwargs,
+    ):
+        """Initialize Azure AI Inference chat completion client.
+
+        Args:
+            model: Azure deployment name or model name
+            api_key: Azure API key (defaults to AZURE_API_KEY env var)
+            endpoint: Azure endpoint URL (defaults to AZURE_ENDPOINT env var)
+            api_version: Azure API version (defaults to AZURE_API_VERSION env var)
+            timeout: Request timeout in seconds
+            max_retries: Maximum number of retries
+            temperature: Sampling temperature (0-2)
+            top_p: Nucleus sampling parameter
+            frequency_penalty: Frequency penalty (-2 to 2)
+            presence_penalty: Presence penalty (-2 to 2)
+            max_tokens: Maximum tokens in response
+            stop: Stop sequences
+            stream: Enable streaming responses
+            **kwargs: Additional parameters
+        """
+        super().__init__(
+            model=model, temperature=temperature, stop=stop or [], **kwargs
+        )
+
+        self.api_key = api_key or os.getenv("AZURE_API_KEY")
+        self.endpoint = (
+            endpoint
+            or os.getenv("AZURE_ENDPOINT")
+            or os.getenv("AZURE_OPENAI_ENDPOINT")
+            or os.getenv("AZURE_API_BASE")
+        )
+        self.api_version = api_version or os.getenv("AZURE_API_VERSION") or "2024-02-01"
+
+        if not self.api_key:
+            raise ValueError(
+                "Azure API key is required. Set AZURE_API_KEY environment variable or pass api_key parameter."
+            )
+        if not self.endpoint:
+            raise ValueError(
+                "Azure endpoint is required. Set AZURE_ENDPOINT environment variable or pass endpoint parameter."
+            )
+
+        self.client = ChatCompletionsClient(
+            endpoint=self.endpoint,
+            credential=AzureKeyCredential(self.api_key),
+        )
+
+        self.top_p = top_p
+        self.frequency_penalty = frequency_penalty
+        self.presence_penalty = presence_penalty
+        self.max_tokens = max_tokens
+        self.stream = stream
+
+        self.is_openai_model = any(
+            prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"]
+        )
+
+    def call(
+        self,
+        messages: str | list[dict[str, str]],
+        tools: list[dict] | None = None,
+        callbacks: list[Any] | None = None,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str | Any:
+        """Call Azure AI Inference chat completions API.
+
+        Args:
+            messages: Input messages for the chat completion
+            tools: List of tool/function definitions
+            callbacks: Callback functions (not used in native implementation)
+            available_functions: Available functions for tool calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            Chat completion response or tool call result
+        """
+        try:
+            # Emit call started event
+            self._emit_call_started_event(
+                messages=messages,
+                tools=tools,
+                callbacks=callbacks,
+                available_functions=available_functions,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+
+            # Format messages for Azure
+            formatted_messages = self._format_messages_for_azure(messages)
+
+            # Prepare completion parameters
+            completion_params = self._prepare_completion_params(
+                formatted_messages, tools
+            )
+
+            # Handle streaming vs non-streaming
+            if self.stream:
+                return self._handle_streaming_completion(
+                    completion_params, available_functions, from_task, from_agent
+                )
+
+            return self._handle_completion(
+                completion_params, available_functions, from_task, from_agent
+            )
+
+        except HttpResponseError as e:
+            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
+
+    def _prepare_completion_params(
+        self,
+        messages: list[dict[str, str]],
+        tools: list[dict] | None = None,
+    ) -> dict[str, Any]:
+        """Prepare parameters for Azure AI Inference chat completion.
+
+        Args:
+            messages: Formatted messages for Azure
+            tools: Tool definitions
+
+        Returns:
+            Parameters dictionary for Azure API
+        """
+        params = {
+            "model": self.model,
+            "messages": messages,
+            "stream": self.stream,
+        }
+
+        # Add optional parameters if set
+        if self.temperature is not None:
+            params["temperature"] = self.temperature
+        if self.top_p is not None:
+            params["top_p"] = self.top_p
+        if self.frequency_penalty is not None:
+            params["frequency_penalty"] = self.frequency_penalty
+        if self.presence_penalty is not None:
+            params["presence_penalty"] = self.presence_penalty
+        if self.max_tokens is not None:
+            params["max_tokens"] = self.max_tokens
+        if self.stop:
+            params["stop"] = self.stop
+
+        # Handle tools/functions for Azure OpenAI models
+        if tools and self.is_openai_model:
+            params["tools"] = self._convert_tools_for_interference(tools)
+            params["tool_choice"] = "auto"
+
+        return params
+
+    def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]:
+        """Convert CrewAI tool format to Azure OpenAI function calling format."""
+
+        from crewai.llms.providers.utils.common import safe_tool_conversion
+
+        azure_tools = []
+
+        for tool in tools:
+            name, description, parameters = safe_tool_conversion(tool, "Azure")
+
+            azure_tool = {
+                "type": "function",
+                "function": {
+                    "name": name,
+                    "description": description,
+                },
+            }
+
+            if parameters:
+                if isinstance(parameters, dict):
+                    azure_tool["function"]["parameters"] = parameters  # type: ignore
+                else:
+                    azure_tool["function"]["parameters"] = dict(parameters)
+
+            azure_tools.append(azure_tool)
+
+        return azure_tools
+
+    def _format_messages_for_azure(
+        self, messages: str | list[dict[str, str]]
+    ) -> list[dict[str, str]]:
+        """Format messages for Azure AI Inference API.
+
+        Args:
+            messages: Input messages
+
+        Returns:
+            List of dict objects
+        """
+        # Use base class formatting first
+        base_formatted = super()._format_messages(messages)
+
+        azure_messages = []
+
+        for message in base_formatted:
+            role = message.get("role")
+            content = message.get("content", "")
+
+            if role == "system":
+                azure_messages.append(dict(content=content))
+            elif role == "user":
+                azure_messages.append(dict(content=content))
+            elif role == "assistant":
+                azure_messages.append(dict(content=content))
+            else:
+                # Default to user message for unknown roles
+                azure_messages.append(dict(content=content))
+
+        return azure_messages
+
+    def _handle_completion(
+        self,
+        params: dict[str, Any],
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str | Any:
+        """Handle non-streaming chat completion."""
+        # Make API call
+        try:
+            response: ChatCompletions = self.client.complete(**params)
+
+            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)
+
+            # 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"],
+            )
+
+        except Exception as e:
+            if is_context_length_exceeded(e):
+                logging.error(f"Context window exceeded: {e}")
+                raise LLMContextLengthExceededError(str(e)) from e
+
+        return content
+
+    def _handle_streaming_completion(
+        self,
+        params: dict[str, Any],
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str:
+        """Handle streaming chat completion."""
+        full_response = ""
+        tool_calls = {}
+
+        # 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,
+                        )
+
+                    # 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": "",
+                                }
+
+                            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
+                                )
+
+        # Handle completed tool calls
+        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}")
+                    continue
+
+                # 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
+
+        # Apply stop words to full response
+        full_response = self._apply_stop_words(full_response)
+
+        # Emit completion event and return full response
+        self._emit_call_completed_event(
+            response=full_response,
+            call_type=LLMCallType.LLM_CALL,
+            from_task=from_task,
+            from_agent=from_agent,
+            messages=params["messages"],
+        )
+
+        return full_response
+
+    def supports_function_calling(self) -> bool:
+        """Check if the model supports function calling."""
+        # Azure OpenAI models support function calling
+        return self.is_openai_model
+
+    def supports_stop_words(self) -> bool:
+        """Check if the model supports stop words."""
+        return True  # Most Azure models support stop sequences
+
+    def get_context_window_size(self) -> int:
+        """Get the context window size for the model."""
+        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
+
+        min_context = 1024
+        max_context = 2097152
+
+        for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
+            if value < min_context or value > max_context:
+                raise ValueError(
+                    f"Context window for {key} must be between {min_context} and {max_context}"
+                )
+
+        # Context window sizes for common Azure models
+        context_windows = {
+            "gpt-4": 8192,
+            "gpt-4o": 128000,
+            "gpt-4o-mini": 200000,
+            "gpt-4-turbo": 128000,
+            "gpt-35-turbo": 16385,
+            "gpt-3.5-turbo": 16385,
+            "text-embedding": 8191,
+        }
+
+        # Find the best match for the model name
+        for model_prefix, size in context_windows.items():
+            if self.model.startswith(model_prefix):
+                return int(size * CONTEXT_WINDOW_USAGE_RATIO)
+
+        # Default context window size
+        return int(8192 * CONTEXT_WINDOW_USAGE_RATIO)
+
+    def _extract_azure_token_usage(self, response: ChatCompletions) -> dict[str, Any]:
+        """Extract token usage from Azure response."""
+        if hasattr(response, "usage") and response.usage:
+            usage = response.usage
+            return {
+                "prompt_tokens": getattr(usage, "prompt_tokens", 0),
+                "completion_tokens": getattr(usage, "completion_tokens", 0),
+                "total_tokens": getattr(usage, "total_tokens", 0),
+            }
+        return {"total_tokens": 0}

lib/crewai/src/crewai/llms/providers/gemini/completion.py

@@ -0,0 +1,497 @@
+import logging
+import os
+from typing import Any
+
+from crewai.events.types.llm_events import LLMCallType
+from crewai.llms.base_llm import BaseLLM
+from crewai.utilities.agent_utils import is_context_length_exceeded
+from crewai.utilities.exceptions.context_window_exceeding_exception import (
+    LLMContextLengthExceededError,
+)
+
+
+try:
+    from google import genai  # type: ignore
+    from google.genai import types  # type: ignore
+    from google.genai.errors import APIError  # type: ignore
+except ImportError:
+    raise ImportError(
+        "Google Gen AI native provider not available, to install: `uv add google-genai`"
+    ) from None
+
+
+class GeminiCompletion(BaseLLM):
+    """Google Gemini native completion implementation.
+
+    This class provides direct integration with the Google Gen AI Python SDK,
+    offering native function calling, streaming support, and proper Gemini formatting.
+    """
+
+    def __init__(
+        self,
+        model: str = "gemini-2.0-flash-001",
+        api_key: str | None = None,
+        project: str | None = None,
+        location: str | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        top_k: int | None = None,
+        max_output_tokens: int | None = None,
+        stop_sequences: list[str] | None = None,
+        stream: bool = False,
+        safety_settings: dict[str, Any] | None = None,
+        **kwargs,
+    ):
+        """Initialize Google Gemini chat completion client.
+
+        Args:
+            model: Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro')
+            api_key: Google API key (defaults to GOOGLE_API_KEY or GEMINI_API_KEY env var)
+            project: Google Cloud project ID (for Vertex AI)
+            location: Google Cloud location (for Vertex AI, defaults to 'us-central1')
+            temperature: Sampling temperature (0-2)
+            top_p: Nucleus sampling parameter
+            top_k: Top-k sampling parameter
+            max_output_tokens: Maximum tokens in response
+            stop_sequences: Stop sequences
+            stream: Enable streaming responses
+            safety_settings: Safety filter settings
+            **kwargs: Additional parameters
+        """
+        super().__init__(
+            model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
+        )
+
+        # Get API configuration
+        self.api_key = (
+            api_key or os.getenv("GOOGLE_API_KEY") or os.getenv("GEMINI_API_KEY")
+        )
+        self.project = project or os.getenv("GOOGLE_CLOUD_PROJECT")
+        self.location = location or os.getenv("GOOGLE_CLOUD_LOCATION") or "us-central1"
+
+        # Initialize client based on available configuration
+        if self.project:
+            # Use Vertex AI
+            self.client = genai.Client(
+                vertexai=True,
+                project=self.project,
+                location=self.location,
+            )
+        elif self.api_key:
+            # Use Gemini Developer API
+            self.client = genai.Client(api_key=self.api_key)
+        else:
+            raise ValueError(
+                "Either GOOGLE_API_KEY/GEMINI_API_KEY (for Gemini API) or "
+                "GOOGLE_CLOUD_PROJECT (for Vertex AI) must be set"
+            )
+
+        # Store completion parameters
+        self.top_p = top_p
+        self.top_k = top_k
+        self.max_output_tokens = max_output_tokens
+        self.stream = stream
+        self.safety_settings = safety_settings or {}
+        self.stop_sequences = stop_sequences or []
+
+        # Model-specific settings
+        self.is_gemini_2 = "gemini-2" in model.lower()
+        self.is_gemini_1_5 = "gemini-1.5" in model.lower()
+        self.supports_tools = self.is_gemini_1_5 or self.is_gemini_2
+
+    def call(
+        self,
+        messages: str | list[dict[str, str]],
+        tools: list[dict] | None = None,
+        callbacks: list[Any] | None = None,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str | Any:
+        """Call Google Gemini generate content API.
+
+        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)
+            available_functions: Available functions for tool calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            Chat completion response or tool call result
+        """
+        try:
+            self._emit_call_started_event(
+                messages=messages,
+                tools=tools,
+                callbacks=callbacks,
+                available_functions=available_functions,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+            self.tools = tools
+
+            formatted_content, system_instruction = self._format_messages_for_gemini(
+                messages
+            )
+
+            config = self._prepare_generation_config(system_instruction, tools)
+
+            if self.stream:
+                return self._handle_streaming_completion(
+                    formatted_content,
+                    config,
+                    available_functions,
+                    from_task,
+                    from_agent,
+                )
+
+            return self._handle_completion(
+                formatted_content,
+                system_instruction,
+                config,
+                available_functions,
+                from_task,
+                from_agent,
+            )
+
+        except APIError as e:
+            error_msg = f"Google Gemini API error: {e.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"Google Gemini 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
+
+    def _prepare_generation_config(
+        self,
+        system_instruction: str | None = None,
+        tools: list[dict] | None = None,
+    ) -> types.GenerateContentConfig:
+        """Prepare generation config for Google Gemini API.
+
+        Args:
+            system_instruction: System instruction for the model
+            tools: Tool definitions
+
+        Returns:
+            GenerateContentConfig object for Gemini API
+        """
+        self.tools = tools
+        config_params = {}
+
+        # Add system instruction if present
+        if system_instruction:
+            # Convert system instruction to Content format
+            system_content = types.Content(
+                role="user", parts=[types.Part.from_text(text=system_instruction)]
+            )
+            config_params["system_instruction"] = system_content
+
+        # Add generation config parameters
+        if self.temperature is not None:
+            config_params["temperature"] = self.temperature
+        if self.top_p is not None:
+            config_params["top_p"] = self.top_p
+        if self.top_k is not None:
+            config_params["top_k"] = self.top_k
+        if self.max_output_tokens is not None:
+            config_params["max_output_tokens"] = self.max_output_tokens
+        if self.stop_sequences:
+            config_params["stop_sequences"] = self.stop_sequences
+
+        # Handle tools for supported models
+        if tools and self.supports_tools:
+            config_params["tools"] = self._convert_tools_for_interference(tools)
+
+        if self.safety_settings:
+            config_params["safety_settings"] = self.safety_settings
+
+        return types.GenerateContentConfig(**config_params)
+
+    def _convert_tools_for_interference(self, tools: list[dict]) -> list[types.Tool]:
+        """Convert CrewAI tool format to Gemini function declaration format."""
+        gemini_tools = []
+
+        from crewai.llms.providers.utils.common import safe_tool_conversion
+
+        for tool in tools:
+            name, description, parameters = safe_tool_conversion(tool, "Gemini")
+
+            function_declaration = types.FunctionDeclaration(
+                name=name,
+                description=description,
+            )
+
+            # Add parameters if present - ensure parameters is a dict
+            if parameters and isinstance(parameters, dict):
+                function_declaration.parameters = parameters
+
+            gemini_tool = types.Tool(function_declarations=[function_declaration])
+            gemini_tools.append(gemini_tool)
+
+        return gemini_tools
+
+    def _format_messages_for_gemini(
+        self, messages: str | list[dict[str, str]]
+    ) -> tuple[list[types.Content], str | None]:
+        """Format messages for Gemini API.
+
+        Gemini has specific requirements:
+        - System messages are separate system_instruction
+        - Content is organized as Content objects with Parts
+        - Roles are 'user' and 'model' (not 'assistant')
+
+        Args:
+            messages: Input messages
+
+        Returns:
+            Tuple of (formatted_contents, system_instruction)
+        """
+        # Use base class formatting first
+        base_formatted = super()._format_messages(messages)
+
+        contents = []
+        system_instruction = None
+
+        for message in base_formatted:
+            role = message.get("role")
+            content = message.get("content", "")
+
+            if role == "system":
+                # Extract system instruction - Gemini handles it separately
+                if system_instruction:
+                    system_instruction += f"\n\n{content}"
+                else:
+                    system_instruction = content
+            else:
+                # Convert role for Gemini (assistant -> model)
+                gemini_role = "model" if role == "assistant" else "user"
+
+                # Create Content object
+                gemini_content = types.Content(
+                    role=gemini_role, parts=[types.Part.from_text(text=content)]
+                )
+                contents.append(gemini_content)
+
+        return contents, system_instruction
+
+    def _handle_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,
+    ) -> str | Any:
+        """Handle non-streaming content generation."""
+        api_params = {
+            "model": self.model,
+            "contents": contents,
+            "config": config,
+        }
+
+        try:
+            response = self.client.models.generate_content(**api_params)
+
+            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
+                        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,  # type: ignore
+                            from_task=from_task,
+                            from_agent=from_agent,
+                        )
+
+                        if result is not None:
+                            return result
+
+        content = response.text if hasattr(response, "text") else ""
+        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,
+            from_task=from_task,
+            from_agent=from_agent,
+            messages=messages_for_event,
+        )
+
+        return content
+
+    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,
+    ) -> str:
+        """Handle streaming content generation."""
+        full_response = ""
+        function_calls = {}
+
+        api_params = {
+            "model": self.model,
+            "contents": contents,
+            "config": config,
+        }
+
+        for chunk in self.client.models.generate_content_stream(**api_params):
+            if hasattr(chunk, "text") and chunk.text:
+                full_response += chunk.text
+                self._emit_stream_chunk_event(
+                    chunk=chunk.text,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
+
+            if hasattr(chunk, "candidates") and 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 {},
+                                }
+
+        # Handle completed function calls
+        if function_calls and available_functions:
+            for call_data in function_calls.values():
+                function_name = call_data["name"]
+                function_args = call_data["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
+
+        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 full_response
+
+    def supports_function_calling(self) -> bool:
+        """Check if the model supports function calling."""
+        return self.supports_tools
+
+    def supports_stop_words(self) -> bool:
+        """Check if the model supports stop words."""
+        return self._supports_stop_words_implementation()
+
+    def get_context_window_size(self) -> int:
+        """Get the context window size for the model."""
+        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
+
+        min_context = 1024
+        max_context = 2097152
+
+        for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
+            if value < min_context or value > max_context:
+                raise ValueError(
+                    f"Context window for {key} must be between {min_context} and {max_context}"
+                )
+
+        context_windows = {
+            "gemini-2.0-flash": 1048576,  # 1M tokens
+            "gemini-2.0-flash-thinking": 32768,
+            "gemini-2.0-flash-lite": 1048576,
+            "gemini-2.5-flash": 1048576,
+            "gemini-2.5-pro": 1048576,
+            "gemini-1.5-pro": 2097152,  # 2M tokens
+            "gemini-1.5-flash": 1048576,
+            "gemini-1.5-flash-8b": 1048576,
+            "gemini-1.0-pro": 32768,
+            "gemma-3-1b": 32000,
+            "gemma-3-4b": 128000,
+            "gemma-3-12b": 128000,
+            "gemma-3-27b": 128000,
+        }
+
+        # Find the best match for the model name
+        for model_prefix, size in context_windows.items():
+            if self.model.startswith(model_prefix):
+                return int(size * CONTEXT_WINDOW_USAGE_RATIO)
+
+        # Default context window size for Gemini models
+        return int(1048576 * CONTEXT_WINDOW_USAGE_RATIO)  # 1M tokens
+
+    def _extract_token_usage(self, response: dict[str, Any]) -> dict[str, Any]:
+        """Extract token usage from Gemini response."""
+        if hasattr(response, "usage_metadata"):
+            usage = response.usage_metadata
+            return {
+                "prompt_token_count": getattr(usage, "prompt_token_count", 0),
+                "candidates_token_count": getattr(usage, "candidates_token_count", 0),
+                "total_token_count": getattr(usage, "total_token_count", 0),
+                "total_tokens": getattr(usage, "total_token_count", 0),
+            }
+        return {"total_tokens": 0}
+
+    def _convert_contents_to_dict(
+        self, contents: list[types.Content]
+    ) -> list[dict[str, str]]:
+        """Convert contents to dict format."""
+        return [
+            {
+                "role": "assistant"
+                if content_obj.role == "model"
+                else content_obj.role,
+                "content": " ".join(
+                    part.text
+                    for part in content_obj.parts
+                    if hasattr(part, "text") and part.text
+                ),
+            }
+            for content_obj in contents
+        ]

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -0,0 +1,484 @@
+from collections.abc import Iterator
+import json
+import logging
+import os
+from typing import Any
+
+from crewai.events.types.llm_events import LLMCallType
+from crewai.llms.base_llm import BaseLLM
+from crewai.utilities.agent_utils import is_context_length_exceeded
+from crewai.utilities.exceptions.context_window_exceeding_exception import (
+    LLMContextLengthExceededError,
+)
+from openai import OpenAI
+from openai.types.chat import ChatCompletion, ChatCompletionChunk
+from openai.types.chat.chat_completion import Choice
+from openai.types.chat.chat_completion_chunk import ChoiceDelta
+from pydantic import BaseModel
+
+
+class OpenAICompletion(BaseLLM):
+    """OpenAI native completion implementation.
+
+    This class provides direct integration with the OpenAI Python SDK,
+    offering native structured outputs, function calling, and streaming support.
+    """
+
+    def __init__(
+        self,
+        model: str = "gpt-4o",
+        api_key: str | None = None,
+        base_url: str | None = None,
+        organization: str | None = None,
+        project: str | None = None,
+        timeout: float | None = None,
+        max_retries: int = 2,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        frequency_penalty: float | None = None,
+        presence_penalty: float | None = None,
+        max_tokens: int | None = None,
+        max_completion_tokens: int | None = None,
+        seed: int | None = None,
+        stream: bool = False,
+        response_format: dict[str, Any] | type[BaseModel] | None = None,
+        logprobs: bool | None = None,
+        top_logprobs: int | None = None,
+        reasoning_effort: str | None = None,  # For o1 models
+        provider: str | None = None,  # Add provider parameter
+        **kwargs,
+    ):
+        """Initialize OpenAI chat completion client."""
+
+        if provider is None:
+            provider = kwargs.pop("provider", "openai")
+
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key or os.getenv("OPENAI_API_KEY"),
+            base_url=base_url,
+            timeout=timeout,
+            provider=provider,
+            **kwargs,
+        )
+
+        self.client = OpenAI(
+            api_key=api_key or os.getenv("OPENAI_API_KEY"),
+            base_url=base_url,
+            organization=organization,
+            project=project,
+            timeout=timeout,
+            max_retries=max_retries,
+        )
+
+        self.top_p = top_p
+        self.frequency_penalty = frequency_penalty
+        self.presence_penalty = presence_penalty
+        self.max_tokens = max_tokens
+        self.max_completion_tokens = max_completion_tokens
+        self.seed = seed
+        self.stream = stream
+        self.response_format = response_format
+        self.logprobs = logprobs
+        self.top_logprobs = top_logprobs
+        self.reasoning_effort = reasoning_effort
+        self.timeout = timeout
+        self.is_o1_model = "o1" in model.lower()
+        self.is_gpt4_model = "gpt-4" in model.lower()
+
+    def call(
+        self,
+        messages: str | list[dict[str, str]],
+        tools: list[dict] | None = None,
+        callbacks: list[Any] | None = None,
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str | Any:
+        """Call OpenAI chat completion API.
+
+        Args:
+            messages: Input messages for the chat completion
+            tools: list of tool/function definitions
+            callbacks: Callback functions (not used in native implementation)
+            available_functions: Available functions for tool calling
+            from_task: Task that initiated the call
+            from_agent: Agent that initiated the call
+
+        Returns:
+            Chat completion response or tool call result
+        """
+        try:
+            self._emit_call_started_event(
+                messages=messages,
+                tools=tools,
+                callbacks=callbacks,
+                available_functions=available_functions,
+                from_task=from_task,
+                from_agent=from_agent,
+            )
+
+            formatted_messages = self._format_messages(messages)
+
+            completion_params = self._prepare_completion_params(
+                formatted_messages, tools
+            )
+
+            if self.stream:
+                return self._handle_streaming_completion(
+                    completion_params, available_functions, from_task, from_agent
+                )
+
+            return self._handle_completion(
+                completion_params, available_functions, from_task, from_agent
+            )
+
+        except Exception as e:
+            error_msg = f"OpenAI 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
+
+    def _prepare_completion_params(
+        self, messages: list[dict[str, str]], tools: list[dict] | None = None
+    ) -> dict[str, Any]:
+        """Prepare parameters for OpenAI chat completion."""
+        params = {
+            "model": self.model,
+            "messages": messages,
+            "stream": self.stream,
+        }
+
+        params.update(self.additional_params)
+
+        if self.temperature is not None:
+            params["temperature"] = self.temperature
+        if self.top_p is not None:
+            params["top_p"] = self.top_p
+        if self.frequency_penalty is not None:
+            params["frequency_penalty"] = self.frequency_penalty
+        if self.presence_penalty is not None:
+            params["presence_penalty"] = self.presence_penalty
+        if self.max_completion_tokens is not None:
+            params["max_completion_tokens"] = self.max_completion_tokens
+        elif self.max_tokens is not None:
+            params["max_tokens"] = self.max_tokens
+        if self.seed is not None:
+            params["seed"] = self.seed
+        if self.logprobs is not None:
+            params["logprobs"] = self.logprobs
+        if self.top_logprobs is not None:
+            params["top_logprobs"] = self.top_logprobs
+
+        # Handle o1 model specific parameters
+        if self.is_o1_model and self.reasoning_effort:
+            params["reasoning_effort"] = self.reasoning_effort
+
+        # Handle response format for structured outputs
+        if self.response_format:
+            if isinstance(self.response_format, type) and issubclass(
+                self.response_format, BaseModel
+            ):
+                # Convert Pydantic model to OpenAI response format
+                params["response_format"] = {
+                    "type": "json_schema",
+                    "json_schema": {
+                        "name": self.response_format.__name__,
+                        "schema": self.response_format.model_json_schema(),
+                    },
+                }
+            else:
+                params["response_format"] = self.response_format
+
+        if tools:
+            params["tools"] = self._convert_tools_for_interference(tools)
+            params["tool_choice"] = "auto"
+
+        # Filter out CrewAI-specific parameters that shouldn't go to the API
+        crewai_specific_params = {
+            "callbacks",
+            "available_functions",
+            "from_task",
+            "from_agent",
+            "provider",
+            "api_key",
+            "base_url",
+            "timeout",
+            "max_retries",
+        }
+
+        return {k: v for k, v in params.items() if k not in crewai_specific_params}
+
+    def _convert_tools_for_interference(self, tools: list[dict]) -> list[dict]:
+        """Convert CrewAI tool format to OpenAI function calling format."""
+        from crewai.llms.providers.utils.common import safe_tool_conversion
+
+        openai_tools = []
+
+        for tool in tools:
+            name, description, parameters = safe_tool_conversion(tool, "OpenAI")
+
+            openai_tool = {
+                "type": "function",
+                "function": {
+                    "name": name,
+                    "description": description,
+                },
+            }
+
+            if parameters:
+                if isinstance(parameters, dict):
+                    openai_tool["function"]["parameters"] = parameters  # type: ignore
+                else:
+                    openai_tool["function"]["parameters"] = dict(parameters)
+
+            openai_tools.append(openai_tool)
+        return openai_tools
+
+    def _handle_completion(
+        self,
+        params: dict[str, Any],
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str | Any:
+        """Handle non-streaming chat completion."""
+        try:
+            response: ChatCompletion = self.client.chat.completions.create(**params)
+
+            usage = self._extract_openai_token_usage(response)
+
+            self._track_token_usage_internal(usage)
+
+            choice: Choice = response.choices[0]
+            message = choice.message
+
+            if message.tool_calls and available_functions:
+                tool_call = message.tool_calls[0]
+                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)
+
+            if self.response_format and isinstance(self.response_format, type):
+                try:
+                    structured_result = self._validate_structured_output(
+                        content, self.response_format
+                    )
+                    self._emit_call_completed_event(
+                        response=structured_result,
+                        call_type=LLMCallType.LLM_CALL,
+                        from_task=from_task,
+                        from_agent=from_agent,
+                        messages=params["messages"],
+                    )
+                    return structured_result
+                except ValueError as e:
+                    logging.warning(f"Structured output validation failed: {e}")
+
+            self._emit_call_completed_event(
+                response=content,
+                call_type=LLMCallType.LLM_CALL,
+                from_task=from_task,
+                from_agent=from_agent,
+                messages=params["messages"],
+            )
+
+            if usage.get("total_tokens", 0) > 0:
+                logging.info(f"OpenAI API usage: {usage}")
+        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
+
+        return content
+
+    def _handle_streaming_completion(
+        self,
+        params: dict[str, Any],
+        available_functions: dict[str, Any] | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
+    ) -> str:
+        """Handle streaming chat completion."""
+        full_response = ""
+        tool_calls = {}
+
+        # Make streaming API call
+        stream: Iterator[ChatCompletionChunk] = self.client.chat.completions.create(
+            **params
+        )
+
+        for chunk in stream:
+            if not chunk.choices:
+                continue
+
+            choice = chunk.choices[0]
+            delta: ChoiceDelta = choice.delta
+
+            # Handle content streaming
+            if delta.content:
+                full_response += delta.content
+                self._emit_stream_chunk_event(
+                    chunk=delta.content,
+                    from_task=from_task,
+                    from_agent=from_agent,
+                )
+
+            # Handle tool call streaming
+            if delta.tool_calls:
+                for tool_call in 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"]
+                arguments = call_data["arguments"]
+
+                # Skip if function name is empty or arguments are empty
+                if not function_name or not arguments:
+                    continue
+
+                # Check if function exists in available functions
+                if function_name not in available_functions:
+                    logging.warning(
+                        f"Function '{function_name}' not found in available functions"
+                    )
+                    continue
+
+                try:
+                    function_args = json.loads(arguments)
+                except json.JSONDecodeError as e:
+                    logging.error(f"Failed to parse streamed tool arguments: {e}")
+                    continue
+
+                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
+
+        # Apply stop words to full response
+        full_response = self._apply_stop_words(full_response)
+
+        # Emit completion event and return full response
+        self._emit_call_completed_event(
+            response=full_response,
+            call_type=LLMCallType.LLM_CALL,
+            from_task=from_task,
+            from_agent=from_agent,
+            messages=params["messages"],
+        )
+
+        return full_response
+
+    def supports_function_calling(self) -> bool:
+        """Check if the model supports function calling."""
+        return not self.is_o1_model
+
+    def supports_stop_words(self) -> bool:
+        """Check if the model supports stop words."""
+        return not self.is_o1_model
+
+    def get_context_window_size(self) -> int:
+        """Get the context window size for the model."""
+        from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
+
+        min_context = 1024
+        max_context = 2097152
+
+        for key, value in LLM_CONTEXT_WINDOW_SIZES.items():
+            if value < min_context or value > max_context:
+                raise ValueError(
+                    f"Context window for {key} must be between {min_context} and {max_context}"
+                )
+
+        # Context window sizes for OpenAI models
+        context_windows = {
+            "gpt-4": 8192,
+            "gpt-4o": 128000,
+            "gpt-4o-mini": 200000,
+            "gpt-4-turbo": 128000,
+            "gpt-4.1": 1047576,
+            "gpt-4.1-mini-2025-04-14": 1047576,
+            "gpt-4.1-nano-2025-04-14": 1047576,
+            "o1-preview": 128000,
+            "o1-mini": 128000,
+            "o3-mini": 200000,
+            "o4-mini": 200000,
+        }
+
+        # Find the best match for the model name
+        for model_prefix, size in context_windows.items():
+            if self.model.startswith(model_prefix):
+                return int(size * CONTEXT_WINDOW_USAGE_RATIO)
+
+        # 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."""
+        if hasattr(response, "usage") and response.usage:
+            usage = response.usage
+            return {
+                "prompt_tokens": getattr(usage, "prompt_tokens", 0),
+                "completion_tokens": getattr(usage, "completion_tokens", 0),
+                "total_tokens": getattr(usage, "total_tokens", 0),
+            }
+        return {"total_tokens": 0}
+
+    def _format_messages(
+        self, messages: str | list[dict[str, str]]
+    ) -> list[dict[str, str]]:
+        """Format messages for OpenAI API."""
+        # Use base class formatting first
+        base_formatted = super()._format_messages(messages)
+
+        # Apply OpenAI-specific formatting
+        formatted_messages = []
+
+        for message in base_formatted:
+            if self.is_o1_model and message.get("role") == "system":
+                formatted_messages.append(
+                    {"role": "user", "content": f"System: {message['content']}"}
+                )
+            else:
+                formatted_messages.append(message)
+
+        return formatted_messages

lib/crewai/src/crewai/llms/providers/utils/common.py

@@ -0,0 +1,136 @@
+import logging
+import re
+from typing import Any
+
+
+def validate_function_name(name: str, provider: str = "LLM") -> str:
+    """Validate function name according to common LLM provider requirements.
+
+    Most LLM providers (OpenAI, Gemini, Anthropic) have similar requirements:
+    - Must start with letter or underscore
+    - Only alphanumeric, underscore, dot, colon, dash allowed
+    - Maximum length of 64 characters
+    - Cannot be empty
+
+    Args:
+        name: The function name to validate
+        provider: The provider name for error messages
+
+    Returns:
+        The validated function name (unchanged if valid)
+
+    Raises:
+        ValueError: If the function name is invalid
+    """
+    if not name or not isinstance(name, str):
+        raise ValueError(f"{provider} function name cannot be empty")
+
+    if not (name[0].isalpha() or name[0] == "_"):
+        raise ValueError(
+            f"{provider} function name '{name}' must start with a letter or underscore"
+        )
+
+    if len(name) > 64:
+        raise ValueError(
+            f"{provider} function name '{name}' exceeds 64 character limit"
+        )
+
+    # Check for invalid characters (most providers support these)
+    if not re.match(r"^[a-zA-Z_][a-zA-Z0-9_.\-:]*$", name):
+        raise ValueError(
+            f"{provider} function name '{name}' contains invalid characters. "
+            f"Only letters, numbers, underscore, dot, colon, dash allowed"
+        )
+
+    return name
+
+
+def extract_tool_info(tool: dict[str, Any]) -> tuple[str, str, dict[str, Any]]:
+    """Extract tool information from various schema formats.
+
+    Handles both OpenAI/standard format and direct format:
+    - OpenAI format: {"type": "function", "function": {"name": "...", ...}}
+    - Direct format: {"name": "...", "description": "...", ...}
+
+    Args:
+        tool: Tool dictionary in any supported format
+
+    Returns:
+        Tuple of (name, description, parameters)
+
+    Raises:
+        ValueError: If tool format is invalid
+    """
+    if not isinstance(tool, dict):
+        raise ValueError("Tool must be a dictionary")
+
+    # Handle nested function schema format (OpenAI/standard)
+    if "function" in tool:
+        function_info = tool["function"]
+        if not isinstance(function_info, dict):
+            raise ValueError("Tool function must be a dictionary")
+
+        name = function_info.get("name", "")
+        description = function_info.get("description", "")
+        parameters = function_info.get("parameters", {})
+    else:
+        # Direct format
+        name = tool.get("name", "")
+        description = tool.get("description", "")
+        parameters = tool.get("parameters", {})
+
+        # Also check for args_schema (Pydantic format)
+        if not parameters and "args_schema" in tool:
+            if hasattr(tool["args_schema"], "model_json_schema"):
+                parameters = tool["args_schema"].model_json_schema()
+
+    return name, description, parameters
+
+
+def log_tool_conversion(tool: dict[str, Any], provider: str) -> None:
+    """Log tool conversion for debugging.
+
+    Args:
+        tool: The tool being converted
+        provider: The provider name
+    """
+    try:
+        name, description, parameters = extract_tool_info(tool)
+        logging.debug(
+            f"{provider}: Converting tool '{name}' (desc: {description[:50]}...)"
+        )
+        logging.debug(f"{provider}: Tool parameters: {parameters}")
+    except Exception as e:
+        logging.error(f"{provider}: Error extracting tool info: {e}")
+        logging.error(f"{provider}: Tool structure: {tool}")
+
+
+def safe_tool_conversion(
+    tool: dict[str, Any], provider: str
+) -> tuple[str, str, dict[str, Any]]:
+    """Safely extract and validate tool information.
+
+    Combines extraction, validation, and logging for robust tool conversion.
+
+    Args:
+        tool: Tool dictionary to convert
+        provider: Provider name for error messages and logging
+
+    Returns:
+        Tuple of (validated_name, description, parameters)
+
+    Raises:
+        ValueError: If tool is invalid or name validation fails
+    """
+    try:
+        log_tool_conversion(tool, provider)
+
+        name, description, parameters = extract_tool_info(tool)
+
+        validated_name = validate_function_name(name, provider)
+
+        logging.info(f"{provider}: Successfully validated tool '{validated_name}'")
+        return validated_name, description, parameters
+    except Exception as e:
+        logging.error(f"{provider}: Error converting tool: {e}")
+        raise

lib/crewai/src/crewai/tools/tool_usage.py

@@ -1,10 +1,10 @@
 import ast
 import datetime
-import json
-import time
 from difflib import SequenceMatcher
+import json
 from json import JSONDecodeError
 from textwrap import dedent
+import time
 from typing import TYPE_CHECKING, Any, Union
 
 import json5
@@ -29,6 +29,7 @@ from crewai.utilities.agent_utils import (
     render_text_description_and_args,
 )
 
+
 if TYPE_CHECKING:
     from crewai.agents.agent_builder.base_agent import BaseAgent
     from crewai.lite_agent import LiteAgent
@@ -587,7 +588,23 @@ class ToolUsage:
         e: Exception,
     ) -> None:
         event_data = self._prepare_event_data(tool, tool_calling)
-        crewai_event_bus.emit(self, ToolUsageErrorEvent(**{**event_data, "error": e}))
+        event_data.update(
+            {
+                "task_id": str(self.task.id) if self.task else None,
+                "task_name": self.task.name or self.task.description
+                if self.task
+                else None,
+            }
+        )
+        crewai_event_bus.emit(
+            self,
+            ToolUsageErrorEvent(
+                **{
+                    **event_data,
+                    "error": e,
+                }
+            ),
+        )
 
     def on_tool_use_finished(
         self,

lib/crewai/src/crewai/utilities/agent_utils.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 
+from collections.abc import Callable, Sequence
 import json
 import re
-from collections.abc import Callable, Sequence
 from typing import TYPE_CHECKING, Any, Final, Literal, TypedDict
 
 from rich.console import Console
@@ -15,7 +15,6 @@ from crewai.agents.parser import (
     parse,
 )
 from crewai.cli.config import Settings
-from crewai.llm import LLM
 from crewai.llms.base_llm import BaseLLM
 from crewai.tools import BaseTool as CrewAITool
 from crewai.tools.base_tool import BaseTool
@@ -29,11 +28,14 @@ from crewai.utilities.i18n import I18N
 from crewai.utilities.printer import ColoredText, Printer
 from crewai.utilities.types import LLMMessage
 
+
 if TYPE_CHECKING:
     from crewai.agent import Agent
+    from crewai.llm import LLM
     from crewai.task import Task
 
 
+
 class SummaryContent(TypedDict):
     """Structure for summary content entries.
 
@@ -392,8 +394,10 @@ def is_context_length_exceeded(exception: Exception) -> bool:
     Returns:
         bool: True if the exception is due to context length exceeding
     """
-    return LLMContextLengthExceededError(str(exception))._is_context_limit_error(
-        str(exception)
+    return (
+        LLMContextLengthExceededError(str(exception))
+        ._is_context_limit_error(str(exception))
+        ._is_context_limit_error(str(exception))
     )
 
 

lib/crewai/src/crewai/utilities/llm_utils.py

@@ -6,6 +6,7 @@ from crewai.cli.constants import DEFAULT_LLM_MODEL, ENV_VARS, LITELLM_PARAMS
 from crewai.llm import LLM
 from crewai.llms.base_llm import BaseLLM
 
+
 logger = logging.getLogger(__name__)
 
 
@@ -42,7 +43,7 @@ def create_llm(
             or str(llm_value)
         )
         temperature: float | None = getattr(llm_value, "temperature", None)
-        max_tokens: int | None = getattr(llm_value, "max_tokens", None)
+        max_tokens: float | int | None = getattr(llm_value, "max_tokens", None)
         logprobs: int | None = getattr(llm_value, "logprobs", None)
         timeout: float | None = getattr(llm_value, "timeout", None)
         api_key: str | None = getattr(llm_value, "api_key", None)
@@ -59,6 +60,7 @@ def create_llm(
             base_url=base_url,
             api_base=api_base,
         )
+
     except Exception as e:
         logger.debug(f"Error instantiating LLM from unknown object type: {e}")
         return None
@@ -117,6 +119,7 @@ def _llm_via_environment_or_fallback() -> LLM | None:
     elif api_base and not base_url:
         base_url = api_base
 
+    # Initialize llm_params dictionary
     llm_params: dict[str, Any] = {
         "model": model,
         "temperature": temperature,
@@ -140,6 +143,11 @@ def _llm_via_environment_or_fallback() -> LLM | None:
         "callbacks": callbacks,
     }
 
+    unaccepted_attributes = [
+        "AWS_ACCESS_KEY_ID",
+        "AWS_SECRET_ACCESS_KEY",
+        "AWS_REGION_NAME",
+    ]
     set_provider = model_name.partition("/")[0] if "/" in model_name else "openai"
 
     if set_provider in ENV_VARS:
@@ -147,7 +155,7 @@ def _llm_via_environment_or_fallback() -> LLM | None:
         if isinstance(env_vars_for_provider, (list, tuple)):
             for env_var in env_vars_for_provider:
                 key_name = env_var.get("key_name")
-                if key_name and key_name not in UNACCEPTED_ATTRIBUTES:
+                if key_name and key_name not in unaccepted_attributes:
                     env_value = os.environ.get(key_name)
                     if env_value:
                         # Map environment variable names to recognized parameters

lib/crewai/src/crewai/utilities/reasoning_handler.py

@@ -102,21 +102,18 @@ class AgentReasoning:
         try:
             output = self.__handle_agent_reasoning()
 
-            # Emit reasoning completed event
-            try:
-                crewai_event_bus.emit(
-                    self.agent,
-                    AgentReasoningCompletedEvent(
-                        agent_role=self.agent.role,
-                        task_id=str(self.task.id),
-                        plan=output.plan.plan,
-                        ready=output.plan.ready,
-                        attempt=1,
-                        from_task=self.task,
-                    ),
-                )
-            except Exception:  # noqa: S110
-                pass
+            crewai_event_bus.emit(
+                self.agent,
+                AgentReasoningCompletedEvent(
+                    agent_role=self.agent.role,
+                    task_id=str(self.task.id),
+                    plan=output.plan.plan,
+                    ready=output.plan.ready,
+                    attempt=1,
+                    from_task=self.task,
+                    from_agent=self.agent,
+                ),
+            )
 
             return output
         except Exception as e:
@@ -130,10 +127,11 @@ class AgentReasoning:
                         error=str(e),
                         attempt=1,
                         from_task=self.task,
+                        from_agent=self.agent,
                     ),
                 )
-            except Exception:  # noqa: S110
-                pass
+            except Exception as e:
+                logging.error(f"Error emitting reasoning failed event: {e}")
 
             raise
 

lib/crewai/src/crewai/utilities/token_counter_callback.py

@@ -4,10 +4,24 @@ This module provides a callback handler that tracks token usage
 for LLM API calls through the litellm library.
 """
 
-from typing import Any
+from typing import TYPE_CHECKING, Any
+
+
+if TYPE_CHECKING:
+    from litellm.integrations.custom_logger import CustomLogger
+    from litellm.types.utils import Usage
+else:
+    try:
+        from litellm.integrations.custom_logger import CustomLogger
+        from litellm.types.utils import Usage
+    except ImportError:
+
+        class CustomLogger:
+            """Fallback CustomLogger when litellm is not available."""
+
+        class Usage:
+            """Fallback Usage when litellm is not available."""
 
-from litellm.integrations.custom_logger import CustomLogger
-from litellm.types.utils import Usage
 
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.utilities.logger_utils import suppress_warnings

lib/crewai/tests/agents/test_agent.py

@@ -1,14 +1,9 @@
 """Test Agent creation and execution basic functionality."""
 
-# ruff: noqa: S106
 import os
 from unittest import mock
 from unittest.mock import MagicMock, patch
 
-import pytest
-
-from crewai import Agent, Crew, Task
-from crewai.agents.cache import CacheHandler
 from crewai.agents.crew_agent_executor import AgentFinish, CrewAgentExecutor
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.tool_usage_events import ToolUsageFinishedEvent
@@ -17,12 +12,17 @@ from crewai.knowledge.knowledge_config import KnowledgeConfig
 from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
+from crewai.llms.base_llm import BaseLLM
 from crewai.process import Process
-from crewai.tools import tool
 from crewai.tools.tool_calling import InstructorToolCalling
 from crewai.tools.tool_usage import ToolUsage
-from crewai.utilities import RPMController
 from crewai.utilities.errors import AgentRepositoryError
+import pytest
+
+from crewai import Agent, Crew, Task
+from crewai.agents.cache import CacheHandler
+from crewai.tools import tool
+from crewai.utilities import RPMController
 
 
 def test_agent_llm_creation_with_env_vars():
@@ -40,7 +40,7 @@ def test_agent_llm_creation_with_env_vars():
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
 
     # Check if LLM is created correctly
-    assert isinstance(agent.llm, LLM)
+    assert isinstance(agent.llm, BaseLLM)
     assert agent.llm.model == "gpt-4-turbo"
     assert agent.llm.api_key == "test_api_key"
     assert agent.llm.base_url == "https://test-api-base.com"
@@ -50,11 +50,18 @@ def test_agent_llm_creation_with_env_vars():
     del os.environ["OPENAI_API_BASE"]
     del os.environ["OPENAI_MODEL_NAME"]
 
+    if original_api_key:
+        os.environ["OPENAI_API_KEY"] = original_api_key
+    if original_api_base:
+        os.environ["OPENAI_API_BASE"] = original_api_base
+    if original_model_name:
+        os.environ["OPENAI_MODEL_NAME"] = original_model_name
+
     # Create an agent without specifying LLM
     agent = Agent(role="test role", goal="test goal", backstory="test backstory")
 
     # Check if LLM is created correctly
-    assert isinstance(agent.llm, LLM)
+    assert isinstance(agent.llm, BaseLLM)
     assert agent.llm.model != "gpt-4-turbo"
     assert agent.llm.api_key != "test_api_key"
     assert agent.llm.base_url != "https://test-api-base.com"
@@ -456,18 +463,30 @@ def test_agent_custom_max_iterations():
         allow_delegation=False,
     )
 
-    with patch.object(
-        LLM, "call", wraps=LLM("gpt-4o", stop=["\nObservation:"]).call
-    ) as private_mock:
-        task = Task(
-            description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
-            expected_output="The final answer",
-        )
-        agent.execute_task(
-            task=task,
-            tools=[get_final_answer],
-        )
-        assert private_mock.call_count == 3
+    original_call = agent.llm.call
+    call_count = 0
+
+    def counting_call(*args, **kwargs):
+        nonlocal call_count
+        call_count += 1
+        return original_call(*args, **kwargs)
+
+    agent.llm.call = counting_call
+
+    task = Task(
+        description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
+        expected_output="The final answer",
+    )
+    result = agent.execute_task(
+        task=task,
+        tools=[get_final_answer],
+    )
+
+    assert result is not None
+    assert isinstance(result, str)
+    assert len(result) > 0
+    assert call_count > 0
+    assert call_count == 3
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -888,9 +907,8 @@ def test_agent_function_calling_llm():
     crew = Crew(agents=[agent1], tasks=tasks)
     from unittest.mock import patch
 
-    import instructor
-
     from crewai.tools.tool_usage import ToolUsage
+    import instructor
 
     with (
         patch.object(
@@ -1413,7 +1431,7 @@ def test_agent_with_llm():
         llm=LLM(model="gpt-3.5-turbo", temperature=0.7),
     )
 
-    assert isinstance(agent.llm, LLM)
+    assert isinstance(agent.llm, BaseLLM)
     assert agent.llm.model == "gpt-3.5-turbo"
     assert agent.llm.temperature == 0.7
 
@@ -1427,7 +1445,7 @@ def test_agent_with_custom_stop_words():
         llm=LLM(model="gpt-3.5-turbo", stop=stop_words),
     )
 
-    assert isinstance(agent.llm, LLM)
+    assert isinstance(agent.llm, BaseLLM)
     assert set(agent.llm.stop) == set([*stop_words, "\nObservation:"])
     assert all(word in agent.llm.stop for word in stop_words)
     assert "\nObservation:" in agent.llm.stop
@@ -1441,10 +1459,12 @@ def test_agent_with_callbacks():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(model="gpt-3.5-turbo", callbacks=[dummy_callback]),
+        llm=LLM(model="gpt-3.5-turbo", callbacks=[dummy_callback], is_litellm=True),
     )
 
-    assert isinstance(agent.llm, LLM)
+    assert isinstance(agent.llm, BaseLLM)
+    # All LLM implementations now support callbacks consistently
+    assert hasattr(agent.llm, "callbacks")
     assert len(agent.llm.callbacks) == 1
     assert agent.llm.callbacks[0] == dummy_callback
 
@@ -1463,7 +1483,7 @@ def test_agent_with_additional_kwargs():
         ),
     )
 
-    assert isinstance(agent.llm, LLM)
+    assert isinstance(agent.llm, BaseLLM)
     assert agent.llm.model == "gpt-3.5-turbo"
     assert agent.llm.temperature == 0.8
     assert agent.llm.top_p == 0.9
@@ -1580,40 +1600,40 @@ def test_agent_with_all_llm_attributes():
             timeout=10,
             temperature=0.7,
             top_p=0.9,
-            n=1,
+            # n=1,
             stop=["STOP", "END"],
             max_tokens=100,
             presence_penalty=0.1,
             frequency_penalty=0.1,
-            logit_bias={50256: -100},  # Example: bias against the EOT token
+            # logit_bias={50256: -100},  # Example: bias against the EOT token
             response_format={"type": "json_object"},
             seed=42,
             logprobs=True,
             top_logprobs=5,
             base_url="https://api.openai.com/v1",
-            api_version="2023-05-15",
+            # api_version="2023-05-15",
             api_key="sk-your-api-key-here",
         ),
     )
 
-    assert isinstance(agent.llm, LLM)
+    assert isinstance(agent.llm, BaseLLM)
     assert agent.llm.model == "gpt-3.5-turbo"
     assert agent.llm.timeout == 10
     assert agent.llm.temperature == 0.7
     assert agent.llm.top_p == 0.9
-    assert agent.llm.n == 1
+    # assert agent.llm.n == 1
     assert set(agent.llm.stop) == set(["STOP", "END", "\nObservation:"])
     assert all(word in agent.llm.stop for word in ["STOP", "END", "\nObservation:"])
     assert agent.llm.max_tokens == 100
     assert agent.llm.presence_penalty == 0.1
     assert agent.llm.frequency_penalty == 0.1
-    assert agent.llm.logit_bias == {50256: -100}
+    # assert agent.llm.logit_bias == {50256: -100}
     assert agent.llm.response_format == {"type": "json_object"}
     assert agent.llm.seed == 42
     assert agent.llm.logprobs
     assert agent.llm.top_logprobs == 5
     assert agent.llm.base_url == "https://api.openai.com/v1"
-    assert agent.llm.api_version == "2023-05-15"
+    # assert agent.llm.api_version == "2023-05-15"
     assert agent.llm.api_key == "sk-your-api-key-here"
 
 
@@ -1982,7 +2002,7 @@ def test_agent_with_knowledge_sources_works_with_copy():
             assert len(agent_copy.knowledge_sources) == 1
             assert isinstance(agent_copy.knowledge_sources[0], StringKnowledgeSource)
             assert agent_copy.knowledge_sources[0].content == content
-            assert isinstance(agent_copy.llm, LLM)
+            assert isinstance(agent_copy.llm, BaseLLM)
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
@@ -2130,7 +2150,7 @@ def test_litellm_auth_error_handling():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(model="gpt-4"),
+        llm=LLM(model="gpt-4", is_litellm=True),
         max_retry_limit=0,  # Disable retries for authentication errors
     )
 
@@ -2157,16 +2177,15 @@ def test_litellm_auth_error_handling():
 
 def test_crew_agent_executor_litellm_auth_error():
     """Test that CrewAgentExecutor handles LiteLLM authentication errors by raising them."""
-    from litellm.exceptions import AuthenticationError
-
     from crewai.agents.tools_handler import ToolsHandler
+    from litellm.exceptions import AuthenticationError
 
     # Create an agent and executor
     agent = Agent(
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(model="gpt-4", api_key="invalid_api_key"),
+        llm=LLM(model="gpt-4", api_key="invalid_api_key", is_litellm=True),
     )
     task = Task(
         description="Test task",
@@ -2224,7 +2243,7 @@ def test_litellm_anthropic_error_handling():
         role="test role",
         goal="test goal",
         backstory="test backstory",
-        llm=LLM(model="claude-3.5-sonnet-20240620"),
+        llm=LLM(model="claude-3.5-sonnet-20240620", is_litellm=True),
         max_retry_limit=0,
     )
 

lib/crewai/tests/agents/test_lite_agent.py

@@ -3,16 +3,17 @@ from collections import defaultdict
 from typing import cast
 from unittest.mock import Mock, patch
 
-import pytest
-from crewai import LLM, Agent
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.agent_events import LiteAgentExecutionStartedEvent
 from crewai.events.types.tool_usage_events import ToolUsageStartedEvent
-from crewai.flow import Flow, start
 from crewai.lite_agent import LiteAgent, LiteAgentOutput
 from crewai.llms.base_llm import BaseLLM
-from crewai.tools import BaseTool
 from pydantic import BaseModel, Field
+import pytest
+
+from crewai import LLM, Agent
+from crewai.flow import Flow, start
+from crewai.tools import BaseTool
 
 
 # A simple test tool
@@ -197,10 +198,6 @@ def test_lite_agent_structured_output():
         response_format=SimpleOutput,
     )
 
-    print(f"\n=== Agent Result Type: {type(result)}")
-    print(f"=== Agent Result: {result}")
-    print(f"=== Pydantic: {result.pydantic}")
-
     assert result.pydantic is not None, "Should return a Pydantic model"
 
     output = cast(SimpleOutput, result.pydantic)
@@ -295,6 +292,17 @@ def test_sets_parent_flow_when_inside_flow():
     mock_llm.call.return_value = "Test response"
     mock_llm.stop = []
 
+    from crewai.types.usage_metrics import UsageMetrics
+
+    mock_usage_metrics = UsageMetrics(
+        total_tokens=100,
+        prompt_tokens=50,
+        completion_tokens=50,
+        cached_prompt_tokens=0,
+        successful_requests=1,
+    )
+    mock_llm.get_token_usage_summary.return_value = mock_usage_metrics
+
     class MyFlow(Flow):
         @start()
         def start(self):

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+      Based on the initial analysis, suggest areas for deeper investigation. This
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+      attempting to encapsulate a holistic view of the researched topic. Specific
+      data sources include:\n- **Databases**: Publicly accessible databases, such
+      as PubMed for health-related data and the U.S. Census Bureau for demographic
+      statistics.\n- **Surveys**: National surveys conducted by reputable organizations
+      such as Pew Research Center, providing data on public opinion and behavior.\n-
+      **Experiments**: Published studies which have adhered to peer-review standards,
+      ensuring methodological rigor.\n- **Third-party sources**: Reports from think
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+      **Quantitative Data**: Numerical values are predominantly used, including continuous
+      metrics such as age, income levels, and frequency of events. This is suitable
+      for statistical analysis.\n- **Qualitative Data**: Text responses from surveys
+      that capture opinions, experiences, and feedback. This can involve coding responses
+      into categories for easier analysis.\n\n#### 3. **Preliminary Trends**\nInitial
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+      than the surrounding cluster).\n- Potential reasons for these anomalies might
+      include errors in data entry, unique subpopulations not representative of the
+      larger group, or influential cases that merit further exploration.\n\n#### 5.
+      **Comparative Analysis**\nComparative analysis reveals:\n- **Temporal Fluctuations**:
+      Examining the same dataset over time indicates fluctuations in responses, such
+      as changing public opinion on specific social issues.\n- **Segmentation**: Segmenting
+      data by demographic factors (e.g., age, income, education) allows for comparisons
+      that highlight significant differences across groups, reinforcing the stability
+      or volatility of particular trends.\n\n#### 6. **Limitations**\nRecognizing
+      limitations is crucial:\n- **Missing Data**: Instances where values are absent,
+      leading to gaps in the analysis. This may necessitate imputation or exclusion
+      from certain calculations.\n- **Potential Errors**: Occurrences of data entry
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+      of results.\n\n#### 7. **Recommendations for Further Analysis**\nBased on these
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+      **Complex Modeling**: Implement predictive modeling techniques to explore relationships
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+      Investigate additional factors that could impact outcomes (e.g., geographic
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+      Publicly accessible databases, such as PubMed for health-related data and the
+      U.S. Census Bureau for demographic statistics.\n- **Surveys**: National surveys
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+      and feedback. This can involve coding responses into categories for easier analysis.\n\n####
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+      Initial analysis indicates potential correlations, such as between education
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+      ranges (e.g., an income level substantially higher than the surrounding cluster).\n-
+      Potential reasons for these anomalies might include errors in data entry, unique
+      subpopulations not representative of the larger group, or influential cases
+      that merit further exploration.\n\n#### 5. **Comparative Analysis**\nComparative
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+      time indicates fluctuations in responses, such as changing public opinion on
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+      factors (e.g., age, income, education) allows for comparisons that highlight
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+      gaps in the analysis. This may necessitate imputation or exclusion from certain
+      calculations.\n- **Potential Errors**: Occurrences of data entry mistakes can
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+      of results.\n\n#### 7. **Recommendations for Further Analysis**\nBased on these
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+      **Additional Data Collection**: To address gaps and enhance dataset robustness,
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+      **Complex Modeling**: Implement predictive modeling techniques to explore relationships
+      more intricately, adjusting for confounding variables.\n- **Exploratory Variables**:
+      Investigate additional factors that could impact outcomes (e.g., geographic
+      location, socioeconomic status) to enhance comprehension of observed trends.\n\nBy
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+      data sources include:\n- **Databases**: Publicly accessible databases, such
+      as PubMed for health-related data and the U.S. Census Bureau for demographic
+      statistics.\n- **Surveys**: National surveys conducted by reputable organizations
+      such as Pew Research Center, providing data on public opinion and behavior.\n-
+      **Experiments**: Published studies which have adhered to peer-review standards,
+      ensuring methodological rigor.\n- **Third-party sources**: Reports from think
+      tanks and academic institutions which aggregate data from primary research.\n\n####
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+      **Quantitative Data**: Numerical values are predominantly used, including continuous
+      metrics such as age, income levels, and frequency of events. This is suitable
+      for statistical analysis.\n- **Qualitative Data**: Text responses from surveys
+      that capture opinions, experiences, and feedback. This can involve coding responses
+      into categories for easier analysis.\n\n#### 3. **Preliminary Trends**\nInitial
+      trends observed in the dataset include:\n- **Averages**: Calculation of mean
+      and median values to measure central tendency (e.g., average income levels across
+      demographic groups).\n- **Distributions**: Graphical representation using histograms
+      reveals how data points are spread across different categories or values (e.g.,
+      age groups).\n- **Correlations**: Initial analysis indicates potential correlations,
+      such as between education level and income, visualized through scatter plots
+      which depict the relationship between the two variables.\n\n#### 4. **Outliers**\nThe
+      analysis identifies several outliers:\n- Data points significantly exceeding
+      or falling below expected ranges (e.g., an income level substantially higher
+      than the surrounding cluster).\n- Potential reasons for these anomalies might
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lib/crewai/tests/test_crew.py

@@ -1,16 +1,14 @@
 """Test Agent creation and execution basic functionality."""
 
-import json
 from collections import defaultdict
 from concurrent.futures import Future
 from hashlib import md5
+import json
+import re
 from unittest import mock
 from unittest.mock import ANY, MagicMock, patch
 
-import pydantic_core
-import pytest
 from crewai.agent import Agent
-from crewai.agents import CacheHandler
 from crewai.crew import Crew
 from crewai.crews.crew_output import CrewOutput
 from crewai.events.event_bus import crewai_event_bus
@@ -30,7 +28,6 @@ from crewai.events.types.memory_events import (
     MemorySaveFailedEvent,
     MemorySaveStartedEvent,
 )
-from crewai.flow import Flow, start
 from crewai.knowledge.knowledge import Knowledge
 from crewai.knowledge.source.string_knowledge_source import StringKnowledgeSource
 from crewai.llm import LLM
@@ -46,6 +43,11 @@ from crewai.tasks.task_output import TaskOutput
 from crewai.types.usage_metrics import UsageMetrics
 from crewai.utilities.rpm_controller import RPMController
 from crewai.utilities.task_output_storage_handler import TaskOutputStorageHandler
+import pydantic_core
+import pytest
+
+from crewai.agents import CacheHandler
+from crewai.flow import Flow, start
 
 
 @pytest.fixture
@@ -199,7 +201,9 @@ def test_async_task_cannot_include_sequential_async_tasks_in_context(
     # This should raise an error because task2 is async and has task1 in its context without a sync task in between
     with pytest.raises(
         ValueError,
-        match="Task 'Task 2' is asynchronous and cannot include other sequential asynchronous tasks in its context.",
+        match=re.escape(
+            "Task 'Task 2' is asynchronous and cannot include other sequential asynchronous tasks in its context."
+        ),
     ):
         Crew(tasks=[task1, task2, task3, task4, task5], agents=[researcher, writer])
 
@@ -237,7 +241,9 @@ def test_context_no_future_tasks(researcher, writer):
     # This should raise an error because task1 has a context dependency on a future task (task4)
     with pytest.raises(
         ValueError,
-        match="Task 'Task 1' has a context dependency on a future task 'Task 4', which is not allowed.",
+        match=re.escape(
+            "Task 'Task 1' has a context dependency on a future task 'Task 4', which is not allowed."
+        ),
     ):
         Crew(tasks=[task1, task2, task3, task4], agents=[researcher, writer])
 
@@ -568,9 +574,10 @@ def test_crew_with_delegating_agents(ceo, writer):
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_with_delegating_agents_should_not_override_task_tools(ceo, writer):
-    from crewai.tools import BaseTool
     from pydantic import BaseModel, Field
 
+    from crewai.tools import BaseTool
+
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
 
@@ -627,9 +634,10 @@ def test_crew_with_delegating_agents_should_not_override_task_tools(ceo, writer)
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_crew_with_delegating_agents_should_not_override_agent_tools(ceo, writer):
-    from crewai.tools import BaseTool
     from pydantic import BaseModel, Field
 
+    from crewai.tools import BaseTool
+
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
 
@@ -688,9 +696,10 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools(ceo, writer
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_task_tools_override_agent_tools(researcher):
-    from crewai.tools import BaseTool
     from pydantic import BaseModel, Field
 
+    from crewai.tools import BaseTool
+
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
 
@@ -744,9 +753,10 @@ def test_task_tools_override_agent_tools_with_allow_delegation(researcher, write
     Test that task tools override agent tools while preserving delegation tools when allow_delegation=True
     """
 
-    from crewai.tools import BaseTool
     from pydantic import BaseModel, Field
 
+    from crewai.tools import BaseTool
+
     class TestToolInput(BaseModel):
         query: str = Field(..., description="Query to process")
 
@@ -1005,7 +1015,7 @@ def test_crew_kickoff_streaming_usage_metrics():
         role="{topic} Researcher",
         goal="Express hot takes on {topic}.",
         backstory="You have a lot of experience with {topic}.",
-        llm=LLM(model="gpt-4o", stream=True),
+        llm=LLM(model="gpt-4o", stream=True, is_litellm=True),
         max_iter=3,
     )
 
@@ -1773,7 +1783,7 @@ def test_hierarchical_kickoff_usage_metrics_include_manager(researcher):
         agent=researcher,  # *regular* agent
     )
 
-    # ── 2.  Stub out each agent's _token_process.get_summary() ───────────────────
+    # ── 2.  Stub out each agent's token usage methods ───────────────────
     researcher_metrics = UsageMetrics(
         total_tokens=120, prompt_tokens=80, completion_tokens=40, successful_requests=2
     )
@@ -1781,10 +1791,10 @@ def test_hierarchical_kickoff_usage_metrics_include_manager(researcher):
         total_tokens=30, prompt_tokens=20, completion_tokens=10, successful_requests=1
     )
 
-    # Replace the internal _token_process objects with simple mocks
-    researcher._token_process = MagicMock(
-        get_summary=MagicMock(return_value=researcher_metrics)
-    )
+    # Mock the LLM's get_token_usage_summary method for the researcher
+    researcher.llm.get_token_usage_summary = MagicMock(return_value=researcher_metrics)
+
+    # Mock the manager's _token_process since it uses the fallback path
     manager._token_process = MagicMock(
         get_summary=MagicMock(return_value=manager_metrics)
     )
@@ -3334,7 +3344,9 @@ def test_replay_with_invalid_task_id():
     ):
         with pytest.raises(
             ValueError,
-            match="Task with id bf5b09c9-69bd-4eb8-be12-f9e5bae31c2d not found in the crew's tasks.",
+            match=re.escape(
+                "Task with id bf5b09c9-69bd-4eb8-be12-f9e5bae31c2d not found in the crew's tasks."
+            ),
         ):
             crew.replay("bf5b09c9-69bd-4eb8-be12-f9e5bae31c2d")
 
@@ -3813,10 +3825,11 @@ def test_task_tools_preserve_code_execution_tools():
     """
     Test that task tools don't override code execution tools when allow_code_execution=True
     """
-    from crewai.tools import BaseTool
     from crewai_tools import CodeInterpreterTool
     from pydantic import BaseModel, Field
 
+    from crewai.tools import BaseTool
+
     class TestToolInput(BaseModel):
         """Input schema for TestTool."""
 

lib/crewai/tests/test_llm.py

@@ -3,7 +3,6 @@ import os
 from time import sleep
 from unittest.mock import MagicMock, patch
 
-import pytest
 from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.events.event_types import (
     LLMCallCompletedEvent,
@@ -15,33 +14,30 @@ from crewai.events.event_types import (
 from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM
 from crewai.utilities.token_counter_callback import TokenCalcHandler
 from pydantic import BaseModel
+import pytest
 
 
 # TODO: This test fails without print statement, which makes me think that something is happening asynchronously that we need to eventually fix and dive deeper into at a later date
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_llm_callback_replacement():
-    llm1 = LLM(model="gpt-4o-mini")
-    llm2 = LLM(model="gpt-4o-mini")
+    llm1 = LLM(model="gpt-4o-mini", is_litellm=True)
+    llm2 = LLM(model="gpt-4o-mini", is_litellm=True)
 
     calc_handler_1 = TokenCalcHandler(token_cost_process=TokenProcess())
     calc_handler_2 = TokenCalcHandler(token_cost_process=TokenProcess())
 
-    result1 = llm1.call(
+    llm1.call(
         messages=[{"role": "user", "content": "Hello, world!"}],
         callbacks=[calc_handler_1],
     )
-    print("result1:", result1)
     usage_metrics_1 = calc_handler_1.token_cost_process.get_summary()
-    print("usage_metrics_1:", usage_metrics_1)
 
-    result2 = llm2.call(
+    llm2.call(
         messages=[{"role": "user", "content": "Hello, world from another agent!"}],
         callbacks=[calc_handler_2],
     )
     sleep(5)
-    print("result2:", result2)
     usage_metrics_2 = calc_handler_2.token_cost_process.get_summary()
-    print("usage_metrics_2:", usage_metrics_2)
 
     # The first handler should not have been updated
     assert usage_metrics_1.successful_requests == 1
@@ -61,7 +57,7 @@ def test_llm_call_with_string_input():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_llm_call_with_string_input_and_callbacks():
-    llm = LLM(model="gpt-4o-mini")
+    llm = LLM(model="gpt-4o-mini", is_litellm=True)
     calc_handler = TokenCalcHandler(token_cost_process=TokenProcess())
 
     # Test the call method with a string input and callbacks
@@ -127,7 +123,7 @@ def test_llm_call_with_tool_and_string_input():
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_llm_call_with_tool_and_message_list():
-    llm = LLM(model="gpt-4o-mini")
+    llm = LLM(model="gpt-4o-mini", is_litellm=True)
 
     def square_number(number: int) -> int:
         """Returns the square of a number."""
@@ -171,6 +167,7 @@ def test_llm_passes_additional_params():
         model="gpt-4o-mini",
         vertex_credentials="test_credentials",
         vertex_project="test_project",
+        is_litellm=True,
     )
 
     messages = [{"role": "user", "content": "Hello, world!"}]
@@ -223,7 +220,7 @@ def test_get_custom_llm_provider_gemini():
 
 
 def test_get_custom_llm_provider_openai():
-    llm = LLM(model="gpt-4")
+    llm = LLM(model="gpt-4", is_litellm=True)
     assert llm._get_custom_llm_provider() is None
 
 
@@ -380,7 +377,7 @@ def test_context_window_exceeded_error_handling():
     )
     from litellm.exceptions import ContextWindowExceededError
 
-    llm = LLM(model="gpt-4")
+    llm = LLM(model="gpt-4", is_litellm=True)
 
     # Test non-streaming response
     with patch("litellm.completion") as mock_completion:
@@ -397,7 +394,7 @@ def test_context_window_exceeded_error_handling():
         assert "8192 tokens" in str(excinfo.value)
 
     # Test streaming response
-    llm = LLM(model="gpt-4", stream=True)
+    llm = LLM(model="gpt-4", stream=True, is_litellm=True)
     with patch("litellm.completion") as mock_completion:
         mock_completion.side_effect = ContextWindowExceededError(
             "This model's maximum context length is 8192 tokens. However, your messages resulted in 10000 tokens.",
@@ -416,7 +413,7 @@ def test_context_window_exceeded_error_handling():
 @pytest.fixture
 def anthropic_llm():
     """Fixture providing an Anthropic LLM instance."""
-    return LLM(model="anthropic/claude-3-sonnet")
+    return LLM(model="anthropic/claude-3-sonnet", is_litellm=True)
 
 
 @pytest.fixture
@@ -455,40 +452,25 @@ def test_anthropic_model_detection():
         ("claude-instant", True),
         ("claude/v1", True),
         ("gpt-4", False),
-        ("", False),
         ("anthropomorphic", False),  # Should not match partial words
     ]
 
     for model, expected in models:
-        llm = LLM(model=model)
+        llm = LLM(model=model, is_litellm=True)
         assert llm.is_anthropic == expected, f"Failed for model: {model}"
 
 
 def test_anthropic_message_formatting(anthropic_llm, system_message, user_message):
     """Test Anthropic message formatting with fixtures."""
     # Test when first message is system
-    formatted = anthropic_llm._format_messages_for_provider([system_message])
-    assert len(formatted) == 2
-    assert formatted[0]["role"] == "user"
-    assert formatted[0]["content"] == "."
-    assert formatted[1] == system_message
 
-    # Test when first message is already user
-    formatted = anthropic_llm._format_messages_for_provider([user_message])
-    assert len(formatted) == 1
-    assert formatted[0] == user_message
-
-    # Test with empty message list
     formatted = anthropic_llm._format_messages_for_provider([])
     assert len(formatted) == 1
     assert formatted[0]["role"] == "user"
     assert formatted[0]["content"] == "."
 
-    # Test with non-Anthropic model (should not modify messages)
-    non_anthropic_llm = LLM(model="gpt-4")
-    formatted = non_anthropic_llm._format_messages_for_provider([system_message])
-    assert len(formatted) == 1
-    assert formatted[0] == system_message
+    with pytest.raises(TypeError, match="Invalid message format"):
+        anthropic_llm._format_messages_for_provider([{"invalid": "message"}])
 
 
 def test_deepseek_r1_with_open_router():
@@ -499,6 +481,7 @@ def test_deepseek_r1_with_open_router():
         model="openrouter/deepseek/deepseek-r1",
         base_url="https://openrouter.ai/api/v1",
         api_key=os.getenv("OPEN_ROUTER_API_KEY"),
+        is_litellm=True,
     )
     result = llm.call("What is the capital of France?")
     assert isinstance(result, str)
@@ -568,7 +551,7 @@ def mock_emit() -> MagicMock:
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_handle_streaming_tool_calls(get_weather_tool_schema, mock_emit):
-    llm = LLM(model="openai/gpt-4o", stream=True)
+    llm = LLM(model="openai/gpt-4o", stream=True, is_litellm=True)
     response = llm.call(
         messages=[
             {"role": "user", "content": "What is the weather in New York?"},
@@ -599,7 +582,7 @@ def test_handle_streaming_tool_calls_with_error(get_weather_tool_schema, mock_em
     def get_weather_error(location):
         raise Exception("Error")
 
-    llm = LLM(model="openai/gpt-4o", stream=True)
+    llm = LLM(model="openai/gpt-4o", stream=True, is_litellm=True)
     response = llm.call(
         messages=[
             {"role": "user", "content": "What is the weather in New York?"},
@@ -623,7 +606,7 @@ def test_handle_streaming_tool_calls_with_error(get_weather_tool_schema, mock_em
 def test_handle_streaming_tool_calls_no_available_functions(
     get_weather_tool_schema, mock_emit
 ):
-    llm = LLM(model="openai/gpt-4o", stream=True)
+    llm = LLM(model="openai/gpt-4o", stream=True, is_litellm=True)
     response = llm.call(
         messages=[
             {"role": "user", "content": "What is the weather in New York?"},
@@ -642,7 +625,7 @@ def test_handle_streaming_tool_calls_no_available_functions(
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_handle_streaming_tool_calls_no_tools(mock_emit):
-    llm = LLM(model="openai/gpt-4o", stream=True)
+    llm = LLM(model="openai/gpt-4o", stream=True, is_litellm=True)
     response = llm.call(
         messages=[
             {"role": "user", "content": "What is the weather in New York?"},
@@ -663,7 +646,7 @@ def test_handle_streaming_tool_calls_no_tools(mock_emit):
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_llm_call_when_stop_is_unsupported(caplog):
-    llm = LLM(model="o1-mini", stop=["stop"])
+    llm = LLM(model="o1-mini", stop=["stop"], is_litellm=True)
     with caplog.at_level(logging.INFO):
         result = llm.call("What is the capital of France?")
         assert "Retrying LLM call without the unsupported 'stop'" in caplog.text
@@ -675,7 +658,12 @@ def test_llm_call_when_stop_is_unsupported(caplog):
 def test_llm_call_when_stop_is_unsupported_when_additional_drop_params_is_provided(
     caplog,
 ):
-    llm = LLM(model="o1-mini", stop=["stop"], additional_drop_params=["another_param"])
+    llm = LLM(
+        model="o1-mini",
+        stop=["stop"],
+        additional_drop_params=["another_param"],
+        is_litellm=True,
+    )
     with caplog.at_level(logging.INFO):
         result = llm.call("What is the capital of France?")
         assert "Retrying LLM call without the unsupported 'stop'" in caplog.text
@@ -685,7 +673,7 @@ def test_llm_call_when_stop_is_unsupported_when_additional_drop_params_is_provid
 
 @pytest.fixture
 def ollama_llm():
-    return LLM(model="ollama/llama3.2:3b")
+    return LLM(model="ollama/llama3.2:3b", is_litellm=True)
 
 
 def test_ollama_appends_dummy_user_message_when_last_is_assistant(ollama_llm):

lib/crewai/tests/utilities/test_converter.py

@@ -1,12 +1,9 @@
-import json
-import os
-
 # Tests for enums
 from enum import Enum
-from typing import Dict, List, Optional
+import json
+import os
 from unittest.mock import MagicMock, Mock, patch
 
-import pytest
 from crewai.llm import LLM
 from crewai.utilities.converter import (
     Converter,
@@ -21,6 +18,7 @@ from crewai.utilities.converter import (
 )
 from crewai.utilities.pydantic_schema_parser import PydanticSchemaParser
 from pydantic import BaseModel
+import pytest
 
 
 @pytest.fixture(scope="module")
@@ -254,7 +252,7 @@ def test_supports_function_calling_true():
 
 
 def test_supports_function_calling_false():
-    llm = LLM(model="non-existent-model")
+    llm = LLM(model="non-existent-model", is_litellm=True)
     assert llm.supports_function_calling() is False
 
 
@@ -311,17 +309,17 @@ def test_generate_model_description_nested_model():
 
 def test_generate_model_description_optional_field():
     class ModelWithOptionalField(BaseModel):
-        name: Optional[str]
-        age: int
+        name: str
+        age: int | None
 
     description = generate_model_description(ModelWithOptionalField)
-    expected_description = '{\n  "name": Optional[str],\n  "age": int\n}'
+    expected_description = '{\n  "name": str,\n  "age": int | None\n}'
     assert description == expected_description
 
 
 def test_generate_model_description_list_field():
     class ModelWithListField(BaseModel):
-        items: List[int]
+        items: list[int]
 
     description = generate_model_description(ModelWithListField)
     expected_description = '{\n  "items": List[int]\n}'
@@ -330,7 +328,7 @@ def test_generate_model_description_list_field():
 
 def test_generate_model_description_dict_field():
     class ModelWithDictField(BaseModel):
-        attributes: Dict[str, int]
+        attributes: dict[str, int]
 
     description = generate_model_description(ModelWithDictField)
     expected_description = '{\n  "attributes": Dict[str, int]\n}'
@@ -470,7 +468,7 @@ def test_converter_retry_logic():
 def test_converter_with_optional_fields():
     class OptionalModel(BaseModel):
         name: str
-        age: Optional[int]
+        age: int | None
 
     llm = Mock(spec=LLM)
     llm.supports_function_calling.return_value = False
@@ -496,7 +494,7 @@ def test_converter_with_optional_fields():
 # Tests for list fields
 def test_converter_with_list_field():
     class ListModel(BaseModel):
-        items: List[int]
+        items: list[int]
 
     llm = Mock(spec=LLM)
     llm.supports_function_calling.return_value = False

lib/crewai/tests/utilities/test_events.py

@@ -1,17 +1,12 @@
-import os
 from datetime import datetime
+import os
 from unittest.mock import Mock, patch
 
-import pytest
-from pydantic import Field
-
 from crewai.agent import Agent
 from crewai.agents.crew_agent_executor import CrewAgentExecutor
 from crewai.crew import Crew
-from crewai.flow.flow import Flow, listen, start
-from crewai.llm import LLM
-from crewai.task import Task
-from crewai.tools.base_tool import BaseTool
+from crewai.events.event_bus import crewai_event_bus
+from crewai.events.event_listener import EventListener
 from crewai.events.types.agent_events import (
     AgentExecutionCompletedEvent,
     AgentExecutionErrorEvent,
@@ -25,9 +20,6 @@ from crewai.events.types.crew_events import (
     CrewTestResultEvent,
     CrewTestStartedEvent,
 )
-from crewai.events.event_bus import crewai_event_bus
-from crewai.events.event_listener import EventListener
-from crewai.events.types.tool_usage_events import ToolUsageFinishedEvent
 from crewai.events.types.flow_events import (
     FlowCreatedEvent,
     FlowFinishedEvent,
@@ -48,7 +40,14 @@ from crewai.events.types.task_events import (
 )
 from crewai.events.types.tool_usage_events import (
     ToolUsageErrorEvent,
+    ToolUsageFinishedEvent,
 )
+from crewai.flow.flow import Flow, listen, start
+from crewai.llm import LLM
+from crewai.task import Task
+from crewai.tools.base_tool import BaseTool
+from pydantic import Field
+import pytest
 
 
 @pytest.fixture(scope="module")
@@ -195,7 +194,7 @@ def test_crew_emits_kickoff_failed_event(base_agent, base_task):
             error_message = "Simulated crew kickoff failure"
             mock_execute.side_effect = Exception(error_message)
 
-            with pytest.raises(Exception):
+            with pytest.raises(Exception):  # noqa: B017
                 crew.kickoff()
 
         assert len(received_events) == 1
@@ -279,7 +278,7 @@ def test_task_emits_failed_event_on_execution_error(base_agent, base_task):
             agent=agent,
         )
 
-        with pytest.raises(Exception):
+        with pytest.raises(Exception):  # noqa: B017
             agent.execute_task(task=task)
 
             assert len(received_events) == 1
@@ -333,7 +332,7 @@ def test_agent_emits_execution_error_event(base_agent, base_task):
     ) as invoke_mock:
         invoke_mock.side_effect = Exception(error_message)
 
-        with pytest.raises(Exception):
+        with pytest.raises(Exception):  # noqa: B017
             base_agent.execute_task(
                 task=base_task,
             )
@@ -517,7 +516,6 @@ def test_flow_emits_method_execution_started_event():
 
         @crewai_event_bus.on(MethodExecutionStartedEvent)
         def handle_method_start(source, event):
-            print("event in method name", event.method_name)
             received_events.append(event)
 
         class TestFlow(Flow[dict]):
@@ -619,7 +617,7 @@ def test_flow_emits_method_execution_failed_event():
             raise error
 
     flow = TestFlow()
-    with pytest.raises(Exception):
+    with pytest.raises(Exception):  # noqa: B017
         flow.kickoff()
 
     assert len(received_events) == 1
@@ -655,6 +653,7 @@ def test_llm_emits_call_started_event():
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])
+@pytest.mark.isolated
 def test_llm_emits_call_failed_event():
     received_events = []
 
@@ -662,13 +661,18 @@ def test_llm_emits_call_failed_event():
     def handle_llm_call_failed(source, event):
         received_events.append(event)
 
-    error_message = "Simulated LLM call failure"
-    with patch("crewai.llm.litellm.completion", side_effect=Exception(error_message)):
+    error_message = "OpenAI API call failed: Simulated API failure"
+
+    with patch(
+        "crewai.llms.providers.openai.completion.OpenAICompletion._handle_completion"
+    ) as mock_handle_completion:
+        mock_handle_completion.side_effect = Exception("Simulated API failure")
+
         llm = LLM(model="gpt-4o-mini")
         with pytest.raises(Exception) as exc_info:
             llm.call("Hello, how are you?")
 
-        assert str(exc_info.value) == error_message
+        assert str(exc_info.value) == "Simulated API failure"
         assert len(received_events) == 1
         assert received_events[0].type == "llm_call_failed"
         assert received_events[0].error == error_message
@@ -884,8 +888,8 @@ def test_stream_llm_emits_event_with_task_and_agent_info():
     assert len(all_task_name) == 14
 
     assert set(all_agent_roles) == {agent.role}
-    assert set(all_agent_id) == {agent.id}
-    assert set(all_task_id) == {task.id}
+    assert set(all_agent_id) == {str(agent.id)}
+    assert set(all_task_id) == {str(task.id)}
     assert set(all_task_name) == {task.name or task.description}
 
 
@@ -935,8 +939,8 @@ def test_llm_emits_event_with_task_and_agent_info(base_agent, base_task):
     assert len(all_task_name) == 2
 
     assert set(all_agent_roles) == {base_agent.role}
-    assert set(all_agent_id) == {base_agent.id}
-    assert set(all_task_id) == {base_task.id}
+    assert set(all_agent_id) == {str(base_agent.id)}
+    assert set(all_task_id) == {str(base_task.id)}
     assert set(all_task_name) == {base_task.name or base_task.description}
 
 
@@ -991,4 +995,4 @@ def test_llm_emits_event_with_lite_agent():
     assert len(all_task_name) == 0
 
     assert set(all_agent_roles) == {agent.role}
-    assert set(all_agent_id) == {agent.id}
+    assert set(all_agent_id) == {str(agent.id)}

lib/crewai/tests/utilities/test_llm_utils.py

@@ -1,10 +1,16 @@
 import os
 from unittest.mock import patch
 
-import pytest
 from crewai.llm import LLM
+from crewai.llms.base_llm import BaseLLM
 from crewai.utilities.llm_utils import create_llm
-from litellm.exceptions import BadRequestError
+import pytest
+
+
+try:
+    from litellm.exceptions import BadRequestError
+except ImportError:
+    BadRequestError = Exception
 
 
 def test_create_llm_with_llm_instance():
@@ -15,13 +21,19 @@ def test_create_llm_with_llm_instance():
 
 def test_create_llm_with_valid_model_string():
     llm = create_llm(llm_value="gpt-4o")
-    assert isinstance(llm, LLM)
+    assert isinstance(llm, BaseLLM)
     assert llm.model == "gpt-4o"
 
 
 def test_create_llm_with_invalid_model_string():
-    with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
-        llm = create_llm(llm_value="invalid-model")
+    # For invalid model strings, create_llm succeeds but call() fails with API error
+    llm = create_llm(llm_value="invalid-model")
+    assert llm is not None
+    assert isinstance(llm, BaseLLM)
+
+    # The error should occur when making the actual API call
+    # We expect some kind of API error (NotFoundError, etc.)
+    with pytest.raises(Exception):  # noqa: B017
         llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
 
 
@@ -32,16 +44,16 @@ def test_create_llm_with_unknown_object_missing_attributes():
     unknown_obj = UnknownObject()
     llm = create_llm(llm_value=unknown_obj)
 
-    # Attempt to call the LLM and expect it to raise an error due to missing attributes
-    with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
-        llm.call(messages=[{"role": "user", "content": "Hello, world!"}])
+    # Should succeed because str(unknown_obj) provides a model name
+    assert llm is not None
+    assert isinstance(llm, BaseLLM)
 
 
 def test_create_llm_with_none_uses_default_model():
-    with patch.dict(os.environ, {}, clear=True):
-        with patch("crewai.cli.constants.DEFAULT_LLM_MODEL", "gpt-4o"):
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "fake-key"}, clear=True):
+        with patch("crewai.utilities.llm_utils.DEFAULT_LLM_MODEL", "gpt-4o-mini"):
             llm = create_llm(llm_value=None)
-            assert isinstance(llm, LLM)
+            assert isinstance(llm, BaseLLM)
             assert llm.model == "gpt-4o-mini"
 
 
@@ -53,7 +65,7 @@ def test_create_llm_with_unknown_object():
 
     unknown_obj = UnknownObject()
     llm = create_llm(llm_value=unknown_obj)
-    assert isinstance(llm, LLM)
+    assert isinstance(llm, BaseLLM)
     assert llm.model == "gpt-4o"
     assert llm.temperature == 0.7
     assert llm.max_tokens == 1500
@@ -64,13 +76,14 @@ def test_create_llm_from_env_with_unaccepted_attributes():
         os.environ,
         {
             "OPENAI_MODEL_NAME": "gpt-3.5-turbo",
+            "OPENAI_API_KEY": "fake-key",
             "AWS_ACCESS_KEY_ID": "fake-access-key",
             "AWS_SECRET_ACCESS_KEY": "fake-secret-key",
             "AWS_REGION_NAME": "us-west-2",
         },
     ):
         llm = create_llm(llm_value=None)
-        assert isinstance(llm, LLM)
+        assert isinstance(llm, BaseLLM)
         assert llm.model == "gpt-3.5-turbo"
         assert not hasattr(llm, "AWS_ACCESS_KEY_ID")
         assert not hasattr(llm, "AWS_SECRET_ACCESS_KEY")
@@ -84,12 +97,18 @@ def test_create_llm_with_partial_attributes():
 
     obj = PartialAttributes()
     llm = create_llm(llm_value=obj)
-    assert isinstance(llm, LLM)
+    assert isinstance(llm, BaseLLM)
     assert llm.model == "gpt-4o"
     assert llm.temperature is None  # Should handle missing attributes gracefully
 
 
 def test_create_llm_with_invalid_type():
-    with pytest.raises(BadRequestError, match="LLM Provider NOT provided"):
-        llm = create_llm(llm_value=42)
+    # For integers, create_llm succeeds because str(42) becomes "42"
+    llm = create_llm(llm_value=42)
+    assert llm is not None
+    assert isinstance(llm, BaseLLM)
+    assert llm.model == "42"
+
+    # The error should occur when making the actual API call
+    with pytest.raises(Exception):  # noqa: B017
         llm.call(messages=[{"role": "user", "content": "Hello, world!"}])

lib/devtools/pyproject.toml

@@ -0,0 +1,32 @@
+[project]
+name = "crewai-devtools"
+dynamic = ["version"]
+description = "Development tools for version bumping and git automation"
+readme = "README.md"
+authors = [
+    { name = "Greyson R. LaLonde", email = "greyson@crewai.com" },
+]
+requires-python = ">=3.10, <3.14"
+classifiers = ["Private :: Do Not Upload"]
+private = true
+dependencies = [
+    "click>=8.3.0",
+    "toml>=0.10.2",
+    "openai>=1.0.0",
+    "python-dotenv>=1.1.1",
+    "pygithub>=1.59.1",
+    "rich>=13.9.4",
+]
+
+[project.scripts]
+bump-version = "crewai_devtools.cli:bump"
+tag = "crewai_devtools.cli:tag"
+devtools = "crewai_devtools.cli:main"
+
+[build-system]
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[tool.hatch.version]
+path = "src/crewai_devtools/__init__.py"
+

lib/devtools/src/crewai_devtools/__init__.py

@@ -0,0 +1,3 @@
+"""CrewAI development tools."""
+
+__version__ = "1.0.0a3"

lib/devtools/src/crewai_devtools/cli.py

@@ -0,0 +1,706 @@
+"""Development tools for version bumping and git automation."""
+
+import os
+from pathlib import Path
+import subprocess
+import sys
+
+import click
+from dotenv import load_dotenv
+from github import Github
+from openai import OpenAI
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.panel import Panel
+from rich.prompt import Confirm
+
+from crewai_devtools.prompts import RELEASE_NOTES_PROMPT
+
+
+load_dotenv()
+
+console = Console()
+
+
+def run_command(cmd: list[str], cwd: Path | None = None) -> str:
+    """Run a shell command and return output.
+
+    Args:
+        cmd: Command to run as list of strings.
+        cwd: Working directory for command.
+
+    Returns:
+        Command output as string.
+
+    Raises:
+        subprocess.CalledProcessError: If command fails.
+    """
+    result = subprocess.run(  # noqa: S603
+        cmd,
+        cwd=cwd,
+        capture_output=True,
+        text=True,
+        check=True,
+    )
+    return result.stdout.strip()
+
+
+def check_gh_installed() -> None:
+    """Check if GitHub CLI is installed and offer to install it.
+
+    Raises:
+        SystemExit: If gh is not installed and user declines installation.
+    """
+    try:
+        run_command(["gh", "--version"])
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        console.print("[yellow]Warning:[/yellow] GitHub CLI (gh) is not installed")
+        import platform
+
+        if platform.system() == "Darwin":
+            try:
+                run_command(["brew", "--version"])
+                from rich.prompt import Confirm
+
+                if Confirm.ask(
+                    "\n[bold]Would you like to install GitHub CLI via Homebrew?[/bold]",
+                    default=True,
+                ):
+                    try:
+                        console.print("\nInstalling GitHub CLI...")
+                        subprocess.run(
+                            ["brew", "install", "gh"],  # noqa: S607
+                            check=True,
+                        )
+                        console.print(
+                            "[green]✓[/green] GitHub CLI installed successfully"
+                        )
+                        console.print("\nAuthenticating with GitHub...")
+                        subprocess.run(
+                            ["gh", "auth", "login"],  # noqa: S607
+                            check=True,
+                        )
+                        console.print("[green]✓[/green] GitHub authentication complete")
+                        return
+                    except subprocess.CalledProcessError as e:
+                        console.print(
+                            f"[red]Error:[/red] Failed to install or authenticate gh: {e}"
+                        )
+                        console.print(
+                            "\nYou can try running [bold]gh auth login[/bold] manually"
+                        )
+            except (subprocess.CalledProcessError, FileNotFoundError):
+                pass
+
+        console.print("\nPlease install GitHub CLI from: https://cli.github.com/")
+        console.print("\nInstallation instructions:")
+        console.print("  macOS:   brew install gh")
+        console.print(
+            "  Linux:   https://github.com/cli/cli/blob/trunk/docs/install_linux.md"
+        )
+        console.print("  Windows: winget install --id GitHub.cli")
+        sys.exit(1)
+
+
+def check_git_clean() -> None:
+    """Check if git working directory is clean.
+
+    Raises:
+        SystemExit: If there are uncommitted changes.
+    """
+    try:
+        status = run_command(["git", "status", "--porcelain"])
+        if status:
+            console.print(
+                "[red]Error:[/red] You have uncommitted changes. Please commit or stash them first."
+            )
+            sys.exit(1)
+    except subprocess.CalledProcessError as e:
+        console.print(f"[red]Error checking git status:[/red] {e}")
+        sys.exit(1)
+
+
+def update_version_in_file(file_path: Path, new_version: str) -> bool:
+    """Update __version__ attribute in a Python file.
+
+    Args:
+        file_path: Path to Python file.
+        new_version: New version string.
+
+    Returns:
+        True if version was updated, False otherwise.
+    """
+    if not file_path.exists():
+        return False
+
+    content = file_path.read_text()
+    lines = content.splitlines()
+    updated = False
+
+    for i, line in enumerate(lines):
+        if line.strip().startswith("__version__"):
+            lines[i] = f'__version__ = "{new_version}"'
+            updated = True
+            break
+
+    if updated:
+        file_path.write_text("\n".join(lines) + "\n")
+        return True
+
+    return False
+
+
+def update_pyproject_dependencies(file_path: Path, new_version: str) -> bool:
+    """Update workspace dependency versions in pyproject.toml.
+
+    Args:
+        file_path: Path to pyproject.toml file.
+        new_version: New version string.
+
+    Returns:
+        True if any dependencies were updated, False otherwise.
+    """
+    if not file_path.exists():
+        return False
+
+    content = file_path.read_text()
+    lines = content.splitlines()
+    updated = False
+
+    workspace_packages = ["crewai", "crewai-tools", "crewai-devtools"]
+
+    for i, line in enumerate(lines):
+        for pkg in workspace_packages:
+            if f"{pkg}==" in line:
+                stripped = line.lstrip()
+                indent = line[: len(line) - len(stripped)]
+
+                if '"' in line:
+                    lines[i] = f'{indent}"{pkg}=={new_version}",'
+                elif "'" in line:
+                    lines[i] = f"{indent}'{pkg}=={new_version}',"
+                else:
+                    lines[i] = f"{indent}{pkg}=={new_version},"
+
+                updated = True
+
+    if updated:
+        file_path.write_text("\n".join(lines) + "\n")
+        return True
+
+    return False
+
+
+def find_version_files(base_path: Path) -> list[Path]:
+    """Find all __init__.py files that contain __version__.
+
+    Args:
+        base_path: Base directory to search in.
+
+    Returns:
+        List of paths to files containing __version__.
+    """
+    return [
+        init_file
+        for init_file in base_path.rglob("__init__.py")
+        if "__version__" in init_file.read_text()
+    ]
+
+
+def get_packages(lib_dir: Path) -> list[Path]:
+    """Get all packages from lib/ directory.
+
+    Args:
+        lib_dir: Path to lib/ directory.
+
+    Returns:
+        List of package directory paths.
+
+    Raises:
+        SystemExit: If lib/ doesn't exist or no packages found.
+    """
+    if not lib_dir.exists():
+        console.print("[red]Error:[/red] lib/ directory not found")
+        sys.exit(1)
+
+    packages = [p for p in lib_dir.iterdir() if p.is_dir()]
+
+    if not packages:
+        console.print("[red]Error:[/red] No packages found in lib/")
+        sys.exit(1)
+
+    return packages
+
+
+def get_commits_from_last_tag(tag_name: str, version: str) -> tuple[str, str]:
+    """Get commits from the last tag, excluding current version.
+
+    Args:
+        tag_name: Current tag name (e.g., "v1.0.0").
+        version: Current version (e.g., "1.0.0").
+
+    Returns:
+        Tuple of (commit_range, commits) where commits is newline-separated.
+    """
+    try:
+        all_tags = run_command(["git", "tag", "--sort=-version:refname"]).split("\n")
+        prev_tags = [t for t in all_tags if t and t != tag_name and t != f"v{version}"]
+
+        if prev_tags:
+            last_tag = prev_tags[0]
+            commit_range = f"{last_tag}..HEAD"
+            commits = run_command(["git", "log", commit_range, "--pretty=format:%s"])
+        else:
+            commit_range = "HEAD"
+            commits = run_command(["git", "log", "--pretty=format:%s"])
+    except subprocess.CalledProcessError:
+        commit_range = "HEAD"
+        commits = run_command(["git", "log", "--pretty=format:%s"])
+
+    return commit_range, commits
+
+
+def get_github_contributors(commit_range: str) -> list[str]:
+    """Get GitHub usernames from commit range using GitHub API.
+
+    Args:
+        commit_range: Git commit range (e.g., "abc123..HEAD").
+
+    Returns:
+        List of GitHub usernames sorted alphabetically.
+    """
+    try:
+        # Get GitHub token from gh CLI
+        try:
+            gh_token = run_command(["gh", "auth", "token"])
+        except subprocess.CalledProcessError:
+            gh_token = None
+
+        g = Github(login_or_token=gh_token) if gh_token else Github()
+        github_repo = g.get_repo("crewAIInc/crewAI")
+
+        commit_shas = run_command(
+            ["git", "log", commit_range, "--pretty=format:%H"]
+        ).split("\n")
+
+        contributors = set()
+        for sha in commit_shas:
+            if not sha:
+                continue
+            try:
+                commit = github_repo.get_commit(sha)
+                if commit.author and commit.author.login:
+                    contributors.add(commit.author.login)
+
+                if commit.commit.message:
+                    for line in commit.commit.message.split("\n"):
+                        if line.strip().startswith("Co-authored-by:"):
+                            if "<" in line and ">" in line:
+                                email_part = line.split("<")[1].split(">")[0]
+                                if "@users.noreply.github.com" in email_part:
+                                    username = email_part.split("+")[-1].split("@")[0]
+                                    contributors.add(username)
+            except Exception:  # noqa: S112
+                continue
+
+        return sorted(list(contributors))
+
+    except Exception as e:
+        console.print(
+            f"[yellow]Warning:[/yellow] Could not fetch GitHub contributors: {e}"
+        )
+        return []
+
+
+@click.group()
+def cli() -> None:
+    """Development tools for version bumping and git automation."""
+
+
+@click.command()
+@click.argument("version")
+@click.option(
+    "--dry-run", is_flag=True, help="Show what would be done without making changes"
+)
+@click.option("--no-push", is_flag=True, help="Don't push changes to remote")
+def bump(version: str, dry_run: bool, no_push: bool) -> None:
+    """Bump version across all packages in lib/.
+
+    Args:
+        version: New version to set (e.g., 1.0.0, 1.0.0a1).
+        dry_run: Show what would be done without making changes.
+        no_push: Don't push changes to remote.
+    """
+    try:
+        # Check prerequisites
+        check_gh_installed()
+
+        cwd = Path.cwd()
+        lib_dir = cwd / "lib"
+
+        if not dry_run:
+            console.print("Checking git status...")
+            check_git_clean()
+            console.print("[green]✓[/green] Working directory is clean")
+        else:
+            console.print("[dim][DRY RUN][/dim] Would check git status")
+
+        packages = get_packages(lib_dir)
+
+        console.print(f"\nFound {len(packages)} package(s) to update:")
+        for pkg in packages:
+            console.print(f"  - {pkg.name}")
+
+        console.print(f"\nUpdating version to {version}...")
+        updated_files = []
+
+        for pkg in packages:
+            version_files = find_version_files(pkg)
+            for vfile in version_files:
+                if dry_run:
+                    console.print(
+                        f"[dim][DRY RUN][/dim] Would update: {vfile.relative_to(cwd)}"
+                    )
+                else:
+                    if update_version_in_file(vfile, version):
+                        console.print(
+                            f"[green]✓[/green] Updated: {vfile.relative_to(cwd)}"
+                        )
+                        updated_files.append(vfile)
+                    else:
+                        console.print(
+                            f"[red]✗[/red] Failed to update: {vfile.relative_to(cwd)}"
+                        )
+
+            pyproject = pkg / "pyproject.toml"
+            if pyproject.exists():
+                if dry_run:
+                    console.print(
+                        f"[dim][DRY RUN][/dim] Would update dependencies in: {pyproject.relative_to(cwd)}"
+                    )
+                else:
+                    if update_pyproject_dependencies(pyproject, version):
+                        console.print(
+                            f"[green]✓[/green] Updated dependencies in: {pyproject.relative_to(cwd)}"
+                        )
+                        updated_files.append(pyproject)
+
+        if not updated_files and not dry_run:
+            console.print(
+                "[yellow]Warning:[/yellow] No __version__ attributes found to update"
+            )
+
+        if not dry_run:
+            console.print("\nSyncing workspace...")
+            run_command(["uv", "sync"])
+            console.print("[green]✓[/green] Workspace synced")
+        else:
+            console.print("[dim][DRY RUN][/dim] Would run: uv sync")
+
+        branch_name = f"feat/bump-version-{version}"
+        if not dry_run:
+            console.print(f"\nCreating branch {branch_name}...")
+            run_command(["git", "checkout", "-b", branch_name])
+            console.print("[green]✓[/green] Branch created")
+
+            console.print("\nCommitting changes...")
+            run_command(["git", "add", "."])
+            run_command(["git", "commit", "-m", f"feat: bump versions to {version}"])
+            console.print("[green]✓[/green] Changes committed")
+
+            if not no_push:
+                console.print("\nPushing branch...")
+                run_command(["git", "push", "-u", "origin", branch_name])
+                console.print("[green]✓[/green] Branch pushed")
+        else:
+            console.print(f"[dim][DRY RUN][/dim] Would create branch: {branch_name}")
+            console.print(
+                f"[dim][DRY RUN][/dim] Would commit: feat: bump versions to {version}"
+            )
+            if not no_push:
+                console.print(f"[dim][DRY RUN][/dim] Would push branch: {branch_name}")
+
+        if not dry_run and not no_push:
+            console.print("\nCreating pull request...")
+            run_command(
+                [
+                    "gh",
+                    "pr",
+                    "create",
+                    "--base",
+                    "release/v1.0.0",
+                    "--title",
+                    f"feat: bump versions to {version}",
+                    "--body",
+                    "",
+                ]
+            )
+            console.print("[green]✓[/green] Pull request created")
+        elif dry_run:
+            console.print(
+                f"[dim][DRY RUN][/dim] Would create PR: feat: bump versions to {version}"
+            )
+        else:
+            console.print("\nSkipping PR creation (--no-push flag set)")
+
+        console.print(f"\n[green]✓[/green] Version bump to {version} complete!")
+
+    except subprocess.CalledProcessError as e:
+        console.print(f"[red]Error running command:[/red] {e}")
+        if e.stderr:
+            console.print(e.stderr)
+        sys.exit(1)
+    except Exception as e:
+        console.print(f"[red]Error:[/red] {e}")
+        sys.exit(1)
+
+
+@click.command()
+@click.option(
+    "--dry-run", is_flag=True, help="Show what would be done without making changes"
+)
+@click.option("--no-edit", is_flag=True, help="Skip editing release notes")
+def tag(dry_run: bool, no_edit: bool) -> None:
+    """Create and push a version tag on main branch.
+
+    Run this after the version bump PR has been merged.
+    Automatically detects version from __version__ in packages.
+
+    Args:
+        dry_run: Show what would be done without making changes.
+        no_edit: Skip editing release notes.
+    """
+    try:
+        cwd = Path.cwd()
+        lib_dir = cwd / "lib"
+
+        packages = get_packages(lib_dir)
+
+        with console.status("[cyan]Validating package versions..."):
+            versions = {}
+            for pkg in packages:
+                version_files = find_version_files(pkg)
+                for vfile in version_files:
+                    content = vfile.read_text()
+                    for line in content.splitlines():
+                        if line.strip().startswith("__version__"):
+                            ver = line.split("=")[1].strip().strip('"').strip("'")
+                            versions[vfile.relative_to(cwd)] = ver
+                            break
+
+        if not versions:
+            console.print(
+                "[red]✗[/red] Validated package versions: Could not find __version__ in any package"
+            )
+            sys.exit(1)
+
+        unique_versions = set(versions.values())
+        if len(unique_versions) > 1:
+            console.print(
+                "[red]✗[/red] Validated package versions: Version mismatch detected"
+            )
+            for file, ver in versions.items():
+                console.print(f"  {file}: {ver}")
+            sys.exit(1)
+
+        version = unique_versions.pop()
+        console.print(f"[green]✓[/green] Validated packages @ [bold]{version}[/bold]")
+        tag_name = version
+
+        if not dry_run:
+            with console.status("[cyan]Checking out release/v1.0.0 branch..."):
+                try:
+                    run_command(["git", "checkout", "release/v1.0.0"])
+                except subprocess.CalledProcessError as e:
+                    console.print(
+                        f"[red]✗[/red] Checked out release/v1.0.0 branch: {e}"
+                    )
+                    sys.exit(1)
+            console.print("[green]✓[/green] On release/v1.0.0 branch")
+
+            with console.status("[cyan]Pulling latest changes..."):
+                try:
+                    run_command(["git", "pull"])
+                except subprocess.CalledProcessError as e:
+                    console.print(f"[red]✗[/red] Pulled latest changes: {e}")
+                    sys.exit(1)
+            console.print("[green]✓[/green] release/v1.0.0 branch up to date")
+
+        release_notes = f"Release {version}"
+        commits = ""
+
+        with console.status("[cyan]Generating release notes..."):
+            try:
+                prev_bump_commit = run_command(
+                    [
+                        "git",
+                        "log",
+                        "--grep=^feat: bump versions to",
+                        "--format=%H",
+                        "-n",
+                        "2",
+                    ]
+                )
+                commits_list = prev_bump_commit.strip().split("\n")
+
+                if len(commits_list) > 1:
+                    prev_commit = commits_list[1]
+                    commit_range = f"{prev_commit}..HEAD"
+                    commits = run_command(
+                        ["git", "log", commit_range, "--pretty=format:%s"]
+                    )
+
+                    commit_lines = [
+                        line
+                        for line in commits.split("\n")
+                        if not line.startswith("feat: bump versions to")
+                    ]
+                    commits = "\n".join(commit_lines)
+                else:
+                    commit_range, commits = get_commits_from_last_tag(tag_name, version)
+
+            except subprocess.CalledProcessError:
+                commit_range, commits = get_commits_from_last_tag(tag_name, version)
+
+            github_contributors = get_github_contributors(commit_range)
+
+            if commits.strip():
+                client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+                contributors_section = ""
+                if github_contributors:
+                    contributors_section = f"\n\n## Contributors\n\n{', '.join([f'@{u}' for u in github_contributors])}"
+
+                prompt = RELEASE_NOTES_PROMPT.substitute(
+                    version=version,
+                    commits=commits,
+                    contributors_section=contributors_section,
+                )
+
+                response = client.chat.completions.create(
+                    model="gpt-4o-mini",
+                    messages=[
+                        {
+                            "role": "system",
+                            "content": "You are a helpful assistant that generates clear, concise release notes.",
+                        },
+                        {"role": "user", "content": prompt},
+                    ],
+                    temperature=0.7,
+                )
+
+                release_notes = (
+                    response.choices[0].message.content or f"Release {version}"
+                )
+
+        console.print("[green]✓[/green] Generated release notes")
+
+        if commits.strip():
+            try:
+                console.print()
+                md = Markdown(release_notes, justify="left")
+                console.print(
+                    Panel(
+                        md,
+                        title="[bold cyan]Generated Release Notes[/bold cyan]",
+                        border_style="cyan",
+                        padding=(1, 2),
+                    )
+                )
+            except Exception as e:
+                console.print(
+                    f"[yellow]Warning:[/yellow] Could not generate release notes with OpenAI: {e}"
+                )
+                console.print("Using default release notes")
+
+        if not no_edit:
+            if Confirm.ask(
+                "\n[bold]Would you like to edit the release notes?[/bold]", default=True
+            ):
+                edited_notes = click.edit(release_notes)
+                if edited_notes is not None:
+                    release_notes = edited_notes.strip()
+                    console.print("\n[green]✓[/green] Release notes updated")
+                else:
+                    console.print("\n[green]✓[/green] Using original release notes")
+            else:
+                console.print(
+                    "\n[green]✓[/green] Using generated release notes without editing"
+                )
+        else:
+            console.print(
+                "\n[green]✓[/green] Using generated release notes without editing"
+            )
+
+        if not dry_run:
+            with console.status(f"[cyan]Creating tag {tag_name}..."):
+                try:
+                    run_command(["git", "tag", "-a", tag_name, "-m", release_notes])
+                except subprocess.CalledProcessError as e:
+                    console.print(f"[red]✗[/red] Created tag {tag_name}: {e}")
+                    sys.exit(1)
+            console.print(f"[green]✓[/green] Created tag {tag_name}")
+
+            with console.status(f"[cyan]Pushing tag {tag_name}..."):
+                try:
+                    run_command(["git", "push", "origin", tag_name])
+                except subprocess.CalledProcessError as e:
+                    console.print(f"[red]✗[/red] Pushed tag {tag_name}: {e}")
+                    sys.exit(1)
+            console.print(f"[green]✓[/green] Pushed tag {tag_name}")
+
+            is_prerelease = any(
+                indicator in version.lower()
+                for indicator in ["a", "b", "rc", "alpha", "beta", "dev"]
+            )
+
+            with console.status("[cyan]Creating GitHub Release..."):
+                try:
+                    gh_cmd = [
+                        "gh",
+                        "release",
+                        "create",
+                        tag_name,
+                        "--title",
+                        tag_name,
+                        "--notes",
+                        release_notes,
+                    ]
+                    if is_prerelease:
+                        gh_cmd.append("--prerelease")
+
+                    run_command(gh_cmd)
+                except subprocess.CalledProcessError as e:
+                    console.print(f"[red]✗[/red] Created GitHub Release: {e}")
+                    sys.exit(1)
+
+            release_type = "prerelease" if is_prerelease else "release"
+            console.print(
+                f"[green]✓[/green] Created GitHub {release_type} for {tag_name}"
+            )
+
+        console.print(
+            f"\n[green]✓[/green] Packages @ [bold]{version}[/bold] tagged successfully!"
+        )
+
+    except subprocess.CalledProcessError as e:
+        console.print(f"[red]Error running command:[/red] {e}")
+        if e.stderr:
+            console.print(e.stderr)
+        sys.exit(1)
+    except Exception as e:
+        console.print(f"[red]Error:[/red] {e}")
+        sys.exit(1)
+
+
+cli.add_command(bump)
+cli.add_command(tag)
+
+
+def main() -> None:
+    """Entry point for the CLI."""
+    cli()
+
+
+if __name__ == "__main__":
+    main()

lib/devtools/src/crewai_devtools/prompts.py

@@ -0,0 +1,45 @@
+"""Prompt templates for AI-generated content."""
+
+from string import Template
+
+
+RELEASE_NOTES_PROMPT = Template(
+    """Generate concise release notes for version $version based on these commits:
+
+$commits
+
+The commits follow the Conventional Commits standard (feat:, fix:, chore:, etc.).
+
+Use this exact template format:
+
+## What's Changed
+
+### Features
+- [List feat: commits here, using imperative mood like "Add X", "Implement Y"]
+
+### Bug Fixes
+- [List fix: commits here, using imperative mood like "Fix X", "Resolve Y"]
+
+### Documentation
+- [List docs: commits here, using imperative mood like "Update X", "Add Y"]
+
+### Performance
+- [List perf: commits here, using imperative mood like "Improve X", "Optimize Y"]
+
+### Refactoring
+- [List refactor: commits here, using imperative mood like "Refactor X", "Simplify Y"]
+
+### Breaking Changes
+- [List commits with BREAKING CHANGE in footer or ! after type, using imperative mood]$contributors_section
+
+Instructions:
+- Parse conventional commit format (type: description or type(scope): description)
+- Only include sections that have relevant changes from the commits
+- Skip chore:, ci:, test:, and style: commits unless significant
+- Convert commit messages to imperative mood if needed (e.g., "adds" → "Add")
+- Be concise but informative
+- Focus on user-facing changes
+- Use the exact Contributors list provided above, do not modify it
+
+Keep it professional and clear."""
+)

pyproject.toml

@@ -122,9 +122,11 @@ python_functions = "test_*"
 members = [
     "lib/crewai",
     "lib/crewai-tools",
+    "lib/devtools",
 ]
 
 
 [tool.uv.sources]
 crewai = { workspace = true }
 crewai-tools = { workspace = true }
+crewai-devtools = { workspace = true }

uv.lock

@@ -31,6 +31,7 @@ resolution-markers = [
 [manifest]
 members = [
     "crewai",
+    "crewai-devtools",
     "crewai-tools",
 ]
 
@@ -332,14 +333,14 @@ wheels = [
 
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@@ -993,7 +994,6 @@ dependencies = [
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     { name = "json5" },
     { name = "jsonref" },
-    { name = "litellm" },
     { name = "openai" },
     { name = "openpyxl" },
     { name = "opentelemetry-api" },
@@ -1026,6 +1026,9 @@ docling = [
 embeddings = [
     { name = "tiktoken" },
 ]
+litellm = [
+    { name = "litellm" },
+]
 mem0 = [
     { name = "mem0ai" },
 ]
@@ -1066,7 +1069,7 @@ requires-dist = [
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     { name = "jsonref", specifier = ">=1.1.0" },
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+    { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.74.9" },
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@@ -1092,7 +1095,29 @@ requires-dist = [
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-provides-extras = ["aisuite", "aws", "docling", "embeddings", "mem0", "openpyxl", "pandas", "pdfplumber", "qdrant", "tools", "voyageai", "watson"]
+provides-extras = ["aisuite", "aws", "docling", "embeddings", "litellm", "mem0", "openpyxl", "pandas", "pdfplumber", "qdrant", "tools", "voyageai", "watson"]
+
+[[package]]
+name = "crewai-devtools"
+source = { editable = "lib/devtools" }
+dependencies = [
+    { name = "click" },
+    { name = "openai" },
+    { name = "pygithub" },
+    { name = "python-dotenv" },
+    { name = "rich" },
+    { name = "toml" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "click", specifier = ">=8.3.0" },
+    { name = "openai", specifier = ">=1.0.0" },
+    { name = "pygithub", specifier = ">=1.59.1" },
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+    { name = "rich", specifier = ">=13.9.4" },
+    { name = "toml", specifier = ">=0.10.2" },
+]
 
 [[package]]
 name = "crewai-tools"
@@ -1193,7 +1218,7 @@ scrapfly-sdk = [
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 ]
 serpapi = [
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@@ -1477,7 +1502,7 @@ wheels = [
 
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 source = { registry = "https://pypi.org/simple" }
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