Fix AIMindTool import error with minds_sdk 2.0.0

- Remove import of DatabaseConfig which no longer exists in minds_sdk 2.0.0
- Update code to use new minds_sdk API structure
- Create datasources directly via minds_client.datasources.create()
- Pass datasource names (strings) instead of DatabaseConfig objects to minds.create()
- Handle missing optional fields (description, connection_data) with defaults
- Add comprehensive tests to cover the import issue and prevent regression

Fixes #3885

Co-Authored-By: João <joao@crewai.com>

.github/dependabot.yml

@@ -0,0 +1,11 @@
+# To get started with Dependabot version updates, you'll need to specify which 
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+version: 2
+updates:
+  - package-ecosystem: uv # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"

docs/en/concepts/tasks.mdx

@@ -60,6 +60,7 @@ crew = Crew(
 | **Output Pydantic** _(optional)_ | `output_pydantic` | `Optional[Type[BaseModel]]`   | A Pydantic model for task output.                                                                                    |
 | **Callback** _(optional)_        | `callback`        | `Optional[Any]`               | Function/object to be executed after task completion.                                                                |
 | **Guardrail** _(optional)_       | `guardrail`       | `Optional[Callable]`             | Function to validate task output before proceeding to next task.                                                  |
+| **Guardrails** _(optional)_       | `guardrails`       | `Optional[List[Callable] | List[str]]` | List of guardrails to validate task output before proceeding to next task.                                      |
 | **Guardrail Max Retries** _(optional)_ | `guardrail_max_retries` | `Optional[int]`     | Maximum number of retries when guardrail validation fails. Defaults to 3.                                         |
 
 <Note type="warning" title="Deprecated: max_retries">
@@ -223,6 +224,7 @@ By default, the `TaskOutput` will only include the `raw` output. A `TaskOutput`
 | **JSON Dict**     | `json_dict`     | `Optional[Dict[str, Any]]` | A dictionary representing the JSON output of the task.                                             |
 | **Agent**         | `agent`         | `str`                      | The agent that executed the task.                                                                  |
 | **Output Format** | `output_format` | `OutputFormat`             | The format of the task output, with options including RAW, JSON, and Pydantic. The default is RAW. |
+| **Messages**      | `messages`      | `list[LLMMessage]`         | The messages from the last task execution.                                                           |
 
 ### Task Methods and Properties
 
@@ -341,7 +343,11 @@ Task guardrails provide a way to validate and transform task outputs before they
 are passed to the next task. This feature helps ensure data quality and provides
 feedback to agents when their output doesn't meet specific criteria.
 
-Guardrails are implemented as Python functions that contain custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
+CrewAI supports two types of guardrails:
+
+1. **Function-based guardrails**: Python functions with custom validation logic, giving you complete control over the validation process and ensuring reliable, deterministic results.
+
+2. **LLM-based guardrails**: String descriptions that use the agent's LLM to validate outputs based on natural language criteria. These are ideal for complex or subjective validation requirements.
 
 ### Function-Based Guardrails
 
@@ -355,12 +361,12 @@ def validate_blog_content(result: TaskOutput) -> Tuple[bool, Any]:
     """Validate blog content meets requirements."""
     try:
         # Check word count
-        word_count = len(result.split())
+        word_count = len(result.raw.split())
         if word_count > 200:
             return (False, "Blog content exceeds 200 words")
 
         # Additional validation logic here
-        return (True, result.strip())
+        return (True, result.raw.strip())
     except Exception as e:
         return (False, "Unexpected error during validation")
 
@@ -372,6 +378,147 @@ blog_task = Task(
 )
 ```
 
+### LLM-Based Guardrails (String Descriptions)
+
+Instead of writing custom validation functions, you can use string descriptions that leverage LLM-based validation. When you provide a string to the `guardrail` or `guardrails` parameter, CrewAI automatically creates an `LLMGuardrail` that uses the agent's LLM to validate the output based on your description.
+
+**Requirements**:
+- The task must have an `agent` assigned (the guardrail uses the agent's LLM)
+- Provide a clear, descriptive string explaining the validation criteria
+
+```python Code
+from crewai import Task
+
+# Single LLM-based guardrail
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A blog post under 200 words",
+    agent=blog_agent,
+    guardrail="The blog post must be under 200 words and contain no technical jargon"
+)
+```
+
+LLM-based guardrails are particularly useful for:
+- **Complex validation logic** that's difficult to express programmatically
+- **Subjective criteria** like tone, style, or quality assessments
+- **Natural language requirements** that are easier to describe than code
+
+The LLM guardrail will:
+1. Analyze the task output against your description
+2. Return `(True, output)` if the output complies with the criteria
+3. Return `(False, feedback)` with specific feedback if validation fails
+
+**Example with detailed validation criteria**:
+
+```python Code
+research_task = Task(
+    description="Research the latest developments in quantum computing",
+    expected_output="A comprehensive research report",
+    agent=researcher_agent,
+    guardrail="""
+    The research report must:
+    - Be at least 1000 words long
+    - Include at least 5 credible sources
+    - Cover both technical and practical applications
+    - Be written in a professional, academic tone
+    - Avoid speculation or unverified claims
+    """
+)
+```
+
+### Multiple Guardrails
+
+You can apply multiple guardrails to a task using the `guardrails` parameter. Multiple guardrails are executed sequentially, with each guardrail receiving the output from the previous one. This allows you to chain validation and transformation steps.
+
+The `guardrails` parameter accepts:
+- A list of guardrail functions or string descriptions
+- A single guardrail function or string (same as `guardrail`)
+
+**Note**: If `guardrails` is provided, it takes precedence over `guardrail`. The `guardrail` parameter will be ignored when `guardrails` is set.
+
+```python Code
+from typing import Tuple, Any
+from crewai import TaskOutput, Task
+
+def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
+    """Validate word count is within limits."""
+    word_count = len(result.raw.split())
+    if word_count < 100:
+        return (False, f"Content too short: {word_count} words. Need at least 100 words.")
+    if word_count > 500:
+        return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
+    return (True, result.raw)
+
+def validate_no_profanity(result: TaskOutput) -> Tuple[bool, Any]:
+    """Check for inappropriate language."""
+    profanity_words = ["badword1", "badword2"]  # Example list
+    content_lower = result.raw.lower()
+    for word in profanity_words:
+        if word in content_lower:
+            return (False, f"Inappropriate language detected: {word}")
+    return (True, result.raw)
+
+def format_output(result: TaskOutput) -> Tuple[bool, Any]:
+    """Format and clean the output."""
+    formatted = result.raw.strip()
+    # Capitalize first letter
+    formatted = formatted[0].upper() + formatted[1:] if formatted else formatted
+    return (True, formatted)
+
+# Apply multiple guardrails sequentially
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A well-formatted blog post between 100-500 words",
+    agent=blog_agent,
+    guardrails=[
+        validate_word_count,      # First: validate length
+        validate_no_profanity,    # Second: check content
+        format_output             # Third: format the result
+    ],
+    guardrail_max_retries=3
+)
+```
+
+In this example, the guardrails execute in order:
+1. `validate_word_count` checks the word count
+2. `validate_no_profanity` checks for inappropriate language (using the output from step 1)
+3. `format_output` formats the final result (using the output from step 2)
+
+If any guardrail fails, the error is sent back to the agent, and the task is retried up to `guardrail_max_retries` times.
+
+**Mixing function-based and LLM-based guardrails**:
+
+You can combine both function-based and string-based guardrails in the same list:
+
+```python Code
+from typing import Tuple, Any
+from crewai import TaskOutput, Task
+
+def validate_word_count(result: TaskOutput) -> Tuple[bool, Any]:
+    """Validate word count is within limits."""
+    word_count = len(result.raw.split())
+    if word_count < 100:
+        return (False, f"Content too short: {word_count} words. Need at least 100 words.")
+    if word_count > 500:
+        return (False, f"Content too long: {word_count} words. Maximum is 500 words.")
+    return (True, result.raw)
+
+# Mix function-based and LLM-based guardrails
+blog_task = Task(
+    description="Write a blog post about AI",
+    expected_output="A well-formatted blog post between 100-500 words",
+    agent=blog_agent,
+    guardrails=[
+        validate_word_count,  # Function-based: precise word count check
+        "The content must be engaging and suitable for a general audience",  # LLM-based: subjective quality check
+        "The writing style should be clear, concise, and free of technical jargon"  # LLM-based: style validation
+    ],
+    guardrail_max_retries=3
+)
+```
+
+This approach combines the precision of programmatic validation with the flexibility of LLM-based assessment for subjective criteria.
+
 ### Guardrail Function Requirements
 
 1. **Function Signature**:

lib/crewai-tools/pyproject.toml

@@ -12,7 +12,7 @@ dependencies = [
     "pytube>=15.0.0",
     "requests>=2.32.5",
     "docker>=7.1.0",
-    "crewai==1.3.0",
+    "crewai==1.4.1",
     "lancedb>=0.5.4",
     "tiktoken>=0.8.0",
     "beautifulsoup4>=4.13.4",

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

@@ -287,4 +287,4 @@ __all__ = [
     "ZapierActionTools",
 ]
 
-__version__ = "1.3.0"
+__version__ = "1.4.1"

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

@@ -52,7 +52,6 @@ class AIMindTool(BaseTool):
 
         try:
             from minds.client import Client  # type: ignore
-            from minds.datasources import DatabaseConfig  # type: ignore
         except ImportError as e:
             raise ImportError(
                 "`minds_sdk` package not found, please run `pip install minds-sdk`"
@@ -60,23 +59,24 @@ class AIMindTool(BaseTool):
 
         minds_client = Client(api_key=self.api_key)
 
-        # Convert the datasources to DatabaseConfig objects.
-        datasources = []
+        datasource_names = []
         for datasource in self.datasources:
-            config = DatabaseConfig(
-                name=f"{AIMindToolConstants.DATASOURCE_NAME_PREFIX}_{secrets.token_hex(5)}",
+            ds_name = f"{AIMindToolConstants.DATASOURCE_NAME_PREFIX}{secrets.token_hex(5)}"
+
+            minds_client.datasources.create(
+                name=ds_name,
                 engine=datasource["engine"],
-                description=datasource["description"],
-                connection_data=datasource["connection_data"],
-                tables=datasource["tables"],
+                description=datasource.get("description", ""),
+                connection_data=datasource.get("connection_data", {}),
+                replace=True,
             )
-            datasources.append(config)
+            datasource_names.append(ds_name)
 
         # Generate a random name for the Mind.
         name = f"{AIMindToolConstants.MIND_NAME_PREFIX}_{secrets.token_hex(5)}"
 
         mind = minds_client.minds.create(
-            name=name, datasources=datasources, replace=True
+            name=name, datasources=datasource_names, replace=True
         )
 
         self.mind_name = mind.name

lib/crewai-tools/tests/tools/ai_mind_tool_test.py

@@ -0,0 +1,229 @@
+import os
+import sys
+from unittest.mock import MagicMock, patch, Mock
+
+import pytest
+
+from crewai_tools.tools.ai_mind_tool.ai_mind_tool import AIMindTool
+
+
+@pytest.fixture(autouse=True)
+def mock_minds_api_key():
+    with patch.dict(os.environ, {"MINDS_API_KEY": "test_key"}):
+        yield
+
+
+@pytest.fixture
+def mock_minds_sdk():
+    """Mock the minds_sdk package to avoid requiring it to be installed."""
+    mock_minds_module = MagicMock()
+    mock_client_module = MagicMock()
+    
+    mock_client_class = MagicMock()
+    mock_client_instance = MagicMock()
+    mock_client_class.return_value = mock_client_instance
+    
+    mock_datasources = MagicMock()
+    mock_client_instance.datasources = mock_datasources
+    
+    mock_minds = MagicMock()
+    mock_client_instance.minds = mock_minds
+    
+    mock_mind = MagicMock()
+    mock_mind.name = "test_mind_name"
+    mock_minds.create.return_value = mock_mind
+    
+    mock_client_module.Client = mock_client_class
+    mock_minds_module.client = mock_client_module
+    
+    with patch.dict(sys.modules, {"minds": mock_minds_module, "minds.client": mock_client_module}):
+        yield mock_client_instance
+
+
+def test_aimind_tool_imports_correctly_with_new_api(mock_minds_sdk):
+    """Test that AIMindTool can be initialized without DatabaseConfig import error."""
+    datasources = [
+        {
+            "description": "test database",
+            "engine": "postgres",
+            "connection_data": {
+                "user": "test_user",
+                "password": "test_pass",
+                "host": "localhost",
+                "port": 5432,
+                "database": "test_db",
+            },
+            "tables": ["test_table"],
+        }
+    ]
+    
+    tool = AIMindTool(api_key="test_key", datasources=datasources)
+    
+    assert tool.api_key == "test_key"
+    assert tool.mind_name == "test_mind_name"
+
+
+def test_aimind_tool_creates_datasources_with_new_api(mock_minds_sdk):
+    """Test that AIMindTool creates datasources using the new minds_sdk API."""
+    datasources = [
+        {
+            "description": "test database",
+            "engine": "postgres",
+            "connection_data": {
+                "user": "test_user",
+                "password": "test_pass",
+                "host": "localhost",
+                "port": 5432,
+                "database": "test_db",
+            },
+        }
+    ]
+    
+    tool = AIMindTool(api_key="test_key", datasources=datasources)
+    
+    mock_minds_sdk.datasources.create.assert_called_once()
+    call_args = mock_minds_sdk.datasources.create.call_args
+    
+    assert call_args.kwargs["engine"] == "postgres"
+    assert call_args.kwargs["description"] == "test database"
+    assert call_args.kwargs["connection_data"]["user"] == "test_user"
+    assert call_args.kwargs["replace"] is True
+
+
+def test_aimind_tool_handles_missing_optional_fields(mock_minds_sdk):
+    """Test that AIMindTool handles missing optional fields in datasource config."""
+    datasources = [
+        {
+            "engine": "postgres",
+        }
+    ]
+    
+    tool = AIMindTool(api_key="test_key", datasources=datasources)
+    
+    mock_minds_sdk.datasources.create.assert_called_once()
+    call_args = mock_minds_sdk.datasources.create.call_args
+    
+    assert call_args.kwargs["engine"] == "postgres"
+    assert call_args.kwargs["description"] == ""
+    assert call_args.kwargs["connection_data"] == {}
+
+
+def test_aimind_tool_creates_mind_with_datasource_names(mock_minds_sdk):
+    """Test that AIMindTool creates mind with datasource names instead of objects."""
+    datasources = [
+        {
+            "description": "test database 1",
+            "engine": "postgres",
+            "connection_data": {"user": "test_user1"},
+        },
+        {
+            "description": "test database 2",
+            "engine": "mysql",
+            "connection_data": {"user": "test_user2"},
+        },
+    ]
+    
+    tool = AIMindTool(api_key="test_key", datasources=datasources)
+    
+    assert mock_minds_sdk.datasources.create.call_count == 2
+    
+    mock_minds_sdk.minds.create.assert_called_once()
+    call_args = mock_minds_sdk.minds.create.call_args
+    
+    assert isinstance(call_args.kwargs["datasources"], list)
+    assert len(call_args.kwargs["datasources"]) == 2
+    assert all(isinstance(ds, str) for ds in call_args.kwargs["datasources"])
+    assert call_args.kwargs["replace"] is True
+
+
+def test_aimind_tool_raises_error_when_minds_sdk_not_installed():
+    """Test that AIMindTool raises ImportError when minds_sdk is not installed."""
+    with patch.dict(sys.modules, {"minds": None, "minds.client": None}):
+        with pytest.raises(ImportError) as exc_info:
+            AIMindTool(api_key="test_key", datasources=[])
+        
+        error_message = str(exc_info.value)
+        assert "minds_sdk" in error_message or "pip install minds-sdk" in error_message
+
+
+def test_aimind_tool_raises_error_when_api_key_missing():
+    """Test that AIMindTool raises ValueError when API key is not provided."""
+    with patch.dict(os.environ, {}, clear=True):
+        with pytest.raises(ValueError) as exc_info:
+            AIMindTool(datasources=[])
+        
+        assert "API key must be provided" in str(exc_info.value)
+
+
+def test_aimind_tool_uses_env_var_for_api_key(mock_minds_sdk):
+    """Test that AIMindTool uses MINDS_API_KEY environment variable."""
+    with patch.dict(os.environ, {"MINDS_API_KEY": "env_test_key"}):
+        tool = AIMindTool(datasources=[])
+        
+        assert tool.api_key == "env_test_key"
+
+
+def test_aimind_tool_run_method(mock_minds_sdk):
+    """Test that AIMindTool._run method works correctly."""
+    from openai.types.chat import ChatCompletion
+    
+    datasources = [
+        {
+            "engine": "postgres",
+            "description": "test db",
+        }
+    ]
+    
+    tool = AIMindTool(api_key="test_key", datasources=datasources)
+    
+    with patch("crewai_tools.tools.ai_mind_tool.ai_mind_tool.OpenAI") as mock_openai:
+        mock_client = MagicMock()
+        mock_openai.return_value = mock_client
+        
+        mock_completion = MagicMock(spec=ChatCompletion)
+        mock_completion.choices = [MagicMock()]
+        mock_completion.choices[0].message.content = "Test response"
+        mock_client.chat.completions.create.return_value = mock_completion
+        
+        result = tool._run("Test query")
+        
+        assert result == "Test response"
+        mock_client.chat.completions.create.assert_called_once()
+        call_args = mock_client.chat.completions.create.call_args
+        assert call_args.kwargs["model"] == "test_mind_name"
+        assert call_args.kwargs["messages"][0]["content"] == "Test query"
+
+
+def test_aimind_tool_run_raises_error_when_mind_name_not_set():
+    """Test that AIMindTool._run raises ValueError when mind_name is not set."""
+    with patch("openai.OpenAI"):
+        tool = AIMindTool.__new__(AIMindTool)
+        object.__setattr__(tool, "api_key", "test_key")
+        object.__setattr__(tool, "mind_name", None)
+        
+        with pytest.raises(ValueError) as exc_info:
+            tool._run("Test query")
+        
+        assert "Mind name is not set" in str(exc_info.value)
+
+
+def test_aimind_tool_run_raises_error_on_invalid_response():
+    """Test that AIMindTool._run raises ValueError on invalid response."""
+    with patch("crewai_tools.tools.ai_mind_tool.ai_mind_tool.OpenAI") as mock_openai:
+        mock_client = MagicMock()
+        mock_openai.return_value = mock_client
+        
+        mock_client.chat.completions.create.return_value = "invalid_response"
+        
+        tool = AIMindTool.__new__(AIMindTool)
+        object.__setattr__(tool, "api_key", "test_key")
+        object.__setattr__(tool, "mind_name", "test_mind")
+        
+        with pytest.raises(ValueError) as exc_info:
+            tool._run("Test query")
+        
+        assert "Invalid response from AI-Mind" in str(exc_info.value)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

lib/crewai/pyproject.toml

@@ -48,7 +48,7 @@ Repository = "https://github.com/crewAIInc/crewAI"
 
 [project.optional-dependencies]
 tools = [
-    "crewai-tools==1.3.0",
+    "crewai-tools==1.4.1",
 ]
 embeddings = [
     "tiktoken~=0.8.0"

lib/crewai/src/crewai/__init__.py

@@ -40,7 +40,7 @@ def _suppress_pydantic_deprecation_warnings() -> None:
 
 _suppress_pydantic_deprecation_warnings()
 
-__version__ = "1.3.0"
+__version__ = "1.4.1"
 _telemetry_submitted = False
 
 

lib/crewai/src/crewai/agent/core.py

@@ -119,6 +119,7 @@ class Agent(BaseAgent):
 
     _times_executed: int = PrivateAttr(default=0)
     _mcp_clients: list[Any] = PrivateAttr(default_factory=list)
+    _last_messages: list[LLMMessage] = PrivateAttr(default_factory=list)
     max_execution_time: int | None = Field(
         default=None,
         description="Maximum execution time for an agent to execute a task",
@@ -538,6 +539,12 @@ class Agent(BaseAgent):
             event=AgentExecutionCompletedEvent(agent=self, task=task, output=result),
         )
 
+        self._last_messages = (
+            self.agent_executor.messages.copy()
+            if self.agent_executor and hasattr(self.agent_executor, "messages")
+            else []
+        )
+
         self._cleanup_mcp_clients()
 
         return result
@@ -618,22 +625,22 @@ class Agent(BaseAgent):
             response_template=self.response_template,
         ).task_execution()
 
-        stop_sequences = [self.i18n.slice("observation")]
+        stop_words = [self.i18n.slice("observation")]
 
         if self.response_template:
-            stop_sequences.append(
+            stop_words.append(
                 self.response_template.split("{{ .Response }}")[1].strip()
             )
 
         self.agent_executor = CrewAgentExecutor(
-            llm=self.llm,  # type: ignore[arg-type]
+            llm=self.llm,
             task=task,  # type: ignore[arg-type]
             agent=self,
             crew=self.crew,
             tools=parsed_tools,
             prompt=prompt,
             original_tools=raw_tools,
-            stop_sequences=stop_sequences,
+            stop_words=stop_words,
             max_iter=self.max_iter,
             tools_handler=self.tools_handler,
             tools_names=get_tool_names(parsed_tools),
@@ -974,9 +981,7 @@ class Agent(BaseAgent):
         path = parsed.path.replace("/", "_").strip("_")
         return f"{domain}_{path}" if path else domain
 
-    def _get_mcp_tool_schemas(
-        self, server_params: dict[str, Any]
-    ) -> dict[str, dict[str, Any]] | Any:
+    def _get_mcp_tool_schemas(self, server_params: dict) -> dict[str, dict]:
         """Get tool schemas from MCP server for wrapper creation with caching."""
         server_url = server_params["url"]
 
@@ -1008,7 +1013,7 @@ class Agent(BaseAgent):
 
     async def _get_mcp_tool_schemas_async(
         self, server_params: dict[str, Any]
-    ) -> dict[str, dict[str, Any]]:
+    ) -> dict[str, dict]:
         """Async implementation of MCP tool schema retrieval with timeouts and retries."""
         server_url = server_params["url"]
         return await self._retry_mcp_discovery(
@@ -1016,7 +1021,7 @@ class Agent(BaseAgent):
         )
 
     async def _retry_mcp_discovery(
-        self, operation_func: Any, server_url: str
+        self, operation_func, server_url: str
     ) -> dict[str, dict[str, Any]]:
         """Retry MCP discovery operation with exponential backoff, avoiding try-except in loop."""
         last_error = None
@@ -1047,7 +1052,7 @@ class Agent(BaseAgent):
 
     @staticmethod
     async def _attempt_mcp_discovery(
-        operation_func: Any, server_url: str
+        operation_func, server_url: str
     ) -> tuple[dict[str, dict[str, Any]] | None, str, bool]:
         """Attempt single MCP discovery operation and return (result, error_message, should_retry)."""
         try:
@@ -1151,13 +1156,13 @@ class Agent(BaseAgent):
                     Field(..., description=field_description),
                 )
             else:
-                field_definitions[field_name] = (  # type: ignore[assignment]
+                field_definitions[field_name] = (
                     field_type | None,
                     Field(default=None, description=field_description),
                 )
 
         model_name = f"{tool_name.replace('-', '_').replace(' ', '_')}Schema"
-        return create_model(model_name, **field_definitions)  # type: ignore[no-any-return,call-overload]
+        return create_model(model_name, **field_definitions)
 
     def _json_type_to_python(self, field_schema: dict[str, Any]) -> type:
         """Convert JSON Schema type to Python type.
@@ -1177,12 +1182,12 @@ class Agent(BaseAgent):
                 if "const" in option:
                     types.append(str)
                 else:
-                    types.append(self._json_type_to_python(option))  # type: ignore[arg-type]
+                    types.append(self._json_type_to_python(option))
             unique_types = list(set(types))
             if len(unique_types) > 1:
                 result = unique_types[0]
                 for t in unique_types[1:]:
-                    result = result | t  # type: ignore[assignment]
+                    result = result | t
                 return result
             return unique_types[0]
 
@@ -1195,10 +1200,10 @@ class Agent(BaseAgent):
             "object": dict,
         }
 
-        return type_mapping.get(json_type, Any)  # type: ignore[arg-type]
+        return type_mapping.get(json_type, Any)
 
     @staticmethod
-    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict[str, Any]]:
+    def _fetch_amp_mcp_servers(mcp_name: str) -> list[dict]:
         """Fetch MCP server configurations from CrewAI AMP API."""
         # TODO: Implement AMP API call to "integrations/mcps" endpoint
         # Should return list of server configs with URLs
@@ -1343,6 +1348,15 @@ class Agent(BaseAgent):
     def set_fingerprint(self, fingerprint: Fingerprint) -> None:
         self.security_config.fingerprint = fingerprint
 
+    @property
+    def last_messages(self) -> list[LLMMessage]:
+        """Get messages from the last task execution.
+
+        Returns:
+            List of LLM messages from the most recent task execution.
+        """
+        return self._last_messages
+
     def _get_knowledge_search_query(self, task_prompt: str, task: Task) -> str | None:
         """Generate a search query for the knowledge base based on the task description."""
         crewai_event_bus.emit(
@@ -1437,7 +1451,7 @@ class Agent(BaseAgent):
             goal=self.goal,
             backstory=self.backstory,
             llm=self.llm,
-            tools=self.tools,
+            tools=self.tools or [],
             max_iterations=self.max_iter,
             max_execution_time=self.max_execution_time,
             respect_context_window=self.respect_context_window,

lib/crewai/src/crewai/agents/agent_builder/base_agent.py

@@ -137,7 +137,7 @@ class BaseAgent(BaseModel, ABC, metaclass=AgentMeta):
         default=False,
         description="Enable agent to delegate and ask questions among each other.",
     )
-    tools: list[BaseTool] = Field(
+    tools: list[BaseTool] | None = Field(
         default_factory=list, description="Tools at agents' disposal"
     )
     max_iter: int = Field(

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

@@ -73,7 +73,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         max_iter: int,
         tools: list[CrewStructuredTool],
         tools_names: str,
-        stop_sequences: list[str],
+        stop_words: list[str],
         tools_description: str,
         tools_handler: ToolsHandler,
         step_callback: Any = None,
@@ -95,7 +95,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             max_iter: Maximum iterations.
             tools: Available tools.
             tools_names: Tool names string.
-            stop_sequences: Stop sequences list for halting generation.
+            stop_words: Stop word list.
             tools_description: Tool descriptions.
             tools_handler: Tool handler instance.
             step_callback: Optional step callback.
@@ -114,6 +114,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.prompt = prompt
         self.tools = tools
         self.tools_names = tools_names
+        self.stop = stop_words
         self.max_iter = max_iter
         self.callbacks = callbacks or []
         self._printer: Printer = Printer()
@@ -130,7 +131,15 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         self.iterations = 0
         self.log_error_after = 3
         if self.llm:
-            self.llm.stop_sequences.extend(stop_sequences)
+            # This may be mutating the shared llm object and needs further evaluation
+            existing_stop = getattr(self.llm, "stop", [])
+            self.llm.stop = list(
+                set(
+                    existing_stop + self.stop
+                    if isinstance(existing_stop, list)
+                    else self.stop
+                )
+            )
 
     @property
     def use_stop_words(self) -> bool:
@@ -139,7 +148,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
         Returns:
             bool: True if tool should be used or not.
         """
-        return self.llm.supports_stop_words if self.llm else False
+        return self.llm.supports_stop_words() if self.llm else False
 
     def invoke(self, inputs: dict[str, Any]) -> dict[str, Any]:
         """Execute the agent with given inputs.
@@ -205,6 +214,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         llm=self.llm,
                         callbacks=self.callbacks,
                     )
+                    break
 
                 enforce_rpm_limit(self.request_within_rpm_limit)
 
@@ -217,7 +227,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                     from_agent=self.agent,
                     response_model=self.response_model,
                 )
-                formatted_answer = process_llm_response(answer, self.use_stop_words)
+                formatted_answer = process_llm_response(answer, self.use_stop_words)  # type: ignore[assignment]
 
                 if isinstance(formatted_answer, AgentAction):
                     # Extract agent fingerprint if available
@@ -249,11 +259,11 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                         formatted_answer, tool_result
                     )
 
-                self._invoke_step_callback(formatted_answer)
-                self._append_message(formatted_answer.text)
+                self._invoke_step_callback(formatted_answer)  # type: ignore[arg-type]
+                self._append_message(formatted_answer.text)  # type: ignore[union-attr,attr-defined]
 
-            except OutputParserError as e:  # noqa: PERF203
-                formatted_answer = handle_output_parser_exception(
+            except OutputParserError as e:
+                formatted_answer = handle_output_parser_exception(  # type: ignore[assignment]
                     e=e,
                     messages=self.messages,
                     iterations=self.iterations,

lib/crewai/src/crewai/cli/templates/crew/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.3.0"
+    "crewai[tools]==1.4.1"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/cli/templates/flow/pyproject.toml

@@ -5,7 +5,7 @@ description = "{{name}} using crewAI"
 authors = [{ name = "Your Name", email = "you@example.com" }]
 requires-python = ">=3.10,<3.14"
 dependencies = [
-    "crewai[tools]==1.3.0"
+    "crewai[tools]==1.4.1"
 ]
 
 [project.scripts]

lib/crewai/src/crewai/crew.py

@@ -809,6 +809,7 @@ class Crew(FlowTrackable, BaseModel):
                 "json_dict": output.json_dict,
                 "output_format": output.output_format,
                 "agent": output.agent,
+                "messages": output.messages,
             },
             "task_index": task_index,
             "inputs": inputs,
@@ -1236,6 +1237,7 @@ class Crew(FlowTrackable, BaseModel):
                 pydantic=stored_output["pydantic"],
                 json_dict=stored_output["json_dict"],
                 output_format=stored_output["output_format"],
+                messages=stored_output.get("messages", []),
             )
             self.tasks[i].output = task_output
 

lib/crewai/src/crewai/lite_agent.py

@@ -358,6 +358,7 @@ class LiteAgent(FlowTrackable, BaseModel):
             pydantic=formatted_result,
             agent_role=self.role,
             usage_metrics=usage_metrics.model_dump() if usage_metrics else None,
+            messages=self._messages,
         )
 
         # Process guardrail if set

lib/crewai/src/crewai/lite_agent_output.py

@@ -6,6 +6,8 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
+from crewai.utilities.types import LLMMessage
+
 
 class LiteAgentOutput(BaseModel):
     """Class that represents the result of a LiteAgent execution."""
@@ -20,6 +22,7 @@ class LiteAgentOutput(BaseModel):
     usage_metrics: dict[str, Any] | None = Field(
         description="Token usage metrics for this execution", default=None
     )
+    messages: list[LLMMessage] = Field(description="Messages of the agent", default=[])
 
     def to_dict(self) -> dict[str, Any]:
         """Convert pydantic_output to a dictionary."""

lib/crewai/src/crewai/llm.py

@@ -20,7 +20,8 @@ from typing import (
 )
 
 from dotenv import load_dotenv
-from pydantic import BaseModel, Field, model_validator
+import httpx
+from pydantic import BaseModel, Field
 from typing_extensions import Self
 
 from crewai.events.event_bus import crewai_event_bus
@@ -37,6 +38,13 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageStartedEvent,
 )
 from crewai.llms.base_llm import BaseLLM
+from crewai.llms.constants import (
+    ANTHROPIC_MODELS,
+    AZURE_MODELS,
+    BEDROCK_MODELS,
+    GEMINI_MODELS,
+    OPENAI_MODELS,
+)
 from crewai.utilities import InternalInstructor
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -53,6 +61,7 @@ if TYPE_CHECKING:
     from litellm.utils import supports_response_schema
 
     from crewai.agent.core import Agent
+    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
     from crewai.utilities.types import LLMMessage
@@ -318,152 +327,67 @@ class AccumulatedToolArgs(BaseModel):
 
 
 class LLM(BaseLLM):
-    completion_cost: float | None = Field(
-        default=None, description="The completion cost of the LLM."
-    )
-    top_p: float | None = Field(
-        default=None, description="Sampling probability threshold."
-    )
-    n: int | None = Field(
-        default=None, description="Number of completions to generate."
-    )
-    max_completion_tokens: int | None = Field(
-        default=None,
-        description="Maximum number of tokens to generate in the completion.",
-    )
-    max_tokens: int | None = Field(
-        default=None,
-        description="Maximum number of tokens allowed in the prompt + completion.",
-    )
-    presence_penalty: float | None = Field(
-        default=None, description="Penalty on the presence penalty."
-    )
-    frequency_penalty: float | None = Field(
-        default=None, description="Penalty on the frequency penalty."
-    )
-    logit_bias: dict[int, float] | None = Field(
-        default=None,
-        description="Modifies the likelihood of specified tokens appearing in the completion.",
-    )
-    response_format: type[BaseModel] | None = Field(
-        default=None,
-        description="Pydantic model class for structured response parsing.",
-    )
-    seed: int | None = Field(
-        default=None,
-        description="Random seed for reproducibility.",
-    )
-    logprobs: int | None = Field(
-        default=None,
-        description="Number of top logprobs to return.",
-    )
-    top_logprobs: int | None = Field(
-        default=None,
-        description="Number of top logprobs to return.",
-    )
-    api_base: str | None = Field(
-        default=None,
-        description="Base URL for the API endpoint.",
-    )
-    api_version: str | None = Field(
-        default=None,
-        description="API version to use.",
-    )
-    callbacks: list[Any] = Field(
-        default_factory=list,
-        description="List of callback handlers for LLM events.",
-    )
-    reasoning_effort: Literal["none", "low", "medium", "high"] | None = Field(
-        default=None,
-        description="Level of reasoning effort for the LLM.",
-    )
-    context_window_size: int = Field(
-        default=0,
-        description="The context window size of the LLM.",
-    )
-    is_anthropic: bool = Field(
-        default=False,
-        description="Indicates if the model is from Anthropic provider.",
-    )
-    supports_function_calling: bool = Field(
-        default=False,
-        description="Indicates if the model supports function calling.",
-    )
-    supports_stop_words: bool = Field(
-        default=False,
-        description="Indicates if the model supports stop words.",
-    )
-
-    @model_validator(mode="after")
-    def initialize_client(self) -> Self:
-        self.is_anthropic = any(
-            prefix in self.model.lower() for prefix in ANTHROPIC_PREFIXES
-        )
-        try:
-            provider = self._get_custom_llm_provider()
-            self.supports_function_calling = litellm.utils.supports_function_calling(
-                self.model, custom_llm_provider=provider
-            )
-        except Exception as e:
-            logging.error(f"Failed to check function calling support: {e!s}")
-            self.supports_function_calling = False
-        try:
-            params = get_supported_openai_params(model=self.model)
-            self.supports_stop_words = params is not None and "stop" in params
-        except Exception as e:
-            logging.error(f"Failed to get supported params: {e!s}")
-            self.supports_stop_words = False
-
-        with suppress_warnings():
-            callback_types = [type(callback) for callback in self.callbacks]
-            for callback in litellm.success_callback[:]:
-                if type(callback) in callback_types:
-                    litellm.success_callback.remove(callback)
-
-            for callback in litellm._async_success_callback[:]:
-                if type(callback) in callback_types:
-                    litellm._async_success_callback.remove(callback)
-
-            litellm.callbacks = self.callbacks
-
-        with suppress_warnings():
-            success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
-            success_callbacks: list[str | Callable[..., Any] | CustomLogger] = []
-            if success_callbacks_str:
-                success_callbacks = [
-                    cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
-                ]
-
-            failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
-            if failure_callbacks_str:
-                failure_callbacks: list[str | Callable[..., Any] | CustomLogger] = [
-                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
-                ]
-
-                litellm.success_callback = success_callbacks
-                litellm.failure_callback = failure_callbacks
-        return self
-
-    # @computed_field
-    # @property
-    # def is_anthropic(self) -> bool:
-    #     """Determine if the model is from Anthropic provider."""
-    #     anthropic_prefixes = ("anthropic/", "claude-", "claude/")
-    #     return any(prefix in self.model.lower() for prefix in anthropic_prefixes)
+    completion_cost: float | None = None
 
     def __new__(cls, model: str, is_litellm: bool = False, **kwargs: Any) -> LLM:
-        """Factory method that routes to native SDK or falls back to LiteLLM."""
+        """Factory method that routes to native SDK or falls back to LiteLLM.
+
+        Routing priority:
+            1. If 'provider' kwarg is present, use that provider with constants
+            2. If only 'model' kwarg, use constants to infer provider
+            3. If "/" in model name:
+               - Check if prefix is a native provider (openai/anthropic/azure/bedrock/gemini)
+               - If yes, validate model against constants
+               - If valid, route to native SDK; otherwise route 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"
+        explicit_provider = kwargs.get("provider")
 
-        native_class = cls._get_native_provider(provider)
+        if explicit_provider:
+            provider = explicit_provider
+            use_native = True
+            model_string = model
+        elif "/" in model:
+            prefix, _, model_part = model.partition("/")
+
+            provider_mapping = {
+                "openai": "openai",
+                "anthropic": "anthropic",
+                "claude": "anthropic",
+                "azure": "azure",
+                "azure_openai": "azure",
+                "google": "gemini",
+                "gemini": "gemini",
+                "bedrock": "bedrock",
+                "aws": "bedrock",
+            }
+
+            canonical_provider = provider_mapping.get(prefix.lower())
+
+            if canonical_provider and cls._validate_model_in_constants(
+                model_part, canonical_provider
+            ):
+                provider = canonical_provider
+                use_native = True
+                model_string = model_part
+            else:
+                provider = prefix
+                use_native = False
+                model_string = model_part
+        else:
+            provider = cls._infer_provider_from_model(model)
+            use_native = True
+            model_string = model
+
+        native_class = cls._get_native_provider(provider) if use_native else None
         if native_class and not is_litellm and provider in SUPPORTED_NATIVE_PROVIDERS:
             try:
-                model_string = model.partition("/")[2] if "/" in model else model
+                # Remove 'provider' from kwargs if it exists to avoid duplicate keyword argument
+                kwargs_copy = {k: v for k, v in kwargs.items() if k != 'provider'}
                 return cast(
-                    Self, native_class(model=model_string, provider=provider, **kwargs)
+                    Self, native_class(model=model_string, provider=provider, **kwargs_copy)
                 )
             except NotImplementedError:
                 raise
@@ -480,6 +404,63 @@ class LLM(BaseLLM):
         instance.is_litellm = True
         return instance
 
+    @classmethod
+    def _validate_model_in_constants(cls, model: str, provider: str) -> bool:
+        """Validate if a model name exists in the provider's constants.
+
+        Args:
+            model: The model name to validate
+            provider: The provider to check against (canonical name)
+
+        Returns:
+            True if the model exists in the provider's constants, False otherwise
+        """
+        if provider == "openai":
+            return model in OPENAI_MODELS
+
+        if provider == "anthropic" or provider == "claude":
+            return model in ANTHROPIC_MODELS
+
+        if provider == "gemini":
+            return model in GEMINI_MODELS
+
+        if provider == "bedrock":
+            return model in BEDROCK_MODELS
+
+        if provider == "azure":
+            # azure does not provide a list of available models, determine a better way to handle this
+            return True
+
+        return False
+
+    @classmethod
+    def _infer_provider_from_model(cls, model: str) -> str:
+        """Infer the provider from the model name.
+
+        Args:
+            model: The model name without provider prefix
+
+        Returns:
+            The inferred provider name, defaults to "openai"
+        """
+
+        if model in OPENAI_MODELS:
+            return "openai"
+
+        if model in ANTHROPIC_MODELS:
+            return "anthropic"
+
+        if model in GEMINI_MODELS:
+            return "gemini"
+
+        if model in BEDROCK_MODELS:
+            return "bedrock"
+
+        if model in AZURE_MODELS:
+            return "azure"
+
+        return "openai"
+
     @classmethod
     def _get_native_provider(cls, provider: str) -> type | None:
         """Get native provider class if available."""
@@ -512,6 +493,98 @@ class LLM(BaseLLM):
 
         return None
 
+    def __init__(
+        self,
+        model: str,
+        timeout: float | int | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        n: int | None = None,
+        stop: str | list[str] | None = None,
+        max_completion_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,
+        response_format: type[BaseModel] | None = None,
+        seed: int | None = None,
+        logprobs: int | None = None,
+        top_logprobs: int | None = None,
+        base_url: str | None = None,
+        api_base: str | None = None,
+        api_version: str | None = None,
+        api_key: str | None = None,
+        callbacks: list[Any] | None = None,
+        reasoning_effort: Literal["none", "low", "medium", "high"] | None = None,
+        stream: bool = False,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize LLM instance.
+
+        Note: This __init__ method is only called for fallback instances.
+        Native provider instances handle their own initialization in their respective classes.
+        """
+        super().__init__(
+            model=model,
+            temperature=temperature,
+            api_key=api_key,
+            base_url=base_url,
+            timeout=timeout,
+            **kwargs,
+        )
+        self.model = model
+        self.timeout = timeout
+        self.temperature = temperature
+        self.top_p = top_p
+        self.n = n
+        self.max_completion_tokens = max_completion_tokens
+        self.max_tokens = max_tokens
+        self.presence_penalty = presence_penalty
+        self.frequency_penalty = frequency_penalty
+        self.logit_bias = logit_bias
+        self.response_format = response_format
+        self.seed = seed
+        self.logprobs = logprobs
+        self.top_logprobs = top_logprobs
+        self.base_url = base_url
+        self.api_base = api_base
+        self.api_version = api_version
+        self.api_key = api_key
+        self.callbacks = callbacks
+        self.context_window_size = 0
+        self.reasoning_effort = reasoning_effort
+        self.additional_params = kwargs
+        self.is_anthropic = self._is_anthropic_model(model)
+        self.stream = stream
+        self.interceptor = interceptor
+
+        litellm.drop_params = True
+
+        # Normalize self.stop to always be a list[str]
+        if stop is None:
+            self.stop: list[str] = []
+        elif isinstance(stop, str):
+            self.stop = [stop]
+        else:
+            self.stop = stop
+
+        self.set_callbacks(callbacks or [])
+        self.set_env_callbacks()
+
+    @staticmethod
+    def _is_anthropic_model(model: str) -> bool:
+        """Determine if the model is from Anthropic provider.
+
+        Args:
+            model: The model identifier string.
+
+        Returns:
+            bool: True if the model is from Anthropic, False otherwise.
+        """
+        anthropic_prefixes = ("anthropic/", "claude-", "claude/")
+        return any(prefix in model.lower() for prefix in anthropic_prefixes)
+
     def _prepare_completion_params(
         self,
         messages: str | list[LLMMessage],
@@ -1225,6 +1298,8 @@ class LLM(BaseLLM):
                     message["role"] = msg_role
         # --- 5) Set up callbacks if provided
         with suppress_warnings():
+            if callbacks and len(callbacks) > 0:
+                self.set_callbacks(callbacks)
             try:
                 # --- 6) Prepare parameters for the completion call
                 params = self._prepare_completion_params(messages, tools)
@@ -1413,6 +1488,24 @@ class LLM(BaseLLM):
                 "Please remove response_format or use a supported model."
             )
 
+    def supports_function_calling(self) -> bool:
+        try:
+            provider = self._get_custom_llm_provider()
+            return litellm.utils.supports_function_calling(
+                self.model, custom_llm_provider=provider
+            )
+        except Exception as e:
+            logging.error(f"Failed to check function calling support: {e!s}")
+            return False
+
+    def supports_stop_words(self) -> bool:
+        try:
+            params = get_supported_openai_params(model=self.model)
+            return params is not None and "stop" in params
+        except Exception as e:
+            logging.error(f"Failed to get supported params: {e!s}")
+            return False
+
     def get_context_window_size(self) -> int:
         """
         Returns the context window size, using 75% of the maximum to avoid
@@ -1442,6 +1535,60 @@ class LLM(BaseLLM):
                 self.context_window_size = int(value * CONTEXT_WINDOW_USAGE_RATIO)
         return self.context_window_size
 
+    @staticmethod
+    def set_callbacks(callbacks: list[Any]) -> None:
+        """
+        Attempt to keep a single set of callbacks in litellm by removing old
+        duplicates and adding new ones.
+        """
+        with suppress_warnings():
+            callback_types = [type(callback) for callback in callbacks]
+            for callback in litellm.success_callback[:]:
+                if type(callback) in callback_types:
+                    litellm.success_callback.remove(callback)
+
+            for callback in litellm._async_success_callback[:]:
+                if type(callback) in callback_types:
+                    litellm._async_success_callback.remove(callback)
+
+            litellm.callbacks = callbacks
+
+    @staticmethod
+    def set_env_callbacks() -> None:
+        """Sets the success and failure callbacks for the LiteLLM library from environment variables.
+
+        This method reads the `LITELLM_SUCCESS_CALLBACKS` and `LITELLM_FAILURE_CALLBACKS`
+        environment variables, which should contain comma-separated lists of callback names.
+        It then assigns these lists to `litellm.success_callback` and `litellm.failure_callback`,
+        respectively.
+
+        If the environment variables are not set or are empty, the corresponding callback lists
+        will be set to empty lists.
+
+        Examples:
+            LITELLM_SUCCESS_CALLBACKS="langfuse,langsmith"
+            LITELLM_FAILURE_CALLBACKS="langfuse"
+
+        This will set `litellm.success_callback` to ["langfuse", "langsmith"] and
+        `litellm.failure_callback` to ["langfuse"].
+        """
+        with suppress_warnings():
+            success_callbacks_str = os.environ.get("LITELLM_SUCCESS_CALLBACKS", "")
+            success_callbacks: list[str | Callable[..., Any] | CustomLogger] = []
+            if success_callbacks_str:
+                success_callbacks = [
+                    cb.strip() for cb in success_callbacks_str.split(",") if cb.strip()
+                ]
+
+            failure_callbacks_str = os.environ.get("LITELLM_FAILURE_CALLBACKS", "")
+            if failure_callbacks_str:
+                failure_callbacks: list[str | Callable[..., Any] | CustomLogger] = [
+                    cb.strip() for cb in failure_callbacks_str.split(",") if cb.strip()
+                ]
+
+                litellm.success_callback = success_callbacks
+                litellm.failure_callback = failure_callbacks
+
     def __copy__(self) -> LLM:
         """Create a shallow copy of the LLM instance."""
         # Filter out parameters that are already explicitly passed to avoid conflicts
@@ -1502,7 +1649,7 @@ class LLM(BaseLLM):
             **filtered_params,
         )
 
-    def __deepcopy__(self, memo: dict[int, Any] | None) -> LLM:  # type: ignore[override]
+    def __deepcopy__(self, memo: dict[int, Any] | None) -> LLM:
         """Create a deep copy of the LLM instance."""
         import copy
 

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

@@ -13,9 +13,8 @@ import logging
 import re
 from typing import TYPE_CHECKING, Any, Final
 
-from pydantic import AliasChoices, BaseModel, Field, PrivateAttr, field_validator
+from pydantic import BaseModel
 
-from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
 from crewai.events.event_bus import crewai_event_bus
 from crewai.events.types.llm_events import (
     LLMCallCompletedEvent,
@@ -29,7 +28,6 @@ from crewai.events.types.tool_usage_events import (
     ToolUsageFinishedEvent,
     ToolUsageStartedEvent,
 )
-from crewai.llms.hooks import BaseInterceptor
 from crewai.types.usage_metrics import UsageMetrics
 
 
@@ -45,7 +43,7 @@ DEFAULT_SUPPORTS_STOP_WORDS: Final[bool] = True
 _JSON_EXTRACTION_PATTERN: Final[re.Pattern[str]] = re.compile(r"\{.*}", re.DOTALL)
 
 
-class BaseLLM(BaseModel, ABC):
+class BaseLLM(ABC):
     """Abstract base class for LLM implementations.
 
     This class defines the interface that all LLM implementations must follow.
@@ -57,105 +55,70 @@ class BaseLLM(BaseModel, ABC):
     implement proper validation for input parameters and provide clear error
     messages when things go wrong.
 
-
     Attributes:
         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.
     """
 
-    provider: str | re.Pattern[str] = Field(
-        default="openai", description="The provider of the LLM."
-    )
-    model: str = Field(description="The model identifier/name.")
-    temperature: float | None = Field(
-        default=None, ge=0, le=2, description="Temperature for response generation."
-    )
-    api_key: str | None = Field(default=None, description="API key for authentication.")
-    base_url: str | None = Field(default=None, description="Base URL for API calls.")
-    timeout: float | None = Field(default=None, description="Timeout for API calls.")
-    max_retries: int = Field(
-        default=2, description="Maximum number of API requests to make."
-    )
-    max_tokens: int | None = Field(
-        default=None, description="Maximum tokens for response generation."
-    )
-    stream: bool | None = Field(default=False, description="Stream the API requests.")
-    client: Any = Field(description="Underlying LLM client instance.")
-    interceptor: BaseInterceptor[Any, Any] | None = Field(
-        default=None,
-        description="An optional HTTPX interceptor for modifying requests/responses.",
-    )
-    client_params: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Additional parameters for the underlying LLM client.",
-    )
-    supports_stop_words: bool = Field(
-        default=DEFAULT_SUPPORTS_STOP_WORDS,
-        description="Whether or not to support stop words.",
-    )
-    stop_sequences: list[str] = Field(
-        default_factory=list,
-        validation_alias=AliasChoices("stop_sequences", "stop"),
-        description="Stop sequences for generation (synchronized with stop).",
-    )
-    is_litellm: bool = Field(
-        default=False, description="Is this LLM implementation in litellm?"
-    )
-    additional_params: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Additional parameters for LLM calls.",
-    )
-    _token_usage: TokenProcess = PrivateAttr(default_factory=TokenProcess)
+    is_litellm: bool = False
 
-    @field_validator("provider", mode="before")
-    @classmethod
-    def extract_provider_from_model(
-        cls, v: str | re.Pattern[str] | None, info: Any
-    ) -> str | re.Pattern[str]:
-        """Extract provider from model string if not explicitly provided.
+    def __init__(
+        self,
+        model: str,
+        temperature: float | None = None,
+        api_key: str | None = None,
+        base_url: str | None = None,
+        provider: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize the BaseLLM with default attributes.
 
         Args:
-            v: Provided provider value (can be str, Pattern, or None)
-            info: Validation info containing other field values
-
-        Returns:
-            Provider name (str) or Pattern
+            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 provider explicitly provided, validate and return it
-        if v is not None:
-            if not isinstance(v, (str, re.Pattern)):
-                raise ValueError(f"Provider must be str or Pattern, got {type(v)}")
-            return v
+        if not model:
+            raise ValueError("Model name is required and cannot be empty")
 
-        model: str = info.data.get("model", "")
-        if "/" in model:
-            return model.partition("/")[0]
-        return "openai"
+        self.model = model
+        self.temperature = temperature
+        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"
 
-    @field_validator("stop_sequences", mode="before")
-    @classmethod
-    def normalize_stop_sequences(
-        cls, v: str | list[str] | set[str] | None
-    ) -> list[str]:
-        """Validate and normalize stop sequences.
+        stop = kwargs.pop("stop", None)
+        if stop is None:
+            self.stop: list[str] = []
+        elif isinstance(stop, str):
+            self.stop = [stop]
+        elif isinstance(stop, list):
+            self.stop = stop
+        else:
+            self.stop = []
 
-        Converts string to list and handles None values.
-        AliasChoices handles accepting both 'stop' and 'stop_sequences' parameter names.
-        """
-        if v is None:
-            return []
-        if isinstance(v, str):
-            return [v]
-        if isinstance(v, set):
-            return list(v)
-        if isinstance(v, list):
-            return v
-        return []
+        self._token_usage = {
+            "total_tokens": 0,
+            "prompt_tokens": 0,
+            "completion_tokens": 0,
+            "successful_requests": 0,
+            "cached_prompt_tokens": 0,
+        }
 
     @property
-    def stop(self) -> list[str]:
-        """Alias for stop_sequences to maintain backward compatibility."""
-        return self.stop_sequences
+    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(
@@ -208,6 +171,14 @@ class BaseLLM(BaseModel, ABC):
         """
         return tools
 
+    def supports_stop_words(self) -> bool:
+        """Check if the LLM supports stop words.
+
+        Returns:
+            True if the LLM supports stop words, False otherwise.
+        """
+        return DEFAULT_SUPPORTS_STOP_WORDS
+
     def _supports_stop_words_implementation(self) -> bool:
         """Check if stop words are configured for this LLM instance.
 
@@ -535,7 +506,7 @@ class BaseLLM(BaseModel, ABC):
         """
         if "/" in model:
             return model.partition("/")[0]
-        return "openai"
+        return "openai"  # Default provider
 
     def _track_token_usage_internal(self, usage_data: dict[str, Any]) -> None:
         """Track token usage internally in the LLM instance.
@@ -564,11 +535,11 @@ class BaseLLM(BaseModel, ABC):
             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
+        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.
@@ -576,10 +547,4 @@ class BaseLLM(BaseModel, ABC):
         Returns:
             Dictionary with token usage totals
         """
-        return UsageMetrics(
-            prompt_tokens=self._token_usage.prompt_tokens,
-            completion_tokens=self._token_usage.completion_tokens,
-            total_tokens=self._token_usage.total_tokens,
-            successful_requests=self._token_usage.successful_requests,
-            cached_prompt_tokens=self._token_usage.cached_prompt_tokens,
-        )
+        return UsageMetrics(**self._token_usage)

lib/crewai/src/crewai/llms/constants.py

@@ -0,0 +1,558 @@
+from typing import Literal, TypeAlias
+
+
+OpenAIModels: TypeAlias = Literal[
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0125",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-1106",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-3.5-turbo-instruct",
+    "gpt-3.5-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0125-preview",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-turbo-preview",
+    "gpt-4-vision-preview",
+    "gpt-4.1",
+    "gpt-4.1-2025-04-14",
+    "gpt-4.1-mini",
+    "gpt-4.1-mini-2025-04-14",
+    "gpt-4.1-nano",
+    "gpt-4.1-nano-2025-04-14",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-audio-preview",
+    "gpt-4o-audio-preview-2024-10-01",
+    "gpt-4o-audio-preview-2024-12-17",
+    "gpt-4o-audio-preview-2025-06-03",
+    "gpt-4o-mini",
+    "gpt-4o-mini-2024-07-18",
+    "gpt-4o-mini-audio-preview",
+    "gpt-4o-mini-audio-preview-2024-12-17",
+    "gpt-4o-mini-realtime-preview",
+    "gpt-4o-mini-realtime-preview-2024-12-17",
+    "gpt-4o-mini-search-preview",
+    "gpt-4o-mini-search-preview-2025-03-11",
+    "gpt-4o-mini-transcribe",
+    "gpt-4o-mini-tts",
+    "gpt-4o-realtime-preview",
+    "gpt-4o-realtime-preview-2024-10-01",
+    "gpt-4o-realtime-preview-2024-12-17",
+    "gpt-4o-realtime-preview-2025-06-03",
+    "gpt-4o-search-preview",
+    "gpt-4o-search-preview-2025-03-11",
+    "gpt-4o-transcribe",
+    "gpt-4o-transcribe-diarize",
+    "gpt-5",
+    "gpt-5-2025-08-07",
+    "gpt-5-chat",
+    "gpt-5-chat-latest",
+    "gpt-5-codex",
+    "gpt-5-mini",
+    "gpt-5-mini-2025-08-07",
+    "gpt-5-nano",
+    "gpt-5-nano-2025-08-07",
+    "gpt-5-pro",
+    "gpt-5-pro-2025-10-06",
+    "gpt-5-search-api",
+    "gpt-5-search-api-2025-10-14",
+    "gpt-audio",
+    "gpt-audio-2025-08-28",
+    "gpt-audio-mini",
+    "gpt-audio-mini-2025-10-06",
+    "gpt-image-1",
+    "gpt-image-1-mini",
+    "gpt-realtime",
+    "gpt-realtime-2025-08-28",
+    "gpt-realtime-mini",
+    "gpt-realtime-mini-2025-10-06",
+    "o1",
+    "o1-preview",
+    "o1-2024-12-17",
+    "o1-mini",
+    "o1-mini-2024-09-12",
+    "o1-pro",
+    "o1-pro-2025-03-19",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+    "whisper-1",
+]
+OPENAI_MODELS: list[OpenAIModels] = [
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0125",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-1106",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-3.5-turbo-instruct",
+    "gpt-3.5-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0125-preview",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-turbo-preview",
+    "gpt-4-vision-preview",
+    "gpt-4.1",
+    "gpt-4.1-2025-04-14",
+    "gpt-4.1-mini",
+    "gpt-4.1-mini-2025-04-14",
+    "gpt-4.1-nano",
+    "gpt-4.1-nano-2025-04-14",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-audio-preview",
+    "gpt-4o-audio-preview-2024-10-01",
+    "gpt-4o-audio-preview-2024-12-17",
+    "gpt-4o-audio-preview-2025-06-03",
+    "gpt-4o-mini",
+    "gpt-4o-mini-2024-07-18",
+    "gpt-4o-mini-audio-preview",
+    "gpt-4o-mini-audio-preview-2024-12-17",
+    "gpt-4o-mini-realtime-preview",
+    "gpt-4o-mini-realtime-preview-2024-12-17",
+    "gpt-4o-mini-search-preview",
+    "gpt-4o-mini-search-preview-2025-03-11",
+    "gpt-4o-mini-transcribe",
+    "gpt-4o-mini-tts",
+    "gpt-4o-realtime-preview",
+    "gpt-4o-realtime-preview-2024-10-01",
+    "gpt-4o-realtime-preview-2024-12-17",
+    "gpt-4o-realtime-preview-2025-06-03",
+    "gpt-4o-search-preview",
+    "gpt-4o-search-preview-2025-03-11",
+    "gpt-4o-transcribe",
+    "gpt-4o-transcribe-diarize",
+    "gpt-5",
+    "gpt-5-2025-08-07",
+    "gpt-5-chat",
+    "gpt-5-chat-latest",
+    "gpt-5-codex",
+    "gpt-5-mini",
+    "gpt-5-mini-2025-08-07",
+    "gpt-5-nano",
+    "gpt-5-nano-2025-08-07",
+    "gpt-5-pro",
+    "gpt-5-pro-2025-10-06",
+    "gpt-5-search-api",
+    "gpt-5-search-api-2025-10-14",
+    "gpt-audio",
+    "gpt-audio-2025-08-28",
+    "gpt-audio-mini",
+    "gpt-audio-mini-2025-10-06",
+    "gpt-image-1",
+    "gpt-image-1-mini",
+    "gpt-realtime",
+    "gpt-realtime-2025-08-28",
+    "gpt-realtime-mini",
+    "gpt-realtime-mini-2025-10-06",
+    "o1",
+    "o1-preview",
+    "o1-2024-12-17",
+    "o1-mini",
+    "o1-mini-2024-09-12",
+    "o1-pro",
+    "o1-pro-2025-03-19",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+    "whisper-1",
+]
+
+
+AnthropicModels: TypeAlias = Literal[
+    "claude-3-7-sonnet-latest",
+    "claude-3-7-sonnet-20250219",
+    "claude-3-5-haiku-latest",
+    "claude-3-5-haiku-20241022",
+    "claude-haiku-4-5",
+    "claude-haiku-4-5-20251001",
+    "claude-sonnet-4-20250514",
+    "claude-sonnet-4-0",
+    "claude-4-sonnet-20250514",
+    "claude-sonnet-4-5",
+    "claude-sonnet-4-5-20250929",
+    "claude-3-5-sonnet-latest",
+    "claude-3-5-sonnet-20241022",
+    "claude-3-5-sonnet-20240620",
+    "claude-opus-4-0",
+    "claude-opus-4-20250514",
+    "claude-4-opus-20250514",
+    "claude-opus-4-1",
+    "claude-opus-4-1-20250805",
+    "claude-3-opus-latest",
+    "claude-3-opus-20240229",
+    "claude-3-sonnet-20240229",
+    "claude-3-haiku-latest",
+    "claude-3-haiku-20240307",
+]
+ANTHROPIC_MODELS: list[AnthropicModels] = [
+    "claude-3-7-sonnet-latest",
+    "claude-3-7-sonnet-20250219",
+    "claude-3-5-haiku-latest",
+    "claude-3-5-haiku-20241022",
+    "claude-haiku-4-5",
+    "claude-haiku-4-5-20251001",
+    "claude-sonnet-4-20250514",
+    "claude-sonnet-4-0",
+    "claude-4-sonnet-20250514",
+    "claude-sonnet-4-5",
+    "claude-sonnet-4-5-20250929",
+    "claude-3-5-sonnet-latest",
+    "claude-3-5-sonnet-20241022",
+    "claude-3-5-sonnet-20240620",
+    "claude-opus-4-0",
+    "claude-opus-4-20250514",
+    "claude-4-opus-20250514",
+    "claude-opus-4-1",
+    "claude-opus-4-1-20250805",
+    "claude-3-opus-latest",
+    "claude-3-opus-20240229",
+    "claude-3-sonnet-20240229",
+    "claude-3-haiku-latest",
+    "claude-3-haiku-20240307",
+]
+
+GeminiModels: TypeAlias = Literal[
+    "gemini-2.5-pro",
+    "gemini-2.5-pro-preview-03-25",
+    "gemini-2.5-pro-preview-05-06",
+    "gemini-2.5-pro-preview-06-05",
+    "gemini-2.5-flash",
+    "gemini-2.5-flash-preview-05-20",
+    "gemini-2.5-flash-preview-04-17",
+    "gemini-2.5-flash-image",
+    "gemini-2.5-flash-image-preview",
+    "gemini-2.5-flash-lite",
+    "gemini-2.5-flash-lite-preview-06-17",
+    "gemini-2.5-flash-preview-09-2025",
+    "gemini-2.5-flash-lite-preview-09-2025",
+    "gemini-2.5-flash-preview-tts",
+    "gemini-2.5-pro-preview-tts",
+    "gemini-2.5-computer-use-preview-10-2025",
+    "gemini-2.0-flash",
+    "gemini-2.0-flash-001",
+    "gemini-2.0-flash-exp",
+    "gemini-2.0-flash-exp-image-generation",
+    "gemini-2.0-flash-lite",
+    "gemini-2.0-flash-lite-001",
+    "gemini-2.0-flash-lite-preview",
+    "gemini-2.0-flash-lite-preview-02-05",
+    "gemini-2.0-flash-preview-image-generation",
+    "gemini-2.0-flash-thinking-exp",
+    "gemini-2.0-flash-thinking-exp-01-21",
+    "gemini-2.0-flash-thinking-exp-1219",
+    "gemini-2.0-pro-exp",
+    "gemini-2.0-pro-exp-02-05",
+    "gemini-exp-1206",
+    "gemini-1.5-pro",
+    "gemini-1.5-flash",
+    "gemini-1.5-flash-8b",
+    "gemini-flash-latest",
+    "gemini-flash-lite-latest",
+    "gemini-pro-latest",
+    "gemini-2.0-flash-live-001",
+    "gemini-live-2.5-flash-preview",
+    "gemini-2.5-flash-live-preview",
+    "gemini-robotics-er-1.5-preview",
+    "gemini-gemma-2-27b-it",
+    "gemini-gemma-2-9b-it",
+    "gemma-3-1b-it",
+    "gemma-3-4b-it",
+    "gemma-3-12b-it",
+    "gemma-3-27b-it",
+    "gemma-3n-e2b-it",
+    "gemma-3n-e4b-it",
+    "learnlm-2.0-flash-experimental",
+]
+GEMINI_MODELS: list[GeminiModels] = [
+    "gemini-2.5-pro",
+    "gemini-2.5-pro-preview-03-25",
+    "gemini-2.5-pro-preview-05-06",
+    "gemini-2.5-pro-preview-06-05",
+    "gemini-2.5-flash",
+    "gemini-2.5-flash-preview-05-20",
+    "gemini-2.5-flash-preview-04-17",
+    "gemini-2.5-flash-image",
+    "gemini-2.5-flash-image-preview",
+    "gemini-2.5-flash-lite",
+    "gemini-2.5-flash-lite-preview-06-17",
+    "gemini-2.5-flash-preview-09-2025",
+    "gemini-2.5-flash-lite-preview-09-2025",
+    "gemini-2.5-flash-preview-tts",
+    "gemini-2.5-pro-preview-tts",
+    "gemini-2.5-computer-use-preview-10-2025",
+    "gemini-2.0-flash",
+    "gemini-2.0-flash-001",
+    "gemini-2.0-flash-exp",
+    "gemini-2.0-flash-exp-image-generation",
+    "gemini-2.0-flash-lite",
+    "gemini-2.0-flash-lite-001",
+    "gemini-2.0-flash-lite-preview",
+    "gemini-2.0-flash-lite-preview-02-05",
+    "gemini-2.0-flash-preview-image-generation",
+    "gemini-2.0-flash-thinking-exp",
+    "gemini-2.0-flash-thinking-exp-01-21",
+    "gemini-2.0-flash-thinking-exp-1219",
+    "gemini-2.0-pro-exp",
+    "gemini-2.0-pro-exp-02-05",
+    "gemini-exp-1206",
+    "gemini-1.5-pro",
+    "gemini-1.5-flash",
+    "gemini-1.5-flash-8b",
+    "gemini-flash-latest",
+    "gemini-flash-lite-latest",
+    "gemini-pro-latest",
+    "gemini-2.0-flash-live-001",
+    "gemini-live-2.5-flash-preview",
+    "gemini-2.5-flash-live-preview",
+    "gemini-robotics-er-1.5-preview",
+    "gemini-gemma-2-27b-it",
+    "gemini-gemma-2-9b-it",
+    "gemma-3-1b-it",
+    "gemma-3-4b-it",
+    "gemma-3-12b-it",
+    "gemma-3-27b-it",
+    "gemma-3n-e2b-it",
+    "gemma-3n-e4b-it",
+    "learnlm-2.0-flash-experimental",
+]
+
+
+AzureModels: TypeAlias = Literal[
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-35-turbo",
+    "gpt-35-turbo-0125",
+    "gpt-35-turbo-1106",
+    "gpt-35-turbo-16k-0613",
+    "gpt-35-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-vision",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-mini",
+    "gpt-5",
+    "o1",
+    "o1-mini",
+    "o1-preview",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+]
+AZURE_MODELS: list[AzureModels] = [
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-35-turbo",
+    "gpt-35-turbo-0125",
+    "gpt-35-turbo-1106",
+    "gpt-35-turbo-16k-0613",
+    "gpt-35-turbo-instruct-0914",
+    "gpt-4",
+    "gpt-4-0314",
+    "gpt-4-0613",
+    "gpt-4-1106-preview",
+    "gpt-4-0125-preview",
+    "gpt-4-32k",
+    "gpt-4-32k-0314",
+    "gpt-4-32k-0613",
+    "gpt-4-turbo",
+    "gpt-4-turbo-2024-04-09",
+    "gpt-4-vision",
+    "gpt-4o",
+    "gpt-4o-2024-05-13",
+    "gpt-4o-2024-08-06",
+    "gpt-4o-2024-11-20",
+    "gpt-4o-mini",
+    "gpt-5",
+    "o1",
+    "o1-mini",
+    "o1-preview",
+    "o3-mini",
+    "o3",
+    "o4-mini",
+]
+
+
+BedrockModels: TypeAlias = Literal[
+    "ai21.jamba-1-5-large-v1:0",
+    "ai21.jamba-1-5-mini-v1:0",
+    "amazon.nova-lite-v1:0",
+    "amazon.nova-lite-v1:0:24k",
+    "amazon.nova-lite-v1:0:300k",
+    "amazon.nova-micro-v1:0",
+    "amazon.nova-micro-v1:0:128k",
+    "amazon.nova-micro-v1:0:24k",
+    "amazon.nova-premier-v1:0",
+    "amazon.nova-premier-v1:0:1000k",
+    "amazon.nova-premier-v1:0:20k",
+    "amazon.nova-premier-v1:0:8k",
+    "amazon.nova-premier-v1:0:mm",
+    "amazon.nova-pro-v1:0",
+    "amazon.nova-pro-v1:0:24k",
+    "amazon.nova-pro-v1:0:300k",
+    "amazon.titan-text-express-v1",
+    "amazon.titan-text-express-v1:0:8k",
+    "amazon.titan-text-lite-v1",
+    "amazon.titan-text-lite-v1:0:4k",
+    "amazon.titan-tg1-large",
+    "anthropic.claude-3-5-haiku-20241022-v1:0",
+    "anthropic.claude-3-5-sonnet-20240620-v1:0",
+    "anthropic.claude-3-5-sonnet-20241022-v2:0",
+    "anthropic.claude-3-7-sonnet-20250219-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0:200k",
+    "anthropic.claude-3-haiku-20240307-v1:0:48k",
+    "anthropic.claude-3-opus-20240229-v1:0",
+    "anthropic.claude-3-opus-20240229-v1:0:12k",
+    "anthropic.claude-3-opus-20240229-v1:0:200k",
+    "anthropic.claude-3-opus-20240229-v1:0:28k",
+    "anthropic.claude-3-sonnet-20240229-v1:0",
+    "anthropic.claude-3-sonnet-20240229-v1:0:200k",
+    "anthropic.claude-3-sonnet-20240229-v1:0:28k",
+    "anthropic.claude-haiku-4-5-20251001-v1:0",
+    "anthropic.claude-instant-v1:2:100k",
+    "anthropic.claude-opus-4-1-20250805-v1:0",
+    "anthropic.claude-opus-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-5-20250929-v1:0",
+    "anthropic.claude-v2:0:100k",
+    "anthropic.claude-v2:0:18k",
+    "anthropic.claude-v2:1:18k",
+    "anthropic.claude-v2:1:200k",
+    "cohere.command-r-plus-v1:0",
+    "cohere.command-r-v1:0",
+    "cohere.rerank-v3-5:0",
+    "deepseek.r1-v1:0",
+    "meta.llama3-1-70b-instruct-v1:0",
+    "meta.llama3-1-8b-instruct-v1:0",
+    "meta.llama3-2-11b-instruct-v1:0",
+    "meta.llama3-2-1b-instruct-v1:0",
+    "meta.llama3-2-3b-instruct-v1:0",
+    "meta.llama3-2-90b-instruct-v1:0",
+    "meta.llama3-3-70b-instruct-v1:0",
+    "meta.llama3-70b-instruct-v1:0",
+    "meta.llama3-8b-instruct-v1:0",
+    "meta.llama4-maverick-17b-instruct-v1:0",
+    "meta.llama4-scout-17b-instruct-v1:0",
+    "mistral.mistral-7b-instruct-v0:2",
+    "mistral.mistral-large-2402-v1:0",
+    "mistral.mistral-small-2402-v1:0",
+    "mistral.mixtral-8x7b-instruct-v0:1",
+    "mistral.pixtral-large-2502-v1:0",
+    "openai.gpt-oss-120b-1:0",
+    "openai.gpt-oss-20b-1:0",
+    "qwen.qwen3-32b-v1:0",
+    "qwen.qwen3-coder-30b-a3b-v1:0",
+    "twelvelabs.pegasus-1-2-v1:0",
+]
+BEDROCK_MODELS: list[BedrockModels] = [
+    "ai21.jamba-1-5-large-v1:0",
+    "ai21.jamba-1-5-mini-v1:0",
+    "amazon.nova-lite-v1:0",
+    "amazon.nova-lite-v1:0:24k",
+    "amazon.nova-lite-v1:0:300k",
+    "amazon.nova-micro-v1:0",
+    "amazon.nova-micro-v1:0:128k",
+    "amazon.nova-micro-v1:0:24k",
+    "amazon.nova-premier-v1:0",
+    "amazon.nova-premier-v1:0:1000k",
+    "amazon.nova-premier-v1:0:20k",
+    "amazon.nova-premier-v1:0:8k",
+    "amazon.nova-premier-v1:0:mm",
+    "amazon.nova-pro-v1:0",
+    "amazon.nova-pro-v1:0:24k",
+    "amazon.nova-pro-v1:0:300k",
+    "amazon.titan-text-express-v1",
+    "amazon.titan-text-express-v1:0:8k",
+    "amazon.titan-text-lite-v1",
+    "amazon.titan-text-lite-v1:0:4k",
+    "amazon.titan-tg1-large",
+    "anthropic.claude-3-5-haiku-20241022-v1:0",
+    "anthropic.claude-3-5-sonnet-20240620-v1:0",
+    "anthropic.claude-3-5-sonnet-20241022-v2:0",
+    "anthropic.claude-3-7-sonnet-20250219-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0",
+    "anthropic.claude-3-haiku-20240307-v1:0:200k",
+    "anthropic.claude-3-haiku-20240307-v1:0:48k",
+    "anthropic.claude-3-opus-20240229-v1:0",
+    "anthropic.claude-3-opus-20240229-v1:0:12k",
+    "anthropic.claude-3-opus-20240229-v1:0:200k",
+    "anthropic.claude-3-opus-20240229-v1:0:28k",
+    "anthropic.claude-3-sonnet-20240229-v1:0",
+    "anthropic.claude-3-sonnet-20240229-v1:0:200k",
+    "anthropic.claude-3-sonnet-20240229-v1:0:28k",
+    "anthropic.claude-haiku-4-5-20251001-v1:0",
+    "anthropic.claude-instant-v1:2:100k",
+    "anthropic.claude-opus-4-1-20250805-v1:0",
+    "anthropic.claude-opus-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-20250514-v1:0",
+    "anthropic.claude-sonnet-4-5-20250929-v1:0",
+    "anthropic.claude-v2:0:100k",
+    "anthropic.claude-v2:0:18k",
+    "anthropic.claude-v2:1:18k",
+    "anthropic.claude-v2:1:200k",
+    "cohere.command-r-plus-v1:0",
+    "cohere.command-r-v1:0",
+    "cohere.rerank-v3-5:0",
+    "deepseek.r1-v1:0",
+    "meta.llama3-1-70b-instruct-v1:0",
+    "meta.llama3-1-8b-instruct-v1:0",
+    "meta.llama3-2-11b-instruct-v1:0",
+    "meta.llama3-2-1b-instruct-v1:0",
+    "meta.llama3-2-3b-instruct-v1:0",
+    "meta.llama3-2-90b-instruct-v1:0",
+    "meta.llama3-3-70b-instruct-v1:0",
+    "meta.llama3-70b-instruct-v1:0",
+    "meta.llama3-8b-instruct-v1:0",
+    "meta.llama4-maverick-17b-instruct-v1:0",
+    "meta.llama4-scout-17b-instruct-v1:0",
+    "mistral.mistral-7b-instruct-v0:2",
+    "mistral.mistral-large-2402-v1:0",
+    "mistral.mistral-small-2402-v1:0",
+    "mistral.mixtral-8x7b-instruct-v0:1",
+    "mistral.pixtral-large-2502-v1:0",
+    "openai.gpt-oss-120b-1:0",
+    "openai.gpt-oss-20b-1:0",
+    "qwen.qwen3-32b-v1:0",
+    "qwen.qwen3-coder-30b-a3b-v1:0",
+    "twelvelabs.pegasus-1-2-v1:0",
+]

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

@@ -5,14 +5,11 @@ import logging
 import os
 from typing import TYPE_CHECKING, Any, cast
 
-from pydantic import BaseModel, Field, PrivateAttr, computed_field, model_validator
-from typing_extensions import Self
+from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO
 from crewai.llms.base_llm import BaseLLM
 from crewai.llms.hooks.transport import HTTPTransport
-from crewai.llms.providers.utils.common import safe_tool_conversion
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -21,8 +18,7 @@ from crewai.utilities.types import LLMMessage
 
 
 if TYPE_CHECKING:
-    from crewai.agent import Agent
-    from crewai.task import Task
+    from crewai.llms.hooks.base import BaseInterceptor
 
 try:
     from anthropic import Anthropic
@@ -35,19 +31,6 @@ except ImportError:
     ) from None
 
 
-ANTHROPIC_CONTEXT_WINDOWS: dict[str, int] = {
-    "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,
-}
-
-
 class AnthropicCompletion(BaseLLM):
     """Anthropic native completion implementation.
 
@@ -55,69 +38,110 @@ class AnthropicCompletion(BaseLLM):
     offering native tool use, streaming support, and proper message formatting.
     """
 
-    model: str = Field(
-        default="claude-3-5-sonnet-20241022",
-        description="Anthropic model name (e.g., 'claude-3-5-sonnet-20241022')",
-    )
-    max_tokens: int = Field(
-        default=4096,
-        description="Maximum number of allowed tokens in response.",
-    )
-    top_p: float | None = Field(
-        default=None,
-        description="Nucleus sampling parameter.",
-    )
-    _client: Anthropic = PrivateAttr(
-        default_factory=Anthropic,
-    )
+    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,
+        client_params: dict[str, Any] | None = None,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
+        **kwargs: Any,
+    ):
+        """Initialize Anthropic chat completion client.
 
-    @model_validator(mode="after")
-    def initialize_client(self) -> Self:
-        """Initialize the Anthropic client after Pydantic validation.
-
-        This runs after all field validation is complete, ensuring that:
-        - All BaseLLM fields are set (model, temperature, stop_sequences, etc.)
-        - Field validators have run (stop_sequences is normalized to set[str])
-        - API key and other configuration is ready
+        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
+            client_params: Additional parameters for the Anthropic client
+            interceptor: HTTP interceptor for modifying requests/responses at transport level.
+            **kwargs: Additional parameters
         """
+        super().__init__(
+            model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
+        )
+
+        # Client params
+        self.interceptor = interceptor
+        self.client_params = client_params
+        self.base_url = base_url
+        self.timeout = timeout
+        self.max_retries = max_retries
+
+        self.client = Anthropic(**self._get_client_params())
+
+        # 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
+
+    @property
+    def stop(self) -> list[str]:
+        """Get stop sequences sent to the API."""
+        return self.stop_sequences
+
+    @stop.setter
+    def stop(self, value: list[str] | str | None) -> None:
+        """Set stop sequences.
+
+        Synchronizes stop_sequences to ensure values set by CrewAgentExecutor
+        are properly sent to the Anthropic API.
+
+        Args:
+            value: Stop sequences as a list, single string, or None
+        """
+        if value is None:
+            self.stop_sequences = []
+        elif isinstance(value, str):
+            self.stop_sequences = [value]
+        elif isinstance(value, list):
+            self.stop_sequences = value
+        else:
+            self.stop_sequences = []
+
+    def _get_client_params(self) -> dict[str, Any]:
+        """Get client parameters."""
+
         if self.api_key is None:
             self.api_key = os.getenv("ANTHROPIC_API_KEY")
             if self.api_key is None:
                 raise ValueError("ANTHROPIC_API_KEY is required")
 
-        params = self.model_dump(
-            include={"api_key", "base_url", "timeout", "max_retries"},
-            exclude_none=True,
-        )
+        client_params = {
+            "api_key": self.api_key,
+            "base_url": self.base_url,
+            "timeout": self.timeout,
+            "max_retries": self.max_retries,
+        }
 
         if self.interceptor:
             transport = HTTPTransport(interceptor=self.interceptor)
             http_client = httpx.Client(transport=transport)
-            params["http_client"] = http_client
+            client_params["http_client"] = http_client  # type: ignore[assignment]
 
         if self.client_params:
-            params.update(self.client_params)
+            client_params.update(self.client_params)
 
-        self._client = Anthropic(**params)
-        return self
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def is_claude_3(self) -> bool:
-        """Check if the model is Claude 3 or higher."""
-        return "claude-3" in self.model.lower()
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def supports_tools(self) -> bool:
-        """Check if the model supports tool use."""
-        return self.is_claude_3
-
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def supports_function_calling(self) -> bool:
-        """Check if the model supports function calling."""
-        return self.supports_tools
+        return client_params
 
     def call(
         self,
@@ -125,8 +149,8 @@ class AnthropicCompletion(BaseLLM):
         tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Task | None = None,
-        from_agent: Agent | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call Anthropic messages API.
@@ -205,21 +229,25 @@ class AnthropicCompletion(BaseLLM):
         Returns:
             Parameters dictionary for Anthropic API
         """
-        params = self.model_dump(
-            include={
-                "model",
-                "max_tokens",
-                "stream",
-                "temperature",
-                "top_p",
-                "stop_sequences",
-            },
-        )
-        params["messages"] = messages
+        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)
@@ -238,6 +266,8 @@ class AnthropicCompletion(BaseLLM):
                 continue
 
             try:
+                from crewai.llms.providers.utils.common import safe_tool_conversion
+
                 name, description, parameters = safe_tool_conversion(tool, "Anthropic")
             except (ImportError, KeyError, ValueError) as e:
                 logging.error(f"Error converting tool to Anthropic format: {e}")
@@ -311,8 +341,8 @@ class AnthropicCompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Task | None = None,
-        from_agent: Agent | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming message completion."""
@@ -327,7 +357,7 @@ class AnthropicCompletion(BaseLLM):
             params["tool_choice"] = {"type": "tool", "name": "structured_output"}
 
         try:
-            response: Message = self._client.messages.create(**params)
+            response: Message = self.client.messages.create(**params)
 
         except Exception as e:
             if is_context_length_exceeded(e):
@@ -399,8 +429,8 @@ class AnthropicCompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Task | None = None,
-        from_agent: Agent | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming message completion."""
@@ -421,7 +451,7 @@ class AnthropicCompletion(BaseLLM):
         stream_params = {k: v for k, v in params.items() if k != "stream"}
 
         # Make streaming API call
-        with self._client.messages.stream(**stream_params) as stream:
+        with self.client.messages.stream(**stream_params) as stream:
             for event in stream:
                 if hasattr(event, "delta") and hasattr(event.delta, "text"):
                     text_delta = event.delta.text
@@ -495,8 +525,8 @@ class AnthropicCompletion(BaseLLM):
         tool_uses: list[ToolUseBlock],
         params: dict[str, Any],
         available_functions: dict[str, Any],
-        from_task: Task | None = None,
-        from_agent: Agent | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
     ) -> str:
         """Handle the complete tool use conversation flow.
 
@@ -549,7 +579,7 @@ class AnthropicCompletion(BaseLLM):
 
         try:
             # Send tool results back to Claude for final response
-            final_response: Message = self._client.messages.create(**follow_up_params)
+            final_response: Message = self.client.messages.create(**follow_up_params)
 
             # Track token usage for follow-up call
             follow_up_usage = self._extract_anthropic_token_usage(final_response)
@@ -596,24 +626,48 @@ class AnthropicCompletion(BaseLLM):
                 return tool_results[0]["content"]
             raise e
 
+    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 ANTHROPIC_CONTEXT_WINDOWS.items():
+        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)
 
-    @staticmethod
-    def _extract_anthropic_token_usage(response: Message) -> dict[str, Any]:
+    def _extract_anthropic_token_usage(self, response: Message) -> dict[str, Any]:
         """Extract token usage from Anthropic response."""
-        if response.usage:
+        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": usage.input_tokens,
-                "output_tokens": usage.output_tokens,
-                "total_tokens": usage.input_tokens + usage.output_tokens,
+                "input_tokens": input_tokens,
+                "output_tokens": output_tokens,
+                "total_tokens": input_tokens + output_tokens,
             }
         return {"total_tokens": 0}

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

@@ -1,14 +1,12 @@
-from __future__ import annotations
-
 import logging
-from typing import TYPE_CHECKING, Any, cast
+import os
+from typing import Any, cast
 
-from pydantic import BaseModel, Field, PrivateAttr, computed_field, model_validator
-from typing_extensions import Self
+from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
 from crewai.llms.base_llm import BaseLLM
+from crewai.llms.hooks.base import BaseInterceptor
 from crewai.utilities.agent_utils import is_context_length_exceeded
 from crewai.utilities.exceptions.context_window_exceeding_exception import (
     LLMContextLengthExceededError,
@@ -16,11 +14,6 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
 from crewai.utilities.types import LLMMessage
 
 
-if TYPE_CHECKING:
-    from crewai.agent import Agent
-    from crewai.task import Task
-
-
 try:
     from google import genai  # type: ignore[import-untyped]
     from google.genai import types  # type: ignore[import-untyped]
@@ -31,27 +24,6 @@ except ImportError:
     ) from None
 
 
-GEMINI_CONTEXT_WINDOWS: dict[str, int] = {
-    "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,
-}
-
-# Context window validation constraints
-MIN_CONTEXT_WINDOW: int = 1024
-MAX_CONTEXT_WINDOW: int = 2097152
-
-
 class GeminiCompletion(BaseLLM):
     """Google Gemini native completion implementation.
 
@@ -59,140 +31,78 @@ class GeminiCompletion(BaseLLM):
     offering native function calling, streaming support, and proper Gemini formatting.
     """
 
-    model: str = Field(
-        default="gemini-2.0-flash-001",
-        description="Gemini model name (e.g., 'gemini-2.0-flash-001', 'gemini-1.5-pro')",
-    )
-    project: str | None = Field(
-        default=None,
-        description="Google Cloud project ID (for Vertex AI)",
-    )
-    location: str = Field(
-        default="us-central1",
-        description="Google Cloud location (for Vertex AI)",
-    )
-    top_p: float | None = Field(
-        default=None,
-        description="Nucleus sampling parameter",
-    )
-    top_k: int | None = Field(
-        default=None,
-        description="Top-k sampling parameter",
-    )
-    max_output_tokens: int | None = Field(
-        default=None,
-        description="Maximum tokens in response",
-    )
-    safety_settings: dict[str, Any] | None = Field(
-        default=None,
-        description="Safety filter settings",
-    )
-    _client: genai.Client = PrivateAttr(  # type: ignore[no-any-unimported]
-        default_factory=genai.Client,
-    )
+    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,
+        client_params: dict[str, Any] | None = None,
+        interceptor: BaseInterceptor[Any, Any] | None = None,
+        **kwargs: Any,
+    ):
+        """Initialize Google Gemini chat completion client.
 
-    @model_validator(mode="after")
-    def initialize_client(self) -> Self:
-        """Initialize the Anthropic client after Pydantic validation.
-
-        This runs after all field validation is complete, ensuring that:
-        - All BaseLLM fields are set (model, temperature, stop_sequences, etc.)
-        - Field validators have run (stop_sequences is normalized to set[str])
-        - API key and other configuration is ready
+        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
+            client_params: Additional parameters to pass to the Google Gen AI Client constructor.
+                          Supports parameters like http_options, credentials, debug_config, etc.
+            interceptor: HTTP interceptor (not yet supported for Gemini).
+            **kwargs: Additional parameters
         """
-        self._client = genai.Client(**self._get_client_params())
-        return self
+        if interceptor is not None:
+            raise NotImplementedError(
+                "HTTP interceptors are not yet supported for Google Gemini provider. "
+                "Interceptors are currently supported for OpenAI and Anthropic providers only."
+            )
 
-    # 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,
-    # client_params: dict[str, Any] | None = None,
-    # interceptor: BaseInterceptor[Any, Any] | None = None,
-    # **kwargs: Any,
-    # # ):
-    #     """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
-    #         client_params: Additional parameters to pass to the Google Gen AI Client constructor.
-    #                       Supports parameters like http_options, credentials, debug_config, etc.
-    #         interceptor: HTTP interceptor (not yet supported for Gemini).
-    #         **kwargs: Additional parameters
-    #     """
-    #     if interceptor is not None:
-    #         raise NotImplementedError(
-    #             "HTTP interceptors are not yet supported for Google Gemini provider. "
-    #             "Interceptors are currently supported for OpenAI and Anthropic providers only."
-    #         )
-    #
-    #     super().__init__(
-    #         model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
-    #     )
-    #
-    #     # Store client params for later use
-    #     self.client_params = client_params or {}
-    #
-    #     # Get API configuration with environment variable fallbacks
-    #     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"
-    #
-    #     use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
-    #
-    #     self.client = self._initialize_client(use_vertexai)
-    #
-    #     # 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
+        super().__init__(
+            model=model, temperature=temperature, stop=stop_sequences or [], **kwargs
+        )
 
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def is_gemini_2(self) -> bool:
-        """Check if the model is Gemini 2.x."""
-        return "gemini-2" in self.model.lower()
+        # Store client params for later use
+        self.client_params = client_params or {}
 
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def is_gemini_1_5(self) -> bool:
-        """Check if the model is Gemini 1.5.x."""
-        return "gemini-1.5" in self.model.lower()
+        # Get API configuration with environment variable fallbacks
+        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"
 
-    @computed_field  # type: ignore[prop-decorator]
-    @property
-    def supports_tools(self) -> bool:
-        """Check if the model supports tool/function calling."""
-        return self.is_gemini_1_5 or self.is_gemini_2
+        use_vertexai = os.getenv("GOOGLE_GENAI_USE_VERTEXAI", "").lower() == "true"
+
+        self.client = self._initialize_client(use_vertexai)
+
+        # 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
 
     @property
     def stop(self) -> list[str]:
@@ -232,12 +142,6 @@ class GeminiCompletion(BaseLLM):
         if self.client_params:
             client_params.update(self.client_params)
 
-        if self.interceptor:
-            raise NotImplementedError(
-                "HTTP interceptors are not yet supported for Google Gemini provider. "
-                "Interceptors are currently supported for OpenAI and Anthropic providers only."
-            )
-
         if use_vertexai or self.project:
             client_params.update(
                 {
@@ -277,7 +181,7 @@ class GeminiCompletion(BaseLLM):
 
         if (
             hasattr(self, "client")
-            and hasattr(self._client, "vertexai")
+            and hasattr(self.client, "vertexai")
             and self.client.vertexai
         ):
             # Vertex AI configuration
@@ -302,8 +206,8 @@ class GeminiCompletion(BaseLLM):
         tools: list[dict[str, Any]] | None = None,
         callbacks: list[Any] | None = None,
         available_functions: dict[str, Any] | None = None,
-        from_task: Task | None = None,
-        from_agent: Agent | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Call Google Gemini generate content API.
@@ -390,16 +294,7 @@ class GeminiCompletion(BaseLLM):
             GenerateContentConfig object for Gemini API
         """
         self.tools = tools
-        config_params = self.model_dump(
-            include={
-                "temperature",
-                "top_p",
-                "top_k",
-                "max_output_tokens",
-                "stop_sequences",
-                "safety_settings",
-            }
-        )
+        config_params = {}
 
         # Add system instruction if present
         if system_instruction:
@@ -409,6 +304,18 @@ class GeminiCompletion(BaseLLM):
             )
             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
+
         if response_model:
             config_params["response_mime_type"] = "application/json"
             config_params["response_schema"] = response_model.model_json_schema()
@@ -417,6 +324,9 @@ class GeminiCompletion(BaseLLM):
         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(  # type: ignore[no-any-unimported]
@@ -494,8 +404,8 @@ class GeminiCompletion(BaseLLM):
         system_instruction: str | None,
         config: types.GenerateContentConfig,
         available_functions: dict[str, Any] | None = None,
-        from_task: Task | None = None,
-        from_agent: Agent | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming content generation."""
@@ -506,7 +416,7 @@ class GeminiCompletion(BaseLLM):
         }
 
         try:
-            response = self._client.models.generate_content(**api_params)
+            response = self.client.models.generate_content(**api_params)
 
             usage = self._extract_token_usage(response)
         except Exception as e:
@@ -560,8 +470,8 @@ class GeminiCompletion(BaseLLM):
         contents: list[types.Content],
         config: types.GenerateContentConfig,
         available_functions: dict[str, Any] | None = None,
-        from_task: Task | None = None,
-        from_agent: Agent | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming content generation."""
@@ -574,7 +484,7 @@ class GeminiCompletion(BaseLLM):
             "config": config,
         }
 
-        for chunk in self._client.models.generate_content_stream(**api_params):
+        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(
@@ -627,30 +537,52 @@ class GeminiCompletion(BaseLLM):
 
         return full_response
 
-    @computed_field  # type: ignore[prop-decorator]
-    @property
     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
+
     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_WINDOW or value > MAX_CONTEXT_WINDOW:
+            if value < min_context or value > max_context:
                 raise ValueError(
-                    f"Context window for {key} must be between {MIN_CONTEXT_WINDOW} and {MAX_CONTEXT_WINDOW}"
+                    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 GEMINI_CONTEXT_WINDOWS.items():
+        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
 
-    @staticmethod
-    def _extract_token_usage(response: dict[str, Any]) -> dict[str, Any]:
+    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
@@ -662,8 +594,8 @@ class GeminiCompletion(BaseLLM):
             }
         return {"total_tokens": 0}
 
-    @staticmethod
     def _convert_contents_to_dict(  # type: ignore[no-any-unimported]
+        self,
         contents: list[types.Content],
     ) -> list[dict[str, str]]:
         """Convert contents to dict format."""

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

@@ -4,23 +4,16 @@ from collections.abc import Iterator
 import json
 import logging
 import os
-from typing import TYPE_CHECKING, Any, Final
+from typing import TYPE_CHECKING, Any
 
 import httpx
 from openai import APIConnectionError, NotFoundError, 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,
-    Field,
-    PrivateAttr,
-    model_validator,
-)
-from typing_extensions import Self
+from pydantic import BaseModel
 
 from crewai.events.types.llm_events import LLMCallType
-from crewai.llm import CONTEXT_WINDOW_USAGE_RATIO, LLM_CONTEXT_WINDOW_SIZES
 from crewai.llms.base_llm import BaseLLM
 from crewai.llms.hooks.transport import HTTPTransport
 from crewai.utilities.agent_utils import is_context_length_exceeded
@@ -32,28 +25,11 @@ from crewai.utilities.types import LLMMessage
 
 if TYPE_CHECKING:
     from crewai.agent.core import Agent
+    from crewai.llms.hooks.base import BaseInterceptor
     from crewai.task import Task
     from crewai.tools.base_tool import BaseTool
 
 
-OPENAI_CONTEXT_WINDOWS: dict[str, int] = {
-    "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,
-}
-
-MIN_CONTEXT_WINDOW: Final[int] = 1024
-MAX_CONTEXT_WINDOW: Final[int] = 2097152
-
-
 class OpenAICompletion(BaseLLM):
     """OpenAI native completion implementation.
 
@@ -61,125 +37,112 @@ class OpenAICompletion(BaseLLM):
     offering native structured outputs, function calling, and streaming support.
     """
 
-    model: str = Field(
-        default="gpt-4o",
-        description="OpenAI model name (e.g., 'gpt-4o')",
-    )
-    organization: str | None = Field(
-        default=None,
-        description="Name of the OpenAI organization",
-    )
-    project: str | None = Field(
-        default=None,
-        description="Name of the OpenAI project",
-    )
-    api_base: str | None = Field(
-        default=os.getenv("OPENAI_BASE_URL"),
-        description="Base URL for OpenAI API",
-    )
-    default_headers: dict[str, str] | None = Field(
-        default=None,
-        description="Default headers for OpenAI API requests",
-    )
-    default_query: dict[str, Any] | None = Field(
-        default=None,
-        description="Default query parameters for OpenAI API requests",
-    )
-    top_p: float | None = Field(
-        default=None,
-        description="Top-p sampling parameter",
-    )
-    frequency_penalty: float | None = Field(
-        default=None,
-        description="Frequency penalty parameter",
-    )
-    presence_penalty: float | None = Field(
-        default=None,
-        description="Presence penalty parameter",
-    )
-    max_completion_tokens: int | None = Field(
-        default=None,
-        description="Maximum tokens for completion",
-    )
-    seed: int | None = Field(
-        default=None,
-        description="Random seed for reproducibility",
-    )
-    response_format: dict[str, Any] | type[BaseModel] | None = Field(
-        default=None,
-        description="Response format for structured output",
-    )
-    logprobs: bool | None = Field(
-        default=None,
-        description="Whether to include log probabilities",
-    )
-    top_logprobs: int | None = Field(
-        default=None,
-        description="Number of top log probabilities to return",
-    )
-    reasoning_effort: str | None = Field(
-        default=None,
-        description="Reasoning effort level for o1 models",
-    )
-    supports_function_calling: bool = Field(
-        default=True,
-        description="Whether the model supports function calling",
-    )
-    is_o1_model: bool = Field(
-        default=False,
-        description="Whether the model is an o1 model",
-    )
-    is_gpt4_model: bool = Field(
-        default=False,
-        description="Whether the model is a GPT-4 model",
-    )
-    _client: OpenAI = PrivateAttr(
-        default_factory=OpenAI,
-    )
+    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,
+        default_headers: dict[str, str] | None = None,
+        default_query: dict[str, Any] | None = None,
+        client_params: dict[str, Any] | None = None,
+        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,
+        provider: str | None = None,
+        interceptor: BaseInterceptor[httpx.Request, httpx.Response] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize OpenAI chat completion client."""
 
-    @model_validator(mode="after")
-    def initialize_client(self) -> Self:
-        """Initialize the Anthropic client after Pydantic validation.
+        if provider is None:
+            provider = kwargs.pop("provider", "openai")
+
+        self.interceptor = interceptor
+        # Client configuration attributes
+        self.organization = organization
+        self.project = project
+        self.max_retries = max_retries
+        self.default_headers = default_headers
+        self.default_query = default_query
+        self.client_params = client_params
+        self.timeout = timeout
+        self.base_url = base_url
+        self.api_base = kwargs.pop("api_base", None)
+
+        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,
+        )
+
+        client_config = self._get_client_params()
+        if self.interceptor:
+            transport = HTTPTransport(interceptor=self.interceptor)
+            http_client = httpx.Client(transport=transport)
+            client_config["http_client"] = http_client
+
+        self.client = OpenAI(**client_config)
+
+        # Completion parameters
+        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.is_o1_model = "o1" in model.lower()
+        self.is_gpt4_model = "gpt-4" in model.lower()
+
+    def _get_client_params(self) -> dict[str, Any]:
+        """Get OpenAI client parameters."""
 
-        This runs after all field validation is complete, ensuring that:
-        - All BaseLLM fields are set (model, temperature, stop_sequences, etc.)
-        - Field validators have run (stop_sequences is normalized to set[str])
-        - API key and other configuration is ready
-        """
         if self.api_key is None:
             self.api_key = os.getenv("OPENAI_API_KEY")
             if self.api_key is None:
                 raise ValueError("OPENAI_API_KEY is required")
 
-        self.is_o1_model = "o1" in self.model.lower()
-        self.supports_function_calling = not self.is_o1_model
-        self.is_gpt4_model = "gpt-4" in self.model.lower()
-        self.supports_stop_words = not self.is_o1_model
+        base_params = {
+            "api_key": self.api_key,
+            "organization": self.organization,
+            "project": self.project,
+            "base_url": self.base_url
+            or self.api_base
+            or os.getenv("OPENAI_BASE_URL")
+            or None,
+            "timeout": self.timeout,
+            "max_retries": self.max_retries,
+            "default_headers": self.default_headers,
+            "default_query": self.default_query,
+        }
 
-        params = self.model_dump(
-            include={
-                "api_key",
-                "organization",
-                "project",
-                "base_url",
-                "timeout",
-                "max_retries",
-                "default_headers",
-                "default_query",
-            },
-            exclude_none=True,
-        )
-
-        if self.interceptor:
-            transport = HTTPTransport(interceptor=self.interceptor)
-            http_client = httpx.Client(transport=transport)
-            params["http_client"] = http_client
+        client_params = {k: v for k, v in base_params.items() if v is not None}
 
         if self.client_params:
-            params.update(self.client_params)
+            client_params.update(self.client_params)
 
-        self._client = OpenAI(**params)
-        return self
+        return client_params
 
     def call(
         self,
@@ -250,26 +213,38 @@ class OpenAICompletion(BaseLLM):
         self, messages: list[LLMMessage], tools: list[dict[str, BaseTool]] | None = None
     ) -> dict[str, Any]:
         """Prepare parameters for OpenAI chat completion."""
-        params = self.model_dump(
-            include={
-                "model",
-                "stream",
-                "temperature",
-                "top_p",
-                "frequency_penalty",
-                "presence_penalty",
-                "max_completion_tokens",
-                "max_tokens",
-                "seed",
-                "logprobs",
-                "top_logprobs",
-                "reasoning_effort",
-            },
-            exclude_none=True,
-        )
-        params["messages"] = messages
+        params: dict[str, Any] = {
+            "model": self.model,
+            "messages": messages,
+        }
+        if self.stream:
+            params["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
+
         if tools:
             params["tools"] = self._convert_tools_for_interference(tools)
             params["tool_choice"] = "auto"
@@ -321,14 +296,14 @@ class OpenAICompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Task | None = None,
-        from_agent: Agent | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str | Any:
         """Handle non-streaming chat completion."""
         try:
             if response_model:
-                parsed_response = self._client.beta.chat.completions.parse(
+                parsed_response = self.client.beta.chat.completions.parse(
                     **params,
                     response_format=response_model,
                 )
@@ -352,7 +327,7 @@ class OpenAICompletion(BaseLLM):
                     )
                     return structured_json
 
-            response: ChatCompletion = self._client.chat.completions.create(**params)
+            response: ChatCompletion = self.client.chat.completions.create(**params)
 
             usage = self._extract_openai_token_usage(response)
 
@@ -444,8 +419,8 @@ class OpenAICompletion(BaseLLM):
         self,
         params: dict[str, Any],
         available_functions: dict[str, Any] | None = None,
-        from_task: Task | None = None,
-        from_agent: Agent | None = None,
+        from_task: Any | None = None,
+        from_agent: Any | None = None,
         response_model: type[BaseModel] | None = None,
     ) -> str:
         """Handle streaming chat completion."""
@@ -454,7 +429,7 @@ class OpenAICompletion(BaseLLM):
 
         if response_model:
             completion_stream: Iterator[ChatCompletionChunk] = (
-                self._client.chat.completions.create(**params)
+                self.client.chat.completions.create(**params)
             )
 
             accumulated_content = ""
@@ -497,7 +472,7 @@ class OpenAICompletion(BaseLLM):
                 )
                 return accumulated_content
 
-        stream: Iterator[ChatCompletionChunk] = self._client.chat.completions.create(
+        stream: Iterator[ChatCompletionChunk] = self.client.chat.completions.create(
             **params
         )
 
@@ -575,31 +550,58 @@ class OpenAICompletion(BaseLLM):
 
         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_WINDOW or value > MAX_CONTEXT_WINDOW:
+            if value < min_context or value > max_context:
                 raise ValueError(
-                    f"Context window for {key} must be between {MIN_CONTEXT_WINDOW} and {MAX_CONTEXT_WINDOW}"
+                    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 OPENAI_CONTEXT_WINDOWS.items():
+        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)
 
-    @staticmethod
-    def _extract_openai_token_usage(response: ChatCompletion) -> dict[str, Any]:
+    def _extract_openai_token_usage(self, response: ChatCompletion) -> dict[str, Any]:
         """Extract token usage from OpenAI ChatCompletion response."""
-        if response.usage:
+        if hasattr(response, "usage") and response.usage:
             usage = response.usage
             return {
-                "prompt_tokens": usage.prompt_tokens,
-                "completion_tokens": usage.completion_tokens,
-                "total_tokens": usage.total_tokens,
+                "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/task.py

@@ -539,6 +539,7 @@ class Task(BaseModel):
                 json_dict=json_output,
                 agent=agent.role,
                 output_format=self._get_output_format(),
+                messages=agent.last_messages,
             )
 
             if self._guardrails:
@@ -949,6 +950,7 @@ Follow these guidelines:
                 json_dict=json_output,
                 agent=agent.role,
                 output_format=self._get_output_format(),
+                messages=agent.last_messages,
             )
 
         return task_output

lib/crewai/src/crewai/tasks/task_output.py

@@ -6,6 +6,7 @@ from typing import Any
 from pydantic import BaseModel, Field, model_validator
 
 from crewai.tasks.output_format import OutputFormat
+from crewai.utilities.types import LLMMessage
 
 
 class TaskOutput(BaseModel):
@@ -40,6 +41,7 @@ class TaskOutput(BaseModel):
     output_format: OutputFormat = Field(
         description="Output format of the task", default=OutputFormat.RAW
     )
+    messages: list[LLMMessage] = Field(description="Messages of the task", default=[])
 
     @model_validator(mode="after")
     def set_summary(self):

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

@@ -127,7 +127,7 @@ def handle_max_iterations_exceeded(
     messages: list[LLMMessage],
     llm: LLM | BaseLLM,
     callbacks: list[TokenCalcHandler],
-) -> AgentAction | AgentFinish:
+) -> AgentFinish:
     """Handles the case when the maximum number of iterations is exceeded. Performs one more LLM call to get the final answer.
 
     Args:
@@ -139,7 +139,7 @@ def handle_max_iterations_exceeded(
         callbacks: List of callbacks for the LLM call.
 
     Returns:
-        The final formatted answer after exceeding max iterations.
+        AgentFinish with the final answer after exceeding max iterations.
     """
     printer.print(
         content="Maximum iterations reached. Requesting final answer.",
@@ -157,7 +157,7 @@ def handle_max_iterations_exceeded(
 
     # Perform one more LLM call to get the final answer
     answer = llm.call(
-        messages,  # type: ignore[arg-type]
+        messages,
         callbacks=callbacks,
     )
 
@@ -168,8 +168,16 @@ def handle_max_iterations_exceeded(
         )
         raise ValueError("Invalid response from LLM call - None or empty.")
 
-    # Return the formatted answer, regardless of its type
-    return format_answer(answer=answer)
+    formatted = format_answer(answer=answer)
+
+    # If format_answer returned an AgentAction, convert it to AgentFinish
+    if isinstance(formatted, AgentFinish):
+        return formatted
+    return AgentFinish(
+        thought=formatted.thought,
+        output=formatted.text,
+        text=formatted.text,
+    )
 
 
 def format_message_for_llm(
@@ -249,10 +257,10 @@ def get_llm_response(
     """
     try:
         answer = llm.call(
-            messages,  # type: ignore[arg-type]
+            messages,
             callbacks=callbacks,
             from_task=from_task,
-            from_agent=from_agent,
+            from_agent=from_agent,  # type: ignore[arg-type]
             response_model=response_model,
         )
     except Exception as e:
@@ -294,8 +302,8 @@ def handle_agent_action_core(
     formatted_answer: AgentAction,
     tool_result: ToolResult,
     messages: list[LLMMessage] | None = None,
-    step_callback: Callable | None = None,
-    show_logs: Callable | None = None,
+    step_callback: Callable | None = None,  # type: ignore[type-arg]
+    show_logs: Callable | None = None,  # type: ignore[type-arg]
 ) -> AgentAction | AgentFinish:
     """Core logic for handling agent actions and tool results.
 
@@ -481,7 +489,7 @@ def summarize_messages(
             ),
         ]
         summary = llm.call(
-            messages,  # type: ignore[arg-type]
+            messages,
             callbacks=callbacks,
         )
         summarized_contents.append({"content": str(summary)})

lib/crewai/src/crewai/utilities/types.py

@@ -1,6 +1,8 @@
 """Types for CrewAI utilities."""
 
-from typing import Any, Literal, TypedDict
+from typing import Any, Literal
+
+from typing_extensions import TypedDict
 
 
 class LLMMessage(TypedDict):

lib/crewai/tests/agents/test_agent.py

@@ -508,7 +508,47 @@ def test_agent_custom_max_iterations():
     assert isinstance(result, str)
     assert len(result) > 0
     assert call_count > 0
-    assert call_count == 3
+    # With max_iter=1, expect 2 calls:
+    # - Call 1: iteration 0
+    # - Call 2: iteration 1 (max reached, handle_max_iterations_exceeded called, then loop breaks)
+    assert call_count == 2
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+@pytest.mark.timeout(30)
+def test_agent_max_iterations_stops_loop():
+    """Test that agent execution terminates when max_iter is reached."""
+
+    @tool
+    def get_data(step: str) -> str:
+        """Get data for a step. Always returns data requiring more steps."""
+        return f"Data for {step}: incomplete, need to query more steps."
+
+    agent = Agent(
+        role="data collector",
+        goal="collect data using the get_data tool",
+        backstory="You must use the get_data tool extensively",
+        max_iter=2,
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Use get_data tool for step1, step2, step3, step4, step5, step6, step7, step8, step9, and step10. Do NOT stop until you've called it for ALL steps.",
+        expected_output="A summary of all data collected",
+    )
+
+    result = agent.execute_task(
+        task=task,
+        tools=[get_data],
+    )
+
+    assert result is not None
+    assert isinstance(result, str)
+
+    assert agent.agent_executor.iterations <= agent.max_iter + 2, (
+        f"Agent ran {agent.agent_executor.iterations} iterations "
+        f"but should stop around {agent.max_iter + 1}. "
+    )
 
 
 @pytest.mark.vcr(filter_headers=["authorization"])

lib/crewai/tests/agents/test_lite_agent.py

@@ -238,6 +238,27 @@ def test_lite_agent_returns_usage_metrics():
     assert result.usage_metrics["total_tokens"] > 0
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_lite_agent_output_includes_messages():
+    """Test that LiteAgentOutput includes messages from agent execution."""
+    llm = LLM(model="gpt-4o-mini")
+    agent = Agent(
+        role="Research Assistant",
+        goal="Find information about the population of Tokyo",
+        backstory="You are a helpful research assistant who can search for information about the population of Tokyo.",
+        llm=llm,
+        tools=[WebSearchTool()],
+        verbose=True,
+    )
+
+    result = agent.kickoff("What is the population of Tokyo?")
+
+    assert isinstance(result, LiteAgentOutput)
+    assert hasattr(result, "messages")
+    assert isinstance(result.messages, list)
+    assert len(result.messages) > 0
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.mark.asyncio
 async def test_lite_agent_returns_usage_metrics_async():

lib/crewai/tests/cassettes/test_agent_max_iterations_stops_loop.yaml

@@ -0,0 +1,495 @@
+interactions:
+- request:
+    body: '{"trace_id": "REDACTED_TRACE_ID", "execution_type":
+      "crew", "user_identifier": null, "execution_context": {"crew_fingerprint": null,
+      "crew_name": "Unknown Crew", "flow_name": null, "crewai_version": "1.4.0", "privacy_level":
+      "standard"}, "execution_metadata": {"expected_duration_estimate": 300, "agent_count":
+      0, "task_count": 0, "flow_method_count": 0, "execution_started_at": "2025-11-07T18:27:07.650947+00:00"}}'
+    headers:
+      Accept:
+      - '*/*'
+      Accept-Encoding:
+      - gzip, deflate, zstd
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '434'
+      Content-Type:
+      - application/json
+      User-Agent:
+      - CrewAI-CLI/1.4.0
+      X-Crewai-Version:
+      - 1.4.0
+    method: POST
+    uri: https://app.crewai.com/crewai_plus/api/v1/tracing/batches
+  response:
+    body:
+      string: '{"error":"bad_credentials","message":"Bad credentials"}'
+    headers:
+      Connection:
+      - keep-alive
+      Content-Length:
+      - '55'
+      Content-Type:
+      - application/json; charset=utf-8
+      Date:
+      - Fri, 07 Nov 2025 18:27:07 GMT
+      cache-control:
+      - no-store
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lib/crewai/tests/cassettes/test_task_output_includes_messages.yaml

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lib/crewai/tests/llms/anthropic/test_anthropic.py

@@ -36,7 +36,7 @@ def test_anthropic_completion_is_used_when_claude_provider():
 
     from crewai.llms.providers.anthropic.completion import AnthropicCompletion
     assert isinstance(llm, AnthropicCompletion)
-    assert llm.provider == "claude"
+    assert llm.provider == "anthropic"
     assert llm.model == "claude-3-5-sonnet-20241022"
 
 

lib/crewai/tests/llms/azure/test_azure.py

@@ -39,7 +39,7 @@ def test_azure_completion_is_used_when_azure_openai_provider():
 
     from crewai.llms.providers.azure.completion import AzureCompletion
     assert isinstance(llm, AzureCompletion)
-    assert llm.provider == "azure_openai"
+    assert llm.provider == "azure"
     assert llm.model == "gpt-4"
 
 

lib/crewai/tests/llms/google/test_google.py

@@ -24,7 +24,7 @@ def test_gemini_completion_is_used_when_google_provider():
     llm = LLM(model="google/gemini-2.0-flash-001")
 
     assert llm.__class__.__name__ == "GeminiCompletion"
-    assert llm.provider == "google"
+    assert llm.provider == "gemini"
     assert llm.model == "gemini-2.0-flash-001"
 
 

lib/crewai/tests/llms/hooks/test_unsupported_providers.py

@@ -154,7 +154,7 @@ class TestGeminiProviderInterceptor:
         # Gemini provider should raise NotImplementedError
         with pytest.raises(NotImplementedError) as exc_info:
             LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                 interceptor=interceptor,
                 api_key="test-gemini-key",
             )
@@ -169,7 +169,7 @@ class TestGeminiProviderInterceptor:
 
         with pytest.raises(NotImplementedError) as exc_info:
             LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                 interceptor=interceptor,
                 api_key="test-gemini-key",
             )
@@ -181,7 +181,7 @@ class TestGeminiProviderInterceptor:
     def test_gemini_without_interceptor_works(self) -> None:
         """Test that Gemini LLM works without interceptor."""
         llm = LLM(
-            model="gemini/gemini-pro",
+            model="gemini/gemini-2.5-pro",
             api_key="test-gemini-key",
         )
 
@@ -231,7 +231,7 @@ class TestUnsupportedProviderMessages:
 
         with pytest.raises(NotImplementedError) as exc_info:
             LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                 interceptor=interceptor,
                 api_key="test-gemini-key",
             )
@@ -282,7 +282,7 @@ class TestProviderSupportMatrix:
         # Gemini - NOT SUPPORTED
         with pytest.raises(NotImplementedError):
             LLM(
-                model="gemini/gemini-pro",
+                model="gemini/gemini-2.5-pro",
                 interceptor=interceptor,
                 api_key="test",
             )
@@ -315,5 +315,5 @@ class TestProviderSupportMatrix:
         assert not hasattr(bedrock_llm, 'interceptor') or bedrock_llm.interceptor is None
 
         # Gemini - doesn't have interceptor attribute
-        gemini_llm = LLM(model="gemini/gemini-pro", api_key="test")
-        assert not hasattr(gemini_llm, 'interceptor') or gemini_llm.interceptor is None
+        gemini_llm = LLM(model="gemini/gemini-2.5-pro", api_key="test")
+        assert not hasattr(gemini_llm, 'interceptor') or gemini_llm.interceptor is None

lib/crewai/tests/llms/openai/test_openai.py

@@ -16,7 +16,7 @@ def test_openai_completion_is_used_when_openai_provider():
     """
     Test that OpenAICompletion from completion.py is used when LLM uses provider 'openai'
     """
-    llm = LLM(model="openai/gpt-4o")
+    llm = LLM(model="gpt-4o")
 
     assert llm.__class__.__name__ == "OpenAICompletion"
     assert llm.provider == "openai"
@@ -70,7 +70,7 @@ def test_openai_completion_module_is_imported():
         del sys.modules[module_name]
 
     # Create LLM instance - this should trigger the import
-    LLM(model="openai/gpt-4o")
+    LLM(model="gpt-4o")
 
     # Verify the module was imported
     assert module_name in sys.modules
@@ -97,7 +97,7 @@ def test_native_openai_raises_error_when_initialization_fails():
 
         # This should raise ImportError, not fall back to LiteLLM
         with pytest.raises(ImportError) as excinfo:
-            LLM(model="openai/gpt-4o")
+            LLM(model="gpt-4o")
 
         assert "Error importing native provider" in str(excinfo.value)
         assert "Native SDK failed" in str(excinfo.value)
@@ -108,7 +108,7 @@ def test_openai_completion_initialization_parameters():
     Test that OpenAICompletion is initialized with correct parameters
     """
     llm = LLM(
-        model="openai/gpt-4o",
+        model="gpt-4o",
         temperature=0.7,
         max_tokens=1000,
         api_key="test-key"
@@ -311,7 +311,7 @@ def test_openai_completion_call_returns_usage_metrics():
         role="Research Assistant",
         goal="Find information about the population of Tokyo",
         backstory="You are a helpful research assistant.",
-        llm=LLM(model="openai/gpt-4o"),
+        llm=LLM(model="gpt-4o"),
         verbose=True,
     )
 
@@ -331,6 +331,7 @@ def test_openai_completion_call_returns_usage_metrics():
     assert result.token_usage.cached_prompt_tokens == 0
 
 
+@pytest.mark.skip(reason="Allow for litellm")
 def test_openai_raises_error_when_model_not_supported():
     """Test that OpenAICompletion raises ValueError when model not supported"""
 
@@ -354,7 +355,7 @@ def test_openai_client_setup_with_extra_arguments():
     Test that OpenAICompletion is initialized with correct parameters
     """
     llm = LLM(
-        model="openai/gpt-4o",
+        model="gpt-4o",
         temperature=0.7,
         max_tokens=1000,
         top_p=0.5,
@@ -391,7 +392,7 @@ def test_extra_arguments_are_passed_to_openai_completion():
     """
     Test that extra arguments are passed to OpenAICompletion
     """
-    llm = LLM(model="openai/gpt-4o", temperature=0.7, max_tokens=1000, top_p=0.5, max_retries=3)
+    llm = LLM(model="gpt-4o", temperature=0.7, max_tokens=1000, top_p=0.5, max_retries=3)
 
     with patch.object(llm.client.chat.completions, 'create') as mock_create:
         mock_create.return_value = MagicMock(

lib/crewai/tests/test_crew.py

@@ -340,7 +340,7 @@ def test_sync_task_execution(researcher, writer):
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -412,7 +412,7 @@ def test_manager_agent_delegating_to_assigned_task_agent(researcher, writer):
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -513,7 +513,7 @@ def test_manager_agent_delegates_with_varied_role_cases():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
     task.output = mock_task_output
 
@@ -611,7 +611,7 @@ def test_crew_with_delegating_agents_should_not_override_task_tools(ceo, writer)
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -669,7 +669,7 @@ def test_crew_with_delegating_agents_should_not_override_agent_tools(ceo, writer
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -788,7 +788,7 @@ def test_task_tools_override_agent_tools_with_allow_delegation(researcher, write
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     # We mock execute_sync to verify which tools get used at runtime
@@ -1225,7 +1225,7 @@ async def test_async_task_execution_call_count(researcher, writer):
 
     # Create a valid TaskOutput instance to mock the return value
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     # Create a MagicMock Future instance
@@ -1784,7 +1784,7 @@ def test_hierarchical_kickoff_usage_metrics_include_manager(researcher):
         Task,
         "execute_sync",
         return_value=TaskOutput(
-            description="dummy", raw="Hello", agent=researcher.role
+            description="dummy", raw="Hello", agent=researcher.role, messages=[]
         ),
     ):
         crew.kickoff()
@@ -1828,7 +1828,7 @@ def test_hierarchical_crew_creation_tasks_with_agents(researcher, writer):
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -1881,7 +1881,7 @@ def test_hierarchical_crew_creation_tasks_with_async_execution(researcher, write
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     # Create a mock Future that returns our TaskOutput
@@ -2246,11 +2246,13 @@ def test_conditional_task_uses_last_output(researcher, writer):
         description="First task output",
         raw="First success output",  # Will be used by third task's condition
         agent=researcher.role,
+        messages=[],
     )
     mock_third = TaskOutput(
         description="Third task output",
         raw="Third task executed",  # Output when condition succeeds using first task output
         agent=writer.role,
+        messages=[],
     )
 
     # Set up mocks for task execution and conditional logic
@@ -2318,11 +2320,13 @@ def test_conditional_tasks_result_collection(researcher, writer):
         description="Success output",
         raw="Success output",  # Triggers third task's condition
         agent=researcher.role,
+        messages=[],
     )
     mock_conditional = TaskOutput(
         description="Conditional output",
         raw="Conditional task executed",
         agent=writer.role,
+        messages=[],
     )
 
     # Set up mocks for task execution and conditional logic
@@ -2399,6 +2403,7 @@ def test_multiple_conditional_tasks(researcher, writer):
         description="Mock success",
         raw="Success and proceed output",
         agent=researcher.role,
+        messages=[],
     )
 
     # Set up mocks for task execution
@@ -2806,7 +2811,7 @@ def test_manager_agent(researcher, writer):
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     # Because we are mocking execute_sync, we never hit the underlying _execute_core
@@ -3001,6 +3006,7 @@ def test_replay_feature(researcher, writer):
             output_format=OutputFormat.RAW,
             pydantic=None,
             summary="Mocked output for list of ideas",
+            messages=[],
         )
 
         crew.kickoff()
@@ -3052,6 +3058,7 @@ def test_crew_task_db_init():
             output_format=OutputFormat.RAW,
             pydantic=None,
             summary="Write about AI in healthcare...",
+            messages=[],
         )
 
         crew.kickoff()
@@ -3114,6 +3121,7 @@ def test_replay_task_with_context():
         output_format=OutputFormat.RAW,
         pydantic=None,
         summary="Detailed report on AI advancements...",
+        messages=[],
     )
     mock_task_output2 = TaskOutput(
         description="Summarize the AI advancements report.",
@@ -3123,6 +3131,7 @@ def test_replay_task_with_context():
         output_format=OutputFormat.RAW,
         pydantic=None,
         summary="Summary of the AI advancements report...",
+        messages=[],
     )
     mock_task_output3 = TaskOutput(
         description="Write an article based on the AI advancements summary.",
@@ -3132,6 +3141,7 @@ def test_replay_task_with_context():
         output_format=OutputFormat.RAW,
         pydantic=None,
         summary="Article on AI advancements...",
+        messages=[],
     )
     mock_task_output4 = TaskOutput(
         description="Create a presentation based on the AI advancements article.",
@@ -3141,6 +3151,7 @@ def test_replay_task_with_context():
         output_format=OutputFormat.RAW,
         pydantic=None,
         summary="Presentation on AI advancements...",
+        messages=[],
     )
 
     with patch.object(Task, "execute_sync") as mock_execute_task:
@@ -3164,6 +3175,70 @@ def test_replay_task_with_context():
         db_handler.reset()
 
 
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_replay_preserves_messages():
+    """Test that replay preserves messages from stored task outputs."""
+    from crewai.utilities.types import LLMMessage
+
+    agent = Agent(
+        role="Test Agent",
+        goal="Test goal",
+        backstory="Test backstory",
+        allow_delegation=False,
+    )
+
+    task = Task(
+        description="Say hello",
+        expected_output="A greeting",
+        agent=agent,
+    )
+
+    crew = Crew(agents=[agent], tasks=[task], process=Process.sequential)
+
+    mock_messages: list[LLMMessage] = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Say hello"},
+        {"role": "assistant", "content": "Hello!"},
+    ]
+
+    mock_task_output = TaskOutput(
+        description="Say hello",
+        raw="Hello!",
+        agent="Test Agent",
+        messages=mock_messages,
+    )
+
+    with patch.object(Task, "execute_sync", return_value=mock_task_output):
+        crew.kickoff()
+
+    # Verify the task output was stored with messages
+    db_handler = TaskOutputStorageHandler()
+    stored_outputs = db_handler.load()
+    assert stored_outputs is not None
+    assert len(stored_outputs) > 0
+
+    # Verify messages are in the stored output
+    stored_output = stored_outputs[0]["output"]
+    assert "messages" in stored_output
+    assert len(stored_output["messages"]) == 3
+    assert stored_output["messages"][0]["role"] == "system"
+    assert stored_output["messages"][1]["role"] == "user"
+    assert stored_output["messages"][2]["role"] == "assistant"
+
+    # Replay the task and verify messages are preserved
+    with patch.object(Task, "execute_sync", return_value=mock_task_output):
+        replayed_output = crew.replay(str(task.id))
+
+    # Verify the replayed task output has messages
+    assert len(replayed_output.tasks_output) > 0
+    replayed_task_output = replayed_output.tasks_output[0]
+    assert hasattr(replayed_task_output, "messages")
+    assert isinstance(replayed_task_output.messages, list)
+    assert len(replayed_task_output.messages) == 3
+
+    db_handler.reset()
+
+
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_replay_with_context():
     agent = Agent(role="test_agent", backstory="Test Description", goal="Test Goal")
@@ -3181,6 +3256,7 @@ def test_replay_with_context():
         pydantic=None,
         json_dict={},
         output_format=OutputFormat.RAW,
+        messages=[],
     )
     task1.output = context_output
 
@@ -3241,6 +3317,7 @@ def test_replay_with_context_set_to_nullable():
             description="Test Task Output",
             raw="test raw output",
             agent="test_agent",
+            messages=[],
         )
         crew.kickoff()
 
@@ -3264,6 +3341,7 @@ def test_replay_with_invalid_task_id():
         pydantic=None,
         json_dict={},
         output_format=OutputFormat.RAW,
+        messages=[],
     )
     task1.output = context_output
 
@@ -3328,6 +3406,7 @@ def test_replay_interpolates_inputs_properly(mock_interpolate_inputs):
         pydantic=None,
         json_dict={},
         output_format=OutputFormat.RAW,
+        messages=[],
     )
     task1.output = context_output
 
@@ -3386,6 +3465,7 @@ def test_replay_setup_context():
         pydantic=None,
         json_dict={},
         output_format=OutputFormat.RAW,
+        messages=[],
     )
     task1.output = context_output
     crew = Crew(agents=[agent], tasks=[task1, task2], process=Process.sequential)
@@ -3619,6 +3699,7 @@ def test_conditional_should_skip(researcher, writer):
             description="Task 1 description",
             raw="Task 1 output",
             agent="Researcher",
+            messages=[],
         )
 
         result = crew_met.kickoff()
@@ -3653,6 +3734,7 @@ def test_conditional_should_execute(researcher, writer):
             description="Task 1 description",
             raw="Task 1 output",
             agent="Researcher",
+            messages=[],
         )
 
         crew_met.kickoff()
@@ -3824,7 +3906,7 @@ def test_task_tools_preserve_code_execution_tools():
     )
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     with patch.object(
@@ -3878,7 +3960,7 @@ def test_multimodal_flag_adds_multimodal_tools():
     crew = Crew(agents=[multimodal_agent], tasks=[task], process=Process.sequential)
 
     mock_task_output = TaskOutput(
-        description="Mock description", raw="mocked output", agent="mocked agent"
+        description="Mock description", raw="mocked output", agent="mocked agent", messages=[]
     )
 
     # Mock execute_sync to verify the tools passed at runtime
@@ -3942,6 +4024,7 @@ def test_multimodal_agent_image_tool_handling():
         description="Mock description",
         raw="A detailed analysis of the image",
         agent="Image Analyst",
+        messages=[],
     )
 
     with patch.object(Task, "execute_sync") as mock_execute_sync:

lib/crewai/tests/test_llm.py

@@ -710,7 +710,7 @@ def test_native_provider_raises_error_when_supported_but_fails():
             mock_get_native.return_value = mock_provider
 
             with pytest.raises(ImportError) as excinfo:
-                LLM(model="openai/gpt-4", is_litellm=False)
+                LLM(model="gpt-4", is_litellm=False)
 
             assert "Error importing native provider" in str(excinfo.value)
             assert "Native provider initialization failed" in str(excinfo.value)
@@ -725,3 +725,113 @@ def test_native_provider_falls_back_to_litellm_when_not_in_supported_list():
         # Should fall back to LiteLLM
         assert llm.is_litellm is True
         assert llm.model == "groq/llama-3.1-70b-versatile"
+
+
+def test_prefixed_models_with_valid_constants_use_native_sdk():
+    """Test that models with native provider prefixes use native SDK when model is in constants."""
+    # Test openai/ prefix with actual OpenAI model in constants → Native SDK
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        llm = LLM(model="openai/gpt-4o", is_litellm=False)
+        assert llm.is_litellm is False
+        assert llm.provider == "openai"
+
+    # Test anthropic/ prefix with Claude model in constants → Native SDK
+    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+        llm2 = LLM(model="anthropic/claude-opus-4-0", is_litellm=False)
+        assert llm2.is_litellm is False
+        assert llm2.provider == "anthropic"
+
+    # Test gemini/ prefix with Gemini model in constants → Native SDK
+    with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
+        llm3 = LLM(model="gemini/gemini-2.5-pro", is_litellm=False)
+        assert llm3.is_litellm is False
+        assert llm3.provider == "gemini"
+
+
+def test_prefixed_models_with_invalid_constants_use_litellm():
+    """Test that models with native provider prefixes use LiteLLM when model is NOT in constants."""
+    # Test openai/ prefix with non-OpenAI model (not in OPENAI_MODELS) → LiteLLM
+    llm = LLM(model="openai/gemini-2.5-flash", is_litellm=False)
+    assert llm.is_litellm is True
+    assert llm.model == "openai/gemini-2.5-flash"
+
+    # Test openai/ prefix with unknown future model → LiteLLM
+    llm2 = LLM(model="openai/gpt-future-6", is_litellm=False)
+    assert llm2.is_litellm is True
+    assert llm2.model == "openai/gpt-future-6"
+
+    # Test anthropic/ prefix with non-Anthropic model → LiteLLM
+    llm3 = LLM(model="anthropic/gpt-4o", is_litellm=False)
+    assert llm3.is_litellm is True
+    assert llm3.model == "anthropic/gpt-4o"
+
+
+def test_prefixed_models_with_non_native_providers_use_litellm():
+    """Test that models with non-native provider prefixes always use LiteLLM."""
+    # Test groq/ prefix (not a native provider) → LiteLLM
+    llm = LLM(model="groq/llama-3.3-70b", is_litellm=False)
+    assert llm.is_litellm is True
+    assert llm.model == "groq/llama-3.3-70b"
+
+    # Test together/ prefix (not a native provider) → LiteLLM
+    llm2 = LLM(model="together/qwen-2.5-72b", is_litellm=False)
+    assert llm2.is_litellm is True
+    assert llm2.model == "together/qwen-2.5-72b"
+
+
+def test_unprefixed_models_use_native_sdk():
+    """Test that unprefixed models use native SDK when model is in constants."""
+    # gpt-4o is in OPENAI_MODELS → Native OpenAI SDK
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        llm = LLM(model="gpt-4o", is_litellm=False)
+        assert llm.is_litellm is False
+        assert llm.provider == "openai"
+
+    # claude-opus-4-0 is in ANTHROPIC_MODELS → Native Anthropic SDK
+    with patch.dict(os.environ, {"ANTHROPIC_API_KEY": "test-key"}):
+        llm2 = LLM(model="claude-opus-4-0", is_litellm=False)
+        assert llm2.is_litellm is False
+        assert llm2.provider == "anthropic"
+
+    # gemini-2.5-pro is in GEMINI_MODELS → Native Gemini SDK
+    with patch.dict(os.environ, {"GOOGLE_API_KEY": "test-key"}):
+        llm3 = LLM(model="gemini-2.5-pro", is_litellm=False)
+        assert llm3.is_litellm is False
+        assert llm3.provider == "gemini"
+
+
+def test_explicit_provider_kwarg_takes_priority():
+    """Test that explicit provider kwarg takes priority over model name inference."""
+    # Explicit provider=openai should use OpenAI even if model name suggests otherwise
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        llm = LLM(model="gpt-4o", provider="openai", is_litellm=False)
+        assert llm.is_litellm is False
+        assert llm.provider == "openai"
+
+    # Explicit provider for a model with "/" should still use that provider
+    with patch.dict(os.environ, {"OPENAI_API_KEY": "test-key"}):
+        llm2 = LLM(model="gpt-4o", provider="openai", is_litellm=False)
+        assert llm2.is_litellm is False
+        assert llm2.provider == "openai"
+
+
+def test_validate_model_in_constants():
+    """Test the _validate_model_in_constants method."""
+    # OpenAI models
+    assert LLM._validate_model_in_constants("gpt-4o", "openai") is True
+    assert LLM._validate_model_in_constants("gpt-future-6", "openai") is False
+
+    # Anthropic models
+    assert LLM._validate_model_in_constants("claude-opus-4-0", "claude") is True
+    assert LLM._validate_model_in_constants("claude-future-5", "claude") is False
+
+    # Gemini models
+    assert LLM._validate_model_in_constants("gemini-2.5-pro", "gemini") is True
+    assert LLM._validate_model_in_constants("gemini-future", "gemini") is False
+
+    # Azure models
+    assert LLM._validate_model_in_constants("gpt-4o", "azure") is True
+    assert LLM._validate_model_in_constants("gpt-35-turbo", "azure") is True
+
+    # Bedrock models
+    assert LLM._validate_model_in_constants("anthropic.claude-opus-4-1-20250805-v1:0", "bedrock") is True

lib/crewai/tests/test_task.py

@@ -162,6 +162,7 @@ def test_task_callback_returns_task_output():
             "name": task.name or task.description,
             "expected_output": "Bullet point list of 5 interesting ideas.",
             "output_format": OutputFormat.RAW,
+            "messages": [],
         }
         assert output_dict == expected_output
 
@@ -1680,3 +1681,44 @@ def test_task_copy_with_list_context():
     assert isinstance(copied_task2.context, list)
     assert len(copied_task2.context) == 1
     assert copied_task2.context[0] is task1
+
+
+@pytest.mark.vcr(filter_headers=["authorization"])
+def test_task_output_includes_messages():
+    """Test that TaskOutput includes messages from agent execution."""
+    researcher = Agent(
+        role="Researcher",
+        goal="Make the best research and analysis on content about AI and AI agents",
+        backstory="You're an expert researcher, specialized in technology, software engineering, AI and startups. You work as a freelancer and is now working on doing research and analysis for a new customer.",
+        allow_delegation=False,
+    )
+
+    task1 = Task(
+        description="Give me a list of 3 interesting ideas about AI.",
+        expected_output="Bullet point list of 3 ideas.",
+        agent=researcher,
+    )
+
+    task2 = Task(
+        description="Summarize the ideas from the previous task.",
+        expected_output="A summary of the ideas.",
+        agent=researcher,
+    )
+
+    crew = Crew(agents=[researcher], tasks=[task1, task2], process=Process.sequential)
+    result = crew.kickoff()
+
+    # Verify both tasks have messages
+    assert len(result.tasks_output) == 2
+
+    # Check first task output has messages
+    task1_output = result.tasks_output[0]
+    assert hasattr(task1_output, "messages")
+    assert isinstance(task1_output.messages, list)
+    assert len(task1_output.messages) > 0
+
+    # Check second task output has messages
+    task2_output = result.tasks_output[1]
+    assert hasattr(task2_output, "messages")
+    assert isinstance(task2_output.messages, list)
+    assert len(task2_output.messages) > 0

lib/crewai/tests/test_task_guardrails.py

@@ -38,6 +38,7 @@ def test_task_without_guardrail():
     agent.role = "test_agent"
     agent.execute_task.return_value = "test result"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(description="Test task", expected_output="Output")
 
@@ -56,6 +57,7 @@ def test_task_with_successful_guardrail_func():
     agent.role = "test_agent"
     agent.execute_task.return_value = "test result"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test task", expected_output="Output", guardrail=guardrail
@@ -76,6 +78,7 @@ def test_task_with_failing_guardrail():
     agent.role = "test_agent"
     agent.execute_task.side_effect = ["bad result", "good result"]
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test task",
@@ -103,6 +106,7 @@ def test_task_with_guardrail_retries():
     agent.role = "test_agent"
     agent.execute_task.return_value = "bad result"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test task",
@@ -128,6 +132,7 @@ def test_guardrail_error_in_context():
     agent = Mock()
     agent.role = "test_agent"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test task",
@@ -295,6 +300,7 @@ def test_hallucination_guardrail_integration():
     agent.role = "test_agent"
     agent.execute_task.return_value = "test result"
     agent.crew = None
+    agent.last_messages = []
 
     mock_llm = Mock(spec=LLM)
     guardrail = HallucinationGuardrail(
@@ -342,6 +348,7 @@ def test_multiple_guardrails_sequential_processing():
     agent.role = "sequential_agent"
     agent.execute_task.return_value = "original text"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test sequential guardrails",
@@ -391,6 +398,7 @@ def test_multiple_guardrails_with_validation_failure():
     agent.role = "validation_agent"
     agent.execute_task = mock_execute_task
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test guardrails with validation",
@@ -432,6 +440,7 @@ def test_multiple_guardrails_with_mixed_string_and_taskoutput():
     agent.role = "mixed_agent"
     agent.execute_task.return_value = "original"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test mixed return types",
@@ -469,6 +478,7 @@ def test_multiple_guardrails_with_retry_on_middle_guardrail():
     agent.role = "retry_agent"
     agent.execute_task.return_value = "base"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test retry in middle guardrail",
@@ -500,6 +510,7 @@ def test_multiple_guardrails_with_max_retries_exceeded():
     agent.role = "failing_agent"
     agent.execute_task.return_value = "test"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test max retries with multiple guardrails",
@@ -523,6 +534,7 @@ def test_multiple_guardrails_empty_list():
     agent.role = "empty_agent"
     agent.execute_task.return_value = "no guardrails"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test empty guardrails list",
@@ -582,6 +594,7 @@ def test_multiple_guardrails_processing_order():
     agent.role = "order_agent"
     agent.execute_task.return_value = "base"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test processing order",
@@ -625,6 +638,7 @@ def test_multiple_guardrails_with_pydantic_output():
     agent.role = "pydantic_agent"
     agent.execute_task.return_value = "test content"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test guardrails with Pydantic",
@@ -658,6 +672,7 @@ def test_guardrails_vs_single_guardrail_mutual_exclusion():
     agent.role = "exclusion_agent"
     agent.execute_task.return_value = "test"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test mutual exclusion",
@@ -700,6 +715,7 @@ def test_per_guardrail_independent_retry_tracking():
     agent.role = "independent_retry_agent"
     agent.execute_task.return_value = "base"
     agent.crew = None
+    agent.last_messages = []
 
     task = create_smart_task(
         description="Test independent retry tracking",

lib/devtools/src/crewai_devtools/__init__.py

@@ -1,3 +1,3 @@
 """CrewAI development tools."""
 
-__version__ = "1.3.0"
+__version__ = "1.4.1"