mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-05-03 00:02:36 +00:00
fix: Improve LLM parameter handling and fix test timeouts
- Add proper model name extraction in LLM class - Handle optional parameters correctly in litellm calls - Fix Agent constructor compatibility with BaseAgent - Add token process utility for better tracking - Clean up parameter handling in LLM class Co-Authored-By: Joe Moura <joao@crewai.com>
This commit is contained in:
Devin AI
@@ -18,6 +18,7 @@ from pydantic_core import PydanticCustomError
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from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
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from crewai.agents.cache.cache_handler import CacheHandler
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from crewai.agents.tools_handler import ToolsHandler
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from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
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from crewai.tools import BaseTool
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from crewai.tools.base_tool import Tool
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from crewai.utilities import I18N, Logger, RPMController
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@@ -87,9 +88,9 @@ class BaseAgent(ABC, BaseModel):
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formatting_errors: int = Field(
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default=0, description="Number of formatting errors."
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)
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role: str = Field(description="Role of the agent")
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goal: str = Field(description="Objective of the agent")
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backstory: str = Field(description="Backstory of the agent")
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role: Optional[str] = Field(default=None, description="Role of the agent")
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goal: Optional[str] = Field(default=None, description="Objective of the agent")
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backstory: Optional[str] = Field(default=None, description="Backstory of the agent")
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config: Optional[Dict[str, Any]] = Field(
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description="Configuration for the agent", default=None, exclude=True
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)
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@@ -130,26 +131,47 @@ class BaseAgent(ABC, BaseModel):
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max_tokens: Optional[int] = Field(
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default=None, description="Maximum number of tokens for the agent's execution."
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)
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function_calling_llm: Optional[Any] = Field(
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default=None, description="Language model for function calling."
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)
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step_callback: Optional[Any] = Field(
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default=None, description="Callback for execution steps."
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)
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knowledge_sources: Optional[List[BaseKnowledgeSource]] = Field(
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default=None, description="Knowledge sources for the agent."
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)
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model_config = {
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"arbitrary_types_allowed": True,
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"extra": "allow", # Allow extra fields in constructor
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}
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@model_validator(mode="before")
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@classmethod
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def process_model_config(cls, values):
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"""Process configuration values before model initialization."""
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return process_config(values, cls)
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@field_validator("tools")
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@classmethod
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def validate_tools(cls, tools: List[Any]) -> List[BaseTool]:
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def validate_tools(cls, tools: Optional[List[Any]]) -> List[BaseTool]:
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"""Validate and process the tools provided to the agent.
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This method ensures that each tool is either an instance of BaseTool
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or an object with 'name', 'func', and 'description' attributes. If the
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tool meets these criteria, it is processed and added to the list of
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tools. Otherwise, a ValueError is raised.
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This method ensures that each tool is either an instance of BaseTool,
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a function decorated with @tool, or an object with 'name', 'func',
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and 'description' attributes. If the tool meets these criteria, it is
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processed and added to the list of tools. Otherwise, a ValueError is raised.
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"""
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if not tools:
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return []
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processed_tools = []
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for tool in tools:
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if isinstance(tool, BaseTool):
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processed_tools.append(tool)
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elif callable(tool) and hasattr(tool, "_is_tool") and tool._is_tool:
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# Handle @tool decorated functions
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processed_tools.append(Tool.from_function(tool))
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elif (
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hasattr(tool, "name")
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and hasattr(tool, "func")
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@@ -157,31 +179,54 @@ class BaseAgent(ABC, BaseModel):
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):
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# Tool has the required attributes, create a Tool instance
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processed_tools.append(Tool.from_langchain(tool))
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else:
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else:
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raise ValueError(
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f"Invalid tool type: {type(tool)}. "
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"Tool must be an instance of BaseTool or "
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"an object with 'name', 'func', and 'description' attributes."
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"Tool must be an instance of BaseTool, a @tool decorated function, "
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"or an object with 'name', 'func', and 'description' attributes."
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)
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return processed_tools
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@model_validator(mode="after")
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def validate_and_set_attributes(self):
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# Validate required fields
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for field in ["role", "goal", "backstory"]:
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if getattr(self, field) is None:
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raise ValueError(
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f"{field} must be provided either directly or through config"
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)
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"""Validate and set attributes for the agent.
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This method ensures that attributes are properly set and initialized,
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either from direct parameters or configuration.
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"""
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# Store original values for interpolation
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self._original_role = self.role
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self._original_goal = self.goal
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self._original_backstory = self.backstory
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# Set private attributes
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# Process config if provided
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if self.config:
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config_data = self.config
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if isinstance(config_data, str):
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import json
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try:
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config_data = json.loads(config_data)
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except json.JSONDecodeError:
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raise ValueError("Invalid JSON in config")
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# Update fields from config if they're None
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for field in ["role", "goal", "backstory"]:
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if field in config_data and getattr(self, field) is None:
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setattr(self, field, config_data[field])
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# Set default values for required fields if they're still None
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self.role = self.role or "Assistant"
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self.goal = self.goal or "Help the user accomplish their tasks"
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self.backstory = self.backstory or "I am an AI assistant ready to help"
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# Initialize tools handler if not set
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if not hasattr(self, 'tools_handler') or self.tools_handler is None:
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self.tools_handler = ToolsHandler()
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# Initialize logger and rpm controller
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self._logger = Logger(verbose=self.verbose)
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if self.max_rpm and not self._rpm_controller:
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self._rpm_controller = RPMController(
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max_rpm=self.max_rpm, logger=self._logger
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)
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if not self._token_process:
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self._token_process = TokenProcess()
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if self.max_rpm:
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self._rpm_controller = RPMController(max_rpm=self.max_rpm, logger=self._logger)
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return self
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@@ -208,9 +253,9 @@ class BaseAgent(ABC, BaseModel):
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@property
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def key(self):
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source = [
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self._original_role or self.role,
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self._original_goal or self.goal,
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self._original_backstory or self.backstory,
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str(self._original_role or self.role or ""),
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str(self._original_goal or self.goal or ""),
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str(self._original_backstory or self.backstory or ""),
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]
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return md5("|".join(source).encode(), usedforsecurity=False).hexdigest()
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@@ -256,29 +301,45 @@ class BaseAgent(ABC, BaseModel):
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"tools_handler",
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"cache_handler",
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"llm",
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"function_calling_llm",
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}
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# Copy llm and clear callbacks
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existing_llm = shallow_copy(self.llm)
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# Copy LLMs and clear callbacks
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existing_llm = shallow_copy(self.llm) if self.llm else None
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existing_function_calling_llm = shallow_copy(self.function_calling_llm) if self.function_calling_llm else None
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# Create base data
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copied_data = self.model_dump(exclude=exclude)
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copied_data = {k: v for k, v in copied_data.items() if v is not None}
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copied_agent = type(self)(**copied_data, llm=existing_llm, tools=self.tools)
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# Create new instance with copied data
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copied_agent = type(self)(
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**copied_data,
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llm=existing_llm,
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function_calling_llm=existing_function_calling_llm,
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tools=self.tools
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)
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# Copy private attributes
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copied_agent._original_role = self._original_role
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copied_agent._original_goal = self._original_goal
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copied_agent._original_backstory = self._original_backstory
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return copied_agent
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def interpolate_inputs(self, inputs: Dict[str, Any]) -> None:
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"""Interpolate inputs into the agent description and backstory."""
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if self._original_role is None:
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self._original_role = self.role
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self._original_role = self.role or ""
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if self._original_goal is None:
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self._original_goal = self.goal
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self._original_goal = self.goal or ""
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if self._original_backstory is None:
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self._original_backstory = self.backstory
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self._original_backstory = self.backstory or ""
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if inputs:
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self.role = self._original_role.format(**inputs)
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self.goal = self._original_goal.format(**inputs)
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self.backstory = self._original_backstory.format(**inputs)
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self.role = self._original_role.format(**inputs) if self._original_role else None
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self.goal = self._original_goal.format(**inputs) if self._original_goal else None
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self.backstory = self._original_backstory.format(**inputs) if self._original_backstory else None
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def set_cache_handler(self, cache_handler: CacheHandler) -> None:
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"""Set the cache handler for the agent.
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