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KISS: Refactor LiteAgent integration in flows to use Agents instead. … (#2556)
* KISS: Refactor LiteAgent integration in flows to use Agents instead. Update documentation and examples to reflect changes in class usage, including async support and structured output handling. Enhance tests for Agent functionality and ensure compatibility with new features. * lint fix * dropped for clarity
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@@ -1,7 +1,6 @@
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import re
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import shutil
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import subprocess
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from typing import Any, Dict, List, Literal, Optional, Sequence, Union
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from typing import Any, Dict, List, Literal, Optional, Sequence, Type, Union
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from pydantic import Field, InstanceOf, PrivateAttr, model_validator
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@@ -11,6 +10,7 @@ from crewai.agents.crew_agent_executor import CrewAgentExecutor
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from crewai.knowledge.knowledge import Knowledge
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from crewai.knowledge.source.base_knowledge_source import BaseKnowledgeSource
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from crewai.knowledge.utils.knowledge_utils import extract_knowledge_context
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from crewai.lite_agent import LiteAgent, LiteAgentOutput
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from crewai.llm import BaseLLM
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from crewai.memory.contextual.contextual_memory import ContextualMemory
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from crewai.security import Fingerprint
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@@ -449,3 +449,74 @@ class Agent(BaseAgent):
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def set_fingerprint(self, fingerprint: Fingerprint):
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self.security_config.fingerprint = fingerprint
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def kickoff(
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self,
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messages: Union[str, List[Dict[str, str]]],
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response_format: Optional[Type[Any]] = None,
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) -> LiteAgentOutput:
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"""
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Execute the agent with the given messages using a LiteAgent instance.
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This method is useful when you want to use the Agent configuration but
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with the simpler and more direct execution flow of LiteAgent.
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Args:
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messages: Either a string query or a list of message dictionaries.
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If a string is provided, it will be converted to a user message.
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If a list is provided, each dict should have 'role' and 'content' keys.
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response_format: Optional Pydantic model for structured output.
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Returns:
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LiteAgentOutput: The result of the agent execution.
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"""
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lite_agent = LiteAgent(
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role=self.role,
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goal=self.goal,
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backstory=self.backstory,
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llm=self.llm,
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tools=self.tools or [],
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max_iterations=self.max_iter,
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max_execution_time=self.max_execution_time,
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respect_context_window=self.respect_context_window,
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verbose=self.verbose,
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response_format=response_format,
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i18n=self.i18n,
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)
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return lite_agent.kickoff(messages)
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async def kickoff_async(
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self,
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messages: Union[str, List[Dict[str, str]]],
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response_format: Optional[Type[Any]] = None,
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) -> LiteAgentOutput:
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"""
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Execute the agent asynchronously with the given messages using a LiteAgent instance.
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This is the async version of the kickoff method.
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Args:
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messages: Either a string query or a list of message dictionaries.
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If a string is provided, it will be converted to a user message.
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If a list is provided, each dict should have 'role' and 'content' keys.
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response_format: Optional Pydantic model for structured output.
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Returns:
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LiteAgentOutput: The result of the agent execution.
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"""
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lite_agent = LiteAgent(
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role=self.role,
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goal=self.goal,
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backstory=self.backstory,
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llm=self.llm,
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tools=self.tools or [],
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max_iterations=self.max_iter,
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max_execution_time=self.max_execution_time,
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respect_context_window=self.respect_context_window,
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verbose=self.verbose,
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response_format=response_format,
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i18n=self.i18n,
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)
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return await lite_agent.kickoff_async(messages)
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@@ -1,6 +1,4 @@
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import asyncio
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import json
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import re
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import uuid
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from datetime import datetime
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from typing import Any, Callable, Dict, List, Optional, Type, Union, cast
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