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lorenzejay
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---
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title: LiteAgent
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description: A lightweight, single-purpose agent for simple autonomous tasks within the CrewAI framework.
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icon: feather
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---
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## Overview
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A `LiteAgent` is a streamlined version of CrewAI's Agent, designed for simpler, standalone tasks that don't require the full complexity of a crew-based workflow. It's perfect for quick automations, single-purpose tasks, or when you need a lightweight solution.
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<Tip>
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Think of a LiteAgent as a specialized worker that excels at individual tasks.
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While regular Agents are team players in a crew, LiteAgents are solo
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performers optimized for specific operations.
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</Tip>
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## LiteAgent Attributes
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| Attribute | Parameter | Type | Description |
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| :------------------------------- | :---------------- | :--------------------- | :-------------------------------------------------------------- |
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| **Role** | `role` | `str` | Defines the agent's function and expertise. |
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| **Goal** | `goal` | `str` | The specific objective that guides the agent's actions. |
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| **Backstory** | `backstory` | `str` | Provides context and personality to the agent. |
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| **LLM** _(optional)_ | `llm` | `Union[str, LLM, Any]` | Language model powering the agent. Defaults to "gpt-4". |
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| **Tools** _(optional)_ | `tools` | `List[BaseTool]` | Capabilities available to the agent. Defaults to an empty list. |
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| **Verbose** _(optional)_ | `verbose` | `bool` | Enable detailed execution logs. Default is False. |
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| **Response Format** _(optional)_ | `response_format` | `Type[BaseModel]` | Pydantic model for structured output. Optional. |
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## Creating a LiteAgent
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Here's a simple example of creating and using a standalone LiteAgent:
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```python
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from typing import List, cast
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from crewai_tools import SerperDevTool
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from pydantic import BaseModel, Field
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from crewai.lite_agent import LiteAgent
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# Define a structured output format
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class MovieReview(BaseModel):
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title: str = Field(description="The title of the movie")
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rating: float = Field(description="Rating out of 10")
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pros: List[str] = Field(description="List of positive aspects")
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cons: List[str] = Field(description="List of negative aspects")
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# Create a LiteAgent
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critic = LiteAgent(
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role="Movie Critic",
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goal="Provide insightful movie reviews",
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backstory="You are an experienced film critic known for balanced, thoughtful reviews.",
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tools=[SerperDevTool()],
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verbose=True,
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response_format=MovieReview,
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)
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# Use the agent
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query = """
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Review the movie 'Inception'. Include:
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1. Your rating out of 10
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2. Key positive aspects
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3. Areas that could be improved
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"""
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result = critic.kickoff(query)
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# Access the structured output
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review = cast(MovieReview, result.pydantic)
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print(f"\nMovie Review: {review.title}")
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print(f"Rating: {review.rating}/10")
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print("\nPros:")
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for pro in review.pros:
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print(f"- {pro}")
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print("\nCons:")
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for con in review.cons:
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print(f"- {con}")
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```
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This example demonstrates the core features of a LiteAgent:
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- Structured output using Pydantic models
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- Tool integration with WebSearchTool
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- Simple execution with `kickoff()`
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- Easy access to both raw and structured results
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## Using LiteAgent in a Flow
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For more complex scenarios, you can integrate LiteAgents into a Flow. Here's an example of a market research flow:
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````python
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import asyncio
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from typing import Any, Dict, List
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from crewai_tools import SerperDevTool
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from pydantic import BaseModel, Field
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from crewai.agent import Agent
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from crewai.flow.flow import Flow, listen, start
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# Define a structured output format
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class MarketAnalysis(BaseModel):
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key_trends: List[str] = Field(description="List of identified market trends")
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market_size: str = Field(description="Estimated market size")
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competitors: List[str] = Field(description="Major competitors in the space")
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# Define flow state
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class MarketResearchState(BaseModel):
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product: str = ""
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analysis: MarketAnalysis | None = None
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# Create a flow class
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class MarketResearchFlow(Flow[MarketResearchState]):
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@start()
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def initialize_research(self) -> Dict[str, Any]:
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print(f"Starting market research for {self.state.product}")
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return {"product": self.state.product}
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@listen(initialize_research)
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async def analyze_market(self) -> Dict[str, Any]:
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# Create an Agent for market research
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analyst = Agent(
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role="Market Research Analyst",
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goal=f"Analyze the market for {self.state.product}",
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backstory="You are an experienced market analyst with expertise in "
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"identifying market trends and opportunities.",
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tools=[SerperDevTool()],
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verbose=True,
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)
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# Define the research query
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query = f"""
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Research the market for {self.state.product}. Include:
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1. Key market trends
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2. Market size
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3. Major competitors
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Format your response according to the specified structure.
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"""
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# Execute the analysis with structured output format
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result = await analyst.kickoff_async(query, response_format=MarketAnalysis)
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if result.pydantic:
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print("result", result.pydantic)
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else:
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print("result", result)
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# Return the analysis to update the state
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return {"analysis": result.pydantic}
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@listen(analyze_market)
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def present_results(self, analysis) -> None:
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print("\nMarket Analysis Results")
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print("=====================")
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if isinstance(analysis, dict):
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# If we got a dict with 'analysis' key, extract the actual analysis object
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market_analysis = analysis.get("analysis")
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else:
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market_analysis = analysis
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if market_analysis and isinstance(market_analysis, MarketAnalysis):
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print("\nKey Market Trends:")
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for trend in market_analysis.key_trends:
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print(f"- {trend}")
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print(f"\nMarket Size: {market_analysis.market_size}")
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print("\nMajor Competitors:")
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for competitor in market_analysis.competitors:
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print(f"- {competitor}")
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else:
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print("No structured analysis data available.")
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print("Raw analysis:", analysis)
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# Usage example
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async def run_flow():
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flow = MarketResearchFlow()
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result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
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return result
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# Run the flow
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if __name__ == "__main__":
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# Use asyncio.run at the top level only
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asyncio.run(run_flow())
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````
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### 4. Async Support
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LiteAgents support asynchronous execution through the `kickoff_async` method, making them suitable for non-blocking operations in your application.
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## Response Formatting
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LiteAgents support structured output through Pydantic models using the `response_format` parameter. This feature ensures type safety and consistent output structure, making it easier to work with agent responses in your application.
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### Basic Usage
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```python
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from pydantic import BaseModel, Field
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class SearchResult(BaseModel):
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title: str = Field(description="The title of the found content")
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summary: str = Field(description="A brief summary of the content")
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relevance_score: float = Field(description="Relevance score from 0 to 1")
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agent = LiteAgent(
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role="Search Specialist",
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goal="Find and summarize relevant information",
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response_format=SearchResult
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)
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result = await agent.kickoff_async("Find information about quantum computing")
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print(f"Title: {result.pydantic.title}")
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print(f"Summary: {result.pydantic.summary}")
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print(f"Relevance: {result.pydantic.relevance_score}")
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```
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### Handling Responses
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When using `response_format`, the agent's response will be available in two forms:
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1. **Raw Response**: Access the unstructured string response
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```python
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result = await agent.kickoff_async("Analyze the market")
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print(result.raw) # Original LLM response
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```
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2. **Structured Response**: Access the parsed Pydantic model
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```python
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print(result.pydantic) # Parsed response as Pydantic model
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print(result.pydantic.dict()) # Convert to dictionary
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```
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@@ -66,7 +66,6 @@
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"concepts/tasks",
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"concepts/crews",
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"concepts/flows",
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"concepts/lite-agent",
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"concepts/knowledge",
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"concepts/llms",
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"concepts/processes",
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@@ -231,4 +230,4 @@
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"reddit": "https://www.reddit.com/r/crewAIInc/"
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}
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}
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}
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}
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