andre/crewAI
1
0
Fork 0
mirror of https://github.com/crewAIInc/crewAI.git synced 2026-01-10 00:28:31 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

KISS: Refactor LiteAgent integration in flows to use Agents instead. … (#2556)

Browse Source 
* 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
This commit is contained in:
Lorenze Jay
2025-04-09 11:54:45 -07:00
committed by GitHub
parent 10328f3db4
commit b73960cebe
9 changed files with 3160 additions and 293 deletions
Download Patch File Download Diff File Expand all files Collapse all files

94
docs/concepts/flows.mdx
View File

@@ -545,16 +545,20 @@ The `third_method` and `fourth_method` listen to the output of the `second_metho
When you run this Flow, the output will change based on the random boolean value generated by the `start_method`.
## Adding LiteAgent to Flows
## Adding Agents to Flows
LiteAgents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use a LiteAgent within a flow to perform market research:
Agents can be seamlessly integrated into your flows, providing a lightweight alternative to full Crews when you need simpler, focused task execution. Here's an example of how to use an Agent within a flow to perform market research:
```python
from typing import List, cast
from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
import asyncio
from typing import Any, Dict, List
from crewai_tools import SerperDevTool
from pydantic import BaseModel, Field
from crewai.agent import Agent
from crewai.flow.flow import Flow, listen, start
from crewai.lite_agent import LiteAgent
# Define a structured output format
class MarketAnalysis(BaseModel):
@@ -562,28 +566,30 @@ class MarketAnalysis(BaseModel):
market_size: str = Field(description="Estimated market size")
competitors: List[str] = Field(description="Major competitors in the space")
# Define flow state
class MarketResearchState(BaseModel):
product: str = ""
analysis: MarketAnalysis | None = None
# Create a flow class
class MarketResearchFlow(Flow[MarketResearchState]):
@start()
def initialize_research(self):
def initialize_research(self) -> Dict[str, Any]:
print(f"Starting market research for {self.state.product}")
return {"product": self.state.product}
@listen(initialize_research)
def analyze_market(self):
# Create a LiteAgent for market research
analyst = LiteAgent(
async def analyze_market(self) -> Dict[str, Any]:
# Create an Agent for market research
analyst = Agent(
role="Market Research Analyst",
goal=f"Analyze the market for {self.state.product}",
backstory="You are an experienced market analyst with expertise in "
"identifying market trends and opportunities.",
llm="gpt-4o",
tools=[WebsiteSearchTool()],
tools=[SerperDevTool()],
verbose=True,
response_format=MarketAnalysis,
)
# Define the research query
@@ -592,49 +598,65 @@ class MarketResearchFlow(Flow[MarketResearchState]):
1. Key market trends
2. Market size
3. Major competitors
Format your response according to the specified structure.
"""
# Execute the analysis
result = analyst.kickoff(query)
self.state.analysis = cast(MarketAnalysis, result.pydantic)
return result.pydantic
# Execute the analysis with structured output format
result = await analyst.kickoff_async(query, response_format=MarketAnalysis)
if result.pydantic:
print("result", result.pydantic)
else:
print("result", result)
# Return the analysis to update the state
return {"analysis": result.pydantic}
@listen(analyze_market)
def present_results(self):
analysis = self.state.analysis
if analysis is None:
print("No analysis results available")
return
def present_results(self, analysis) -> None:
print("\nMarket Analysis Results")
print("=====================")
print("\nKey Market Trends:")
for trend in analysis.key_trends:
print(f"- {trend}")
if isinstance(analysis, dict):
# If we got a dict with 'analysis' key, extract the actual analysis object
market_analysis = analysis.get("analysis")
else:
market_analysis = analysis
print(f"\nMarket Size: {analysis.market_size}")
if market_analysis and isinstance(market_analysis, MarketAnalysis):
print("\nKey Market Trends:")
for trend in market_analysis.key_trends:
print(f"- {trend}")
print(f"\nMarket Size: {market_analysis.market_size}")
print("\nMajor Competitors:")
for competitor in market_analysis.competitors:
print(f"- {competitor}")
else:
print("No structured analysis data available.")
print("Raw analysis:", analysis)
print("\nMajor Competitors:")
for competitor in analysis.competitors:
print(f"- {competitor}")
# Usage example
flow = MarketResearchFlow()
result = flow.kickoff(inputs={"product": "AI-powered chatbots"})
async def run_flow():
flow = MarketResearchFlow()
result = await flow.kickoff_async(inputs={"product": "AI-powered chatbots"})
return result
# Run the flow
if __name__ == "__main__":
asyncio.run(run_flow())
```
This example demonstrates several key features of using LiteAgents in flows:
This example demonstrates several key features of using Agents in flows:
1. **Structured Output**: Using Pydantic models to define the expected output format (`MarketAnalysis`) ensures type safety and structured data throughout the flow.
2. **State Management**: The flow state (`MarketResearchState`) maintains context between steps and stores both inputs and outputs.
3. **Tool Integration**: LiteAgents can use tools (like `WebsiteSearchTool`) to enhance their capabilities.
If you want to learn more about LiteAgents, check out the [LiteAgent](/concepts/lite-agent) page.
3. **Tool Integration**: Agents can use tools (like `WebsiteSearchTool`) to enhance their capabilities.
## Adding Crews to Flows