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Feat/individual react agent (#2483)

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* WIP

* WIP

* wip

* wip

* WIP

* More WIP

* Its working but needs a massive clean up

* output type works now

* Usage metrics fixed

* more testing

* WIP

* cleaning up

* Update logger

* 99% done. Need to make docs match new example

* cleanup

* drop hard coded examples

* docs

* Clean up

* Fix errors

* Trying to fix CI issues

* more type checker fixes

* More type checking fixes

* Update LiteAgent documentation for clarity and consistency; replace WebsiteSearchTool with SerperDevTool, and improve formatting in examples.

* fix fingerprinting issues

* fix type-checker

* Fix type-checker issue by adding type ignore comment for cache read in ToolUsage class

* Add optional agent parameter to CrewAgentParser and enhance action handling logic

* Remove unused parameters from ToolUsage instantiation in tests and clean up debug print statement in CrewAgentParser.

* Remove deprecated test files and examples for LiteAgent; add comprehensive tests for LiteAgent functionality, including tool usage and structured output handling.

* Remove unused variable 'result' from ToolUsage class to clean up code.

* Add initialization for 'result' variable in ToolUsage class to resolve type-checker warnings

* Refactor agent_utils.py by removing unused event imports and adding missing commas in function definitions. Update test_events.py to reflect changes in expected event counts and adjust assertions accordingly. Modify test_tools_emits_error_events.yaml to include new headers and update response content for consistency with recent API changes.

* Enhance tests in crew_test.py by verifying cache behavior in test_tools_with_custom_caching and ensuring proper agent initialization with added commas in test_crew_kickoff_for_each_works_with_manager_agent_copy.

* Update agent tests to reflect changes in expected call counts and improve response formatting in YAML cassette. Adjusted mock call count from 2 to 3 and refined interaction formats for clarity and consistency.

* Refactor agent tests to update model versions and improve response formatting in YAML cassettes. Changed model references from 'o1-preview' to 'o3-mini' and adjusted interaction formats for consistency. Enhanced error handling in context length tests and refined mock setups for better clarity.

* Update tool usage logging to ensure tool arguments are consistently formatted as strings. Adjust agent test cases to reflect changes in maximum iterations and expected outputs, enhancing clarity in assertions. Update YAML cassettes to align with new response formats and improve overall consistency across tests.

* Update YAML cassette for LLM tests to reflect changes in response structure and model version. Adjusted request and response headers, including updated content length and user agent. Enhanced token limits and request counts for improved testing accuracy.

* Update tool usage logging to store tool arguments as native types instead of strings, enhancing data integrity and usability.

* Refactor agent tests by removing outdated test cases and updating YAML cassettes to reflect changes in tool usage and response formats. Adjusted request and response headers, including user agent and content length, for improved accuracy in testing. Enhanced interaction formats for consistency across tests.

* Add Excalidraw diagram file for visual representation of input-output flow

Created a new Excalidraw file that includes a diagram illustrating the input box, database, and output box with connecting arrows. This visual aid enhances understanding of the data flow within the application.

* Remove redundant error handling for action and final answer in CrewAgentParser. Update tests to reflect this change by deleting the corresponding test case.

---------

Co-authored-by: Lorenze Jay <63378463+lorenzejay@users.noreply.github.com>
Co-authored-by: Lorenze Jay <lorenzejaytech@gmail.com>
This commit is contained in:
Brandon Hancock (bhancock_ai)
2025-04-02 11:54:46 -04:00
committed by GitHub
parent 9b51e1174c
commit efe27bd570
42 changed files with 21642 additions and 12531 deletions
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docs/concepts/flows.mdx
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@@ -545,6 +545,97 @@ 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
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:
```python
from typing import List, cast
from crewai_tools.tools.website_search.website_search_tool import WebsiteSearchTool
from pydantic import BaseModel, Field
from crewai.flow.flow import Flow, listen, start
from crewai.lite_agent import LiteAgent
# Define a structured output format
class MarketAnalysis(BaseModel):
key_trends: List[str] = Field(description="List of identified market trends")
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
class MarketResearchFlow(Flow[MarketResearchState]):
@start()
def initialize_research(self):
print(f"Starting market research for {self.state.product}")
@listen(initialize_research)
def analyze_market(self):
# Create a LiteAgent for market research
analyst = LiteAgent(
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()],
verbose=True,
response_format=MarketAnalysis,
)
# Define the research query
query = f"""
Research the market for {self.state.product}. Include:
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
@listen(analyze_market)
def present_results(self):
analysis = self.state.analysis
if analysis is None:
print("No analysis results available")
return
print("\nMarket Analysis Results")
print("=====================")
print("\nKey Market Trends:")
for trend in analysis.key_trends:
print(f"- {trend}")
print(f"\nMarket Size: {analysis.market_size}")
print("\nMajor Competitors:")
for competitor in analysis.competitors:
print(f"- {competitor}")
# Usage example
flow = MarketResearchFlow()
result = flow.kickoff(inputs={"product": "AI-powered chatbots"})
```
This example demonstrates several key features of using LiteAgents 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.
## Adding Crews to Flows
Creating a flow with multiple crews in CrewAI is straightforward.