Samples: Add Docs Agent

[cristipufu]

No description provided.

[cristipufu]

I've reviewed the PR which adds a new Docs Agent sample to the repository. Here's my detailed feedback:

Overview

This PR adds a new sample that demonstrates how to create a documentation agent using LangGraph and MCP adapters. The agent is designed to automatically write documentation for GitHub repositories based on user requests.

Code Structure

The sample is well-organized and includes:

• main.py - Core agent implementation
• agent.mermaid - Flow visualization
• langgraph.json - Graph configuration
• pyproject.toml - Project dependencies
• uipath.json - UiPath configuration
• .env.example - Environment variables template

Technical Review

Positive Aspects:

1. Well-Structured System Prompt: The documentation agent has a very detailed and well-organized system prompt that covers:

   • Clear role definition
   • Specific capabilities
   • Structured workflow steps
   • Documentation style guidelines
   • Examples and templates
   • Error handling scenarios

2. Clean Architecture:

   • Uses LangGraph's prebuilt agent components effectively
   • Clear separation of concerns between state management and agent logic
   • Good use of async context manager for resource management

3. Proper Error Handling:

   • Uses async context manager for proper cleanup
   • Includes timeout in server connection

4. Dependencies:

   • Clear specification of dependencies with version constraints
   • Modern Python requirement (>=3.10)
   • Uses up-to-date versions of key packages

Suggestions for Improvement:

1. Environment Variables:

dotenv.load_dotenv()

Consider making the .env file path configurable or adding error handling for missing environment variables.

2. Type Hints:
   The IssueState class could benefit from more detailed type hints:

class IssueState(AgentState):
    owner: str
    repo: str
    issueNumber: int

Consider adding docstrings to explain the purpose of each field.

3. Documentation:

• Add docstrings to the make_graph() function explaining its purpose and return type
• Consider adding inline comments explaining complex parts of the system prompt
• Add a README.md file for the sample explaining setup and usage

4. Configuration:
   The .env.example file should include all required environment variables. Currently missing:

UIPATH_ACCESS_TOKEN=***

Security Considerations:

• Good: Using environment variables for sensitive data
• Good: API keys and tokens not hardcoded
• Good: Proper timeout settings for server connections

Performance:

• Using Claude 3 Sonnet model - good balance of performance and cost
• Async implementation allows for efficient handling of I/O operations

Recommendations:

1. Add a comprehensive README.md file
2. Add error handling for missing environment variables
3. Add more type hints and docstrings
4. Consider adding unit tests for the agent
5. Document memory usage and performance characteristics

Overall, this is a well-implemented sample that demonstrates good practices in building AI agents. The code is clean, well-structured, and follows modern Python development practices. The comprehensive system prompt is particularly impressive and should serve as a good example for similar implementations.

[cristipufu]

Consider adding type hints and a docstring to this class to improve code documentation and maintainability:

class IssueState(AgentState):
    """State for the documentation agent containing GitHub issue details.
    
    Attributes:
        owner (str): The GitHub repository owner (user or organization)
        repo (str): The GitHub repository name
        issueNumber (int): The issue number to process
    """
    owner: str
    repo: str
    issueNumber: int

[cristipufu]

The make_graph function would benefit from a docstring explaining its purpose and return type:

@asynccontextmanager
async def make_graph():
    """Create and configure a LangGraph agent for documentation generation.
    
    This function sets up an async context that:
    1. Connects to the MCP server via SSE
    2. Configures the Claude 3 model and tools
    3. Creates a ReAct agent with documentation-specific prompt
    
    Returns:
        AsyncGenerator[Graph, None]: A LangGraph instance configured for documentation tasks
    """

[cristipufu]

The .env.example file should include all required environment variables. Please add:

UIPATH_ACCESS_TOKEN=***

This variable is required by the code but not listed in the example.