multi_agent_github

GitHub Research Agent Example

This example demonstrates how to create a multi-agent system that can search and analyze GitHub repositories to answer technical questions about software implementations, architectures, and code patterns.

Overview

This example creates a system of specialized agents that work together to help users research technical implementations in GitHub:

• GitHub Research Agent: The root agent that coordinates the research process
• Repository Explorer: Finds and explores GitHub repositories in organizations
• Code Search Specialist: Searches for specific code implementations, functions, and patterns
• Issue and PR Specialist: Analyzes issues and pull requests for design discussions and context

Features

• Search for repositories in specific organizations
• Find code implementations using targeted searches
• Analyze issues and PRs for design context
• Answer technical questions like:
  • "How does Temporal implement rate limiting on the server side?"
  • "How does Temporal's Go SDK implement workflow sleep?"

Setup Instructions

1. GitHub Token

For higher API rate limits, set up a GitHub Personal Access Token:

1. Go to GitHub Settings > Developer settings > Personal access tokens
2. Click "Generate new token (classic)"
3. Give your token a name and select the following scopes:
   • repo (Full control of private repositories)
   • read:org (Read organization membership)
4. Make sure to authorize access to the Temporal organization if you plan to search Temporal repositories
5. Click "Generate token" and copy your new token
6. Set the environment variable:

export GITHUB_TOKEN=your_github_token_here

Note: Without a token, you'll be limited to 60 requests per hour. With a token, you get 5,000 requests per hour.

2. Google Cloud Platform Setup for VertexAI

1. Create a GCP project

2. Enable the VertexAI API:

   gcloud services enable aiplatform.googleapis.com

3. Set up authentication:

   gcloud auth application-default login
   # OR set service account key
   export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account-key.json

4. Set your project ID:

   export GCP_PROJECT_ID=your-gcp-project-id

Running the Example

Install dependencies and start the agent:

# Install uv if you haven't already
curl -LsSf https://astral.sh/uv/install.sh | sh

# Run the agent
uv run python -m examples.multi_agent_github.agent

Example Queries

Here are some example questions you can ask:

Temporal-specific Questions

• "How does Temporal implement rate limiting on the server side?"
• "How does Temporal's Go SDK implement workflow sleep?"
• "What's the architecture of Temporal's worker polling mechanism?"
• "How does Temporal handle workflow state persistence?"

Language-specific Searches

• "Show me rate limiting implementations in Go"
• "Find circuit breaker patterns in Java"
• "How is async/await implemented in Python?"

The agent automatically handles rate limiting by using proper headers and error handling.

## Dependencies

- Python 3.10+
- GitHub API access (public repositories)
- Google Cloud Platform account for VertexAI

## Troubleshooting

### Rate Limit Issues
- Set `GITHUB_TOKEN` for higher limits
- The agent will show rate limit errors if exceeded

### Authentication Issues
- Verify `GCP_PROJECT_ID` is set correctly
- Check VertexAI API is enabled
- Ensure proper GCP authentication is configured

### Search Quality
- Use specific technical terms in your queries
- Include organization names when looking for specific implementations
- The agent works best with concrete technical questions

## Example Session

🤖 GitHub Research Agent started!

How does Temporal implement rate limiting on the server side?

🤖 Agent is thinking... 💭 I need to search for Temporal's rate limiting implementation. Let me start by exploring their repositories... 💭 Found the main temporal repository. Now searching for rate limiting code... 💭 Looking for rate limiter implementations in Go...

🤖 Agent: Based on my analysis of Temporal's codebase, here's how rate limiting is implemented on the server side:

Summary

Temporal implements rate limiting using a token bucket algorithm with Redis as the backing store...

[Detailed technical explanation with code references]