Supervisor-Based Agent System for GitHub Automation

This project implements a supervisor-based agent system designed to automate GitHub-related tasks. It leverages LangChain, Streamlit, and other libraries to create a modular, state-driven workflow for managing repositories, commits, and other GitHub operations.

Key Features

• Modular Agents: Specialized agents for tasks such as creating repositories, commits, and README files.
• Supervisor Agent: Orchestrates task execution by delegating to other agents.
• State Management: Integration with LangChain for managing state and tool invocation.
• Streamlit UI: Provides an interactive user interface for input and workflow execution.

Agents Overview

• create_repo: Creates a new GitHub repository.
• create_readme_file: Generates the content for a README file.
• create_commit: Creates a commit in a specified repository.
• generate_code: Generates Python code based on a given prompt.
• list_repo_branches: Lists all branches of a specified repository.
• general_assistant: Handles general-purpose tasks and queries.

Workflow

The system uses a supervisor agent to analyze user input and delegate tasks to the appropriate agents. The workflow is managed using a state graph, ensuring modularity and flexibility.

Checkpointing and Memory

• In-Memory Store: Used for temporary storage of state and checkpoints.
• SQLite Checkpointing: Provides persistent storage for workflow checkpoints.

Example Query

• Create a Repository: "Create a private repository named my-repo with a description 'My new project'."
• Generate a README: "Generate a README file for the repository my-repo."

Prerequisites

• Python 3.8 or higher
• A valid GitHub access token
• Google API key for LLM integration

Installation

1. Clone the repository:

   git clone <repository-url>
   cd <repository-directory>

2. Create a virtual environment and activate it:

   python3 -m venv .venv
   source .venv/bin/activate

3. Install requirements

   pip install -r requirements.txt

4. Create a .env file in the root directory and add the environment variables defined in example.env:

Run the Streamlit application:

   streamlit run supervisor_based_1.py

Enter your query in the input field and click the "Run" button to execute the workflow.