For the GitHub MCP: Smart Orchestration & Scalable Innovation

Future-proof your AI workflows with LangGraph’s Selector MCP Server – where smart orchestration meets scalable, GitHub-native innovation for modern dev teams." )

Visit Repository

✨ Developer Tools

4.8(92 reviews)

138 saves

64 comments

39% of users reported increased productivity after just one week

About For the GitHub MCP

What is For the GitHub MCP: Smart Orchestration & Scalable Innovation?

This framework combines modular component processing (MCP) with advanced workflow automation, enabling developers to dynamically manage GitHub workflows through Dockerized microservices. Its smart orchestration engine adapts to real-time data inputs from APIs like OpenAI and AbuseIPDB, while scalable innovation allows easy expansion via pluggable modules.

How to Use For the GitHub MCP: Smart Orchestration & Scalable Innovation?

Begin by cloning the repository and configuring sensitive credentials in the .env file. Build Docker containers for each MCP component (e.g., GitHub, Slack, and Selector AI modules), then execute the main selectorplus.py script. Interactions occur through terminal prompts that trigger orchestrated workflows across connected services.

For the GitHub MCP Features

Key Features of For the GitHub MCP: Smart Orchestration & Scalable Innovation?

Modular Docker architecture: Independent containers for GitHub, sequential thinking, and maps modules ensure clean separation of concerns.
API agnosticism: Supports integration with OpenAI, Google Maps, and Slack through standardized environment variables.
Dynamic tracing: LangSmith tracing capability provides end-to-end visibility into workflow execution paths.

Use Cases of For the GitHub MCP: Smart Orchestration & Scalable Innovation?

Automate code review workflows by combining GitHub API actions with AI-driven analysis. Create cross-platform incident response pipelines linking Slack alerts with AbuseIPDB checks. Developers can also prototype new features in isolated MCP containers before integrating into the main workflow engine.

For the GitHub MCP FAQ

FAQ from For the GitHub MCP: Smart Orchestration & Scalable Innovation?

Why use Docker for each module?: Ensures dependency isolation and simplifies scaling individual components independently.
Can I omit certain API keys?: Mandatory keys (e.g., SELECTOR_AI_API_KEY) are required for core functionality, while others like GOOGLE_MAPS_API_KEY depend on specific workflows.
What if containers fail to start?: Check Docker daemon status and ensure all prerequisites like Python 3.8+ are properly installed.

Content

SelectorPlus LangGraph Project Setup This guide will walk you through the steps to set up and run the SelectorPlus LangGraph project.

Prerequisites Docker: Ensure Docker is installed on your system. Python 3.8+: Make sure Python 3.8 or a later version is installed. Git: For cloning the repository. Setup Instructions Clone the Repository:

git clone [<your_repository_url>](https://github.com/automateyournetwork/Selector-)
cd Selector-

Create a .env File:

In the root directory of your project, create a file named .env.

Copy and paste the following environment variables into the .env file:

OPENAI_API_KEY=
WEATHER_API_KEY=
ABUSEIPDB_API_KEY=
LANGSMITH_TRACING=true
LANGSMITH_API_KEY=""
LANGSMITH_ENDPOINT="https://api.smith.langchain.com"
LANGSMITH_PROJECT="SelectorPlus"
GITHUB_TOKEN=""
GOOGLE_MAPS_API_KEY=""
SLACK_BOT_TOKEN=""
SLACK_TEAM_ID=""
SELECTOR_AI_API_KEY=
SELECTOR_URL=

Important: Keep your .env file secure, as it contains sensitive API keys. Do not commit it to version control.

Build Docker Images:

Navigate to each directory containing a Dockerfile and build the Docker images.

Bash

For the GitHub MCP

docker build -t github-mcp ./github

For the Google Maps MCP

docker build -t maps-mcp ./google_maps

For the Sequential Thinking MCP

docker build -t sequential-thinking-mcp ./sequentialthinking

For the Slack MCP

docker build -t slack-mcp ./slack

For the Selector AI MCP

docker build -t selector-mcp ./selector Run the LangGraph Application:

Navigate to the directory containing your main Python script (the one that runs the LangGraph application).

Run the Python script:

python selectorplus.py

Interact with the Application:

Follow the prompts in the terminal to interact with your LangGraph application.

The application will use the Docker containers and environment variables to execute the tools and interact with external services.

Important Notes

API Keys: Ensure that all API keys are valid and have the necessary permissions.

Docker Containers: Make sure that all Docker containers are running correctly. You can check the status of your containers using docker ps.

Error Handling: Pay close attention to the logs and error messages in the terminal to diagnose any issues.

Security: Be cautious when handling API keys and sensitive information.

This README should provide a clear and concise guide to setting up and running your LangGraph project. If you encounter any issues, refer to the logs and error messages for further debugging.