GitHub MCP Server Integration

This is a demonstration of using AIGNE Framework and GitHub MCP Server to interact with GitHub repositories. The example now supports both one-shot and interactive chat modes, along with customizable model settings and pipeline input/output.

flowchart LR

in(In)
out(Out)
agent(AI Agent)
github(GitHub MCP Agent)
searchRepos(Search Repositories)
getContents(Get Contents)
createFile(Create/Update File)
issue(Issues & PRs)

in --> agent <--> github

subgraph MCP Agent
  github <--> searchRepos
  github <--> getContents
  github <--> createFile
  github <--> issue
end

agent --> out

classDef inputOutput fill:#f9f0ed,stroke:#debbae,stroke-width:2px,color:#b35b39,font-weight:bolder;
classDef processing fill:#F0F4EB,stroke:#C2D7A7,stroke-width:2px,color:#6B8F3C,font-weight:bolder;

class in inputOutput
class out inputOutput
class agent processing
class github processing
class searchRepos processing
class getContents processing
class createFile processing
class issue processing

Following is a sequence diagram of the workflow to search for repositories and access contents:

sequenceDiagram
participant User
participant AI as AI Agent
participant G as GitHub MCP Agent
participant SR as Search Repositories
participant GC as Get Contents

User ->> AI: Search for repositories related to 'modelcontextprotocol'
AI ->> G: Use search_repositories function
G ->> SR: Execute search with query='modelcontextprotocol'
SR ->> G: Return repository list results
G ->> AI: Formatted repository information
AI ->> User: Found these repositories: modelcontextprotocol/servers, etc.
User ->> AI: Get README from the servers repo
AI ->> G: Use get_file_contents function
G ->> GC: Access README.md in modelcontextprotocol/servers
GC ->> G: Return file content
G ->> AI: Formatted file content
AI ->> User: Here's the README content: ...

Prerequisites

• Node.js (>=20.0) and npm installed on your machine
• An OpenAI API key for interacting with OpenAI's services
• GitHub Personal Access Token with appropriate permissions
• Optional dependencies (if running the example from source code):
  • Bun for running unit tests & examples
  • Pnpm for package management

Quick Start (No Installation Required)

Run the Example

export GITHUB_TOKEN=YOUR_GITHUB_TOKEN # Set your GitHub token

npx -y @aigne/example-mcp-github # Run the example

Connect to an AI Model

As an example, running npx -y @aigne/example-mcp-github requires an AI model. If this is your first run, you need to connect one.

• Connect via the official AIGNE Hub

Choose the first option and your browser will open the official AIGNE Hub page. Follow the prompts to complete the connection. If you're a new user, the system automatically grants 400,000 tokens for you to use.

• Connect via a self-hosted AIGNE Hub

Choose the second option, enter the URL of your self-hosted AIGNE Hub, and follow the prompts to complete the connection. If you need to set up a self-hosted AIGNE Hub, visit the Blocklet Store to install and deploy it: Blocklet Store.

• Connect via a third-party model provider

Using OpenAI as an example, you can configure the provider's API key via environment variables. After configuration, run the example again:

export OPENAI_API_KEY="" # Set your OpenAI API key here

For more details on third-party model configuration (e.g., OpenAI, DeepSeek, Google Gemini), see .env.local.example.

After configuration, run the example again.

Debugging

The aigne observe command starts a local web server to monitor and analyze agent execution data. It provides a user-friendly interface to inspect traces, view detailed call information, and understand your agent’s behavior during runtime. This tool is essential for debugging, performance tuning, and gaining insight into how your agent processes information and interacts with tools and models.

Start the observation server.

View a list of recent executions.

Installation

Clone the Repository

git clone https://github.com/AIGNE-io/aigne-framework

Install Dependencies

cd aigne-framework/examples/mcp-github

pnpm install

Run the Example

pnpm start # Run in one-shot mode (default)

# Run in interactive chat mode
pnpm start -- --interactive

# Use pipeline input
echo "Search for repositories related to 'modelcontextprotocol'" | pnpm start

Run Options

The example supports the following command-line parameters:

Parameter	Description	Default
--interactive	Run in interactive chat mode	Disabled (one-shot mode)
--model <provider[:model]>	AI model to use in format 'provider[:model]' where model is optional. Examples: 'openai' or 'openai:gpt-4o-mini'	openai
--temperature <value>	Temperature for model generation	Provider default
--top-p <value>	Top-p sampling value	Provider default
--presence-penalty <value>	Presence penalty value	Provider default
--frequency-penalty <value>	Frequency penalty value	Provider default
--log-level <level>	Set logging level (ERROR, WARN, INFO, DEBUG, TRACE)	INFO
--input, -i <input>	Specify input directly	None

Examples

# Run in chat mode (interactive)
pnpm start -- --interactive

# Set logging level
pnpm start -- --log-level DEBUG

# Use pipeline input
echo "Search for repositories related to 'modelcontextprotocol'" | pnpm start

Example

The following example demonstrates how to use the GitHub MCP server to search for repositories:

import { AIAgent, AIGNE, MCPAgent } from "@aigne/core";
import { OpenAIChatModel } from "@aigne/core/models/openai-chat-model.js";

// Load environment variables
const { OPENAI_API_KEY, GITHUB_TOKEN } = process.env;

// Initialize OpenAI model
const model = new OpenAIChatModel({
  apiKey: OPENAI_API_KEY,
});

// Initialize GitHub MCP agent
const githubMCPAgent = await MCPAgent.from({
  command: "npx",
  args: ["-y", "@modelcontextprotocol/server-github"],
  env: {
    GITHUB_TOKEN,
  },
});

// Create AIGNE
const aigne = new AIGNE({
  model,
  skills: [githubMCPAgent],
});

// Create AI agent with GitHub-specific instructions
const agent = AIAgent.from({
  instructions: `\
## GitHub Interaction Assistant
You are an assistant that helps users interact with GitHub repositories.
You can perform various GitHub operations like:
1. Searching repositories
2. Getting file contents
3. Creating or updating files
4. Creating issues and pull requests
5. And many more GitHub operations

Always provide clear, concise responses with relevant information from GitHub.
`,
});

// Example: Search for repositories
const result = await aigne.invoke(
  agent,
  "Search for repositories related to 'modelcontextprotocol'",
);

console.log(result);
// Output:
// I found several repositories related to 'modelcontextprotocol':
//
// 1. **modelcontextprotocol/servers** - MCP servers for various APIs and services
// 2. **modelcontextprotocol/modelcontextprotocol** - The main ModelContextProtocol repository
// ...

// Shutdown the aigne when done
await aigne.shutdown();

Available GitHub Operations

The GitHub MCP server provides a wide range of operations including:

1. Repository Operations:

   • Search repositories
   • Create repositories
   • Get repository information

2. File Operations:

   • Get file contents
   • Create or update files
   • Push multiple files in a single commit

3. Issue and PR Operations:

   • Create issues
   • Create pull requests
   • Add comments
   • Merge pull requests

4. Search Operations:

   • Search code
   • Search issues
   • Search users

5. Commit Operations:

   • List commits
   • Get commit details

License

This project is licensed under the MIT License.