https://github.com/ctvs/a2a-langgraph-agent
A sample project to demonstrate how a langgraph ReAct agent can be wrapped with the A2A protocol
https://github.com/ctvs/a2a-langgraph-agent
a2a langchain langgraph llm-agent mcp mcp-client openai react-agent
Last synced: 3 months ago
JSON representation
A sample project to demonstrate how a langgraph ReAct agent can be wrapped with the A2A protocol
- Host: GitHub
- URL: https://github.com/ctvs/a2a-langgraph-agent
- Owner: ctvs
- License: mit
- Created: 2025-07-01T04:53:39.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-01T05:08:22.000Z (3 months ago)
- Last Synced: 2025-07-01T06:23:00.437Z (3 months ago)
- Topics: a2a, langchain, langgraph, llm-agent, mcp, mcp-client, openai, react-agent
- Language: TypeScript
- Homepage:
- Size: 43.9 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# A2A LangGraph ReAct Agent
A TypeScript project that implements an A2A-compatible agent using LangGraph's ReAct agent with Model Context Protocol (MCP) integration.
## Project Structure
```
a2a-langgraph-agent/
├── src/ # TypeScript source files
│ ├── index.ts # Server entry point
│ ├── cli.ts # CLI client for interacting with the agent
│ └── a2a/ # A2A implementation
│ ├── executor.ts # LangGraph React agent executor
│ ├── agentCard.ts # Agent card definition
│ ├── tools.ts # Local tool definitions
│ └── mcp/ # Model Context Protocol integration
│ ├── client.ts # MCP client implementation
│ └── mcp-servers.json # MCP server configurations
├── dist/ # Compiled JavaScript output (generated)
├── tsconfig.json # TypeScript configuration
├── package.json # Project configuration and dependencies
└── README.md # This file
```
## Prerequisites
- Node.js (v16 or higher recommended)
- npm (comes with Node.js)
## Installation
Clone the repository and install dependencies:
```bash
git clone
cd a2a-langgraph-agent
npm install
```
### OpenAI API Key Configuration
The agent uses OpenAI's models through LangChain. You'll need to set up your OpenAI API key as an environment variable:
```bash
# For Linux/macOS
export OPENAI_API_KEY=your-api-key-here
# For Windows (Command Prompt)
set OPENAI_API_KEY=your-api-key-here
# For Windows (PowerShell)
$env:OPENAI_API_KEY="your-api-key-here"
```
Alternatively, you can create a `.env` file in the project root:
```
OPENAI_API_KEY=your-api-key-here
```
## Running the Agent
### Start the Agent Server
In one terminal, start the agent server:
```bash
npm run dev
```
This will start the A2A server on http://localhost:3000.
### Use the CLI Client
In another terminal, use the CLI client to interact with the agent:
```bash
npm run cli
```
You can also specify a different server URL:
```bash
npm run cli http://localhost:3000
```
## CLI Commands
- Type your message and press Enter to send it to the agent
- `/new` - Start a new session (clears task and context IDs)
- `/exit` - Exit the CLI
## Available Scripts
In the project directory, you can run:
### `npm run build`
Compiles the TypeScript code to JavaScript in the `dist` directory.
### `npm run start`
Runs the compiled JavaScript from the `dist` directory.
### `npm run dev`
Runs the agent server directly using tsx without compiling.
### `npm run cli`
Runs the CLI client to interact with the agent.
## Features
- **A2A Protocol Support**: Implements the A2A protocol for agent communication
- **LangGraph React Agent**: Uses LangChain's React agent for reasoning and tool use
- **Built-in Tools**: Includes calculator and time tools out of the box
- **MCP Integration**: Connects to external Model Context Protocol servers
- **CLI Client**: Interactive command-line interface for testing the agent
## Built-in Tools
The agent comes with several built-in tools defined in `src/a2a/tools.ts`:
### Calculator Tool
Performs basic mathematical operations:
- Addition
- Subtraction
- Multiplication
- Division
Example usage: "Calculate 25 divided by 5" or "What's 123 + 456?"
### Current Time Tool
Returns the current date and time.
Example usage: "What time is it?" or "Tell me the current date"
## MCP Integration
### Configuring MCP Servers
The agent can connect to external Model Context Protocol (MCP) servers to extend its capabilities. MCP servers are configured in the `src/a2a/mcp/mcp-servers.json` file.
Example configuration:
```json
{
"github": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-github"
],
"env": {
"GITHUB_PERSONAL_ACCESS_TOKEN": "your-github-token"
}
}
}
```
Each entry in the JSON file represents an MCP server with:
- A unique name (e.g., "github")
- The command to run the server
- Command arguments
- Environment variables needed by the server
The agent will automatically start these MCP servers when it initializes and make their tools available to the LangGraph React agent.
### Available MCP Servers
The project is configured to work with various MCP servers, including:
- **GitHub**: Interact with GitHub repositories, issues, and pull requests
- **Weather**: Get weather forecasts and conditions
- **Search**: Perform web searches
- **File System**: Access and manipulate files
To use these servers, you'll need to configure them in the `mcp-servers.json` file with appropriate credentials.
## Dependencies
- **@a2a-js/sdk**: A2A JavaScript SDK
- **@langchain/langgraph**: LangChain's LangGraph library for agent workflows
- **@langchain/openai**: LangChain's OpenAI integration
- **@langchain/mcp-adapters**: Model Context Protocol adapters for LangChain
- **express**: Web server framework
- **uuid**: For generating unique IDs
- **zod**: Schema validation for tool definitions
## License
MIT