agenite

Agenite is a modern, modular framework for building AI agents using TypeScript. It emphasizes type safety and is designed to enhance the developer experience. It simplifies the creation, composition, and control of agents while offering powerful capabilities.

GitHub Stars

65

User Rating

Not Rated

Favorites

0

Views

21

Forks

9

Issues

2

Installation
Difficulty
Intermediate
Estimated Time
10-20 minutes
Requirements
Node.js: 18.0.0 or higher
npm: 8.0.0 or higher

Installation

Installation

Prerequisites

Please specify required software and versions:
Node.js: 18.0.0 or higher
npm: 8.0.0 or higher

Installation Steps

1. Clone Repository

bash
git clone https://github.com/subeshb1/agenite.git
cd agenite

2. Install Dependencies

bash
npm install

3. Start Server

bash
npm start

Troubleshooting

Common Issues

Issue: Server won't start Solution: Check Node.js version and reinstall dependencies.

Configuration

Configuration

Basic Configuration

Claude Desktop Setup

Edit ~/.config/claude-desktop/claude_desktop_config.json (macOS/Linux) or %APPDATA%\Claude\claude_desktop_config.json (Windows):
json
{
  "mcpServers": {
    "agenite-server": {
      "command": "node",
      "args": ["server.js"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

Environment Variables

Set the following environment variables as needed:
bash
export API_KEY="your-api-key"
export DEBUG="true"

Configuration Examples

Basic Configuration

json
{
  "mcpServers": {
    "example-mcp": {
      "command": "node",
      "args": ["server.js"],
      "env": {
        "PORT": "3000",
        "LOG_LEVEL": "info"
      }
    }
  }
}

Examples

Examples

Basic Usage

Here are basic usage examples for the MCP server:

Programmatic Usage

javascript
// JavaScript example (Node.js)
const { MCPClient } = require('@modelcontextprotocol/client');

const client = new MCPClient();
await client.connect();

// Execute tool
const result = await client.callTool('toolName', {
  parameter1: 'value1',
  parameter2: 'value2'
});

console.log(result);

Use Cases

Automating customer support using AI agents.
Enhancing application flexibility by switching between different AI providers.
Streamlining data analysis through integration with databases and file systems.
Utilizing multi-agent systems for distributing and executing complex tasks.