What is Agenite

Agenite is a powerful TypeScript framework designed for building sophisticated AI agents. It offers a modular, type-safe, and flexible architecture that simplifies the creation, composition, and control of AI agents with advanced capabilities.

Use cases

Use cases for Agenite include building chatbots, virtual assistants, automated data processing agents, and any AI-driven applications that require integration with external APIs and data sources.

How to use

To use Agenite, developers can install the package via npm and start building AI agents by leveraging its type-safe features, tool integrations, and provider-agnostic capabilities. The framework supports seamless switching between various AI service providers.

Key features

Key features of Agenite include type safety and excellent developer experience, robust tool integration, provider agnosticism, advanced architecture with bidirectional flow and state management, and a standardized model context protocol (MCP) for connecting LLMs to data sources.

Where to use

Agenite can be used in various fields such as software development, AI research, and any application requiring sophisticated AI agents capable of interacting with multiple data sources and service providers.

🤖 Agenite

A modern, modular, and type-safe framework for building AI agents using typescript

What is Agenite?

Agenite is a powerful TypeScript framework designed for building sophisticated AI agents. It provides a modular, type-safe, and flexible architecture that makes it easy to create, compose, and control AI agents with advanced capabilities.

✨ Key features

• Type safety and developer experience

  • Built from the ground up with TypeScript
  • Robust type checking for tools and agent configurations
  • Excellent IDE support and autocompletion

• Tool integration

  • First-class support for function calling
  • Built-in JSON Schema validation
  • Structured error handling
  • Easy API integration

• Provider agnostic

  • Support for OpenAI, Anthropic, AWS Bedrock, and Ollama
  • Consistent interface across providers
  • Easy extension for new providers

• Advanced architecture

  • Bidirectional flow using JavaScript generators
  • Step-based execution model
  • Built-in state management with reducers
  • Flexible middleware system

• Model context protocol (MCP)

  • Standardized protocol for connecting LLMs to data sources
  • Client implementation for interacting with MCP servers
  • Access to web content, filesystem, databases, and more

📦 Available packages

For a typical setup, you’ll need the core packages and at least one provider:

# Install core packages
npm install @agenite/agent @agenite/tool @agenite/llm

# Install your preferred provider
npm install @agenite/openai
# OR
npm install @agenite/bedrock

🚀 Quick start

import { Agent } from '@agenite/agent';
import { Tool } from '@agenite/tool';
import { BedrockProvider } from '@agenite/bedrock';
import { prettyLogger } from '@agenite/pretty-logger';

// Create a calculator tool
const calculatorTool = new Tool<{ expression: string }>({
  name: 'calculator',
  description: 'Perform basic math operations',
  inputSchema: {
    type: 'object',
    properties: {
      expression: { type: 'string' },
    },
    required: ['expression'],
  },
  execute: async ({ input }) => {
    try {
      const result = new Function('return ' + input.expression)();
      return { isError: false, data: result.toString() };
    } catch (error) {
      if (error instanceof Error) {
        return { isError: true, data: error.message };
      }
      return { isError: true, data: 'Unknown error' };
    }
  },
});

// Create an agent
const agent = new Agent({
  name: 'math-buddy',
  provider: new BedrockProvider({
    model: 'anthropic.claude-3-5-sonnet-20240620-v1:0',
  }),
  tools: [calculatorTool],
  instructions: 'You are a helpful math assistant.',
  middlewares: [prettyLogger()],
});

// Example usage
const result = await agent.execute({
  messages: [
    {
      role: 'user',
      content: [{ type: 'text', text: 'What is 1234 * 5678?' }],
    },
  ],
});

🏗️ Core concepts

Agents

Agents are the central building blocks in Agenite. An agent:

• Orchestrates interactions between LLMs and tools
• Manages conversation state and context
• Handles tool execution and results
• Supports nested execution for complex workflows
• Provides streaming capabilities for real-time interactions

Tools

Tools extend agent capabilities by providing specific functionalities:

• Strong type safety with TypeScript
• JSON Schema validation for inputs
• Flexible error handling
• Easy API integration

Providers

Currently supported LLM providers:

• OpenAI API (GPT models)
• Anthropic API (Claude models)
• AWS Bedrock (Claude, Titan models)
• Local models via Ollama

Model Context Protocol (MCP)

MCP is a standardized protocol for connecting LLMs to data sources:

• Client implementation for interacting with MCP servers
• Access to web content, filesystem, databases, and more
• Similar to how USB-C provides universal hardware connections

🔄 Advanced features

Multi-agent systems

// Create specialist agents
const calculatorAgent = new Agent({
  name: 'calculator-specialist',
  provider,
  tools: [calculatorTool],
  description: 'Specializes in mathematical calculations',
});

const weatherAgent = new Agent({
  name: 'weather-specialist',
  provider,
  tools: [weatherTool],
  description: 'Provides weather information',
});

// Create a coordinator agent
const coordinatorAgent = new Agent({
  name: 'coordinator',
  provider,
  agents: [calculatorAgent, weatherAgent],
  instructions: 'Coordinate between specialist agents to solve complex problems.',
});

Step-based execution

// Create an iterator for fine-grained control
const iterator = agent.iterate({
  messages: [{ role: 'user', content: [{ type: 'text', text: 'Calculate 25 divided by 5, then multiply by 3' }] }],
  stream: true,
});

// Process the stream with custom handling
for await (const chunk of iterator) {
  switch (chunk.type) {
    case 'agenite.llm-call.streaming':
      console.log(chunk.content);
      break;
    case 'agenite.tool-call.params':
      console.log('Using tool:', chunk.toolUseBlocks);
      break;
    case 'agenite.tool-result':
      console.log('Tool result:', chunk.result);
      break;
  }
}

📚 Documentation

For comprehensive documentation, visit docs.agenite.com:

• Introduction
• Quick start guide
• Core concepts
• Examples
• API reference

🤝 Community

• GitHub Discussions
• Discord Community

🛠️ Development

git clone https://github.com/subeshb1/agenite.git
cd agenite
pnpm install
pnpm build

📄 License

MIT

🌟 Star history