Content
# 🤖 Agenite
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<img src="./apps/docs/images/hero-dark.png" alt="Agenite Logo" height="200"/>
<p><strong>A modern, modular, and type-safe framework for building AI agents using typescript</strong></p>
</div>
<div align="center">
[](https://github.com/subeshb1/agenite/blob/main/LICENSE)
[](https://www.npmjs.com/package/@agenite/agent)
[](https://www.typescriptlang.org/)
[](https://github.com/subeshb1/agenite/pulls)
</div>
## 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
| Package | Description | Installation |
|---------|-------------|--------------|
| **Core packages** | | |
| `@agenite/agent` | Core agent orchestration framework for managing LLM interactions, tool execution, and state management | `npm install @agenite/agent` |
| `@agenite/tool` | Tool definition framework with type safety, schema validation, and error handling | `npm install @agenite/tool` |
| `@agenite/llm` | Base provider interface layer that enables abstraction across different LLM providers | `npm install @agenite/llm` |
| **Provider packages** | | |
| `@agenite/openai` | Integration with OpenAI's API for GPT models with function calling support | `npm install @agenite/openai` |
| `@agenite/anthropic` | Integration with Anthropic's API for Claude models | `npm install @agenite/anthropic` |
| `@agenite/bedrock` | AWS Bedrock integration supporting Claude and other models | `npm install @agenite/bedrock` |
| `@agenite/ollama` | Integration with Ollama for running models locally | `npm install @agenite/ollama` |
| **MCP package** | | |
| `@agenite/mcp` | Model Context Protocol client for connecting to standardized data sources and tools | `npm install @agenite/mcp` |
| **Middleware packages** | | |
| `@agenite/pretty-logger` | Colorful console logging middleware for debugging agent execution | `npm install @agenite/pretty-logger` |
For a typical setup, you'll need the core packages and at least one provider:
```bash
# 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
```typescript
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
```typescript
// 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
```typescript
// 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](https://docs.agenite.com):
- [Introduction](https://docs.agenite.com/introduction)
- [Quick start guide](https://docs.agenite.com/quickstart)
- [Core concepts](https://docs.agenite.com/core-concepts/overview)
- [Examples](https://docs.agenite.com/examples)
- [API reference](https://docs.agenite.com/api-reference/agent)
## 🤝 Community
- [GitHub Discussions](https://github.com/subeshb1/agenite/discussions)
- [Discord Community](https://discord.gg/v3TXcD6tUH)
## 🛠️ Development
```bash
git clone https://github.com/subeshb1/agenite.git
cd agenite
pnpm install
pnpm build
```
## 📄 License
MIT
## 🌟 Star history
[](https://star-history.com/#subeshb1/agenite&Date)
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