clear-ai-core

Clear AI - A modern TypeScript framework for building AI-powered applications with tool execution and workflow orchestration. Perfect for CLI tools, APIs, and server applications.

📚 Documentation

📖 Full Documentation - Complete guides, API reference, and examples

• Getting Started
• Architecture Overview
• API Reference
• Package Guides

🚀 Quick Start

npm install clear-ai-core

import { ClearAI } from "clear-ai-core";

// Initialize the framework for CLI usage
const ai = new ClearAI({
  llm: {
    openaiApiKey: "your-key",
    ollamaBaseUrl: "http://localhost:11434",
  },
  server: {
    port: 3001,
  },
});

// Start everything
await ai.init();

// Access services
const mcpServer = ai.getMCP();
const llmService = ai.getLLM();
const toolService = ai.getTools();

📦 What's Included

MCP (Model Context Protocol) - clear-ai-mcp-basic

• MCPServer - Full MCP protocol implementation
• ToolRegistry - Dynamic tool registration and management
• Built-in Tools - API calls, JSON processing, file operations

Shared Services - clear-ai-shared

• SimpleLangChainService - Multi-provider LLM integration
• ToolExecutionService - Tool registration and execution
• SimpleWorkflowService - Workflow orchestration
• Logger - Structured logging utilities

Server - clear-ai-server

• Express API - RESTful endpoints for tool execution
• Workflow Execution - LangGraph workflow orchestration
• Health Monitoring - System health and status endpoints

Client - @clear-ai/client (Private - Local Development Only)

• React Components - Pre-built UI components with Storybook
• Theme System - Multiple visual themes
• Web Interface - Browser-based tool execution interface

🎯 Usage Examples

Basic Tool Execution

import { MCPServer, ToolRegistry } from "clear-ai-mcp-basic";

const server = new MCPServer();
const tools = server.getToolRegistry();

// Execute an API call
const result = await tools.executeTool("api_call", {
  url: "https://api.example.com/users/1",
  method: "GET",
});

LLM Integration

import { SimpleLangChainService } from "clear-ai-shared";

const llm = new SimpleLangChainService({
  openaiApiKey: "your-key",
  ollamaBaseUrl: "http://localhost:11434",
});

const response = await llm.complete("Hello, world!", {
  model: "ollama",
  temperature: 0.7,
});

Workflow Execution

import { SimpleWorkflowService, ToolExecutionService } from "clear-ai-shared";

const toolService = new ToolExecutionService(llmConfig);
const workflow = new SimpleWorkflowService(llmConfig, toolService);

const result = await workflow.executeWorkflow(
  "Get weather data and format it nicely"
);

Server API

import { createServer } from "clear-ai-server";

const server = createServer({
  port: 3001,
  mcpConfig: { tools: ["api_call", "json_reader"] },
  llmConfig: { openaiApiKey: "your-key" },
});

await server.start();

CLI Application

import { ClearAI, MCPServer } from "clear-ai-core";

async function main() {
  const ai = new ClearAI({
    llm: { openaiApiKey: process.env.OPENAI_API_KEY },
    server: { port: 3001 },
  });

  await ai.init();

  // Use the MCP server for tool execution
  const mcpServer = ai.getMCP();
  const result = await mcpServer.getToolRegistry().executeTool("api_call", {
    url: "https://api.example.com/data",
    method: "GET",
  });

  console.log("Result:", result);
}

main().catch(console.error);

🏗️ Architecture

clear-ai-core
├── clear-ai-mcp-basic # Model Context Protocol
├── clear-ai-shared    # Shared services & utilities
├── clear-ai-server    # Express API server
└── @clear-ai/client   # React web interface (private)

🔧 Configuration

Environment Variables

# LLM Configuration
OPENAI_API_KEY=your-key
OLLAMA_BASE_URL=http://localhost:11434
MISTRAL_API_KEY=your-key
GROQ_API_KEY=your-key

# Langfuse (Observability)
LANGFUSE_SECRET_KEY=your-key
LANGFUSE_PUBLIC_KEY=your-key
LANGFUSE_BASE_URL=https://cloud.langfuse.com

# Server Configuration
PORT=3001
NODE_ENV=production

Framework Configuration

const config: ClearAIConfig = {
  mcp: {
    tools: ["api_call", "json_reader", "file_reader"],
  },
  llm: {
    openaiApiKey: process.env.OPENAI_API_KEY,
    ollamaBaseUrl: process.env.OLLAMA_BASE_URL,
    langfuseSecretKey: process.env.LANGFUSE_SECRET_KEY,
  },
  server: {
    port: 3001,
    cors: { origin: ["http://localhost:3000"] },
  },
};

🛠️ Development

Prerequisites

• Node.js >= 18.0.0
• npm >= 10.0.0

Setup

# Clone the repository
git clone https://github.com/wsyeabsera/clear-ai.git
cd clear-ai

# Install dependencies
npm install

# Build all packages
npm run build

# Start development servers
npm run dev

Available Scripts

• npm run dev - Start all packages in development mode
• npm run build - Build all packages
• npm run lint - Run ESLint on all packages
• npm run type-check - Run TypeScript type checking
• npm run clean - Clean all build artifacts

📚 Documentation

• Getting Started - Quick start guide
• API Reference - Complete API documentation
• Tutorials - Step-by-step tutorials
• Architecture - System architecture overview

📖 Documentation Access

From NPM

When you install the package, you can access documentation via:

npm docs clear-ai-core
# or
npm home clear-ai-core

Direct Links

• Main Documentation: https://wsyeabsera.github.io/Clear-AI/
• Getting Started: https://wsyeabsera.github.io/Clear-AI/docs/intro
• API Reference: https://wsyeabsera.github.io/Clear-AI/docs/api/overview

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Commit your changes: git commit -m 'Add amazing feature'
4. Push to the branch: git push origin feature/amazing-feature
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• LangChain - LLM framework
• Model Context Protocol - Tool protocol
• Express.js - Web framework
• React - UI library

📞 Support

• 📖 Documentation: GitHub Docs
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

Made with ❤️ by the Clear AI Team