AutoLearn은 ** Model Context Protocol (MCP) 서버** 이며, AI Agent를 동적으로 만들고 재사용할 수 있는 새로운 ** skills** (기능 코드 워크플로우) 을 자연 언어로 사용합니다.

정적 MCP 서버와 달리 AutoLearn은 ** 코딩 에이전트**를 소개합니다.

• 추측을 crystalized memory (Python function)로 변환합니다.
• 자동으로 수정할 수 있습니다.
• 업데이트 ** MCP spec ** 역동적으로 그래서 consuming 에이전트는 새로운 기술을 즉시 사용할 수 있습니다.

더 알아보기: https://www.autolearn.dev/

이 프로젝트는 **frontend 데모 앱 ** 사용자가 할 수있는 곳:

• AutoLearn을 사용하는 consuming 대리인과 채팅.
• 새로운 기술에 대한 자동 생성 된 Python 코드를 참조하십시오.
• 실시간 업데이트된 MCP spec 보기.
• 상호 작용하는 기술을 실행합니다.

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특징

• Dynamic Skill Creation: OpenAI 통합을 통해 Natural language → Python code 워크플로우
• Crystalized Memory: 실행 가능한 코드로 보존된 사용 또는 복잡한 이유
• MCP Server: HTTP 전송에 전체 JSON-RPC 2.0 프로토콜 준수
• Real-Time 업데이트: 스킬에 대한 WebSocket 이벤트 added, Skill executed, mcp updated

AutoLearn

AutoLearn is a Model Context Protocol (MCP) server that lets AI agents dynamically create and reuse new skills (functional code workflows) from natural language.

Unlike static MCP servers, AutoLearn introduces a coding agent that:

• Converts reasoning traces into crystalized memory (Python functions).
• Decides automatically which workflows to crystalize.
• Updates its MCP spec dynamically so consuming agents can use new skills immediately.

Learn More: https://www.autolearn.dev/

The project includes a frontend demo app where users can:

• Chat with a consuming agent that uses AutoLearn.
• See auto-generated Python code for new skills.
• View the updated MCP spec in real time.
• Execute skills interactively.

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Features

• Dynamic Skill Creation: Natural language → Python code workflows via OpenAI integration
• Crystalized Memory: Frequently used or complex reasoning preserved as executable code
• MCP Server: Full JSON-RPC 2.0 protocol compliance over HTTP transport
• Real-Time Updates: WebSocket events for skill_added, skill_executed, mcp_updated
• Persistence: SQLite database for skills, sessions, and operational data
• Frontend Demo: T3 stack with chat, skill viewer, MCP spec viewer, and execution panel
• Comprehensive Testing: 54/54 tests passing with full integration coverage

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Tech Stack

• Backend: Python 3.11+, FastAPI, OpenAI API
• Frontend: T3 Stack (Next.js, TypeScript, Tailwind CSS, tRPC) with shadcn/ui components
• Testing: Pytest (backend), Vitest (frontend)

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Repository Structure

autolearn/
├── backend/          # FastAPI MCP server + skill engine
│   ├── app.py        # FastAPI application
│   ├── schemas.py    # Pydantic models
│   ├── skill_engine.py # Skill registry and execution
│   └── openai_client.py # OpenAI integration
├── frontend/         # T3 Stack frontend (Next.js, TypeScript, Tailwind, tRPC)
│   ├── src/          # Source code
│   │   ├── components/   # UI components using shadcn/ui
│   │   ├── pages/    # Next.js pages
│   │   └── server/   # tRPC router definitions
├── tests/            # Unit and integration tests
├── docs/             # Documentation (PRD, design notes)
├── skills.db         # SQLite database for skill persistence
└── README.md

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Getting Started

Prerequisites

• Python 3.11+
• OpenAI API key

1. Clone the Repository

git clone https://github.com/tarkaai/autolearn.git
cd autolearn

2. Setup Backend and Frontend

python -m venv .venv
source .venv/bin/activate   # or .venv\Scripts\activate on Windows
pip install -e .   # Install in development mode

# Navigate to the frontend directory
cd frontend

# Install dependencies
npm install