LC-StudyLab is an open-source platform demonstrating the full capabilities of LangChain v1.0.3, designed for developers to systematically learn and practice agent development. It serves as a learning resource, a template for production-grade AI agent systems, and a reference for best practices.

How It Works

This project employs a modular, five-stage design, progressively introducing core LangChain features: basic agents, RAG, LangGraph workflows, DeepAgents multi-agent systems, and Guardrails security. Built on Python 3.10+ with LangChain 1.0.3, LangGraph 1.0.2, and FastAPI, this phased approach allows users to master agent development incrementally, leveraging FAISS for vector storage and Pydantic for validation.

Quick Start & Requirements

Docker offers a recommended one-click deployment: clone, configure .env with OPENAI_API_KEY, and run docker-compose up -d. Local development requires Python 3.10+, Node.js 18+, and pnpm. Backend setup involves installing Python dependencies (requirements.txt) and configuring .env; frontend uses pnpm install and .env.local. Access frontend at http://localhost:3000 and API docs at http://localhost:8000/docs.

Highlighted Details

• Comprehensive LangChain v1.0.3 coverage, including advanced RAG with multi-format document loading and FAISS indexing, and stateful LangGraph workflows with persistence and human-in-the-loop.
• Implements DeepAgents for multi-agent collaboration, enabling sub-agent specialization and automated research plan generation.
• Integrates Guardrails for robust security, featuring input/output filtering, prompt injection detection, sensitive data handling, and Pydantic-based structured output validation.
• Full-stack architecture with FastAPI backend and Next.js frontend using shadcn/ui and Tailwind CSS.

Maintenance & Community

Core LangChain features and initial frontend framework are complete. Ongoing development focuses on frontend enhancements, performance optimization, and expanding test coverage. Contributions are welcomed via standard GitHub pull requests.

Licensing & Compatibility

Released under the MIT License, permitting commercial use and integration into proprietary applications without significant restrictions.

Limitations & Caveats

Frontend functionality enhancements and performance optimizations are still under active development. The project relies on external LLM APIs (e.g., OpenAI), necessitating API key configuration and incurring potential usage costs.