Ecosystem & Stack: langchain

An open-source MCP server connecting AI agents, IDEs, and applications to enterprise databases for data interaction, schema exploration, and code generation.

An enterprise-grade, production-ready framework for orchestrating complex multi-agent systems, designed for scalable AI applications and seamless integration.

A lightweight Python library that makes Anthropic Model Context Protocol (MCP) tools compatible with LangChain and LangGraph.

Transforms diverse unstructured data into structured Neo4j Knowledge Graphs using Large Language Models (LLMs) and LangChain.

A comprehensive, job-oriented guide for AI Agent development, covering core technologies, practical projects, and interview preparation.

Aix-DB is an AI-powered conversational BI system that transforms natural language into data insights and visualizations using LLMs, RAG, and a multi-agent architecture.

Accelerates the development and deployment of production-ready AI agents on Google Cloud with pre-built templates, CI/CD, evaluation, and observability.

A comprehensive Chinese-language guide to getting started with LangChain, enabling developers to build powerful applications powered by large language models.

Learn to design, train, and deploy a real-time financial advisor LLM system using a hands-on, three-pipeline approach.

An open-source LangChain implementation of the ChatGPT Code Interpreter, enabling sandboxed Python code execution for LLM applications.

A modular, API-driven RAG framework designed for building scalable, production-ready AI applications, addressing the complexities of deploying RAG systems beyond prototyping.

Deep Lake is an AI data runtime and database optimized for deep learning, offering multimodal data storage, querying, vector search, and streaming for LLM and deep learning applications.

An interactive learning platform providing tutorials and demos to master Milvus for building AI applications with unstructured data.

A comprehensive repository offering practical implementations and evaluation guidance for advanced and agentic Retrieval-Augmented Generation (RAG) techniques.

A comprehensive Linux-based tutorial for deploying and fine-tuning open-source LLMs/MLLMs, tailored for Chinese beginners.

An open-source project providing Chinese LLaMA-2 and Alpaca-2 large language models with enhanced Chinese capabilities and support for ultra-long contexts up to 64K.

A comprehensive guide to deploying large language models (LLMs) locally on CPU using Ollama, making advanced AI accessible without powerful GPUs.