LangChain - AI SDKs and Libraries Tool

Overview

LangChain is an open-source Python framework for building context-aware applications powered by large language models (LLMs). It provides standard interfaces and composable building blocks—LLM wrappers, prompt templates, chains, agents, memory, embeddings, vector store connectors, and document loaders—so developers can combine retrieval, reasoning, and tool use into production-ready pipelines. Popular high-level primitives include RetrievalQA and ConversationalRetrievalChain for retrieval-augmented generation (RAG), and an agent system that lets LLMs call external tools and perform multi-step problem solving. Designed for interoperability, LangChain integrates with many LLM providers (OpenAI, Anthropic, Hugging Face, Cohere), common vector stores (FAISS, Chroma, Pinecone, Milvus, Qdrant, Weaviate), and embedding APIs. It supports synchronous and asynchronous workflows, streaming outputs, output parsers/schemas, callback hooks for observability, and local or cloud-hosted deployments. According to the GitHub repository, LangChain is a widely adopted project (123,831 stars, MIT license) with active development and a large contributor base, making it a practical choice for teams building RAG systems, multi-tool agents, chat assistants, and document understanding applications.

GitHub Statistics

• Stars: 123,831
• Forks: 20,402
• Contributors: 470
• License: MIT
• Primary Language: Python
• Last Updated: 2026-01-09T16:22:49Z
• Latest Release: langchain-core==1.2.6

The GitHub repository is highly active and mature: 123,831 stars, 20,402 forks, and 470 contributors (source: repository metadata). Last commit recorded on 2026-01-09 indicates continuous development. Frequent merges, a large contributor count, and many forks suggest strong community adoption and rapid iteration. Issues and PRs are actively processed; the project maintains backward compatibility while adding connectors, agent improvements, and performance optimizations. The codebase is primarily Python, supports async and sync APIs, and is released under the MIT license.

Installation

Install via pip:

pip install langchain

pip install git+https://github.com/langchain-ai/langchain

Key Features

• Composable chains: link prompts, LLM calls, and post-processing into reusable pipelines.
• Agent frameworks: tools and toolkits let LLMs call external APIs and take multi-step actions.
• Memory primitives: short-term and long-term memory stores for conversational state.
• Retrieval-augmented generation: RetrievalQA and ConversationalRetrievalChain for RAG workflows.
• Vector store connectors: integrations for FAISS, Chroma, Pinecone, Milvus, Qdrant, Weaviate.
• Embeddings support: unified interface for embedding APIs from OpenAI, Cohere, Hugging Face, and more.
• Document loaders: ready-made loaders for PDFs, HTML, Notion, Google Drive, and common formats.
• Prompt templates and output parsers: structured prompts, partials, and schema-validated outputs.
• Streaming & async: streaming LLM responses and full asynchronous APIs for high-throughput apps.
• Callbacks and tracing: hooks for logging, telemetry, and observability during chain execution.

Community

LangChain has a large, active community with thousands of forks and hundreds of contributors. The project maintains active issue and PR handling on GitHub, community channels (forum/Discord), and an ecosystem of third-party connectors and examples. According to the repository metadata, frequent commits and broad contributor participation indicate healthy momentum and strong ecosystem support.

Key Information

• Category: SDKs and Libraries
• Type: AI SDKs and Libraries Tool