LangChain
by Community
The agent engineering platform.
OSS
LangChain
Added 1 June 2026
Overview
LangChain is a Python framework for building applications with large language models through composable chains, agents, and memory abstractions. It provides tools to connect LLMs to external data sources, APIs, and reasoning loops, reducing boilerplate for common patterns like retrieval-augmented generation and multi-step workflows.
Best for
Best for
Python developers building prototype or production LLM applications that need to orchestrate multiple tools and data sources
Use cases
- Building retrieval-augmented generation (RAG) systems that query documents before generating responses
- Creating multi-step agent workflows that reason and call external tools or APIs
- Prototyping LLM applications with pluggable model and data source integrations
Notes
LangChain is a Python framework for building applications with large language models through composable chains, agents, and memory abstractions. It provides tools to connect LLMs to external data sources, APIs, and reasoning loops, reducing boilerplate for common patterns like retrieval-augmented generation and multi-step workflows.
138,234 stars on GitHub. Last updated 2026-06-01. Licensed MIT.
Use cases
- Building retrieval-augmented generation (RAG) systems that query documents before generating responses
- Creating multi-step agent workflows that reason and call external tools or APIs
- Prototyping LLM applications with pluggable model and data source integrations
Pros
- Large ecosystem of integrations with LLM providers, vector stores, and external tools
- Well-established patterns for common tasks like document loading, chunking, and memory management
- Active community with extensive documentation and examples
Cons
- Steep learning curve for complex agent design and debugging chains in production
- Frequent API changes and deprecations across versions can break existing code
- Abstraction layers can obscure underlying LLM behavior and increase latency
Indexed from awesome-llm and enriched against its public facts.
Pros
- Large ecosystem of integrations with LLM providers, vector stores, and external tools
- Well-established patterns for common tasks like document loading, chunking, and memory management
- Active community with extensive documentation and examples
Cons
- Steep learning curve for complex agent design and debugging chains in production
- Frequent API changes and deprecations across versions can break existing code
- Abstraction layers can obscure underlying LLM behavior and increase latency
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Together AI
Various
Build what's next on the AI Native Cloud. Full-stack AI platform for inference, fine-tuning, and GPU clusters — powered by cutting-edge research.
AdalFlow
Community
AdalFlow: The library to build & auto-optimize LLM applications.
AI.JSX
Community
The AI Application Framework for Javascript
AutoGen
Microsoft
Microsoft's framework for multi-agent conversations. Agents that talk to each other to solve hard problems.
Command R-35B
Community
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Dify
Community
Production-ready platform for agentic workflow development.
DSPy
Stanford NLP
Programming, not prompting. Declare what you want, compile prompts and weights against an objective.
Epsilla
Community
An all-in-one LLM Agent platform with your private data and knowledge, delivers your production-ready AI Agents on Day 1.
Haystack
Community
Create agentic, context engineered AI systems using Haystack’s modular and customizable building blocks, built for real-world, production-ready applications.
Intelli
Community
Build multi-model chatbots and agents from intent.
Just-Chat
Community
Make your LLM agent and chat with it simple and fast!
Langroid
Community
Harness LLMs with Multi-Agent Programming
LazyLLM
Community
Easiest and laziest way for building multi-agent LLMs applications.
LiteChain
Community
Build robust LLM applications with true composability 🔗
LLFn
Community
A light-weight framework for creating applications using LLMs
LlamaIndex
LlamaIndex
The data framework for LLM apps. RAG, ingestion, structured extraction, agents over your data.
llm-chain
Community
llm-chain is a powerful rust crate for building chains in large language models allowing you to summarise text and complete complex tasks
Llmware
Community
Unified framework for building enterprise RAG pipelines with small, specialized models
magentic
Community
Seamlessly integrate LLMs as Python functions
Mastra
Mastra
TypeScript-first agent framework. Workflows, agents, tools, memory, evals, in one consistent shape.
MiniChain
Community
A tiny library for coding with large language models.
ModelFusion
Community
The TypeScript library for building AI applications.
OpenAGI
Community
OpenAGI: When LLM Meets Domain Experts
Rigging
Community
Lightweight LLM Interaction Framework
Scale Spellbook
Community
Accelerate and scale Generative AI across your enterprise with the platform to transform your data into customized enterprise-ready Generative AI applications.
Semantic Kernel
Microsoft
Microsoft's enterprise-flavoured framework for AI agents. .NET-first, with Python and Java siblings.
SimpleAIChat
Community
Python package for easily interfacing with chat apps, with robust features and minimal code complexity.
TypeChat
Community
TypeChat is a library that makes it easy to build natural language interfaces using types.
Vercel AI SDK
Vercel
The de facto TypeScript SDK for AI apps. Streaming, tools, multi-model, and now an agent loop.
Wordware
Community
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