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The leading framework for building LLM-powered applications
LangChain is an open-source framework that simplifies building applications powered by large language models with chains, agents, and memory.
Intelligence Coverage
LangChain is an open-source framework for developing applications powered by large language models, created by Harrison Chase in 2022. It provides abstractions for chaining LLM calls, managing prompts, integrating with vector databases, building agents with tools, and managing conversation memory. LangChain has become the de facto standard for building RAG (Retrieval-Augmented Generation) systems, chatbots, autonomous agents, and document Q&A applications. Its ecosystem includes LangSmith for observability and LangServe for deployment.
LLM chaining & prompt management
RAG (Retrieval-Augmented Generation)
Agent & tool use framework
Vector store integrations (Pinecone, Chroma)
Conversation memory management
LangSmith for observability
LangServe for API deployment
Python & JavaScript SDKs
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LangChain founded
by LangChain Inc.
LLM chaining & prompt management
RAG (Retrieval-Augmented Generation)
Agent & tool use framework
Vector store integrations (Pinecone, Chroma)
LangChain today
Active platform with 4+ years of development
LangChain Community
Official Discord and GitHub Discussions community for LangChain — the leading framework for building LLM applications. Get help with integrations, share projects, discuss new features, and connect with the team. Active community of 100,000+ developers building with LLMs.
LangChain
The most widely used framework for building LLM-powered applications. LangChain provides abstractions for chains, agents, RAG pipelines, tool integrations, and memory. Supports Python and JavaScript. 90,000+ GitHub stars, 2,000+ integrations, and a huge ecosystem of extensions.
RAG (Retrieval-Augmented Generation)
A technique that combines a language model with real-time document retrieval to produce grounded, up-to-date answers.
MCP (Model Context Protocol)
An open standard by Anthropic that lets AI assistants connect to external tools, data sources, and services through a unified protocol.
Agentic AI
AI systems that can autonomously plan, take multi-step actions, use tools, and achieve goals without step-by-step human instruction.
Vector Database
A database purpose-built to store and query high-dimensional vector embeddings, enabling semantic similarity search at scale.
Fine-Tuning
Training a pre-trained language model on a smaller, task-specific dataset to improve its performance on a particular domain or task.
Embeddings
Dense numerical vector representations of text, images, or other data that capture semantic meaning in a form machines can compare.
Prompt Engineering
The discipline of designing and refining input prompts to elicit more accurate, reliable, and useful responses from AI language models.
LLM (Large Language Model)
A deep learning model trained on vast quantities of text that can generate, summarise, translate, and reason about language at human level or above.
AI Intelligence Review
DeepAITool Editorial TeamLast Updated
Jun 2, 2026
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