LangChain is the most popular framework for building applications with large language models. It provides a unified interface to work with any LLM provider (OpenAI, Anthropic, Google, local models), tools for retrieval-augmented generation (RAG), and primitives for building autonomous agents. Founded by Harrison Chase in 2022, the company raised $40M+ in funding and powers AI applications at Elastic, Notion, Replit, and thousands of startups. In 2025, LangChain split into separate packages: langchain-core for primitives, langchain for high-level abstractions, and langgraph for agentic workflows.
Key Statistics
80K+
GitHub stars
Source: GitHub Jan 2026
2000+
Contributors
Source: GitHub Jan 2026
50+
LLM provider integrations
Source: LangChain Docs
50+
Vector store integrations
Source: LangChain Docs
Pros and Cons
✓ Pros
- • Most comprehensive LLM application framework
- • Provider-agnostic - easily switch between models
- • Extensive integrations and community
- • Great for RAG and document Q&A
- • LangSmith makes debugging much easier
✗ Cons
- • Can be over-engineered for simple use cases
- • API changes frequently between versions
- • Learning curve to understand all abstractions
- • Performance overhead vs direct API calls
- • Some examples use outdated patterns
Core Concepts
LangChain organizes AI application development around several key abstractions. Models are the LLM connections (ChatOpenAI, ChatAnthropic, etc.). Prompts are templates for constructing inputs. Chains are sequences of operations. Agents are LLMs that can use tools to accomplish tasks. Memory persists conversation state across interactions.
- → Models: Unified interface to Claude, GPT, Gemini, local models, etc.
- → Prompts: Templates with variables for consistent LLM interactions
- → Chains: Sequential operations (prompt → model → output → model → output)
- → Agents: LLMs with tool access that decide what actions to take
- → Memory: Conversation history for multi-turn interactions
- → Retrievers: Connect LLMs to your data (RAG)
RAG (Retrieval-Augmented Generation)
RAG is LangChain's killer feature - letting you give LLMs access to your private data. You chunk documents, create embeddings, store them in a vector database, then retrieve relevant chunks when answering questions. This grounds the LLM in your actual data instead of relying on its training knowledge. LangChain provides integrations for 50+ vector stores and 100+ document loaders.
- → Document Loaders: PDF, HTML, Markdown, databases, APIs
- → Text Splitters: Chunk documents for embedding
- → Embeddings: OpenAI, Cohere, HuggingFace models
- → Vector Stores: Pinecone, Weaviate, Chroma, pgvector
- → Retrievers: Similarity search with optional reranking
LangGraph for Agents
LangGraph is the newer LangChain library for building stateful, multi-step agents. Instead of simple chains, LangGraph lets you define complex workflows as graphs with conditional logic, parallel execution, and human-in-the-loop checkpoints. It's now the recommended approach for any agentic application.
- → Graph-based workflows with conditional branches
- → Parallel tool execution for faster agents
- → Human-in-the-loop checkpoints for approval steps
- → Streaming of intermediate steps
- → State persistence across sessions
Key Features
langchain offers these core capabilities:
- → Unified interface to 50+ LLM providers
- → RAG support with 50+ vector store integrations
- → 100+ document loaders for any data source
- → LangGraph for complex agentic workflows
- → LangSmith for tracing and debugging
- → LangServe for deploying chains as REST APIs
- → Active community with extensive examples
Use Cases
Here are the most common ways people use langchain:
- → Question-answering over private documents (RAG)
- → Chatbots with memory and context awareness
- → Autonomous agents that use tools and APIs
- → Data extraction and structuring from unstructured text
- → Multi-step reasoning and task decomposition
- → Integrating multiple LLMs in one application
Getting Started
Follow these steps to set up langchain:
- → Install: pip install langchain langchain-openai langchain-community
- → Set API keys as environment variables
- → Import ChatOpenAI or ChatAnthropic for your model
- → Create a prompt template with ChatPromptTemplate
- → Chain together: prompt | model | parser
- → For RAG: add document loader, splitter, embeddings, vector store
Official Resources
Key Takeaways
- → Most comprehensive LLM application framework
- → Provider-agnostic - easily switch between models
- → Extensive integrations and community
- → Great for RAG and document Q&A
Related Searches
Frequently Asked Questions
When should I use LangChain vs direct API calls?
Use LangChain when you need RAG (document Q&A), agents (tools and multi-step reasoning), or multi-provider support. For simple chatbots with a single provider, direct API calls may be simpler and faster.
What's the difference between LangChain and LlamaIndex?
LangChain is broader - chains, agents, and general LLM orchestration. LlamaIndex specializes in RAG and data indexing. Many projects use both: LlamaIndex for data ingestion and retrieval, LangChain for the overall application logic.
Should I use Chains or LangGraph?
For simple sequential workflows, Chains are fine. For anything with conditional logic, loops, or complex agent behavior, use LangGraph. It's becoming the recommended approach for all agentic applications.
How do I use LangChain with OpenClaw?
You can build custom AgentSkills for OpenClaw using LangChain for complex RAG or multi-step reasoning. The LangChain agent can be wrapped as an OpenClaw skill that gets triggered by user requests.
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