Key Features
Agentic Workflows
Create agents that use tools, memory, and reasoning to complete tasks.
Chain Composition
Build modular chains for prompt engineering, retrieval, and generation.
Memory & Context
Maintain conversation history and long-term memory across sessions.
Tool Integration
Connect to APIs, code interpreters, search engines, and databases.
Multi-Model Support
Works with OpenAI, Anthropic, Cohere, Hugging Face, and local models.
How It Works
Install LangChain
Use `pip install langchain` or `npm install langchain` for JS support.
Choose a Model
Connect to LLMs like GPT-4, Claude, or local models via wrappers.
Build Chains or Agents
Compose chains or define agents with tools and memory.
Integrate Tools
Add search, calculator, code interpreter, or custom APIs.
Deploy & Monitor
Run locally or in production with observability and tracing.
Code Example
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, Tool
llm = ChatOpenAI(model="gpt-4")
tools = [Tool(name="Calculator", func=lambda x: eval(x), description="Performs math")]
agent = initialize_agent(tools, llm, agent_type="zero-shot-react-description")
response = agent.run("What is 17 * 23?")
print(response)Use Cases
AI Agents
Build autonomous agents that reason and use tools to solve tasks.
RAG Systems
Combine LLMs with vector search for grounded, document-aware responses.
Chatbots
Create multi-turn conversational bots with memory and context.
Workflow Automation
Automate tasks using chains of LLM calls and tool invocations.
Developer Tools
Build code assistants, data explorers, and debugging agents.
Integrations & Resources
Explore LangChain’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Popular Integrations
- OpenAI, Anthropic, Cohere, Hugging Face
- Pinecone, Weaviate, FAISS, Qdrant for vector search
- LangSmith for observability and tracing
- Streamlit and Gradio for UI demos
- Zapier and Airbyte for data pipelines
- FastAPI and Flask for deployment
Helpful Resources
FAQ
Common questions about LangChain’s capabilities, usage, and ecosystem.