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Create agents that use tools, memory, and reasoning to complete tasks.
Build modular chains for prompt engineering, retrieval, and generation.
Maintain conversation history and long-term memory across sessions.
Connect to APIs, code interpreters, search engines, and databases.
Works with OpenAI, Anthropic, Cohere, Hugging Face, and local models.
Use `pip install langchain` or `npm install langchain` for JS support.
Connect to LLMs like GPT-4, Claude, or local models via wrappers.
Compose chains or define agents with tools and memory.
Add search, calculator, code interpreter, or custom APIs.
Run locally or in production with observability and tracing.
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)Build autonomous agents that reason and use tools to solve tasks.
Combine LLMs with vector search for grounded, document-aware responses.
Create multi-turn conversational bots with memory and context.
Automate tasks using chains of LLM calls and tool invocations.
Build code assistants, data explorers, and debugging agents.
Explore LangChain’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about LangChain’s capabilities, usage, and ecosystem.