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LangChain

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Platform and open-source framework for building LLM applications with chains, agents, retrieval, and observability. LangChain provides integrations for models, vector stores, and deployment via LangSmith and LangGraph.

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How it works / How to use

Platform and open-source framework for building LLM applications with chains, agents, retrieval, and observability. LangChain provides integrations for models, vector stores, and deployment via LangSmith and LangGraph.

  1. Create an account and API key in LangChain following the official docs.
  2. Pick the model endpoint that matches latency, cost, and capability needs.
  3. Prototype with a short prompt and log token usage.
  4. Add retries, timeouts, and content filters appropriate to your application.

How to prompt

LangChain's composable primitives map cleanly to retrieval-augmented generation stacks.

Sketch a LangChain RAG pipeline: document loaders, chunking strategy, embedding model, vector store, and retriever for a company wiki chatbot.

Best output tips

  • Set spend alerts where the dashboard allows.
  • Cache stable completions when safe to reduce cost.
  • Rotate API keys on a regular schedule.
  • Verify current plans and limits on the official LangChain website.

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Product Details

Pricing, features, limits and latest updates

LangChain

Platform and open-source framework for building LLM applications with chains, agents, retrieval, and observability. LangChain provides integrations for models, vector stores, and deployment via LangSmith and LangGraph.

No listed price

Pricing Plans

No listed plans.

Key Features

API

api workflow (www.langchain.com).

Open Source

open source workflow (www.langchain.com).

Automation

automation workflow (www.langchain.com).

Agents

agents workflow (www.langchain.com).

Ideas / Prompt experiences

Share a prompt that worked for you. Username and email are shown with your submission. External links are not allowed.

Example prompt

RAG pipeline design

Prompt

Sketch a LangChain RAG pipeline: document loaders, chunking strategy, embedding model, vector store, and retriever for a company wiki chatbot.

Short explanation

LangChain's composable primitives map cleanly to retrieval-augmented generation stacks.

Example prompt

Agent with tools

Prompt

Build a LangChain agent that can search the web, query a SQL database, and summarize results in markdown.

Short explanation

Tool-calling agents are a primary LangChain use case for multi-source answers.

Share your experience

Required fields are marked. Variation, result, and explanation are optional.

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