Overview
LangChain is an open-source framework for building applications and agents powered by large language models (LLMs). It provides standardized abstractions, pre-built components, and integrations so developers can connect models to tools, data sources, and workflows with minimal boilerplate.
Pricing
Framework (LangChain OSS)
- Free to use and self-host as an open-source Python/TypeScript library under a permissive license.
- Ideal for developers and teams who want to build LLM apps and agents without vendor lock-in.
LangSmith (Observability & Evaluation)
- Developer plan: 1 free seat with a limited number of traces per month, suitable for getting started.
- Plus / Team plans from about 39 USD per seat per month, with higher trace limits, longer retention, and collaboration features.
Enterprise
- Enterprise options add advanced security, SSO, compliance, and self-hosting for LangSmith and related services.
- Designed for companies running critical AI agents and LLM applications in production at scale.
Key Features
- Agent & chain abstractions – Pre-built patterns for agents, tools, and multi-step chains that handle planning, tool calling, and control flow.
- RAG tooling – Components for document loading, text splitting, embeddings, vector stores, and retrievers to build retrieval-augmented generation systems.
- Memory & context – Built-in memory modules to keep conversation state and long-term context for agent interactions.
- Ecosystem integrations – Connectors for major LLM providers, vector databases, data stores, and external tools (Python & JS).
- Production tooling – With LangSmith and LangGraph, teams get tracing, evaluation, debugging, and durable execution for complex agent systems.
Best Use Cases
- AI agents & copilots – Build custom agents that call tools, browse data, and take multi-step actions inside apps and workflows.
- RAG assistants – Domain-specific chatbots and assistants that ground answers in private docs, databases, and knowledge bases.aws.
- Developer copilots & code tools – Integrate LLMs into IDEs, CLIs, or internal tools for code generation, refactoring, and review.
- Analytics & data apps – Natural-language interfaces for querying data, generating reports, and orchestrating data workflows via LLMs.
- Multi-agent systems – Use LangGraph/LangChain patterns to coordinate multiple agents with durable state and fine-grained control.
Pros
- ✅ Highly modular & flexible – Components like prompt templates, retrievers, tools, and memory can be composed like building blocks for many patterns.
- ✅ Rich ecosystem – Large community, extensive documentation, and many integrations with models, vector DBs, and infra providers.
- ✅ Agent-focused tooling – Pre-built agent architectures and the LangGraph platform make building reliable, long-running agents easier.
- ✅ Free core & commercial add-ons – Open-source framework plus optional paid observability (LangSmith) for production teams.
Cons
- ❌ Steeper learning curve – Rich abstractions, many modules, and evolving best practices can be overwhelming for newcomers.
- ❌ Extra infra pieces needed – Serious RAG and agent systems still require setting up vector stores, model providers, and observability.
- ❌ Commercial tooling for production – For larger teams, full observability and governance usually require paid LangSmith or similar tools.
Official Website
LangChain – Official website: https://www.langchain.comlangchain
Documentation: https://docs.langchain.comlangchain
Release Date: October 2022
Last Updated: December 2025