LangChain
Open SourceBuild agents with intuitive orchestration and integration.
Published 27 September 2026 · Last updated 1 October 2026
Scores
Popularity4/5
The reference point most LLM app tutorials, courses, and third-party integrations are still written against, with the framework reaching a 1.0 release in October 2025. The bleeding edge of the agent-framework conversation has since moved to LangGraph and newer entrants, leaving LangChain the mature, well-trodden starting point rather than what developers are actively buzzing about.
Learning Curve3/5
Basic chains are quick to assemble with the huge library of existing examples, but building genuinely stateful multi-agent workflows with LangGraph, or wiring up LangSmith observability well, takes real investment.
Flexibility5/5
A massive integration ecosystem across models, tools, vector stores, and memory backends, plus LangGraph's low-level orchestration primitives, make it applicable to nearly any agent architecture a developer wants to build.
Performance4/5
LangGraph's durable execution and human-in-the-loop support are built for production-grade multi-agent systems, and the framework itself adds minimal overhead since it's a thin orchestration layer over whichever models are configured.
Portability5/5
MIT-licensed with no usage limits on the core framework or LangGraph, and provider-agnostic across virtually every major LLM, so there's no lock-in to use it.
About LangChain
LangChain is an open-source agent framework, a Python and JavaScript library (MIT licensed) for combining interoperable components, prompts, models, tools, memory, and third-party integrations, into LLM-powered applications. Rather than a standalone product, it is imported directly into a developer's own codebase, and many agent tutorials and third-party integrations are written against it.
The ecosystem has split into purpose-built pieces since LangChain 1.0. LangGraph is the low-level orchestration layer for stateful, multi-agent workflows with human-in-the-loop steps and durable execution, also MIT licensed. LangSmith is the observability and evaluation platform: it traces agent runs, helps debug failures, and extends into deployment with Engine, Sandboxes, Fleet, and an LLM Gateway.
The core framework and LangGraph are free and open source, with no licensing fees or commercial-use restrictions, so the only running cost is the model usage an application consumes. LangSmith is a separate commercial product with its own plans, and using it is optional.
Key Features
- Interoperable components for models, prompts, tools, and memory across a large integration ecosystem
- A ready-made agent loop (create_agent) built on LangGraph
- LangGraph for stateful multi-agent workflows with human-in-the-loop steps and durable execution
- Partner packages for model providers and vector stores, such as langchain-openai, langchain-anthropic, and langchain-qdrant
- Built-in tracing to LangSmith, switched on with environment variables
- Python and JavaScript libraries, MIT licensed, with no usage limits on the core framework or LangGraph
Pros
- The most-referenced agent framework, with the broadest ecosystem of tutorials, integrations, and community answers
- The core framework and LangGraph are fully free and open source, with no commercial-use restrictions
- LangGraph's durable execution and human-in-the-loop support suit production multi-agent workflows, not only prototypes
- Works with LangSmith, Langfuse, and other tracing tools, so observability is not tied to one vendor
Cons
- LangChain, LangGraph, and LangSmith are three products, each with its own learning curve
- Tracing, evaluation, and deployment come from LangSmith or another tool added on top, not from the framework
- As a library it needs real Python or JavaScript skill, with no low-code interface
- The framework's early history included rapid, sometimes breaking API changes, which the 1.0 releases aim to stabilize
LangChain Pricing
Open Source- · Free, MIT-licensed core framework and LangGraph orchestration, no usage limits
- · No licensing fees or commercial-use restrictions
Tech Stacks with LangChain
Python AI Agent
ProjectA code-first AI agent in Python. An agent framework such as LangGraph or Pydantic AI runs the loop, FastAPI serves it, PostgreSQL (your own, Neon, or Supabase) stores its state, and a model such as Claude or OpenAI does the reasoning.
TypeScript AI Agent
ProjectA code-first AI agent in TypeScript. Next.js holds the agent and its chat interface in one app, a framework such as the Vercel AI SDK or Mastra runs the loop, PostgreSQL (your own, Neon, or Supabase) keeps the conversations, and a model such as Claude or OpenAI does the reasoning.
Python RAG App
ProjectA retrieval-augmented generation (RAG) app in Python. A framework such as LlamaIndex or LangChain loads, splits, and indexes your documents, a vector store such as PostgreSQL with pgvector or Qdrant finds the passages that match a question, FastAPI serves the answers, and a model such as Claude or OpenAI writes them from what was retrieved.
Tools Related to LangChain
Works well with LangChain(1)
Langflow is a visual canvas for composing LangChain-style components, giving a drag-and-drop way to build the flows LangChain expresses in code.
Integrates with LangChain(8)
LangChain's langchain-chroma partner package wraps Chroma as a vector store, and Chroma's minimal setup makes it the default in many LangChain RAG tutorials.
LangChain's langchain-pinecone partner package wraps Pinecone as a vector store, a common managed retrieval backend for LangChain RAG applications.
LangChain's langchain-qdrant partner package provides a Qdrant vector store with dense, sparse, and hybrid retrieval, often chosen when the vector database has to be self-hosted.
LangChain has built-in LangSmith tracing: setting the LangSmith environment variables sends every chain and agent run to LangSmith for debugging and evaluation.
Langfuse ships a LangChain callback handler that records chains, tool calls, and LangGraph runs as nested traces, an open-source option for LangChain teams that want to self-host their observability.
LangChain's first-party langchain-anthropic package provides the ChatAnthropic model class, so LangChain chains and agents run on Claude models.
Built on (2)
LangChain is a Python framework for building LLM applications and agents from composable components.
LangChain.js is LangChain's JavaScript and TypeScript edition, sharing the Python library's core abstractions.
Alternatives to LangChain(8)
LlamaIndex and LangChain are the two most widely used LLM application frameworks. LlamaIndex goes deeper on retrieval, indexing, and document parsing; LangChain is broader on tool orchestration, integrations, and agent control flow.
CrewAI and LangChain are two of the leading agent frameworks — CrewAI centres on role-based multi-agent crews, LangChain on a broad component/integration toolkit. Teams typically pick one as their primary framework.
Mastra positions itself as the TypeScript-first alternative to LangChain, covering the same RAG, memory, and multi-agent workflow ground with typed, JS-native primitives instead of LangChain's Python-first component library.
LangChain's create_agent gives a ready-made agent loop built on LangGraph, while LangGraph itself is the lower-level runtime for hand-built graphs with explicit state and branching. Pick LangChain for standard agents, LangGraph for custom control flow.
The OpenAI Agents SDK is a small set of primitives (agents, handoffs, guardrails, sessions) that gets a multi-agent setup running quickly; LangChain offers a far larger library of integrations and components for teams that need retrieval, many providers, and custom chains.
Pydantic AI positions itself as a leaner, type-safe alternative to LangChain for Python agents, trading LangChain's huge integration surface for stricter validation and IDE support.
Vendor
LangChain
Website →Tags
Details
- Maintained
- Yes
- Agent type
- Framework
- Primary language
- Python
- Open source
- Yes
- Hosting
- Self-hosted
- GitHub stars
- 147k
- Stars updated
- 2026-09-23