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LangChain

by LangChain, Inc. · Framework · also known as langchain (PyPI), LangChain.js

Open-source Python and JavaScript framework whose create_agent harness builds tool-using agents on the LangGraph runtime with swappable model providers and middleware

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Link verified Nov 2, 2026

Overview

LangChain is an MIT-licensed framework from LangChain, Inc. for building LLM applications in Python and JavaScript. After its 1.0 redesign the library centers on create_agent, a configurable agent harness: you pass a model identifier such as a provider-prefixed string, a list of plain functions as tools and a system prompt, then add middleware for guardrails, retries, routing or custom tool policies. A standard model interface covers OpenAI, Anthropic, Google, Bedrock, Azure, Ollama, Hugging Face and many other providers, so switching vendors is mostly a string change, and a large integrations catalog supplies document loaders, vector stores and retrievers for retrieval-augmented generation.

Agents created with LangChain run on LangGraph, which supplies durable execution, persistence and human-in-the-loop controls, and they trace to LangSmith when an environment variable is set. Deep Agents, a batteries-included harness with context compression, a virtual filesystem and subagents, is built on the same primitives. The framework's strengths are breadth of integrations and a common vocabulary across providers; its recurring criticisms are abstraction overhead and API churn across major versions, so teams with simple needs sometimes call provider SDKs directly, and teams with complex control flow drop down to LangGraph.

Capabilities

  • `create_agent` harness with tools, prompts and middleware
  • Standard chat-model interface across many providers
  • Document loaders, text splitters, vector-store and retriever integrations
  • Middleware for guardrails, retries and routing
  • Runs on the LangGraph runtime for persistence and human-in-the-loop
  • Python and JavaScript packages with LangSmith tracing

Typical workflows

  1. Build a tool-calling agent and swap the model provider via a string
  2. Assemble a RAG pipeline from loaders, splitters and a vector store
  3. Trace agent runs in LangSmith during development

Strengths

  • Largest integration catalog among agent frameworks
  • Provider-agnostic model interface
  • MIT license and active community

Limitations

  • Abstraction layers can obscure provider-specific behavior
  • Frequent API changes between major versions
  • Complex control flow is better expressed directly in LangGraph

Prerequisites

  • Python 3.10+ or Node.js
  • API key for at least one model provider or a local runtime such as Ollama

Who it is for

  • Application developers building LLM features and agents
  • Teams standardizing on one interface across providers

Alternatives & comparison

  • CrewAI

    Open-source Python framework for role-based multi-agent crews and event-driven flows, with a commercial enterprise console for deploying, monitoring and triggering automations

    MixedOpen source
  • DSPy

    Python framework for programming language-model pipelines with typed signatures and modules, then optimizing prompts and weights automatically against a metric

    Open sourceOpen source
  • Instructor

    Python library for extracting validated structured data from LLM responses using Pydantic models, with automatic retries, streaming partials and support for many providers

    Open sourceOpen source
  • LangGraph

    Low-level orchestration framework and runtime for stateful, long-running agents expressed as graphs, with durable execution, checkpointing, human-in-the-loop and streaming

    Open sourceOpen source
  • LiteLLM

    Open-source Python SDK and self-hosted AI gateway that calls 100+ LLM providers in the OpenAI format, with routing, fallbacks, virtual keys, budgets, spend tracking and logging callbacks

    MixedOpen source
  • LlamaIndex

    Open-source Python framework for retrieval-augmented agents over your data, with connectors, indexes, query engines and event-driven workflows, alongside the commercial LlamaParse document platform

    Open sourceOpen source
  • Model Context Protocol

    Open JSON-RPC standard for connecting AI applications to tools, data and prompts through servers, now stewarded under the Linux Foundation with SDKs in many languages

    Open sourceOpen source
  • OpenAI Agents SDK

    Lightweight open-source Python and TypeScript framework for agents, handoffs, guardrails, sessions and tracing, defaulting to OpenAI's Responses API with adapters for other providers

    Open sourceOpen source
  • Pydantic AI

    Type-safe Python agent framework from the Pydantic team with validated structured outputs, dependency injection, swappable model providers, MCP, durable execution and evals

    Open sourceOpen source
Compare LangChain with its alternatives →

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Confidence
High
Reviewed
Nov 1, 2026
Links
Verified · Nov 2, 2026

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