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Developer Frameworks & Infrastructure

LangChain

Framework for building LLM-powered applications

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Open Source

Free (open source)

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macOS - Windows - Linux

Self-hosted or local

Category

Developer Frameworks & Infrastructure

133 tools

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Last updated

Sep 23

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Build retrieval-augmented assistants

Wire loaders, splitters, vector stores, and retrievers into a chat pipeline that answers from your documents.

Pricing checked Sep 2026

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About LangChain

LangChain is the most widely adopted open-source framework for building LLM applications, in Python and JavaScript. It standardizes the plumbing: a common interface over model providers, prompt templates, output parsers, document loaders, vector store integrations, retrievers, and agent runtimes, so prototypes do not hard-wire a single vendor.

LangGraph, its companion library, models agents as stateful graphs with checkpoints, retries, and human-in-the-loop steps, which is how production agent workflows survive failures. LangSmith, a separate service, adds tracing, datasets, and evaluation so prompts and runs can be inspected and scored.

The frameworks are MIT licensed and free; LangSmith is priced per seat or by usage above a free developer tier. LangChain suits teams moving a prototype toward a product, though its abstractions churn quickly, upgrades can break code, and debugging is easier with LangSmith than without.

  • Broadest integration catalog in LLM tooling
  • LangGraph handles stateful, resumable agent workflows well
  • Model-agnostic: swap providers through one interface
  • Open source with fast-moving, active development
  • APIs churn fast and upgrades can break working code
  • Abstractions can hide what the model actually receives
  • Debugging complex chains is harder than plain code
Install via pipLocal / CLI
$pip install langchain langchain-community

Open source framework for building context-aware reasoning applications with language models.

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The layer builders stand on: training frameworks, model hubs, agent orchestration, vector databases, and pay-per-second inference APIs. Repository health, license clarity, and self-hosting paths are part of every listing so teams can evaluate lock-in at a glance.

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