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LLM Application Framework · Public

langchain

LLM application framework for checking model, prompt, tool, retrieval, and chain integration boundaries.

LLM appsRAGTool callingPythonFramework migration
Best fitDevelopers building Python LLM apps, RAG workflows, tool calling, or agent prototypes that need a shared abstraction layer.

Check whether this project matches your task before installing it.

What it can doStructured LLM app starting paths, RAG/tool-calling checks, migration reminders, permission boundaries, and acceptance checks

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot139k stars

23k forks · 3.7k contributors

Last verification date: 2026-06-29 Verification method: source evidence, semantic profile, public page gate, and static build acceptance.

Official first step Read manual preview Source repository

Publication status · 2026-06-29

What is langchain?

01

Quick decision

Use this section to decide whether the project is worth a deeper read.
Best forDevelopers building Python LLM apps, RAG workflows, tool calling, or agent prototypes that need a shared abstraction layer.

Match the project to your task before installing it.

CapabilityStructured LLM app starting paths, RAG/tool-calling checks, migration reminders, permission boundaries, and acceptance checks

LLM application framework for checking model, prompt, tool, retrieval, and chain integration boundaries.

Repositorylangchain-ai/langchain

139k stars · 23k forks

02

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.
1

Repository Structure and Package Layout

Related topics: Core Abstractions and the Runnable Interface (LCEL), Building Modern Agents: Factory, Middleware, and Tool Execution, Integrations, Text Splitters, and Classic LangChain

Source: https://github.com/langchain-ai/langchain / Human Manual
2

Core Abstractions and the Runnable Interface (LCEL)

Related topics: Repository Structure and Package Layout, Building Modern Agents: Factory, Middleware, and Tool Execution

Source: https://github.com/langchain-ai/langchain / Human Manual
3

Building Modern Agents: Factory, Middleware, and Tool Execution

Related topics: Core Abstractions and the Runnable Interface (LCEL), Integrations, Text Splitters, and Classic LangChain

Source: https://github.com/langchain-ai/langchain / Human Manual
4

Integrations, Text Splitters, and Classic LangChain

Related topics: Repository Structure and Package Layout, Core Abstractions and the Runnable Interface (LCEL), Building Modern Agents: Factory, Middleware, and Tool Execution

Source: https://github.com/langchain-ai/langchain / Human Manual
5

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

Sources: https://github.com/langchain-ai/langchain, Human Manual, Project Pack evidence, and downstream validation signals.

03

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.
Stars139k stars
Forks23k forks
Contributors3.7k contributors
Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using langchain with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

04

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.
1

Try the prompt first

Test the workflow without installing the upstream project.

preview
2

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual
3

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context
4

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify
pip install langchain

Official start command · https://github.com/langchain-ai/langchain#readme · verified: yes

05

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

langchain Manual

The langchain-ai/langchain repository is organized as a monorepo that hosts multiple independently versioned Python packages. The root README.md describes LangChain as "a framework for bui...

Open the full manual
  1. https://github.com/langchain-ai/langchain Project Manual
  2. Table of Contents
  3. Repository Structure and Package Layout
  4. Related Pages
  5. Overview
  6. High-Level Layout
  7. Core Libraries
  8. `langchain-core`
1

Repository Structure and Package Layout

Related topics: Core Abstractions and the Runnable Interface (LCEL), Building Modern Agents: Factory, Middleware, and Tool Execution, Integrations, Text Splitters, and Classic LangChain

Source: https://github.com/langchain-ai/langchain / Human Manual
2

Core Abstractions and the Runnable Interface (LCEL)

Related topics: Repository Structure and Package Layout, Building Modern Agents: Factory, Middleware, and Tool Execution

Source: https://github.com/langchain-ai/langchain / Human Manual
3

Building Modern Agents: Factory, Middleware, and Tool Execution

Related topics: Core Abstractions and the Runnable Interface (LCEL), Integrations, Text Splitters, and Classic LangChain

Source: https://github.com/langchain-ai/langchain / Human Manual
4

Integrations, Text Splitters, and Classic LangChain

Related topics: Repository Structure and Package Layout, Core Abstractions and the Runnable Interface (LCEL), Building Modern Agents: Factory, Middleware, and Tool Execution

Source: https://github.com/langchain-ai/langchain / Human Manual
5

Doramagic Pitfall Log

Source-linked risks stay visible on the manual page so the preview does not read like a recommendation.

Source: Doramagic discovery, validation, and Project Pack records

06

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual
BundleComplete Project PackAssetAI Context PackAssetBoundary & Risk CardAssetHuman ManualAssetPitfall LogAssetPrompt PreviewAssetQuick StartEvidenceREPO_INSPECTION.json

07

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

08

Pitfall Log and verification risks

Doramagic surfaces high-risk items before users treat a candidate capability as verified.
high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Installation risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Configuration risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Security or permission risk requires verification

Developers may expose sensitive permissions or credentials: Add `BackendIntegrationTests` for Deep Agents `BackendProtocol` implementations

high

Security or permission risk requires verification

Developers may expose sensitive permissions or credentials: Feature: Memory write validation hooks to prevent prompt injection persistence (OWASP ASI-06)

high

Security or permission risk requires verification

May increase setup, validation, or first-run risk for the user.

high

Security or permission risk requires verification

May increase setup, validation, or first-run risk for the user.