LangChain is a framework for building software agents and applications powered by large language models, using reusable components and integrations. Developers use it to connect models, tools, and other services when creating AI applications and agent workflows. The catalogue includes MCP integrations and instructions related to LangChain.
Borrowing it
Nothing to install: this file belongs to langchain-ai/langchain. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/langchain-ai/langchain/master/AGENTS.mdgit clone --depth 1 https://github.com/langchain-ai/langchainWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/langchain-ai/langchain/agents-md)<a href="https://agentmods.dev/instructions/langchain-ai/langchain/agents-md"><img src="https://agentmods.dev/badge/instructions/langchain-ai/langchain/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/langchain-ai/langchain/agents-md"><img src="https://agentmods.dev/badge/instructions/langchain-ai/langchain/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.04680 | $0.04680 |
| Opus 5.5 | $0.01872 | $0.01872 |
| Sonnet 5.5 | $0.00936 | $0.00936 |
| Haiku 4.5 | $0.00468 | $0.00468 |
Grade A, and why
langchain AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 22d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 391 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Global development guidelines for the LangChain monorepo
This document provides context to understand the LangChain Python project and assist with development.
Corridor security analysis
When Corridor's analyzePlan tool is available, create a plan and use the tool to analyze it before generating or modifying code. Apply the resulting security guidance before writing code.
Project architecture and context
Monorepo structure
This is a Python monorepo with multiple independently versioned packages that use uv.
langchain/
├── libs/
│ ├── core/ # `langchain-core` primitives and base abstractions
│ ├── langchain/ # `langchain-classic` (legacy, no new features)
│ ├── langchain_v1/ # Actively maintained `langchain` package
│ ├── partners/ # Third-party integrations
│ │ ├── openai/ # OpenAI models and embeddings
│ │ ├── anthropic/ # Anthropic (Claude) integration
│ │ ├── ollama/ # Local model support
│ │ └── ... (other integrations maintained by the LangChain team)
│ ├── text-splitters/ # Document chunking utilities
│ ├── standard-tests/ # Shared test suite for integrations
│ └── model-profiles/ # Model configuration profiles
├── .github/ # CI/CD workflows and templates
├── .vscode/ # VSCode IDE standard settings and recommended extensions
├── openwiki/ # Generated just-in-time evidence index (optional reading)
└── README.md # Information about LangChain
- Core layer (
langchain-core): Base abstractions, interfaces, and protocols. Users should not need to know about this layer directly. - Implementation layer (
langchain): Concrete implementations and high-level public utilities - Integration layer (
partners/): Third-party service integrations. Note that this monorepo is not exhaustive of all LangChain integrations; some are maintained in separate repos, such aslangchain-ai/langchain-googleandlangchain-ai/langchain-aws. Usually these repos are cloned at the same level as this monorepo, so if needed, you can refer to their code directly by navigating to../langchain-google/from this monorepo. - Testing layer (
standard-tests/): Standardized integration tests for partner integrations
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 22d ago Changed · +5 lines · +211 tokens per session 2d6d7ad9785a
- 1mo ago Changed · +13 lines · +124 tokens per session 09ec43df6946
- 1mo ago First seen · 373 lines · 4,345 tokens per session scan A f86c75b9e890
langchain AGENTS.md is an instructions file published in the GitHub repository langchain-ai/langchain (147,886 stars, last pushed today), licensed MIT. It adds 4,680 tokens to every session, about $0.0187 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, cache eligibility and rollout and applying the notice (cutover).
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.
vscode copilot-instructions.md
Copilot instructions for microsoft/vscode, covering vs code copilot instructions, project overview, root folders, core architecture (src/ folder) and built-in extensions (extensions/ folder).
generative-ai-for-beginners AGENTS.md
AGENTS.md instructions for microsoft/generative-ai-for-beginners, covering agents.md, project overview, setup commands, initial repository setup and clone the repository.