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.
Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/claude-md)<a href="https://agentmods.dev/instructions/langchain-ai/langchain/claude-md"><img src="https://agentmods.dev/badge/instructions/langchain-ai/langchain/claude-md.svg" alt="Measured on agentmods" 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 | $0.04469 | $0.04469 |
| Opus 5 | $0.02235 | $0.02235 |
| Sonnet 5 | $0.00894 | $0.00894 |
| Haiku 4.5 | $0.00447 | $0.00447 |
Grade A, and why
langchain CLAUDE.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 yesterday.
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.
This is a copy
100% identical to langchain AGENTS.md — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 386 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
└── 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.
- yesterday Changed · +13 lines · +124 tokens per session 09ec43df6946
- 6d ago First seen · 373 lines · 4,345 tokens per session scan A f86c75b9e890
langchain CLAUDE.md is an instructions file published in the GitHub repository langchain-ai/langchain (145,734 stars, last pushed today), licensed MIT. It adds 4,469 tokens to every session, about $0.0223 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to langchain AGENTS.md, differing in 0 lines, and is treated as a copy.
Other instructions, from other repositories
opencodex AGENTS.md
AGENTS.md instructions for lidge-jun/opencodex, covering agents.md, what this project is, repository layout, optional subsystems stay off the core path and the devlog directory.
litellm AGENTS.md
AGENTS.md instructions for BerriAI/litellm, a project described as: The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM].
litellm GEMINI.md
Gemini CLI instructions for BerriAI/litellm, a project described as: The fastest, litest AI Gateway. Rust core with Python SDK. Call 100+ LLM APIs in OpenAI (or native) format with cost tracking, guardrails, load balancing, and logging [Bedrock, Azure, OpenAI, Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM].
flock AGENTS.md
Instructions for Onelevenvy/flock, covering agents.md, project overview, build & development commands, rust backend and build the cli.
daiv AGENTS.md
AGENTS.md instructions for srtab/daiv, covering agents.md, commands (verified), single test / pattern, translations and repo map (only what's non-obvious).
litellm CLAUDE.md
Claude Code instructions for BerriAI/litellm, covering think before coding and simplicity first.