Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/langchain-ai/langgraph/agents-mdgit clone --depth 1 https://github.com/langchain-ai/langgraphWhat 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 | $0.00477 | $0.00477 |
| Opus 5 | $0.00238 | $0.00238 |
| Sonnet 5 | $0.00095 | $0.00095 |
| Haiku 4.5 | $0.00048 | $0.00048 |
Grade A, and why
langgraph 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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- langgraph CLAUDE.md — 88% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS Instructions
This repository is a monorepo. Each library lives in a subdirectory under libs/.
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.
When you modify code in any library, run the following commands in that library's directory before creating a pull request:
make format– run code formattersmake lint– run the lintermake test– execute the test suite
To run a particular test file or to pass additional pytest options you can specify the TEST variable:
TEST=path/to/test.py make test
Other pytest arguments can also be supplied inside the TEST variable.
Libraries
The repository contains several Python and JavaScript/TypeScript libraries. Below is a high-level overview:
- checkpoint – base interfaces for LangGraph checkpointers.
- checkpoint-postgres – Postgres implementation of the checkpoint saver.
- checkpoint-sqlite – SQLite implementation of the checkpoint saver.
- cli – official command-line interface for LangGraph.
- langgraph – core framework for building stateful, multi-actor agents.
- prebuilt – high-level APIs for creating and running agents and tools.
- sdk-js – JS/TS SDK for interacting with the LangGraph REST API.
- sdk-py – Python SDK for the LangGraph Server API.
Dependency map
The diagram below lists downstream libraries for each production dependency as
declared in that library's pyproject.toml (or package.json).
checkpoint
├── checkpoint-postgres
├── checkpoint-sqlite
├── prebuilt
└── langgraph
prebuilt
└── langgraph
sdk-py
├── langgraph
└── cli
sdk-js (standalone)
Changes to a library may impact all of its dependents shown above.
- Do NOT use Sphinx-style double backtick formatting (
``code``). Use single backticks (`code`) for inline code references in docstrings and comments.
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 First seen · 66 lines · 477 tokens per session scan A e5a0e5318267
langgraph AGENTS.md is an instructions file published in the GitHub repository langchain-ai/langgraph (40,717 stars, last pushed 2d ago), licensed MIT. It adds 477 tokens to every session, about $0.0024 per session on Opus 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
rowboat CLAUDE.md
Instructions for rowboatlabs/rowboat, covering claude.md - ai coding agent context, quick reference commands, electron app (apps/x), monorepo structure and electron app architecture (apps/x).
langroid CLAUDE.md
Instructions for langroid/langroid, covering claude.md, commands, development, testing and linting and type checking.
pm-claude-skills AGENTS.md
AGENTS.md instructions for mohitagw15856/pm-claude-skills, covering agents.md, what this repo is, working conventions and house prose rules (the mini standard).
autogen copilot-instructions.md
Copilot instructions for microsoft/autogen, covering autogen multi-agent ai framework, working effectively, prerequisites and environment setup, python development workflow and quick validation (under 1 second each).
Proma AGENTS.md
AGENTS.md instructions for proma-ai/Proma, covering proma 工程约定, 必须遵守, 常用命令, 开发 and 构建 / 类型检查 / 测试.
mem9 AGENTS.md
Instructions for unvulcanised-watercress762/mem9, covering what this repo is, high-level modules, commands, go server build / verify and single go test.