LangGraph.js is a JavaScript and TypeScript framework for building language-model agents as graphs, where steps and transitions define how an agent operates. It is for developers who need agents that can handle multi-step workflows reliably.
Borrowing it
Nothing to install: this file belongs to langchain-ai/langgraphjs. 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/langgraphjs/main/AGENTS.mdgit clone --depth 1 https://github.com/langchain-ai/langgraphjsWrote 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/langgraphjs/agents-md)<a href="https://agentmods.dev/instructions/langchain-ai/langgraphjs/agents-md"><img src="https://agentmods.dev/badge/instructions/langchain-ai/langgraphjs/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/langgraphjs/agents-md"><img src="https://agentmods.dev/badge/instructions/langchain-ai/langgraphjs/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 | $0.00499 | $0.00499 |
| Opus 5.5 | $0.00200 | $0.00200 |
| Sonnet 5.5 | $0.00100 | $0.00100 |
| Haiku 4.5 | $0.00050 | $0.00050 |
Grade A, and why
langgraphjs 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 today.
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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repository instructions
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.
Direction: LangGraph is a runtime
LangGraph is consolidating as the low-level runtime for agents and workflows: graph execution, checkpointing, streaming, and interrupt/resume. Critical production systems and agent harnesses run on it, so correctness and stability come first. It is moving away from being a friendly SDK for building agents directly; agent development belongs one layer up, on a harness — Deep Agents (github.com/langchain-ai/deepagents, a separate repo) is the recommended one.
Optimize contributions for runtime correctness, reliability, performance, and simplicity — not for the shortest agent example.
- One way to do each thing. A change never adds a way; removals are always deliberate (see below). The number of ways only goes down. No aliases, overloads, extra accepted types, implicit defaults, or customization hooks for convenience: ergonomics alone never justify new public surface.
- The mandate applies to everything that implements runtime behavior, in
whichever repo it lives: the graph engine (
langgraph,langgraph-core), the checkpointers (checkpoint*), and the server (langgraph-api). The client SDKs and CLI stay ergonomic but are thin, faithful clients, never a second implementation of runtime behavior. - Agent-level packages (
prebuilt,langgraph-supervisor,langgraph-swarm,langgraph-cua) are maintenance-only: fix bugs, don't add agent features. New examples and docs that build agents should use Deep Agents. - Remove deliberately, not opportunistically. Removing or changing public behavior must be the stated purpose of the PR and go through the deprecation process — replacement in place, removal version recorded, migration note. Never drop a default or overload as a side effect of an unrelated change.
- Behavior changes land in Python and JS together (the implementations live in separate repos; note in the PR when the other one needs a matching change).
- If a task asks for something this section rules out, say so and propose the runtime-level or harness-level alternative instead of quietly implementing it.
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.
- today Changed · +33 lines · +445 tokens per session 4e0f9118167a
- 1mo ago First seen · 10 lines · 54 tokens per session scan A 21610e2d42a5
langgraphjs AGENTS.md is an instructions file published in the GitHub repository langchain-ai/langgraphjs (3,377 stars, last pushed today), licensed MIT. It adds 499 tokens to every session, about $0.0020 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).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
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).
deno copilot-instructions.md
Copilot instructions for denoland/deno, covering deno development guide for github copilot, network access, high level overview, key directories and key files to understand first.