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/langchain/agents-mdgit clone --depth 1 https://github.com/langchain-ai/langchainWhat 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 | $0.04345 | $0.04345 |
| Opus 5 | $0.02173 | $0.02173 |
| Sonnet 5 | $0.00869 | $0.00869 |
| Haiku 4.5 | $0.00434 | $0.00434 |
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 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:
- langchain CLAUDE.md — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 373 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 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 (145,288 stars, last pushed 2d ago), licensed MIT. It adds 4,345 tokens to every session, about $0.0217 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.
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