LangChain AWS is a monorepo containing LangChain and LangGraph components for using Amazon Web Services in applications. It provides integrations for language models, vector stores, retrievers, graphs, agents, tools, and checkpoint storage across AWS services.
Borrowing it
Nothing to install: this file belongs to langchain-ai/langchain-aws. 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-aws/main/AGENTS.mdgit clone --depth 1 https://github.com/langchain-ai/langchain-awsWrote 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-aws/agents-md)<a href="https://agentmods.dev/instructions/langchain-ai/langchain-aws/agents-md"><img src="https://agentmods.dev/badge/instructions/langchain-ai/langchain-aws/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-aws/agents-md"><img src="https://agentmods.dev/badge/instructions/langchain-ai/langchain-aws/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 | $0.01740 | $0.01740 |
| Opus 5.5 | $0.00696 | $0.00696 |
| Sonnet 5.5 | $0.00348 | $0.00348 |
| Haiku 4.5 | $0.00174 | $0.00174 |
Grade A, and why
langchain-aws 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- langchain-aws CLAUDE.md — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 198 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.
Project architecture and context
This is a monorepo containing LangChain and LangGraph integrations for AWS services. The repository contains two main packages:
langchain-aws: Core LangChain integrations for AWS services (Bedrock, SageMaker, Kendra, etc.)langgraph-checkpoint-aws: LangGraph checkpointing solutions using AWS serviceslangchain-agentcore-codeinterpreter: AgentCore Code Interpreter sandbox backend for Deep Agents
langchain-aws Structure
The package is organized by AWS service integration type:
- LLMs:
llms/- Bedrock LLM and SageMaker Endpoint integrations - Chat Models:
chat_models/- ChatBedrock, ChatBedrockConverse, SageMaker chat models - Embeddings:
embeddings/- BedrockEmbeddings for vector generation - Retrievers:
retrievers/- Kendra, Knowledge Bases, S3 Vectors retrieval - Vector Stores:
vectorstores/- InMemoryDB and S3 Vectors storage - Graphs:
graphs/- Neptune graph database integrations - Tools:
tools/- AgentCore browser and code interpreter toolkits - Agents:
agents/- Bedrock Agents integration utilities - Chains:
chains/- Neptune Cypher and SPARQL QA chains
Key Components
- Unified AWS Client Configuration: Automatic boto3 client patching with framework user-agent headers
- Debug Logging: Controlled via
LANGCHAIN_AWS_DEBUGenvironment variable - Optional Dependencies: Tools functionality requires extra dependencies (
pip install langchain-aws[tools])
langgraph-checkpoint-aws Structure
- Core Savers: Async and sync checkpoint saving implementations
- AgentCore Integration: Memory service integration for LangGraph agents
- Session Management: AWS Bedrock session management integration
langchain-agentcore-codeinterpreter Structure
Amazon Bedrock AgentCore Code Interpreter sandbox backend for Deep Agents. Lives in libs/agentcore-codeinterpreter/.
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.
- 1mo ago First seen · 198 lines · 1,740 tokens per session scan A b5576e8748ea
langchain-aws AGENTS.md is an instructions file published in the GitHub repository langchain-ai/langchain-aws (353 stars, last pushed today), licensed MIT. It adds 1,740 tokens to every session, about $0.0070 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.