Supabasellms.txtsupabase / learn
supabase/supabase/apps/learn/public/llms.txt
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llms.txt examples
How real projects summarise themselves for language models.
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Supabasellms.txt
Microsoftllms.txtai-agents-for-beginners / 15-browser-use
microsoft/ai-agents-for-beginners/15-browser-use/llms.txt
https://api.browser-use.com/api/v1/openapi.json get /balance Returns the user's current API credit balance, which includes both monthly subscription credits and any additional purchased credits. Required for monitoring usage and ensuring sufficient credits for task execution. https://api.browser-use.com/api/v1/openapi.json get /me Returns a boolean value indicating if the API key is valid and the user is authenticated. https://api.browser-use.com/api/v1/openapi.json post /uploads/presigned-url Returns a presigned url for uploading a file to the user's files bucket. After uploading a file, the user can use the includedfilenames field in the…
LangChainllms.txtlanggraph
langchain-ai/langgraph/docs/llms.txt
Markdown index of the LangGraph (Python) documentation.
OpenAIllms.txtopenai-agents-python
openai/openai-agents-python/docs/llms-full.txt
Extended reference map for the OpenAI Agents SDK documentation site. Use these curated links when assembling prompts that need authoritative guidance on building, operating, and extending agentic applications with the SDK. The Agents SDK delivers a focused set of Python primitives—agents, tools, guardrails, handoffs, sessions, and tracing—plus voice and realtime interfaces. The pages below provide detailed walkthroughs, architectural patterns, and API-level documentation for integrating those capabilities into production systems.
OpenAIllms.txtopenai-agents-python
openai/openai-agents-python/docs/llms.txt
Official documentation for building production-ready agentic applications with the OpenAI Agents SDK, a Python toolkit that equips LLM-powered assistants with tools, guardrails, handoffs, sessions, tracing, voice, and realtime capabilities. The SDK focuses on a concise set of primitives so you can orchestrate multi-agent workflows without heavy abstractions. These pages explain how to install the library, design agents, coordinate tools, handle results, and extend the platform to new modalities.
Googlellms.txtadk-python
google/adk-python/llms-full.txt
The machine-readable llms.txt documentation for Agent Development Kit (ADK) is no longer hosted statically in this repository. It is now automatically generated and hosted on the ADK documentation site. Please use the links below to access the source of truth.
Googlellms.txtadk-python
google/adk-python/llms.txt
The machine-readable llms.txt documentation for Agent Development Kit (ADK) is no longer hosted statically in this repository. It is now automatically generated and hosted on the ADK documentation site. Please use the links below to access the source of truth.
Googlellms.txtfilament
google/filament/docs/llms.txt
Filament is a real-time physically based rendering engine for Android, iOS, Linux, macOS, Windows, and WASM.
Googlellms.txtosv-scanner
google/osv-scanner/llms.txt
A high-performance vulnerability scanner for Open Source Vulnerabilities (OSV), written in Go. osv-scanner acts as the presentation and orchestration layer, while osv-scalibr (https://github.com/google/osv-scalibr) provides the core analysis engine.
AWSllms.txtamazon-sagemaker-examples
aws/amazon-sagemaker-examples/llms.txt
Example notebooks for building, training, and deploying models with the Amazon SageMaker Python SDK v3. This repository targets v3 only; pip install sagemaker installs v3. SDK v3 is modular (sagemaker-core, sagemaker-train, sagemaker-serve, sagemaker-mlops) and is not backward compatible with v2. v2 examples are archived, not maintained. This file follows the llms.txt convention (https://llmstxt.org): a curated index plus explicit instructions so AI coding agents generate correct, current (v3) code. - SDK-first: for any SageMaker task (train, deploy, process, pipelines), use the…
LangChainllms.txtlanggraphjs
langchain-ai/langgraphjs/docs/docs/llms.txt
These guides are designed to help you get started with LangGraph. These guides provide explanations of the key concepts behind the LangGraph framework.
Microsoftllms.txtskills
microsoft/skills/docs/llms-full.txt
Microsoft Foundry (formerly Azure AI Foundry) is a unified Azure platform-as-a-service for enterprise AI operations, model builders, and application development. It combines production-grade infrastructure with friendly interfaces for building agents, deploying models, and developing generative AI applications. Portal: https://ai.azure.com SDK Installation: Authentication Pattern: Environment Variables:
Microsoftllms.txtskills
microsoft/skills/docs/llms.txt
Microsoft Foundry (formerly Azure AI Foundry) is a unified Azure platform-as-a-service for enterprise AI operations, model builders, and application development. It provides a comprehensive set of AI capabilities for building agents, deploying models, and developing generative AI applications with built-in enterprise-readiness including tracing, monitoring, evaluations, and safety controls. Important information:
Googlellms.txtzerocopy / v1
google/zerocopy/anneal/v1/llms-full.txt
Historical V1 documentation: This document describes the prototype in anneal/v1/. It remains authoritative for using and maintaining V1, but it is not current Anneal design authority. See ../PRINCIPLES.md for current project principles. Known differences are historical and need not be reconciled here. Anneal is a toolchain for formally verifying unsafe Rust code using Charon, Aeneas, and Lean 4, with a specific focus on memory layout and correctness boundaries. CRITICAL INSTRUCTION FOR AGENTS: If you are new to Anneal, you MUST…
Googlellms.txtzerocopy / v1
google/zerocopy/anneal/v1/llms.txt
Historical V1 documentation: This document describes the prototype in anneal/v1/. It remains authoritative for using and maintaining V1, but it is not current Anneal design authority. See ../PRINCIPLES.md for current project principles. Known differences are historical and need not be reconciled here. Anneal is a toolchain for formally verifying unsafe Rust code using Charon, Aeneas, and Lean 4, with a specific focus on memory layout and correctness boundaries. CRITICAL INSTRUCTION FOR AGENTS: If you are new to Anneal, you MUST…
AWSllms.txtsagemaker-python-sdk
aws/sagemaker-python-sdk/llms.txt
Source repository and documentation for the Amazon SageMaker Python SDK v3. pip install sagemaker installs v3. SDK v3 is modular (sagemaker-core, sagemaker-train, sagemaker-serve, sagemaker-mlops) and is not backward compatible with v2. v2 is on a maintenance branch (security/bug fixes only) and should not be used as a basis for new code. This file follows the llms.txt convention (https://llmstxt.org): a curated index plus explicit instructions so AI coding agents generate correct, current (v3) SDK code. - SDK-first: for any SageMaker task…
Googlellms.txtgenai-processors
google-gemini/genai-processors/llms.txt
The GenAI Processors library provides composable, async building blocks for generative AI pipelines.
NVIDIAllms.txtaistore
NVIDIA/aistore/docs/llms.txt
AIStore (AIS) is a lightweight, open-source distributed object storage system by NVIDIA designed for AI/ML workloads. It provides multi-cloud unified access (AWS S3, GCS, Azure, OCI), linear scalability, high-performance I/O with no routing overhead, and AI-optimized features including sharding, ETL, batch operations, and PyTorch/TensorFlow integration. - Distributed Sort (dSort): Reshard and shuffle massive datasets - Blob Downloader: Chunked parallel downloads of large remote objects - Get-Batch: Bulk object retrieval for training data pipelines -
Microsoftllms.txtWhatTheHack / mcp-info
microsoft/WhatTheHack/068-AzureOpenAIApps/Student/Resources/data/mcp-info/llms-full.txt
A list of applications that support MCP integrations This page provides an overview of applications that support the Model Context Protocol (MCP). Each client may support different MCP features, allowing for varying levels of integration with MCP servers.
NVIDIAllms.txtdcgm-exporter
NVIDIA/dcgm-exporter/llms.txt
This file is for LLMs and agents generating or modifying DCGM Exporter metrics, configuration, deployment examples, and validation plans. Current repository code and checked-in artifacts are the mechanical source of truth. Do not invent flags, environment variables, metric labels, DCGM field names, exporter-owned counter names, Helm values, or package behavior. If required facts are not present in this repository, mark them unknown and ask for source material. Use this order: 1. pkg/cmd/app.go defines CLI flags, environment variables, defaults, startup behavior,…
About llms.txt
What is llms.txt?
A markdown file at a website's root that gives language models a short guide to the site and links to the pages worth reading.
What's llms-full.txt?
The same idea with the content included, so an agent can read the documentation in one request.
Who reads it?
Agents and tools that fetch documentation on someone's behalf. It's a proposal, not a standard, and support varies.
How do I write one?
Start with a one-line summary, then sections of links with a sentence each. The examples here show what real projects do.