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/azure/gpt-rag/agents-mdgit clone --depth 1 https://github.com/Azure/GPT-RAGWrote 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/azure/gpt-rag/agents-md)<a href="https://agentmods.dev/instructions/azure/gpt-rag/agents-md"><img src="https://agentmods.dev/badge/instructions/azure/gpt-rag/agents-md.svg" alt="Measured on agentmods" 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 | $0.01538 | $0.01538 |
| Opus 5 | $0.00769 | $0.00769 |
| Sonnet 5 | $0.00308 | $0.00308 |
| Haiku 4.5 | $0.00154 | $0.00154 |
Grade A, and why
GPT-RAG 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 3d 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.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GPT-RAG agent operating contract
This file is the stable, repository-wide contract for engineering agents.
Detailed procedures belong in .github/skills/, and file-specific rules belong
in .github/instructions/. Product documentation remains on the docs branch
and at https://azure.github.io/GPT-RAG/.
Priority
Follow, in this order:
- Security, privacy, authorization, and platform instructions.
- Task requirements and acceptance criteria.
- Executable configuration and versioned contracts in the repository.
.github/copilot-instructions.md, this contract, and applicable scoped instructions.- Local conventions observed in the affected code.
When information is insufficient, do not guess behavior that could affect data, contracts, identity, security, releases, or production. Record the uncertainty and obtain a human decision.
What this repository is
GPT-RAG is an enterprise-grade Retrieval-Augmented Generation solution accelerator on Azure. It provides architecture and deployment assets for secure, scalable, observable AI applications using Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure Container Apps, Azure App Configuration, Key Vault, Cosmos DB, and Azure Monitor.
Its shipped capabilities include hybrid and agentic retrieval, NL2SQL, multimodal ingestion and retrieval, SharePoint integration, configurable agent strategies and MCP tools, Entra ID authentication, document-level authorization, feedback and conversation persistence, Responsible AI controls, observability, Bring Your Own VNet, and optional network isolation. The published documentation describes the currently supported combinations and deployment modes.
This repository is the platform and configuration core of a multi-repository solution:
manifest.jsonis the authoritative GPT-RAG release manifest and pins the runtime component repositories and versions..gitmodulesandmanifest.jsonpin the AI Landing Zone infrastructure.main.parameters.jsondefines the GPT-RAG deployment topology and parameters.- Runtime application code lives in the component repositories referenced by
manifest.json; do not duplicate it here. infra/is populated from thebicep-ptn-aiml-landing-zonesubmodule. Do not edit it by hand because local changes are replaced during provisioning.
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.
- 3d ago First seen · 165 lines · 1,538 tokens per session scan A 6d0be6bfb1e7
GPT-RAG AGENTS.md is an instructions file published in the GitHub repository Azure/GPT-RAG (1,169 stars, last pushed 16d ago), licensed MIT. It adds 1,538 tokens to every session, about $0.0077 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
aisearch-openai-rag-audio AGENTS.md
Instructions for Azure-Samples/aisearch-openai-rag-audio, covering instructions for coding agents, code layout, running the code, prerequisites and local development setup.
azure-openai-rag-workshop AGENTS.md
Instructions for Azure-Samples/azure-openai-rag-workshop, covering azure openai rag workshop (node.js), key technologies and frameworks, constraints and requirements, challenges and mitigation strategies and development workflow.
azure-search-openai-demo AGENTS.md
Instructions for Azure-Samples/azure-search-openai-demo, covering instructions for coding agents, overall code layout, adding new data, adding a new azd environment variable and adding a new setting to "developer settings" in rag app.
UltraRAG AGENTS.md
AGENTS.md instructions for OpenBMB/UltraRAG, covering agents.md, 1) project identity, 2) repository map (what matters most), 3) mental model of the system and 4) two-phase execution lifecycle.
azure-search-openai-demo bicep.instructions.md
Infrastructure as Code with Bicep.
chat-with-your-data-solution-accelerator copilot-instructions.md
Instructions for Azure-Samples/chat-with-your-data-solution-accelerator, covering chat with your data (cwyd) — repository instructions, repository layout (truth), mandatory references — consult before editing, external pattern sources — read-only and hard rules.