GPT-RAG is an Azure solution accelerator for building agentic retrieval-augmented generation systems, where agents use connected organizational sources to ground their answers. It is for organizations deploying secure RAG applications with Microsoft Foundry, Microsoft Agent Framework, and Azure infrastructure. The catalogue add-ons provide agents, skills, and instructions for deploying and working with the accelerator.
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.
git 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/agents/azure/gpt-rag/architecture)<a href="https://agentmods.dev/agents/azure/gpt-rag/architecture"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag/architecture/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/agents/azure/gpt-rag/architecture"><img src="https://agentmods.dev/badge/agents/azure/gpt-rag/architecture.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 | $0.00041 | $0.00233 |
| Opus 5.5 | $0.00016 | $0.00093 |
| Sonnet 5.5 | $0.00008 | $0.00047 |
| Haiku 4.5 | $0.00004 | $0.00023 |
Grade A, and why
architecture 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.
What it actually says
GPT-RAG architecture
Follow AGENTS.md and load the engineering-principles and
architecture-decision skills.
Start from the operator or user outcome, constraints, and a small set of measurable architectural characteristics. Compare alternatives in the context of GPT-RAG's multi-repository release model, Azure identity and network boundaries, document-level authorization, cost, operability, migration, and reversibility.
Treat manifest.json, versioned contracts, infrastructure parameters, and
runtime component behavior as executable sources of truth. Do not turn a
framework or Azure service preference into a requirement without evidence.
Record significant decisions under docs/adr/.
Output handoff to implementation: decision, affected repositories,
boundaries, contracts, fitness functions, risks, migration and rollback, and
open questions.
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 · 27 lines · 41 tokens per session scan A a473df3a16ca
architecture is an agent published in the GitHub repository Azure/GPT-RAG (1,179 stars, last pushed 20d ago), licensed MIT. It adds 41 tokens to every session and 233 once invoked, about $0.0002 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.
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Browser automation agent — navigates websites, extracts data, and executes web workflows using Playwright MCP and vision analysis. Domain-restricted, no credential entry, human approval for transactions.
FinOps Enablement Lead
Builds and runs the training, documentation, champions network, and internal communication that turns individual engineers and product managers into daily FinOps practitioners. Scales the FinOps culture past the central team.
Cloud Workload Cost Estimator
Pre-deployment cost modeling for new workloads, architecture alternatives, and migration plans. Produces estimates the FinOps and Engineering teams both trust, with explicit assumptions and sensitivity ranges.