rag

A way to give an AI agent relevant information from a search before it answers. Retrieval-Augmented Generation, or RAG, combines searching a knowledge source with generating a response.

In plain words
What is it for?
Use it to connect agents to sources such as Azure AI Search, Microsoft Foundry, or Neo4j, and to add search context during conversations.
Why use it?
It helps agents answer using current or domain-specific information instead of relying only on what the AI model already knows.

Agent

Install

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.

agentmods
npx agentmods add agents/managedcode/dotnet-skills/rag
Clone the repo
git clone --depth 1 https://github.com/managedcode/dotnet-skills
Per session 15 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 3,154 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00015 $0.03154
Opus 5 $0.00008 $0.01577
Sonnet 5 $0.00003 $0.00631
Haiku 4.5 $0.00002 $0.00315

Measured yesterday against content hash 5533c7e0082d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

rag 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.

catalog/Frameworks/Microsoft-Agent-Framework/skills/microsoft-agent-framework/references/official-docs/agents/rag.md · 341 lines

How it starts

The opening of the file, as written. The whole thing — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RAG

Microsoft Agent Framework supports adding Retrieval Augmented Generation (RAG) capabilities to agents easily by adding AI Context Providers to the agent.

For conversation/session patterns alongside retrieval, see Conversations & Memory overview. For service-specific setup, see Azure AI Search, Microsoft Foundry, and Neo4j.

::: zone pivot="programming-language-csharp"

Using TextSearchProvider

The TextSearchProvider class is an out-of-the-box implementation of a RAG context provider. It supports different modes of operation, e.g. doing a search for each agent run with chat history, or advertising function tools for doing searches.

It can easily be attached to a ChatClientAgent using the AIContextProviders option.

// Configure the options for the TextSearchProvider.
TextSearchProviderOptions textSearchOptions = new()
{
    SearchTime = TextSearchProviderOptions.TextSearchBehavior.BeforeAIInvoke,
};

// Create the AI agent with the TextSearchProvider.
AIAgent agent = azureOpenAIClient
    .GetChatClient(deploymentName)
    .AsAIAgent(new ChatClientAgentOptions
    {
        ChatOptions = new() { Instructions = "You are a helpful support specialist. Answer questions using the provided context and cite the source document when available." },
        AIContextProviders = [new TextSearchProvider(SearchAdapter, textSearchOptions)]
    });

Read the full file on GitHub · 341 lines

Changes

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.

  1. yesterday First seen · 341 lines · 15 tokens per session scan A 5533c7e0082d

Subscribe to this mod's changes

rag is an agent published in the GitHub repository managedcode/dotnet-skills (477 stars, last pushed 2d ago), licensed MIT. It adds 15 tokens to every session and 3,154 once invoked, about $0.0001 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.