Borrowing it
Nothing to install: this file belongs to managedcode/dotPilot. 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/managedcode/dotPilot/main/.codex/skills/dotnet-microsoft-agent-framework/references/official-docs/user-guide/agents/agent-rag.mdgit clone --depth 1 https://github.com/managedcode/dotPilotWrote 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/managedcode/dotpilot/agent-rag)<a href="https://agentmods.dev/agents/managedcode/dotpilot/agent-rag"><img src="https://agentmods.dev/badge/agents/managedcode/dotpilot/agent-rag/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/managedcode/dotpilot/agent-rag"><img src="https://agentmods.dev/badge/agents/managedcode/dotpilot/agent-rag.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 | $0.00015 | $0.02729 |
| Opus 5.5 | $0.00006 | $0.01092 |
| Sonnet 5.5 | $0.00003 | $0.00546 |
| Haiku 4.5 | $0.00002 | $0.00273 |
Grade A, and why
agent-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 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.
This is a copy
100% identical to agent-rag — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Retrieval Augmented Generation (RAG)
Microsoft Agent Framework supports adding Retrieval Augmented Generation (RAG) capabilities to agents easily by adding AI Context Providers to the agent.
::: zone pivot="programming-language-csharp"
Using TextSearchProvider
The TextSearchProvider class is an out-of-the-box implementation of a RAG context provider.
It can easily be attached to a ChatClientAgent using the AIContextProviderFactory option to provide RAG capabilities to the agent.
The factory is an async function that receives a context object and a cancellation token.
// Create the AI agent with the TextSearchProvider as the AI context provider.
AIAgent agent = azureOpenAIClient
.GetChatClient(deploymentName)
.AsAIAgent(new ChatClientAgentOptions
{
ChatOptions = new() { Instructions = "You are a helpful support specialist for Contoso Outdoors. Answer questions using the provided context and cite the source document when available." },
AIContextProviderFactory = (ctx, ct) => new ValueTask<AIContextProvider>(
new TextSearchProvider(SearchAdapter, ctx.SerializedState, ctx.JsonSerializerOptions, textSearchOptions))
});
The TextSearchProvider requires a function that provides the search results given a query. This can be implemented using any search technology, e.g. Azure AI Search, or a web search engine.
Here is an example of a mock search function that returns pre-defined results based on the query.
SourceName and SourceLink are optional, but if provided will be used by the agent to cite the source of the information when answering the user's question.
static Task<IEnumerable<TextSearchProvider.TextSearchResult>> SearchAdapter(string query, CancellationToken cancellationToken)
{
// The mock search inspects the user's question and returns pre-defined snippets
// that resemble documents stored in an external knowledge source.
List<TextSearchProvider.TextSearchResult> results = new();
if (query.Contains("return", StringComparison.OrdinalIgnoreCase) || query.Contains("refund", StringComparison.OrdinalIgnoreCase))
{
results.Add(new()
{
SourceName = "Contoso Outdoors Return Policy",
SourceLink = "https://contoso.com/policies/returns",
Text = "Customers may return any item within 30 days of delivery. Items should be unused and include original packaging. Refunds are issued to the original payment method within 5 business days of inspection."
});
}
return Task.FromResult<IEnumerable<TextSearchProvider.TextSearchResult>>(results);
}
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 · 304 lines · 15 tokens per session scan A e21cea9b85dc
agent-rag is an agent published in the GitHub repository managedcode/dotPilot (25 stars, last pushed 6mo ago), licensed MIT. It adds 15 tokens to every session and 2,729 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-rag, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
ai-engineer
An AI and machine-learning engineering agent for adding language models and other AI features to software. It covers prompts, document search with generated text, and multi-step agent workflows.
Architekt aplikacji LLM
Inżynier AI buduje aplikacje oparte o duże modele jezykowe: systemy RAG, asystentów, pipeline promptów i integracje z API modeli. Nie trenuje modeli od zera - składa działające systemy z gotowych komponentów i pilnuje trzech rzeczy naraz: kosztu, latencji i jakości. Każde twierdzenie o jakości popiera ewaluacja, nie…
vector-db-init-agent
Guided setup wizard for the vector-db plugin. Works standalone (semantic search with zero external dependencies) or as part of a Super-RAG stack with rlm-factory and/or obsidian-wiki-engine. Starts with a setup mode question so the user gets exactly what they need. Installs Python dependencies, creates…
super-rag-setup-agent
Multi-plugin initialization orchestrator for the Super-RAG knowledge stack. Provisions any combination of rlm-factory (RLM summary ledger), vector-db (semantic embedding index), and obsidian-wiki-engine (Karpathy-style LLM wiki) in a single guided session. Starts with a plugin audit and lets the user choose which…
ai-ml-engineer
AI/ML Engineer specialising in prompt engineering, RAG architecture, LLM evaluation, AI safety, and agent orchestration. Use when: "build an AI feature", "LLM", "ChatGPT", "Claude API", "prompt engineering", "RAG", "vector database", "embeddings", "fine-tuning", "AI agent", "LangChain", "LangGraph", "evaluation"…
vector-db-cleanup
Removes stale chunks from the ChromaDB vector store for files that have been deleted or renamed on disk. Dry-run by default — shows what would be removed without deleting. Trigger when the user says "clean the vector database", "remove stale vector entries", "vector DB has orphaned chunks", or "sync the vector store…