Andes.Extensions.AI 0.3.1

Andes.Extensions.AI

Middleware extensions for Microsoft.Extensions.AI: per-request and per-tool token usage tracking, and streaming status propagation for IChatClient pipelines.

Add one line to your pipeline and get:

• Token usage tracking — input/output/total tokens for the main assistant, per model turn, and attributed to each tool call (including LLM calls nested inside tools), rolled up into a ChatUsageReport.
• Streaming progress statuses — synthetic ChatProgressContent updates interleaved into the live stream so your UI can show "Calling GetWeather Tool", sub-statuses reported from inside the tool ("Extracting…", "Processing…"), and completion — while the model and tools are still working.
• Out-of-band observers — implement IChatProgressObserver to receive the same events and the final report without parsing the stream.
• Privacy by default — progress events never carry prompt content, tool arguments, or tool results unless explicitly opted in.

Install

dotnet add package Andes.Extensions.AI

Quickstart

Register the middleware before UseFunctionInvocation() — the tracker must wrap the tools that the function-invoking client executes:

using Andes.Extensions.AI;
using Microsoft.Extensions.AI;

IChatClient client = innerClient          // any IChatClient (Azure OpenAI, OpenAI, Ollama, ...)
    .AsBuilder()
    .UseToolTracking()
    .UseFunctionInvocation()
    .Build();

AIFunction weather = AIFunctionFactory.Create(
    (string city) =>
    {
        ChatProgress.Report("Extracting...");   // sub-status under "Calling GetWeather Tool"
        return $"Sunny in {city}";
    },
    "GetWeather");

await foreach (var update in client.GetStreamingResponseAsync(
    "What's the weather in Quito?",
    new ChatOptions { Tools = [weather] }))
{
    foreach (var content in update.Contents)
    {
        switch (content)
        {
            case ChatProgressContent progress:
                Console.WriteLine($"[{progress.Progress.Kind}] {progress.Progress.Message}");
                break;
            case UsageReportContent usage:
                Console.WriteLine($"Total tokens: {usage.Report.TotalUsage.TotalTokenCount}");
                break;
        }
    }

    Console.Write(update.Text);
}

Tools that are themselves LLM-backed (for example, agents exposed as functions) can attribute their own usage to the calling scope with ChatProgress.ReportUsage(...) — or simply run their own UseToolTracking() pipeline, whose total rolls up automatically.

Before persisting responses into conversation history, remove the synthetic content:

ChatResponse response = updates.ToChatResponse().StripProgressContent();

MCP tools

First-class MCP support ships as a satellite package, Andes.Extensions.AI.Mcp, so the core stays dependency-lean:

dotnet add package Andes.Extensions.AI.Mcp

McpClientTool instances classify as ToolKind.McpTool and render as "Calling {Server} MCP", and the server's progress notifications are bridged into ToolProgress updates with numeric Progress/ProgressTotal values:

IList<McpClientTool> mcpTools = await mcpClient.ListToolsAsync();

IChatClient client = innerClient
    .AsBuilder()
    .UseToolTracking(options => options.UseMcpToolClassification())
    .UseFunctionInvocation()
    .Build();

var chatOptions = new ChatOptions { Tools = mcpTools.WithTracking(mcpClient) };

See MCP support for details.

Agent tools

Microsoft Agent Framework agents run as tracked tools through their own satellite package, Andes.Extensions.AI.Agent:

dotnet add package Andes.Extensions.AI.Agent

Agents wrapped with WithTracking() classify as ToolKind.Agent and render as "Calling {Agent} Agent", and each run's AgentResponse.Usage is attributed to the calling tool's scope — a plain agent.AsAIFunction() exposes neither:

AIAgent weatherAgent = weatherChatClient.AsAIAgent(
    instructions: "You answer questions about the weather.",
    name: "Weather Agent",
    tools: [AIFunctionFactory.Create(GetWeather)]);

IChatClient client = innerClient
    .AsBuilder()
    .UseToolTracking(options => options.UseAgentToolClassification())
    .UseFunctionInvocation()
    .Build();

var chatOptions = new ChatOptions { Tools = [weatherAgent.WithTracking()] };

Agents nest (v0.3): a WithTracking-wrapped agent used as a tool of another agent — or invoked directly inside a tool body — opens its own child scope, rendering live as a child activity card with its own statuses, duration, and token usage in the report:

✓ Research Agent  agent  6.0s · 1,317 tok
  ├── Calling SearchNotes Tool
  ├── Searching notes…
  ├── Calling Packing_Agent Tool
  └── ✓ Packing Agent  agent  2.4s · 504 tok
      └── Checking essentials…

The same applies to nested MCP tools, and any tool can give a sub-operation its own child card with ChatProgress.BeginToolScope(new ToolDescriptor { ... }).

See Agent support for details.

UI

A serializable status contract for streaming progress to a UI ships as its own satellite package, Andes.Extensions.AI.UI — a matching C# and TypeScript shape, so a Blazor app and a SPA render the same activity tree from the same JSON:

dotnet add package Andes.Extensions.AI.UI

Stream AssistantStatusSnapshot instead of parsing ChatResponseUpdate yourself. Each activity carries a clean DisplayName plus a separate Kind badge — never a composed "Calling … MCP/Agent/Tool" string, so the kind word is never repeated:

await foreach (AssistantStatusSnapshot snapshot in client
    .GetStreamingResponseAsync("prompt", chatOptions)
    .ToStatusSnapshotsAsync())
{
    foreach (AssistantActivity activity in snapshot.Activities)
    {
        Console.WriteLine($"{activity.DisplayName} [{activity.Kind}] — {activity.State}");
    }
}

For an HTTP surface, stream ToUiEventsAsync() instead and serialize each event with the package's AssistantUiJsonContext over server-sent events; a browser or Blazor client folds them with the shipped TypeScript foldAssistantEvents or the C# AssistantStatusReducer. See UI support for details.

Samples

samples/Andes.Extensions.AI.Demo is an interactive console chat that exercises all four packages in one tracked pipeline and renders live activity — function/MCP/agent cards, progress bars, token usage — Claude-Code-style with Spectre.Console:

cp samples/Andes.Extensions.AI.Demo/appsettings.sample.json samples/Andes.Extensions.AI.Demo/appsettings.json
# fill in the AzureOpenAI section, then:
dotnet run --project samples/Andes.Extensions.AI.Demo

See the sample README for what each file demonstrates.

Documentation

• Getting started
• Architecture
• MCP support
• Agent support
• UI support
• Example: the Progress Board — every tool kind in one stream
• Example: the UI contract, three ways
• Sample: the interactive demo console app
• Release notes

License

MIT