.NET: Persist messages during function call loop

dotnet/agent-framework-dotnet.slnx

@@ -57,6 +57,7 @@
     <Project Path="samples/02-agents/Agents/Agent_Step16_Declarative/Agent_Step16_Declarative.csproj" />
     <Project Path="samples/02-agents/Agents/Agent_Step17_AdditionalAIContext/Agent_Step17_AdditionalAIContext.csproj" />
     <Project Path="samples/02-agents/Agents/Agent_Step18_CompactionPipeline/Agent_Step18_CompactionPipeline.csproj" />
+    <Project Path="samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Agent_Step19_InFunctionLoopCheckpointing.csproj" />
   </Folder>
   <Folder Name="/Samples/02-agents/DeclarativeAgents/">
     <Project Path="samples/02-agents/DeclarativeAgents/ChatClient/DeclarativeChatClientAgents.csproj" />

dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Agent_Step19_InFunctionLoopCheckpointing.csproj

@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/Program.cs

@@ -0,0 +1,210 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how the PersistChatHistoryAfterEachServiceCall option causes
+// chat history to be persisted after each individual call to the AI service, rather than
+// only at the end of the full agent run. When an agent uses tools, FunctionInvokingChatClient
+// loops multiple times (service call → tool execution → service call), and by default the
+// chat history is only persisted once the entire loop finishes. With this option enabled,
+// intermediate messages (tool calls and results) are persisted after each service call,
+// allowing you to inspect or recover them even if the process is interrupted mid-loop.
+//
+// The sample runs two multi-turn conversations: one using non-streaming (RunAsync) and one
+// using streaming (RunStreamingAsync), to demonstrate correct behavior in both modes.
+
+using System.ComponentModel;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
+
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AzureOpenAIClient openAIClient = new(new Uri(endpoint), new DefaultAzureCredential());
+IChatClient chatClient = openAIClient.GetChatClient(deploymentName).AsIChatClient();
+
+// Define multiple tools so the model makes several tool calls in a single run.
+[Description("Get the current weather for a city.")]
+static string GetWeather([Description("The city name.")] string city) =>
+    city.ToUpperInvariant() switch
+    {
+        "SEATTLE" => "Seattle: 55°F, cloudy with light rain.",
+        "NEW YORK" => "New York: 72°F, sunny and warm.",
+        "LONDON" => "London: 48°F, overcast with fog.",
+        "DUBLIN" => "Dublin: 43°F, overcast with fog.",
+        _ => $"{city}: weather data not available."
+    };
+
+[Description("Get the current time in a city.")]
+static string GetTime([Description("The city name.")] string city) =>
+    city.ToUpperInvariant() switch
+    {
+        "SEATTLE" => "Seattle: 9:00 AM PST",
+        "NEW YORK" => "New York: 12:00 PM EST",
+        "LONDON" => "London: 5:00 PM GMT",
+        "DUBLIN" => "Dublin: 5:00 PM GMT",
+        _ => $"{city}: time data not available."
+    };
+
+// Create the agent with PersistChatHistoryAfterEachServiceCall enabled.
+// The in-memory ChatHistoryProvider is used by default when no explicit provider is set,
+// so we can inspect the chat history via session.TryGetInMemoryChatHistory().
+AIAgent agent = chatClient.AsAIAgent(
+    new ChatClientAgentOptions
+    {
+        Name = "WeatherAssistant",
+        ChatOptions = new()
+        {
+            Instructions = "You are a helpful assistant. When asked about multiple cities, call the appropriate tool for each city.",
+            Tools = [AIFunctionFactory.Create(GetWeather), AIFunctionFactory.Create(GetTime)]
+        },
+        PersistChatHistoryAfterEachServiceCall = true,
+    });
+
+await RunNonStreamingAsync();
+await RunStreamingAsync();
+
+async Task RunNonStreamingAsync()
+{
+    int lastChatHistorySize = 0;
+
+    Console.ForegroundColor = ConsoleColor.Yellow;
+    Console.WriteLine("\n=== Non-Streaming Mode ===");
+    Console.ResetColor();
+
+    AgentSession session = await agent.CreateSessionAsync();
+
+    // First turn — ask about multiple cities so the model calls tools.
+    const string Prompt = "What's the weather and time in Seattle, New York, and London?";
+    PrintUserMessage(Prompt);
+
+    var response = await agent.RunAsync(Prompt, session);
+    PrintAgentResponse(response.Text);
+    PrintChatHistory(session, "After run", ref lastChatHistorySize);
+
+    // Second turn — follow-up to verify chat history is correct.
+    const string FollowUp1 = "And Dublin?";
+    PrintUserMessage(FollowUp1);
+
+    response = await agent.RunAsync(FollowUp1, session);
+    PrintAgentResponse(response.Text);
+    PrintChatHistory(session, "After second run", ref lastChatHistorySize);
+
+    // Third turn — follow-up to verify chat history is correct.
+    const string FollowUp2 = "Which city is the warmest?";
+    PrintUserMessage(FollowUp2);
+
+    response = await agent.RunAsync(FollowUp2, session);
+    PrintAgentResponse(response.Text);
+    PrintChatHistory(session, "After third run", ref lastChatHistorySize);
+}
+
+async Task RunStreamingAsync()
+{
+    int lastChatHistorySize = 0;
+
+    Console.ForegroundColor = ConsoleColor.Yellow;
+    Console.WriteLine("\n=== Streaming Mode ===");
+    Console.ResetColor();
+
+    AgentSession session = await agent.CreateSessionAsync();
+
+    // First turn — ask about multiple cities so the model calls tools.
+    const string Prompt = "What's the weather and time in Seattle, New York, and London?";
+    PrintUserMessage(Prompt);
+
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+
+    await foreach (var update in agent.RunStreamingAsync(Prompt, session))
+    {
+        Console.Write(update);
+
+        // During streaming we should be able to see updates to the chat history
+        // before the full run completes, as each service call is made and persisted.
+        PrintChatHistory(session, "During run", ref lastChatHistorySize);
+    }
+
+    Console.WriteLine();
+    PrintChatHistory(session, "After run", ref lastChatHistorySize);
+
+    // Second turn — follow-up to verify chat history is correct.
+    const string FollowUp1 = "And Dublin?";
+    PrintUserMessage(FollowUp1);
+
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+
+    await foreach (var update in agent.RunStreamingAsync(FollowUp1, session))
+    {
+        Console.Write(update);
+
+        // During streaming we should be able to see updates to the chat history
+        // before the full run completes, as each service call is made and persisted.
+        PrintChatHistory(session, "During second run", ref lastChatHistorySize);
+    }
+
+    Console.WriteLine();
+    PrintChatHistory(session, "After second run", ref lastChatHistorySize);
+
+    // Third turn — follow-up to verify chat history is correct.
+    const string FollowUp2 = "Which city is the warmest?";
+    PrintUserMessage(FollowUp2);
+
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+
+    await foreach (var update in agent.RunStreamingAsync(FollowUp2, session))
+    {
+        Console.Write(update);
+
+        // During streaming we should be able to see updates to the chat history
+        // before the full run completes, as each service call is made and persisted.
+        PrintChatHistory(session, "During third run", ref lastChatHistorySize);
+    }
+
+    Console.WriteLine();
+    PrintChatHistory(session, "After third run", ref lastChatHistorySize);
+}
+
+void PrintUserMessage(string message)
+{
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[User] ");
+    Console.ResetColor();
+    Console.WriteLine(message);
+}
+
+void PrintAgentResponse(string? text)
+{
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+    Console.WriteLine(text);
+}
+
+// Helper to print the current chat history from the session.
+void PrintChatHistory(AgentSession session, string label, ref int lastChatHistorySize)
+{
+    if (session.TryGetInMemoryChatHistory(out var history) && history.Count != lastChatHistorySize)
+    {
+        Console.ForegroundColor = ConsoleColor.DarkGray;
+        Console.WriteLine($"\n  [{label} — Chat history: {history.Count} message(s)]");
+        foreach (var msg in history)
+        {
+            var preview = msg.Text?.Length > 80 ? msg.Text[..80] + "…" : msg.Text;
+            var contentTypes = string.Join(", ", msg.Contents.Select(c => c.GetType().Name));
+            Console.WriteLine($"    {msg.Role,-12} | {(string.IsNullOrWhiteSpace(preview) ? $"[{contentTypes}]" : preview)}");
+        }
+
+        Console.ResetColor();
+
+        lastChatHistorySize = history.Count;
+    }
+}

dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing/README.md

@@ -0,0 +1,62 @@
+# In-Function-Loop Checkpointing
+
+This sample demonstrates how the `PersistChatHistoryAfterEachServiceCall` option on `ChatClientAgentOptions` causes chat history to be saved after each individual call to the AI service, rather than only at the end of the full agent run.
+
+## What This Sample Shows
+
+When an agent uses tools, the `FunctionInvokingChatClient` loops multiple times (service call → tool execution → service call → …). By default, chat history is only persisted once the entire loop finishes. With `PersistChatHistoryAfterEachServiceCall` enabled:
+
+- A `ChatHistoryPersistingChatClient` decorator is automatically inserted into the chat client pipeline
+- After each service call, the decorator notifies the `ChatHistoryProvider` (and any `AIContextProvider` instances) with the new messages
+- Only **new** messages are sent to providers on each notification — messages that were already persisted in an earlier call within the same run are deduplicated automatically
+- The end-of-run persistence in `ChatClientAgent` is skipped to avoid double-persisting
+
+This is useful for:
+- **Crash recovery** — if the process is interrupted mid-loop, the intermediate tool calls and results are already persisted
+- **Observability** — you can inspect the chat history while the agent is still running (e.g., during streaming)
+- **Long-running tool loops** — agents with many sequential tool calls benefit from incremental persistence
+
+## How It Works
+
+The sample asks the agent about the weather and time in three cities. The model calls the `GetWeather` and `GetTime` tools for each city, resulting in multiple service calls within a single `RunStreamingAsync` invocation. After the run completes, the sample prints the full chat history to show all the intermediate messages that were persisted along the way.
+
+### Pipeline Architecture
+
+```
+ChatClientAgent
+  └─ FunctionInvokingChatClient    (handles tool call loop)
+       └─ ChatHistoryPersistingChatClient  (persists after each service call)
+            └─ Leaf IChatClient            (Azure OpenAI)
+```
+
+## Prerequisites
+
+- .NET 10 SDK or later
+- Azure OpenAI service endpoint and model deployment
+- Azure CLI installed and authenticated
+
+**Note**: This sample uses `DefaultAzureCredential`. Sign in with `az login` before running. For production, prefer a specific credential such as `ManagedIdentityCredential`. For more information, see the [Azure CLI authentication documentation](https://learn.microsoft.com/cli/azure/authenticate-azure-cli-interactively).
+
+## Environment Variables
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"  # Required
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-4o-mini"                       # Optional, defaults to gpt-4o-mini
+```
+
+## Running the Sample
+
+```powershell
+cd dotnet/samples/02-agents/Agents/Agent_Step19_InFunctionLoopCheckpointing
+dotnet run
+```
+
+## Expected Behavior
+
+The sample runs two conversation turns:
+
+1. **First turn** — asks about weather and time in three cities. The model calls `GetWeather` and `GetTime` tools (potentially in parallel or sequentially), then provides a summary. The chat history dump after the run shows all the intermediate tool call and result messages.
+
+2. **Second turn** — asks a follow-up question ("Which city is the warmest?") that uses the persisted conversation context. The chat history dump shows the full accumulated conversation.
+
+The chat history printout uses `session.TryGetInMemoryChatHistory()` to inspect the in-memory storage.

dotnet/samples/02-agents/Agents/README.md

@@ -45,6 +45,7 @@ Before you begin, ensure you have the following prerequisites:
 |[Declarative agent](./Agent_Step16_Declarative/)|This sample demonstrates how to declaratively define an agent.|
 |[Providing additional AI Context to an agent using multiple AIContextProviders](./Agent_Step17_AdditionalAIContext/)|This sample demonstrates how to inject additional AI context into a ChatClientAgent using multiple custom AIContextProvider components that are attached to the agent.|
 |[Using compaction pipeline with an agent](./Agent_Step18_CompactionPipeline/)|This sample demonstrates how to use a compaction pipeline to efficiently limit the size of the conversation history for an agent.|
+|[In-function-loop checkpointing](./Agent_Step19_InFunctionLoopCheckpointing/)|This sample demonstrates how to persist chat history after each service call during a tool-calling loop, enabling crash recovery and mid-run observability.|
 
 ## Running the samples from the console