.NET Compaction - Introducing compaction strategies and pipeline (#4533)

* Checkpoint * Checkpoint * Stable * Strategies * Updated * Encoding * Formatting * Cleanup * Formatting * Tests * Tuning * Update tests * Test update * Remove working solution * Add sample to solution * Sample readyme * Experimental * Format * Formatting * Encoding * Support IChatReducer * Sample output formatting * Initial plan * Replace CompactingChatClient with MessageCompactionContextProvider Co-authored-by: crickman <66376200+crickman@users.noreply.github.com> * Boundary condition * Fix encoding * Fix cast * Test coverage * Namespace * Improvements * Efficiency * Cleanup * Detect service managed conversation * Fix namespace * Fix merge * Fix test expectation * Update dotnet/src/Microsoft.Agents.AI.Abstractions/InMemoryChatHistoryProvider.cs Co-authored-by: westey <164392973+westey-m@users.noreply.github.com> * Address PR comments (x1) * Update comment * Update comments * Clean-up * Format output * Sync sample comment * Fix condition * Adjust data-flow * Address comments (x2) * Direct compaction * Fix summarization content * Argument check / fix count calculation * Minor follow-up * Diagnostics * Minor updates * Fix state test * Fix sliding window perf * Stable state keys * Increase size computation * Formatting * Add README.md for Agent_Step18_CompactionPipeline sample (#4574) * Sample comments * Updated * Update dotnet/src/Microsoft.Agents.AI/Compaction/MessageIndex.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update dotnet/tests/Microsoft.Agents.AI.UnitTests/Compaction/CompactionProviderTests.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update dotnet/src/Microsoft.Agents.AI/Compaction/MessageIndex.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Address copilot comments * Fix namespace * Comments / convensions * Prefix `MessageGroup` and `MessageIndex` * Fix sliding window * Update dotnet/src/Microsoft.Agents.AI/Compaction/SummarizationCompactionStrategy.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update dotnet/src/Microsoft.Agents.AI.Abstractions/InMemoryChatHistoryProvider.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Python alignment * Fix merge * Fix equality, readme, and sample * Readme update and ToolResult fix * Update dotnet/src/Microsoft.Agents.AI/Compaction/SummarizationCompactionStrategy.cs Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Update dotnet/samples/02-agents/Agents/Agent_Step18_CompactionPipeline/README.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Simplify readme * Update dotnet/samples/02-agents/Agents/Agent_Step18_CompactionPipeline/README.md Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> * Remove example * Remove unused --------- Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: westey <164392973+westey-m@users.noreply.github.com> Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
2026-06-16 21:04:09 +08:00 · 2026-03-11 00:41:39 +00:00
parent 97b6c9951a
commit d3f0c33180
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@@ -0,0 +1,21 @@
+<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" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>
@@ -0,0 +1,120 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates how to use a CompactionProvider with a compaction pipeline
+// as an AIContextProvider for an agent's in-run context management. The pipeline chains multiple
+// compaction strategies from gentle to aggressive:
+//   1. ToolResultCompactionStrategy - Collapses old tool-call groups into concise summaries
+//   2. SummarizationCompactionStrategy - LLM-compresses older conversation spans
+//   3. SlidingWindowCompactionStrategy - Keeps only the most recent N user turns
+//   4. TruncationCompactionStrategy - Emergency token-budget backstop
+
+using System.ComponentModel;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Compaction;
+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());
+
+// Create a chat client for the agent and a separate one for the summarization strategy.
+// Using the same model for simplicity; in production, use a smaller/cheaper model for summarization.
+IChatClient agentChatClient = openAIClient.GetChatClient(deploymentName).AsIChatClient();
+IChatClient summarizerChatClient = openAIClient.GetChatClient(deploymentName).AsIChatClient();
+
+// Define a tool the agent can use, so we can see tool-result compaction in action.
+[Description("Look up the current price of a product by name.")]
+static string LookupPrice([Description("The product name to look up.")] string productName) =>
+    productName.ToUpperInvariant() switch
+    {
+        "LAPTOP" => "The laptop costs $999.99.",
+        "KEYBOARD" => "The keyboard costs $79.99.",
+        "MOUSE" => "The mouse costs $29.99.",
+        _ => $"Sorry, I don't have pricing for '{productName}'."
+    };
+
+// Configure the compaction pipeline with one of each strategy, ordered least to most aggressive.
+PipelineCompactionStrategy compactionPipeline =
+    new(// 1. Gentle: collapse old tool-call groups into short summaries
+        new ToolResultCompactionStrategy(CompactionTriggers.MessagesExceed(7)),
+
+        // 2. Moderate: use an LLM to summarize older conversation spans into a concise message
+        new SummarizationCompactionStrategy(summarizerChatClient, CompactionTriggers.TokensExceed(0x500)),
+
+        // 3. Aggressive: keep only the last N user turns and their responses
+        new SlidingWindowCompactionStrategy(CompactionTriggers.TurnsExceed(4)),
+
+        // 4. Emergency: drop oldest groups until under the token budget
+        new TruncationCompactionStrategy(CompactionTriggers.TokensExceed(0x8000)));
+
+// Create the agent with a CompactionProvider that uses the compaction pipeline.
+AIAgent agent =
+    agentChatClient
+        .AsBuilder()
+        // Note: Adding the CompactionProvider at the builder level means it will be applied to all agents
+        // built from this builder and will manage context for both agent messages and tool calls.
+        .UseAIContextProviders(new CompactionProvider(compactionPipeline))
+        .BuildAIAgent(
+            new ChatClientAgentOptions
+            {
+                Name = "ShoppingAssistant",
+                ChatOptions = new()
+                {
+                    Instructions =
+                        """
+                        You are a helpful, but long winded, shopping assistant.
+                        Help the user look up prices and compare products.
+                        When responding, Be sure to be extra descriptive and use as
+                        many words as possible without sounding ridiculous.
+                        """,
+                    Tools = [AIFunctionFactory.Create(LookupPrice)]
+                },
+                // Note: AIContextProviders may be specified here instead of ChatClientBuilder.UseAIContextProviders.
+                // Specifying compaction at the agent level skips compaction in the function calling loop.
+                //AIContextProviders = [new CompactionProvider(compactionPipeline)]
+            });
+
+AgentSession session = await agent.CreateSessionAsync();
+
+// Helper to print chat history size
+void PrintChatHistory()
+{
+    if (session.TryGetInMemoryChatHistory(out var history))
+    {
+        Console.ForegroundColor = ConsoleColor.Cyan;
+        Console.WriteLine($"\n[Messages: #{history.Count}]\n");
+        Console.ResetColor();
+    }
+}
+
+// Run a multi-turn conversation with tool calls to exercise the pipeline.
+string[] prompts =
+[
+    "What's the price of a laptop?",
+    "How about a keyboard?",
+    "And a mouse?",
+    "Which product is the cheapest?",
+    "Can you compare the laptop and the keyboard for me?",
+    "What was the first product I asked about?",
+    "Thank you!",
+];
+
+foreach (string prompt in prompts)
+{
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[User] ");
+    Console.ResetColor();
+    Console.WriteLine(prompt);
+    Console.ForegroundColor = ConsoleColor.Cyan;
+    Console.Write("\n[Agent] ");
+    Console.ResetColor();
+    Console.WriteLine(await agent.RunAsync(prompt, session));
+
+    PrintChatHistory();
+}
@@ -0,0 +1,132 @@
+# Compaction Pipeline
+
+This sample demonstrates how to use a `CompactionProvider` with a `PipelineCompactionStrategy` to manage long conversation histories in a token-efficient way. The pipeline chains four compaction strategies, ordered from gentle to aggressive, so that the least disruptive strategy runs first and more aggressive strategies only activate when necessary.
+
+## What This Sample Shows
+
+- **`CompactionProvider`** — an `AIContextProvider` that applies a compaction strategy before each agent invocation, keeping only the most relevant messages within the model's context window
+- **`PipelineCompactionStrategy`** — chains multiple compaction strategies into an ordered pipeline; each strategy evaluates its own trigger independently and operates on the output of the previous one
+- **`ToolResultCompactionStrategy`** — collapses older tool-call groups into concise inline summaries, activated by a message-count trigger
+- **`SummarizationCompactionStrategy`** — uses an LLM to compress older conversation spans into a single summary message, activated by a token-count trigger
+- **`SlidingWindowCompactionStrategy`** — retains only the most recent N user turns and their responses, activated by a turn-count trigger
+- **`TruncationCompactionStrategy`** — emergency backstop that drops the oldest groups until the conversation fits within a hard token budget
+- **`CompactionTriggers`** — factory methods (`MessagesExceed`, `TokensExceed`, `TurnsExceed`, `GroupsExceed`, `HasToolCalls`, `All`, `Any`) that control when each strategy activates
+
+## Concepts
+
+### Message groups
+
+The compaction engine organizes messages into atomic *groups* that are treated as indivisible units during compaction. A group is either:
+
+| Group kind | Contents |
+|---|---|
+| `System` | System prompt message(s) |
+| `User` | A single user message |
+| `ToolCall` | One assistant message with tool calls + the matching tool result messages |
+| `AssistantText` | A single assistant text-only message |
+| `Summary` | One or more messages summarizing earlier conversation spans, produced by compaction strategies |
+
+`Summary` groups (`CompactionGroupKind.Summary`) are created by compaction strategies (for example, `SummarizationCompactionStrategy`) and do not originate directly from user or assistant messages.
+Strategies exclude entire groups rather than individual messages, preserving the tool-call/result pairing required by most model APIs.
+
+### Compaction triggers
+
+A `CompactionTrigger` is a predicate evaluated against the current `MessageIndex`. When the trigger fires, the strategy performs compaction; when it does not fire, the strategy is skipped. Available triggers are:
+
+| Trigger | Activates when… |
+|---|---|
+| `CompactionTriggers.Always` | Always (unconditional) |
+| `CompactionTriggers.Never` | Never (disabled) |
+| `CompactionTriggers.MessagesExceed(n)` | Included message count > n |
+| `CompactionTriggers.TokensExceed(n)` | Included token count > n |
+| `CompactionTriggers.TurnsExceed(n)` | Included user-turn count > n |
+| `CompactionTriggers.GroupsExceed(n)` | Included group count > n |
+| `CompactionTriggers.HasToolCalls()` | At least one included tool-call group exists |
+| `CompactionTriggers.All(...)` | All supplied triggers fire (logical AND) |
+| `CompactionTriggers.Any(...)` | Any supplied trigger fires (logical OR) |
+
+### Pipeline ordering
+
+Order strategies from **least aggressive** to **most aggressive**. The pipeline runs every strategy whose trigger is met. Earlier strategies reduce the conversation gently so that later, more destructive strategies may not need to activate at all.
+
+```
+1. ToolResultCompactionStrategy  – gentle:    replaces verbose tool results with a short label
+2. SummarizationCompactionStrategy – moderate: LLM-summarizes older turns
+3. SlidingWindowCompactionStrategy – aggressive: drops turns beyond the window
+4. TruncationCompactionStrategy   – emergency:  hard token-budget enforcement
+```
+
+## 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_Step18_CompactionPipeline
+dotnet run
+```
+
+## Expected Behavior
+
+The sample runs a seven-turn shopping-assistant conversation with tool calls. After each turn it prints the full message count so you can observe the pipeline compaction doesn't alter the source conversation.
+
+Each of the four compaction strategies has a deliberately low threshold so that it activates during the short demonstration conversation. In a production scenario you would raise the thresholds to match your model's context window and cost requirements.
+
+## Customizing the Pipeline
+
+### Using a single strategy
+
+If you only need one compaction strategy, pass it directly to `CompactionProvider` without wrapping it in a pipeline:
+
+```csharp
+CompactionProvider provider =
+    new(new SlidingWindowCompactionStrategy(CompactionTriggers.TurnsExceed(20)));
+```
+
+### Ad-hoc compaction outside the provider pipeline
+
+`CompactionProvider.CompactAsync` applies a strategy to an arbitrary list of messages without an active agent session:
+
+```csharp
+IEnumerable<ChatMessage> compacted = await CompactionProvider.CompactAsync(
+    new TruncationCompactionStrategy(CompactionTriggers.TokensExceed(8000)),
+    existingMessages);
+```
+
+### Using a different model for summarization
+
+The `SummarizationCompactionStrategy` accepts any `IChatClient`. Use a smaller, cheaper model to reduce summarization cost:
+
+```csharp
+IChatClient summarizerChatClient = openAIClient.GetChatClient("gpt-4o-mini").AsIChatClient();
+new SummarizationCompactionStrategy(summarizerChatClient, CompactionTriggers.TokensExceed(4000))
+```
+
+### Registering through `ChatClientAgentOptions`
+
+`CompactionProvider` can also be specified directly on `ChatClientAgentOptions` instead of calling `UseAIContextProviders` on the `ChatClientBuilder`:
+
+```csharp
+AIAgent agent = agentChatClient
+    .AsBuilder()
+    .BuildAIAgent(new ChatClientAgentOptions
+    {
+        AIContextProviders = [new CompactionProvider(compactionPipeline)]
+    });
+```
+
+This places the compaction provider at the agent level instead of the chat client level, which allows you to use different compaction strategies for different agents that share the same chat client.
+
+> Note: In this mode the `CompactionProvider` is not engaged during the tool calling loop. Agent-level `AIContextProviders` run before chat history is stored, so any synthetic summary messages produced by `CompactionProvider` can become part of the persisted history when using `ChatHistoryProvider`. If you want to compact only the request context while preserving the original stored history, register `CompactionProvider` on the `ChatClientBuilder` via `UseAIContextProviders(...)` instead of on `ChatClientAgentOptions`.
@@ -44,6 +44,7 @@ Before you begin, ensure you have the following prerequisites:
 |[Deep research with an agent](./Agent_Step15_DeepResearch/)|This sample demonstrates how to use the Deep Research Tool to perform comprehensive research on complex topics|
 |[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.|

 ## Running the samples from the console