Merge branch 'main' into feat/durable_task

This commit is contained in:
Shyju Krishnankutty
2026-03-10 14:28:08 -07:00
committed by GitHub
Unverified
42 changed files with 1745 additions and 364 deletions
+3 -3
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@@ -64,7 +64,7 @@ Approaches observed from the compared SDKs:
| AutoGen | **Approach 1** Separates messages into Agent-Agent (maps to Primary) and Internal (maps to Secondary) and these are returned as separate properties on the agent response object. See [types of messages](https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/tutorial/messages.html#types-of-messages) and [Response](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.Response) | **Approach 2** Returns a stream of internal events and the last item is a Response object. See [ChatAgent.on_messages_stream](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.ChatAgent.on_messages_stream) |
| OpenAI Agent SDK | **Approach 1** Separates new_items (Primary+Secondary) from final output (Primary) as separate properties on the [RunResult](https://github.com/openai/openai-agents-python/blob/main/src/agents/result.py#L39) | **Approach 1** Similar to non-streaming, has a way of streaming updates via a method on the response object which includes all data, and then a separate final output property on the response object which is populated only when the run is complete. See [RunResultStreaming](https://github.com/openai/openai-agents-python/blob/main/src/agents/result.py#L136) |
| Google ADK | **Approach 2** [Emits events](https://google.github.io/adk-docs/runtime/#step-by-step-breakdown) with [FinalResponse](https://github.com/google/adk-java/blob/main/core/src/main/java/com/google/adk/events/Event.java#L232) true (Primary) / false (Secondary) and callers have to filter out those with false to get just the final response message | **Approach 2** Similar to non-streaming except [events](https://google.github.io/adk-docs/runtime/#streaming-vs-non-streaming-output-partialtrue) are emitted with [Partial](https://github.com/google/adk-java/blob/main/core/src/main/java/com/google/adk/events/Event.java#L133) true to indicate that they are streaming messages. A final non partial event is also emitted. |
| AWS (Strands) | **Approach 3** Returns an [AgentResult](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent_result/) (Primary) with messages and a reason for the run's completion. | **Approach 2** [Streams events](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent/#strands.agent.agent.Agent.stream_async) (Primary+Secondary) including, response text, current_tool_use, even data from "callbacks" (strands plugins) |
| AWS (Strands) | **Approach 3** Returns an [AgentResult](https://strandsagents.com/docs/api/python/strands.agent.agent_result/#agentresult) (Primary) with messages and a reason for the run's completion. | **Approach 2** [Streams events](https://strandsagents.com/docs/user-guide/concepts/streaming/) (Primary+Secondary) including, response text, current_tool_use, even data from "callbacks" (strands plugins) |
| LangGraph | **Approach 2** A mixed list of all [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) | **Approach 2** A mixed list of all [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) |
| Agno | **Combination of various approaches** Returns a [RunResponse](https://docs.agno.com/reference/agents/run-response) object with text content, messages (essentially chat history including inputs and instructions), reasoning and thinking text properties. Secondary events could potentially be extracted from messages. | **Approach 2** Returns [RunResponseEvent](https://docs.agno.com/reference/agents/run-response#runresponseevent-types-and-attributes) objects including tool call, memory update, etc, information, where the [RunResponseCompletedEvent](https://docs.agno.com/reference/agents/run-response#runresponsecompletedevent) has similar properties to RunResponse|
| A2A | **Approach 3** Returns a [Task or Message](https://a2aproject.github.io/A2A/latest/specification/#71-messagesend) where the message is the final result (Primary) and task is a reference to a long running process. | **Approach 2** Returns a [stream](https://a2aproject.github.io/A2A/latest/specification/#72-messagestream) that contains task updates (Secondary) and a final message (Primary) |
@@ -496,7 +496,7 @@ We need to decide what AIContent types, each agent response type will be mapped
|-|-|
| AutoGen | **Approach 1** Supports [configuring an agent](https://microsoft.github.io/autogen/stable/user-guide/agentchat-user-guide/tutorial/agents.html#structured-output) at agent creation. |
| Google ADK | **Approach 1** Both [input and output schemas can be specified for LLM Agents](https://google.github.io/adk-docs/agents/llm-agents/#structuring-data-input_schema-output_schema-output_key) at construction time. This option is specific to this agent type and other agent types do not necessarily support |
| AWS (Strands) | **Approach 2** Supports a special invocation method called [structured_output](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent/#strands.agent.agent.Agent.structured_output) |
| AWS (Strands) | **Approach 2** Supports a special invocation method called [structured_output](https://strandsagents.com/docs/user-guide/concepts/agents/structured-output/) |
| LangGraph | **Approach 1** Supports [configuring an agent](https://langchain-ai.github.io/langgraph/agents/agents/?h=structured#6-configure-structured-output) at agent construction time, and a [structured response](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) can be retrieved as a special property on the agent response |
| Agno | **Approach 1** Supports [configuring an agent](https://docs.agno.com/input-output/structured-output/agent) at agent construction time |
| A2A | **Informal Approach 2** Doesn't formally support schema negotiation, but [hints can be provided via metadata](https://a2a-protocol.org/latest/specification/#97-structured-data-exchange-requesting-and-providing-json) at invocation time |
@@ -508,7 +508,7 @@ We need to decide what AIContent types, each agent response type will be mapped
|-|-|
| AutoGen | Supports a [stop reason](https://microsoft.github.io/autogen/stable/reference/python/autogen_agentchat.base.html#autogen_agentchat.base.TaskResult.stop_reason) which is a freeform text string |
| Google ADK | [No equivalent present](https://github.com/google/adk-python/blob/main/src/google/adk/events/event.py) |
| AWS (Strands) | Exposes a [stop_reason](https://strandsagents.com/latest/documentation/docs/api-reference/python/types/event_loop/#strands.types.event_loop.StopReason) property on the [AgentResult](https://strandsagents.com/latest/documentation/docs/api-reference/python/agent/agent_result/) class with options that are tied closely to LLM operations. |
| AWS (Strands) | Exposes a `stop_reason` property on the [AgentResult](https://strandsagents.com/docs/api/python/strands.agent.agent_result/#agentresult) class with options that are tied closely to LLM operations. |
| LangGraph | No equivalent present, output contains only [messages](https://langchain-ai.github.io/langgraph/agents/run_agents/#output-format) |
| Agno | [No equivalent present](https://docs.agno.com/reference/agents/run-response) |
| A2A | No equivalent present, response only contains a [message](https://a2a-protocol.org/latest/specification/#64-message-object) or [task](https://a2a-protocol.org/latest/specification/#61-task-object). |
+1
View File
@@ -14,6 +14,7 @@
"src\\Microsoft.Agents.AI.Declarative\\Microsoft.Agents.AI.Declarative.csproj",
"src\\Microsoft.Agents.AI.DevUI\\Microsoft.Agents.AI.DevUI.csproj",
"src\\Microsoft.Agents.AI.DurableTask\\Microsoft.Agents.AI.DurableTask.csproj",
"src\\Microsoft.Agents.AI.FoundryMemory\\Microsoft.Agents.AI.FoundryMemory.csproj",
"src\\Microsoft.Agents.AI.Hosting.A2A.AspNetCore\\Microsoft.Agents.AI.Hosting.A2A.AspNetCore.csproj",
"src\\Microsoft.Agents.AI.Hosting.A2A\\Microsoft.Agents.AI.Hosting.A2A.csproj",
"src\\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore\\Microsoft.Agents.AI.Hosting.AGUI.AspNetCore.csproj",
@@ -4,7 +4,6 @@ using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
using Microsoft.Extensions.AI;
using OpenAI.Chat;
WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
@@ -27,7 +26,7 @@ ChatClient chatClient = new AzureOpenAIClient(
new DefaultAzureCredential())
.GetChatClient(deploymentName);
AIAgent agent = chatClient.AsIChatClient().AsAIAgent(
AIAgent agent = chatClient.AsAIAgent(
name: "AGUIAssistant",
instructions: "You are a helpful assistant.");
@@ -10,7 +10,6 @@
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
</ItemGroup>
<ItemGroup>
@@ -82,7 +82,7 @@ ChatClient chatClient = new AzureOpenAIClient(
new DefaultAzureCredential())
.GetChatClient(deploymentName);
ChatClientAgent agent = chatClient.AsIChatClient().AsAIAgent(
ChatClientAgent agent = chatClient.AsAIAgent(
name: "AGUIAssistant",
instructions: "You are a helpful assistant with access to restaurant information.",
tools: tools);
@@ -10,7 +10,6 @@
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
</ItemGroup>
<ItemGroup>
@@ -4,7 +4,6 @@ using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
using Microsoft.Extensions.AI;
using OpenAI.Chat;
WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
@@ -27,7 +26,7 @@ ChatClient chatClient = new AzureOpenAIClient(
new DefaultAzureCredential())
.GetChatClient(deploymentName);
AIAgent agent = chatClient.AsIChatClient().AsAIAgent(
AIAgent agent = chatClient.AsAIAgent(
name: "AGUIAssistant",
instructions: "You are a helpful assistant.");
@@ -10,7 +10,6 @@
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
</ItemGroup>
<ItemGroup>
@@ -60,7 +60,7 @@ ChatClient openAIChatClient = new AzureOpenAIClient(
new DefaultAzureCredential())
.GetChatClient(deploymentName);
ChatClientAgent baseAgent = openAIChatClient.AsIChatClient().AsAIAgent(
ChatClientAgent baseAgent = openAIChatClient.AsAIAgent(
name: "AGUIAssistant",
instructions: "You are a helpful assistant in charge of approving expenses",
tools: tools);
@@ -10,7 +10,6 @@
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
</ItemGroup>
<ItemGroup>
@@ -4,7 +4,6 @@ using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
using Microsoft.Extensions.AI;
using Microsoft.Extensions.Options;
using OpenAI.Chat;
using RecipeAssistant;
@@ -37,7 +36,7 @@ ChatClient chatClient = new AzureOpenAIClient(
new DefaultAzureCredential())
.GetChatClient(deploymentName);
AIAgent baseAgent = chatClient.AsIChatClient().AsAIAgent(
AIAgent baseAgent = chatClient.AsAIAgent(
name: "RecipeAgent",
instructions: """
You are a helpful recipe assistant. When users ask you to create or suggest a recipe,
@@ -10,7 +10,6 @@
<ItemGroup>
<PackageReference Include="Azure.AI.OpenAI" />
<PackageReference Include="Azure.Identity" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
</ItemGroup>
<ItemGroup>
@@ -12,11 +12,8 @@ using OpenAI.Responses;
string endpoint = Environment.GetEnvironmentVariable("AZURE_AI_PROJECT_ENDPOINT") ?? throw new InvalidOperationException("AZURE_AI_PROJECT_ENDPOINT is not set.");
string deploymentName = Environment.GetEnvironmentVariable("AZURE_AI_MODEL_DEPLOYMENT_NAME") ?? "gpt-4o-mini";
// Memory store configuration
// NOTE: Memory stores must be created beforehand via Azure Portal or Python SDK.
// The .NET SDK currently only supports using existing memory stores with agents.
string memoryStoreName = Environment.GetEnvironmentVariable("AZURE_AI_MEMORY_STORE_ID") ?? throw new InvalidOperationException("AZURE_AI_MEMORY_STORE_ID is not set.");
string embeddingModelName = Environment.GetEnvironmentVariable("AZURE_AI_EMBEDDING_DEPLOYMENT_NAME") ?? "text-embedding-ada-002";
string memoryStoreName = Environment.GetEnvironmentVariable("AZURE_AI_MEMORY_STORE_ID") ?? $"foundry-memory-sample-{Guid.NewGuid():N}";
const string AgentInstructions = """
You are a helpful assistant that remembers past conversations.
@@ -32,71 +29,57 @@ const string AgentNameNative = "MemorySearchAgent-NATIVE";
string userScope = $"user_{Environment.MachineName}";
// Get a client to create/retrieve/delete server side agents with Azure Foundry Agents.
AIProjectClient aiProjectClient = new(new Uri(endpoint), new AzureCliCredential());
DefaultAzureCredential credential = new();
AIProjectClient aiProjectClient = new(new Uri(endpoint), credential);
// Ensure the memory store exists and has memories to retrieve.
await EnsureMemoryStoreAsync();
// Create the Memory Search tool configuration
MemorySearchPreviewTool memorySearchTool = new(memoryStoreName, userScope)
{
// Optional: Configure how quickly new memories are indexed (in seconds)
UpdateDelay = 1,
// Optional: Configure search behavior
SearchOptions = new MemorySearchToolOptions
{
// Additional search options can be configured here if needed
}
};
MemorySearchPreviewTool memorySearchTool = new(memoryStoreName, userScope) { UpdateDelay = 0 };
// Create agent using Option 1 (MEAI) or Option 2 (Native SDK)
AIAgent agent = await CreateAgentWithMEAI();
// AIAgent agent = await CreateAgentWithNativeSDK();
Console.WriteLine("Agent created with Memory Search tool. Starting conversation...\n");
// Conversation 1: Share some personal information
Console.WriteLine("User: My name is Alice and I love programming in C#.");
AgentResponse response1 = await agent.RunAsync("My name is Alice and I love programming in C#.");
Console.WriteLine($"Agent: {response1.Messages.LastOrDefault()?.Text}\n");
// Allow time for memory to be indexed
await Task.Delay(2000);
// Conversation 2: Test if the agent remembers
Console.WriteLine("User: What's my name and what programming language do I prefer?");
AgentResponse response2 = await agent.RunAsync("What's my name and what programming language do I prefer?");
Console.WriteLine($"Agent: {response2.Messages.LastOrDefault()?.Text}\n");
// Inspect memory search results if available in raw response items
// Note: Memory search tool call results appear as AgentResponseItem types
foreach (var message in response2.Messages)
try
{
if (message.RawRepresentation is AgentResponseItem agentResponseItem &&
agentResponseItem is MemorySearchToolCallResponseItem memorySearchResult)
{
Console.WriteLine($"Memory Search Status: {memorySearchResult.Status}");
Console.WriteLine($"Memory Search Results Count: {memorySearchResult.Results.Count}");
Console.WriteLine("Agent created with Memory Search tool. Starting conversation...\n");
foreach (var result in memorySearchResult.Results)
// The agent uses the memory search tool to recall stored information.
Console.WriteLine("User: What's my name and what programming language do I prefer?");
AgentResponse response = await agent.RunAsync("What's my name and what programming language do I prefer?");
Console.WriteLine($"Agent: {response.Messages.LastOrDefault()?.Text}\n");
// Inspect memory search results if available in raw response items.
foreach (var message in response.Messages)
{
if (message.RawRepresentation is MemorySearchToolCallResponseItem memorySearchResult)
{
var memoryItem = result.MemoryItem;
Console.WriteLine($" - Memory ID: {memoryItem.MemoryId}");
Console.WriteLine($" Scope: {memoryItem.Scope}");
Console.WriteLine($" Content: {memoryItem.Content}");
Console.WriteLine($" Updated: {memoryItem.UpdatedAt}");
Console.WriteLine($"Memory Search Status: {memorySearchResult.Status}");
Console.WriteLine($"Memory Search Results Count: {memorySearchResult.Results.Count}");
foreach (var result in memorySearchResult.Results)
{
var memoryItem = result.MemoryItem;
Console.WriteLine($" - Memory ID: {memoryItem.MemoryId}");
Console.WriteLine($" Scope: {memoryItem.Scope}");
Console.WriteLine($" Content: {memoryItem.Content}");
Console.WriteLine($" Updated: {memoryItem.UpdatedAt}");
}
}
}
}
finally
{
// Cleanup: Delete the agent and memory store.
Console.WriteLine("\nCleaning up...");
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
Console.WriteLine("Agent deleted.");
await aiProjectClient.MemoryStores.DeleteMemoryStoreAsync(memoryStoreName);
Console.WriteLine("Memory store deleted.");
}
// Cleanup: Delete the agent (memory store persists and should be cleaned up separately if needed)
Console.WriteLine("\nCleaning up agent...");
await aiProjectClient.Agents.DeleteAgentAsync(agent.Name);
Console.WriteLine("Agent deleted successfully.");
// NOTE: Memory stores are long-lived resources and are NOT deleted with the agent.
// To delete a memory store, use the Azure Portal or Python SDK:
// await project_client.memory_stores.delete(memory_store.name)
// --- Agent Creation Options ---
#pragma warning disable CS8321 // Local function is declared but never used
// Option 1 - Using MemorySearchTool wrapped as MEAI AITool
@@ -122,3 +105,36 @@ async Task<AIAgent> CreateAgentWithNativeSDK()
})
);
}
// Helpers — kept at the bottom so the main agent flow above stays clean.
async Task EnsureMemoryStoreAsync()
{
Console.WriteLine($"Creating memory store '{memoryStoreName}'...");
try
{
await aiProjectClient.MemoryStores.GetMemoryStoreAsync(memoryStoreName);
Console.WriteLine("Memory store already exists.");
}
catch (System.ClientModel.ClientResultException ex) when (ex.Status == 404)
{
MemoryStoreDefaultDefinition definition = new(deploymentName, embeddingModelName);
await aiProjectClient.MemoryStores.CreateMemoryStoreAsync(memoryStoreName, definition, "Sample memory store for Memory Search demo");
Console.WriteLine("Memory store created.");
}
Console.WriteLine("Storing memories from a prior conversation...");
MemoryUpdateOptions memoryOptions = new(userScope) { UpdateDelay = 0 };
memoryOptions.Items.Add(ResponseItem.CreateUserMessageItem("My name is Alice and I love programming in C#."));
MemoryUpdateResult updateResult = await aiProjectClient.MemoryStores.WaitForMemoriesUpdateAsync(
memoryStoreName: memoryStoreName,
options: memoryOptions,
pollingInterval: 500);
if (updateResult.Status == MemoryStoreUpdateStatus.Failed)
{
throw new InvalidOperationException($"Memory update failed: {updateResult.ErrorDetails}");
}
Console.WriteLine($"Memory update completed (status: {updateResult.Status}).\n");
}
@@ -10,7 +10,7 @@ using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Extensions.AI;
using ChatClient = OpenAI.Chat.ChatClient;
using OpenAI.Chat;
namespace AGUIDojoServer;
@@ -36,7 +36,7 @@ internal static class ChatClientAgentFactory
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
return chatClient.AsIChatClient().AsAIAgent(
return chatClient.AsAIAgent(
name: "AgenticChat",
description: "A simple chat agent using Azure OpenAI");
}
@@ -45,7 +45,7 @@ internal static class ChatClientAgentFactory
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
return chatClient.AsIChatClient().AsAIAgent(
return chatClient.AsAIAgent(
name: "BackendToolRenderer",
description: "An agent that can render backend tools using Azure OpenAI",
tools: [AIFunctionFactory.Create(
@@ -59,7 +59,7 @@ internal static class ChatClientAgentFactory
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
return chatClient.AsIChatClient().AsAIAgent(
return chatClient.AsAIAgent(
name: "HumanInTheLoopAgent",
description: "An agent that involves human feedback in its decision-making process using Azure OpenAI");
}
@@ -68,7 +68,7 @@ internal static class ChatClientAgentFactory
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
return chatClient.AsIChatClient().AsAIAgent(
return chatClient.AsAIAgent(
name: "ToolBasedGenerativeUIAgent",
description: "An agent that uses tools to generate user interfaces using Azure OpenAI");
}
@@ -76,7 +76,7 @@ internal static class ChatClientAgentFactory
public static AIAgent CreateAgenticUI(JsonSerializerOptions options)
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
var baseAgent = chatClient.AsIChatClient().AsAIAgent(new ChatClientAgentOptions
var baseAgent = chatClient.AsAIAgent(new ChatClientAgentOptions
{
Name = "AgenticUIAgent",
Description = "An agent that generates agentic user interfaces using Azure OpenAI",
@@ -119,7 +119,7 @@ internal static class ChatClientAgentFactory
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
var baseAgent = chatClient.AsIChatClient().AsAIAgent(
var baseAgent = chatClient.AsAIAgent(
name: "SharedStateAgent",
description: "An agent that demonstrates shared state patterns using Azure OpenAI");
@@ -130,7 +130,7 @@ internal static class ChatClientAgentFactory
{
ChatClient chatClient = s_azureOpenAIClient!.GetChatClient(s_deploymentName!);
var baseAgent = chatClient.AsIChatClient().AsAIAgent(new ChatClientAgentOptions
var baseAgent = chatClient.AsAIAgent(new ChatClientAgentOptions
{
Name = "PredictiveStateUpdatesAgent",
Description = "An agent that demonstrates predictive state updates using Azure OpenAI",
@@ -74,7 +74,7 @@ AzureOpenAIClient azureOpenAIClient = new AzureOpenAIClient(
ChatClient chatClient = azureOpenAIClient.GetChatClient(deploymentName);
// Create AI agent
ChatClientAgent agent = chatClient.AsIChatClient().AsAIAgent(
ChatClientAgent agent = chatClient.AsAIAgent(
name: "ChatAssistant",
instructions: "You are a helpful assistant.");
@@ -162,7 +162,7 @@ dotnet run
Edit the instructions in `Server/Program.cs`:
```csharp
ChatClientAgent agent = chatClient.AsIChatClient().AsAIAgent(
ChatClientAgent agent = chatClient.AsAIAgent(
name: "ChatAssistant",
instructions: "You are a helpful coding assistant specializing in C# and .NET.");
```
@@ -6,7 +6,6 @@ using Azure.AI.OpenAI;
using Azure.Identity;
using Microsoft.Agents.AI;
using Microsoft.Agents.AI.Hosting.AGUI.AspNetCore;
using Microsoft.Extensions.AI;
using OpenAI.Chat;
WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
@@ -28,7 +27,7 @@ AzureOpenAIClient azureOpenAIClient = new(
ChatClient chatClient = azureOpenAIClient.GetChatClient(deploymentName);
ChatClientAgent agent = chatClient.AsIChatClient().AsAIAgent(
ChatClientAgent agent = chatClient.AsAIAgent(
name: "ChatAssistant",
instructions: "You are a helpful assistant.");
@@ -12,6 +12,7 @@ using Microsoft.AspNetCore.Authentication.JwtBearer;
using Microsoft.AspNetCore.Authorization;
using Microsoft.Extensions.AI;
using OpenAI;
using OpenAI.Chat;
WebApplicationBuilder builder = WebApplication.CreateBuilder(args);
@@ -89,7 +90,6 @@ builder.Services.AddSingleton<AIAgent>(sp =>
return new OpenAIClient(apiKey)
.GetChatClient(model)
.AsIChatClient()
.AsAIAgent(
name: "ExpenseApprovalAgent",
instructions: "You are an expense approval assistant. You can list pending expenses "
@@ -10,7 +10,6 @@
<ItemGroup>
<PackageReference Include="Microsoft.AspNetCore.Authentication.JwtBearer" />
<PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
</ItemGroup>
<ItemGroup>
@@ -13,10 +13,6 @@
</PropertyGroup>
<Import Project="$(RepoRoot)/dotnet/nuget/nuget-package.props" />
<PropertyGroup>
<!-- Disable packing until we are ready to release this as a nuget -->
<IsPackable>false</IsPackable>
</PropertyGroup>
<ItemGroup>
<ProjectReference Include="..\Microsoft.Agents.AI.Abstractions\Microsoft.Agents.AI.Abstractions.csproj" />
@@ -3,6 +3,7 @@
#pragma warning disable CS0618 // Type or member is obsolete - Internal use of obsolete types for backward compatibility
using System;
using System.Collections.Concurrent;
using System.Collections.Generic;
using System.Diagnostics;
using System.Linq;
@@ -133,7 +134,25 @@ internal sealed class ExecutorProtocol(MessageRouter router, ISet<Type> sendType
public bool CanHandle(Type type) => router.CanHandle(type);
public bool CanOutput(Type type) => this._yieldTypes.Contains(new(type));
private readonly ConcurrentDictionary<Type, bool> _canOutputCache = new();
public bool CanOutput(Type type)
{
return this._canOutputCache.GetOrAdd(type, this.CanOutputCore);
}
private bool CanOutputCore(Type type)
{
foreach (TypeId yieldType in this._yieldTypes)
{
if (yieldType.IsMatchPolymorphic(type))
{
return true;
}
}
return false;
}
public ProtocolDescriptor Describe() => new(this.Router.IncomingTypes, yieldTypes, sendTypes, this.Router.HasCatchAll);
}
@@ -14,6 +14,8 @@ namespace AzureAIAgentsPersistent.IntegrationTests;
public class AzureAIAgentsPersistentCreateTests
{
private const string SkipCodeInterpreterReason = "Azure AI Code Interpreter intermittently fails to execute uploaded files in CI";
private readonly PersistentAgentsClient _persistentAgentsClient = new(TestConfiguration.GetRequiredValue(TestSettings.AzureAIProjectEndpoint), TestAzureCliCredentials.CreateAzureCliCredential());
[Theory]
@@ -131,11 +133,11 @@ public class AzureAIAgentsPersistentCreateTests
}
}
[Fact]
[Fact(Skip = SkipCodeInterpreterReason)]
public Task CreateAgent_CreatesAgentWithCodeInterpreter_ChatClientAgentOptionsAsync()
=> this.CreateAgent_CreatesAgentWithCodeInterpreterAsync("CreateWithChatClientAgentOptionsAsync");
[RetryFact(Constants.RetryCount, Constants.RetryDelay)]
[Fact(Skip = SkipCodeInterpreterReason)]
public Task CreateAgent_CreatesAgentWithCodeInterpreter_FoundryOptionsAsync()
=> this.CreateAgent_CreatesAgentWithCodeInterpreterAsync("CreateWithFoundryOptionsAsync");
@@ -0,0 +1,276 @@
// Copyright (c) Microsoft. All rights reserved.
using System.Collections.Generic;
using System.Linq;
using System.Threading;
using System.Threading.Tasks;
using FluentAssertions;
namespace Microsoft.Agents.AI.Workflows.UnitTests;
/// <summary>
/// Regression tests for polymorphic output type handling in workflows.
/// Verifies that executors can return derived types when the declared output type is a base class.
/// </summary>
/// <remarks>
/// This addresses GitHub issue #4134: InvalidOperationException when returning derived type as workflow output.
/// </remarks>
public partial class PolymorphicOutputTests
{
#region Test Type Hierarchy
/// <summary>
/// Base class used as declared output type.
/// </summary>
public class BaseOutput
{
public virtual string Name => "BaseOutput";
}
/// <summary>
/// Derived class returned at runtime.
/// </summary>
public class DerivedOutput : BaseOutput
{
public override string Name => "DerivedOutput";
}
/// <summary>
/// Second-level derived class for testing multiple inheritance levels.
/// </summary>
public class GrandchildOutput : DerivedOutput
{
public override string Name => "GrandchildOutput";
}
/// <summary>
/// Unrelated class that should NOT be accepted as output.
/// </summary>
public class UnrelatedOutput
{
public string Name => "UnrelatedOutput";
}
#endregion
#region Test Executors
/// <summary>
/// Executor that declares BaseOutput as yield type but returns DerivedOutput.
/// </summary>
internal sealed class DerivedOutputExecutor() : Executor(nameof(DerivedOutputExecutor))
{
protected override ProtocolBuilder ConfigureProtocol(ProtocolBuilder protocolBuilder)
{
return protocolBuilder.ConfigureRoutes(routeBuilder =>
routeBuilder.AddHandler<string, BaseOutput>(this.HandleAsync));
}
private async ValueTask<BaseOutput> HandleAsync(string input, IWorkflowContext context, CancellationToken cancellationToken)
{
await Task.Delay(10, cancellationToken);
// Arrange: Return a derived type where the method signature declares the base type
return new DerivedOutput();
}
}
/// <summary>
/// Executor that declares BaseOutput as yield type but returns GrandchildOutput (two levels deep).
/// </summary>
internal sealed class GrandchildOutputExecutor() : Executor(nameof(GrandchildOutputExecutor))
{
protected override ProtocolBuilder ConfigureProtocol(ProtocolBuilder protocolBuilder)
{
return protocolBuilder.ConfigureRoutes(routeBuilder =>
routeBuilder.AddHandler<string, BaseOutput>(this.HandleAsync));
}
private async ValueTask<BaseOutput> HandleAsync(string input, IWorkflowContext context, CancellationToken cancellationToken)
{
await Task.Delay(10, cancellationToken);
// Arrange: Return a grandchild type (two inheritance levels)
return new GrandchildOutput();
}
}
/// <summary>
/// Executor that attempts to return an unrelated type - should fail validation.
/// This executor intentionally bypasses type safety to test runtime validation.
/// </summary>
internal sealed class UnrelatedOutputExecutor() : Executor(nameof(UnrelatedOutputExecutor))
{
protected override ProtocolBuilder ConfigureProtocol(ProtocolBuilder protocolBuilder)
{
return protocolBuilder.ConfigureRoutes(routeBuilder =>
routeBuilder.AddHandler<string, BaseOutput>(this.HandleAsync));
}
private async ValueTask<BaseOutput> HandleAsync(string input, IWorkflowContext context, CancellationToken cancellationToken)
{
// Arrange: Attempt to yield an unrelated type - should throw
UnrelatedOutput unrelated = new();
await context.YieldOutputAsync(unrelated, cancellationToken).ConfigureAwait(false);
// This line should not be reached
return new BaseOutput();
}
}
/// <summary>
/// Executor that returns the exact declared type (baseline test).
/// </summary>
internal sealed class ExactTypeExecutor() : Executor(nameof(ExactTypeExecutor))
{
protected override ProtocolBuilder ConfigureProtocol(ProtocolBuilder protocolBuilder)
{
return protocolBuilder.ConfigureRoutes(routeBuilder =>
routeBuilder.AddHandler<string, BaseOutput>(this.HandleAsync));
}
private ValueTask<BaseOutput> HandleAsync(string input, IWorkflowContext context, CancellationToken cancellationToken)
{
BaseOutput result = new();
return new ValueTask<BaseOutput>(result);
}
}
#endregion
#region Tests
/// <summary>
/// Verifies that returning a derived type when the declared output type is a base class succeeds.
/// This is the main regression test for GitHub issue #4134.
/// </summary>
[Fact]
public async Task ReturningDerivedType_WhenBaseTypeIsDeclared_ShouldSucceedAsync()
{
// Arrange
DerivedOutputExecutor executor = new();
WorkflowBuilder builder = new WorkflowBuilder(executor).WithOutputFrom(executor);
Workflow workflow = builder.Build();
// Act
List<WorkflowEvent> events = [];
await using StreamingRun run = await InProcessExecution.RunStreamingAsync(workflow, "test input");
await foreach (WorkflowEvent evt in run.WatchStreamAsync())
{
events.Add(evt);
}
// Assert
events.Should().NotBeEmpty("workflow should produce events");
List<WorkflowOutputEvent> outputEvents = events.OfType<WorkflowOutputEvent>().ToList();
outputEvents.Should().ContainSingle("workflow should produce exactly one output event");
WorkflowOutputEvent outputEvent = outputEvents.Single();
outputEvent.Data.Should().BeOfType<DerivedOutput>("output should be the derived type");
((DerivedOutput)outputEvent.Data!).Name.Should().Be("DerivedOutput");
// Verify no error events
List<WorkflowErrorEvent> errorEvents = events.OfType<WorkflowErrorEvent>().ToList();
errorEvents.Should().BeEmpty("workflow should not produce error events");
}
/// <summary>
/// Verifies that returning a grandchild type (multiple inheritance levels) succeeds.
/// </summary>
[Fact]
public async Task ReturningGrandchildType_WhenBaseTypeIsDeclared_ShouldSucceedAsync()
{
// Arrange
GrandchildOutputExecutor executor = new();
WorkflowBuilder builder = new WorkflowBuilder(executor).WithOutputFrom(executor);
Workflow workflow = builder.Build();
// Act
List<WorkflowEvent> events = [];
await using StreamingRun run = await InProcessExecution.RunStreamingAsync(workflow, "test input");
await foreach (WorkflowEvent evt in run.WatchStreamAsync())
{
events.Add(evt);
}
// Assert
events.Should().NotBeEmpty("workflow should produce events");
List<WorkflowOutputEvent> outputEvents = events.OfType<WorkflowOutputEvent>().ToList();
outputEvents.Should().ContainSingle("workflow should produce exactly one output event");
WorkflowOutputEvent outputEvent = outputEvents.Single();
outputEvent.Data.Should().BeOfType<GrandchildOutput>("output should be the grandchild type");
((GrandchildOutput)outputEvent.Data!).Name.Should().Be("GrandchildOutput");
// Verify no error events
List<WorkflowErrorEvent> errorEvents = events.OfType<WorkflowErrorEvent>().ToList();
errorEvents.Should().BeEmpty("workflow should not produce error events");
}
/// <summary>
/// Verifies that returning an unrelated type still throws InvalidOperationException.
/// This ensures the fix doesn't break the existing validation for truly incompatible types.
/// </summary>
[Fact]
public async Task ReturningUnrelatedType_WhenBaseTypeIsDeclared_ShouldFailAsync()
{
// Arrange
UnrelatedOutputExecutor executor = new();
WorkflowBuilder builder = new WorkflowBuilder(executor).WithOutputFrom(executor);
Workflow workflow = builder.Build();
// Act
List<WorkflowEvent> events = [];
await using StreamingRun run = await InProcessExecution.RunStreamingAsync(workflow, "test input");
await foreach (WorkflowEvent evt in run.WatchStreamAsync())
{
events.Add(evt);
}
// Assert: Should have an error event with InvalidOperationException message
List<WorkflowErrorEvent> errorEvents = events.OfType<WorkflowErrorEvent>().ToList();
errorEvents.Should().ContainSingle("workflow should produce exactly one error event");
WorkflowErrorEvent errorEvent = errorEvents.Single();
string errorMessage = errorEvent.Data?.ToString() ?? string.Empty;
errorMessage.Should().Contain("Cannot output object of type UnrelatedOutput");
errorMessage.Should().Contain("BaseOutput");
}
/// <summary>
/// Verifies that returning the exact declared type still works (baseline test).
/// </summary>
[Fact]
public async Task ReturningExactType_WhenSameTypeIsDeclared_ShouldSucceedAsync()
{
// Arrange: Create an executor that returns the exact declared type
ExactTypeExecutor executor = new();
WorkflowBuilder builder = new WorkflowBuilder(executor).WithOutputFrom(executor);
Workflow workflow = builder.Build();
// Act
List<WorkflowEvent> events = [];
await using StreamingRun run = await InProcessExecution.RunStreamingAsync(workflow, "test input");
await foreach (WorkflowEvent evt in run.WatchStreamAsync())
{
events.Add(evt);
}
// Assert
events.Should().NotBeEmpty("workflow should produce events");
List<WorkflowOutputEvent> outputEvents = events.OfType<WorkflowOutputEvent>().ToList();
outputEvents.Should().ContainSingle("workflow should produce exactly one output event");
WorkflowOutputEvent outputEvent = outputEvents.Single();
outputEvent.Data.Should().BeOfType<BaseOutput>("output should be the exact base type");
// Verify no error events
List<WorkflowErrorEvent> errorEvents = events.OfType<WorkflowErrorEvent>().ToList();
errorEvents.Should().BeEmpty("workflow should not produce error events");
}
#endregion
}
@@ -19,6 +19,8 @@ namespace OpenAIAssistant.IntegrationTests;
public class OpenAIAssistantClientExtensionsTests
{
private const string SkipCodeInterpreterReason = "OpenAI Assistant Code Interpreter intermittently fails in CI";
private readonly AssistantClient _assistantClient = new OpenAIClient(TestConfiguration.GetRequiredValue(TestSettings.OpenAIApiKey)).GetAssistantClient();
private readonly OpenAIFileClient _fileClient = new OpenAIClient(TestConfiguration.GetRequiredValue(TestSettings.OpenAIApiKey)).GetOpenAIFileClient();
@@ -81,7 +83,7 @@ public class OpenAIAssistantClientExtensionsTests
}
}
[Theory]
[Theory(Skip = SkipCodeInterpreterReason)]
[InlineData("CreateWithChatClientAgentOptionsAsync")]
[InlineData("CreateWithChatClientAgentOptionsSync")]
[InlineData("CreateWithParamsAsync")]
@@ -44,7 +44,7 @@ from ._context import CapturingRunnerContext
from ._entities import create_agent_entity
from ._errors import IncomingRequestError
from ._orchestration import AgentOrchestrationContextType, AgentTask, AzureFunctionsAgentExecutor
from ._serialization import deserialize_value, serialize_value
from ._serialization import deserialize_value, serialize_value, strip_pickle_markers
from ._workflow import (
SOURCE_HITL_RESPONSE,
SOURCE_ORCHESTRATOR,
@@ -515,6 +515,10 @@ class AgentFunctionApp(DFAppBase):
except ValueError:
return self._build_error_response("Request body must be valid JSON.")
# Sanitize untrusted HTTP input before it reaches pickle.loads().
# See strip_pickle_markers() docstring for details on the attack vector.
response_data = strip_pickle_markers(response_data)
# Send the response as an external event
# The request_id is used as the event name for correlation
await client.raise_event(
@@ -22,9 +22,14 @@ import importlib
import logging
from contextlib import suppress
from dataclasses import is_dataclass
from typing import Any
from typing import Any, cast
from agent_framework._workflows._checkpoint_encoding import decode_checkpoint_value, encode_checkpoint_value
from agent_framework._workflows._checkpoint_encoding import (
_PICKLE_MARKER, # pyright: ignore[reportPrivateUsage]
_TYPE_MARKER, # pyright: ignore[reportPrivateUsage]
decode_checkpoint_value,
encode_checkpoint_value,
)
from pydantic import BaseModel
logger = logging.getLogger(__name__)
@@ -48,6 +53,41 @@ def resolve_type(type_key: str) -> type | None:
return None
# ============================================================================
# Pickle marker sanitization (security)
# ============================================================================
def strip_pickle_markers(data: Any) -> Any:
"""Recursively strip pickle/type markers from untrusted data.
The core checkpoint encoding uses ``__pickled__`` and ``__type__`` markers to
roundtrip arbitrary Python objects via *pickle*. If an attacker crafts an
HTTP payload that contains these markers, the data would flow into
``pickle.loads()`` and enable **arbitrary code execution**.
This function walks the incoming data structure and replaces any ``dict``
that contains either marker key with ``None``, neutralising the attack
vector while leaving all other data untouched.
It **must** be called on every value that originates from an untrusted
source (e.g. ``req.get_json()``) *before* the value is passed to
``deserialize_value`` / ``decode_checkpoint_value``.
"""
if isinstance(data, dict):
if _PICKLE_MARKER in data or _TYPE_MARKER in data:
logger.debug("Stripped pickle/type markers from untrusted input.")
return None
typed_dict = cast(dict[str, Any], data)
return {k: strip_pickle_markers(v) for k, v in typed_dict.items()}
if isinstance(data, list):
typed_list = cast(list[Any], data) # type: ignore[redundant-cast]
return [strip_pickle_markers(item) for item in typed_list]
return data
# ============================================================================
# Serialize / Deserialize
# ============================================================================
@@ -117,7 +157,10 @@ def reconstruct_to_type(value: Any, target_type: type) -> Any:
if not isinstance(value, dict):
return value
# Try decoding if data has pickle markers (from checkpoint encoding)
# Try decoding if data has pickle markers (from checkpoint encoding).
# NOTE: This function is general-purpose. Callers that handle untrusted
# data (e.g. HITL responses) MUST call strip_pickle_markers() before
# passing data here. See _deserialize_hitl_response in _workflow.py.
decoded = deserialize_value(value)
if not isinstance(decoded, dict):
return decoded
@@ -50,7 +50,7 @@ from azure.durable_functions import DurableOrchestrationContext
from ._context import CapturingRunnerContext
from ._orchestration import AzureFunctionsAgentExecutor
from ._serialization import deserialize_value, reconstruct_to_type, resolve_type, serialize_value
from ._serialization import deserialize_value, reconstruct_to_type, resolve_type, serialize_value, strip_pickle_markers
logger = logging.getLogger(__name__)
@@ -961,6 +961,13 @@ def _deserialize_hitl_response(response_data: Any, response_type_str: str | None
type(response_data).__name__,
)
if response_data is None:
return None
# Sanitize untrusted external input before deserialization.
# HITL response data originates from an HTTP POST and must not contain
# pickle/type markers that would reach pickle.loads().
response_data = strip_pickle_markers(response_data)
if response_data is None:
return None
@@ -969,7 +976,7 @@ def _deserialize_hitl_response(response_data: Any, response_type_str: str | None
logger.debug("Response data is not a dict, returning as-is: %s", type(response_data).__name__)
return response_data
# Try to deserialize using the type hint
# Try to reconstruct using the type hint (Pydantic / dataclass)
if response_type_str:
response_type = resolve_type(response_type_str)
if response_type:
@@ -979,6 +986,8 @@ def _deserialize_hitl_response(response_data: Any, response_type_str: str | None
return result
logger.warning("Could not resolve response type: %s", response_type_str)
# Fall back to generic deserialization
logger.debug("Falling back to generic deserialization")
return deserialize_value(response_data)
# No type hint available - return the sanitized dict as-is.
# We intentionally do NOT call deserialize_value() here because HITL
# response data is untrusted and must never flow into pickle.loads().
logger.debug("No type hint; returning sanitized data as-is")
return response_data # type: ignore[reportUnknownVariableType]
@@ -21,6 +21,7 @@ from agent_framework_azurefunctions._serialization import (
deserialize_value,
reconstruct_to_type,
serialize_value,
strip_pickle_markers,
)
@@ -353,7 +354,11 @@ class TestReconstructToType:
assert result.comment == "Great"
def test_reconstruct_from_checkpoint_markers(self) -> None:
"""Test that data with checkpoint markers is decoded via deserialize_value."""
"""Test that data with checkpoint markers is decoded via deserialize_value.
reconstruct_to_type is general-purpose and handles trusted checkpoint
data. Untrusted HITL callers must call strip_pickle_markers() first.
"""
original = SampleData(value=99, name="marker-test")
encoded = serialize_value(original)
@@ -372,3 +377,73 @@ class TestReconstructToType:
result = reconstruct_to_type(data, Unrelated)
assert result == data
def test_reconstruct_strips_injected_pickle_markers(self) -> None:
"""End-to-end: strip_pickle_markers + reconstruct_to_type blocks attack.
This mirrors the real HITL flow where callers sanitize before reconstruction.
"""
malicious = {"__pickled__": "gASVDgAAAAAAAACMBHRlc3SULg==", "__type__": "builtins:str"}
sanitized = strip_pickle_markers(malicious)
result = reconstruct_to_type(sanitized, str)
assert result is None
class TestStripPickleMarkers:
"""Security tests for strip_pickle_markers — the defence-in-depth layer
that prevents untrusted HTTP input from reaching pickle.loads()."""
def test_strips_top_level_pickle_marker(self) -> None:
"""A dict containing __pickled__ must be replaced with None."""
data = {"__pickled__": "PAYLOAD", "__type__": "os:system"}
assert strip_pickle_markers(data) is None
def test_strips_top_level_type_marker_only(self) -> None:
"""Even __type__ alone (without __pickled__) must be neutralised."""
data = {"__type__": "os:system", "other": "value"}
assert strip_pickle_markers(data) is None
def test_strips_nested_pickle_marker(self) -> None:
"""Pickle markers nested inside a dict must be neutralised."""
data = {"safe": "value", "nested": {"__pickled__": "PAYLOAD", "__type__": "os:system"}}
result = strip_pickle_markers(data)
assert result == {"safe": "value", "nested": None}
def test_strips_pickle_marker_in_list(self) -> None:
"""Pickle markers inside a list element must be neutralised."""
data = [{"__pickled__": "PAYLOAD"}, "safe"]
result = strip_pickle_markers(data)
assert result == [None, "safe"]
def test_strips_deeply_nested_marker(self) -> None:
"""Deeply nested pickle markers must be neutralised."""
data = {"a": {"b": {"c": {"__pickled__": "deep"}}}}
result = strip_pickle_markers(data)
assert result == {"a": {"b": {"c": None}}}
def test_preserves_safe_dict(self) -> None:
"""Dicts without pickle markers must be left untouched."""
data = {"approved": True, "reason": "Looks good"}
assert strip_pickle_markers(data) == data
def test_preserves_primitives(self) -> None:
"""Primitive values must pass through unchanged."""
assert strip_pickle_markers("hello") == "hello"
assert strip_pickle_markers(42) == 42
assert strip_pickle_markers(None) is None
assert strip_pickle_markers(True) is True
def test_preserves_safe_list(self) -> None:
"""Lists without pickle markers must be left untouched."""
data = [1, "two", {"key": "value"}]
assert strip_pickle_markers(data) == data
def test_mixed_safe_and_malicious(self) -> None:
"""Only the malicious entries should be stripped; safe entries remain."""
data = {
"user_input": "hello",
"evil": {"__pickled__": "PAYLOAD", "__type__": "os:system"},
"count": 42,
}
result = strip_pickle_markers(data)
assert result == {"user_input": "hello", "evil": None, "count": 42}
@@ -33,7 +33,13 @@ from ._clients import BaseChatClient, SupportsChatGetResponse
from ._mcp import LOG_LEVEL_MAPPING, MCPTool
from ._middleware import AgentMiddlewareLayer, MiddlewareTypes
from ._serialization import SerializationMixin
from ._sessions import AgentSession, BaseContextProvider, BaseHistoryProvider, InMemoryHistoryProvider, SessionContext
from ._sessions import (
AgentSession,
BaseContextProvider,
BaseHistoryProvider,
InMemoryHistoryProvider,
SessionContext,
)
from ._tools import (
FunctionInvocationLayer,
FunctionTool,
@@ -532,7 +538,14 @@ class BaseAgent(SerializationMixin):
if stream_callback is None:
# Use non-streaming mode
return (await self.run(input_text, stream=False, session=parent_session, **forwarded_kwargs)).text
return (
await self.run(
input_text,
stream=False,
session=parent_session,
**forwarded_kwargs,
)
).text
# Use streaming mode - accumulate updates and create final response
response_updates: list[AgentResponseUpdate] = []
@@ -951,7 +964,9 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
**ctx["filtered_kwargs"],
)
def _propagate_conversation_id(update: AgentResponseUpdate) -> AgentResponseUpdate:
def _propagate_conversation_id(
update: AgentResponseUpdate,
) -> AgentResponseUpdate:
"""Eagerly propagate conversation_id to session as updates arrive.
This ensures session.service_session_id is set even when the user
@@ -975,8 +990,8 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
return self._finalize_response_updates(updates, response_format=rf)
return (
ResponseStream # type: ignore[reportUnknownMemberType]
.from_awaitable(_get_stream())
ResponseStream
.from_awaitable(_get_stream()) # type: ignore[reportUnknownMemberType]
.map(
transform=partial(
map_chat_to_agent_update,
@@ -1002,7 +1017,9 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
)
@staticmethod
def _extract_conversation_id_from_streaming_response(response: AgentResponse[Any]) -> str | None:
def _extract_conversation_id_from_streaming_response(
response: AgentResponse[Any],
) -> str | None:
"""Extract conversation_id from streaming raw updates, if present."""
raw = response.raw_representation
if raw is None:
@@ -1039,6 +1056,10 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
input_messages = normalize_messages(messages)
# `store` in runtime or agent options takes precedence over client-level storage
# indicators. An explicit `store=False` forces local (in-memory) history injection,
# even if the client is configured to use service-side storage by default.
store_ = opts.get("store", self.default_options.get("store", getattr(self.client, "STORES_BY_DEFAULT", False)))
# Auto-inject InMemoryHistoryProvider when session is provided, no context providers
# registered, and no service-side storage indicators
if (
@@ -1046,8 +1067,7 @@ class RawAgent(BaseAgent, Generic[OptionsCoT]): # type: ignore[misc]
and not self.context_providers
and not session.service_session_id
and not opts.get("conversation_id")
and not opts.get("store")
and not (getattr(self.client, "STORES_BY_DEFAULT", False) and opts.get("store") is not False)
and not store_
):
self.context_providers.append(InMemoryHistoryProvider())
+34 -29
View File
@@ -2776,6 +2776,7 @@ class ResponseStream(AsyncIterable[UpdateT], Generic[UpdateT, FinalT]):
except StopAsyncIteration:
self._consumed = True
await self._run_cleanup_hooks()
await self.get_final_response()
raise
except Exception:
await self._run_cleanup_hooks()
@@ -2825,34 +2826,38 @@ class ResponseStream(AsyncIterable[UpdateT], Generic[UpdateT, FinalT]):
await self._get_stream()
if self._inner_stream is None:
raise RuntimeError("Inner stream not available")
if not self._finalized:
if not self._finalized and not self._consumed:
# Consume outer stream (which delegates to inner) if not already consumed
if not self._consumed:
async for _ in self:
pass
async for _ in self:
pass
# First, finalize the inner stream and run its result hooks
# Re-check: __anext__ auto-finalization may have already finalized this stream
if not self._finalized:
# This ensures inner post-processing (e.g., context provider notifications) runs
inner_stream = self._inner_stream
inner_result: Any
if inner_stream._finalizer is not None:
inner_finalizer = inner_stream._finalizer
inner_result = inner_finalizer(inner_stream._updates)
if isawaitable(inner_result):
inner_result = await inner_result
else:
inner_result = list(inner_stream._updates)
# Skip if inner stream was already finalized (e.g., via auto-finalization on iteration)
if not self._inner_stream._finalized:
inner_stream = self._inner_stream
inner_result: Any
if inner_stream._finalizer is not None:
inner_finalizer = inner_stream._finalizer
inner_result = inner_finalizer(inner_stream._updates)
if isawaitable(inner_result):
inner_result = await inner_result
else:
inner_result = list(inner_stream._updates)
# Run inner stream's result hooks
inner_hooks = cast(list[Callable[[Any], Any | Awaitable[Any] | None]], inner_stream._result_hooks)
for hook in inner_hooks:
hooked_result = hook(inner_result)
if isawaitable(hooked_result):
hooked_result = await hooked_result
if hooked_result is not None:
inner_result = hooked_result
inner_stream._final_result = inner_result
inner_stream._finalized = True
# Run inner stream's result hooks
inner_hooks = cast(list[Callable[[Any], Any | Awaitable[Any] | None]], inner_stream._result_hooks)
for hook in inner_hooks:
hooked_result = hook(inner_result)
if isawaitable(hooked_result):
hooked_result = await hooked_result
if hooked_result is not None:
inner_result = hooked_result
inner_stream._final_result = inner_result
inner_stream._finalized = True
else:
inner_result = self._inner_stream._final_result
# Now finalize the outer stream with its own finalizer
# If outer has no finalizer, use inner's result (preserves from_awaitable behavior)
@@ -2877,12 +2882,12 @@ class ResponseStream(AsyncIterable[UpdateT], Generic[UpdateT, FinalT]):
self._finalized = True
return self._final_result # type: ignore[return-value]
if not self._finalized:
if not self._consumed:
async for _ in self:
pass
if not self._finalized and not self._consumed:
async for _ in self:
pass
# Use finalizer if configured, otherwise return collected updates
# Re-check: __anext__ auto-finalization may have already finalized this stream
if not self._finalized:
result: Any
if self._finalizer is not None:
result = self._finalizer(self._updates)
+211 -41
View File
@@ -50,7 +50,9 @@ async def test_agent_run_with_content(agent: SupportsAgentRun) -> None:
async def test_agent_run_streaming(agent: SupportsAgentRun) -> None:
async def collect_updates(updates: AsyncIterable[AgentResponseUpdate]) -> list[AgentResponseUpdate]:
async def collect_updates(
updates: AsyncIterable[AgentResponseUpdate],
) -> list[AgentResponseUpdate]:
return [u async for u in updates]
updates = await collect_updates(agent.run("test", stream=True))
@@ -72,7 +74,9 @@ async def test_chat_client_agent_init(client: SupportsChatGetResponse) -> None:
assert agent.description == "Test"
async def test_chat_client_agent_init_with_name(client: SupportsChatGetResponse) -> None:
async def test_chat_client_agent_init_with_name(
client: SupportsChatGetResponse,
) -> None:
agent_id = str(uuid4())
agent = Agent(client=client, id=agent_id, name="Test Agent", description="Test")
@@ -108,7 +112,13 @@ async def test_chat_client_agent_streaming_response_format_from_default_options(
json_text = '{"greeting": "Hello"}'
client.streaming_responses.append( # type: ignore[attr-defined]
[ChatResponseUpdate(contents=[Content.from_text(json_text)], role="assistant", finish_reason="stop")]
[
ChatResponseUpdate(
contents=[Content.from_text(json_text)],
role="assistant",
finish_reason="stop",
)
]
)
agent = Agent(client=client, default_options={"response_format": Greeting})
@@ -134,7 +144,13 @@ async def test_chat_client_agent_streaming_response_format_from_run_options(
json_text = '{"greeting": "Hi"}'
client.streaming_responses.append( # type: ignore[attr-defined]
[ChatResponseUpdate(contents=[Content.from_text(json_text)], role="assistant", finish_reason="stop")]
[
ChatResponseUpdate(
contents=[Content.from_text(json_text)],
role="assistant",
finish_reason="stop",
)
]
)
agent = Agent(client=client)
@@ -149,14 +165,18 @@ async def test_chat_client_agent_streaming_response_format_from_run_options(
assert result.value.greeting == "Hi"
async def test_chat_client_agent_create_session(client: SupportsChatGetResponse) -> None:
async def test_chat_client_agent_create_session(
client: SupportsChatGetResponse,
) -> None:
agent = Agent(client=client)
session = agent.create_session()
assert isinstance(session, AgentSession)
async def test_chat_client_agent_prepare_session_and_messages(client: SupportsChatGetResponse) -> None:
async def test_chat_client_agent_prepare_session_and_messages(
client: SupportsChatGetResponse,
) -> None:
from agent_framework._sessions import InMemoryHistoryProvider
agent = Agent(client=client, context_providers=[InMemoryHistoryProvider()])
@@ -175,7 +195,9 @@ async def test_chat_client_agent_prepare_session_and_messages(client: SupportsCh
assert result_messages[1].text == "Test"
async def test_prepare_session_does_not_mutate_agent_chat_options(client: SupportsChatGetResponse) -> None:
async def test_prepare_session_does_not_mutate_agent_chat_options(
client: SupportsChatGetResponse,
) -> None:
tool = {"type": "code_interpreter"}
agent = Agent(client=client, tools=[tool])
@@ -195,7 +217,9 @@ async def test_prepare_session_does_not_mutate_agent_chat_options(client: Suppor
assert len(agent.default_options["tools"]) == 1
async def test_chat_client_agent_run_with_session(chat_client_base: SupportsChatGetResponse) -> None:
async def test_chat_client_agent_run_with_session(
chat_client_base: SupportsChatGetResponse,
) -> None:
mock_response = ChatResponse(
messages=[Message(role="assistant", contents=[Content.from_text("test response")])],
conversation_id="123",
@@ -357,7 +381,43 @@ async def test_chat_client_agent_streaming_session_id_set_without_get_final_resp
assert session.service_session_id == "resp_123"
async def test_chat_client_agent_update_session_messages(client: SupportsChatGetResponse) -> None:
async def test_chat_client_agent_streaming_session_history_saved_without_get_final_response(
chat_client_base: SupportsChatGetResponse,
) -> None:
"""Test that session history is saved after streaming iteration without get_final_response().
Auto-finalization on iteration completion should trigger after_run providers,
persisting conversation history to the session.
"""
from agent_framework._sessions import InMemoryHistoryProvider
chat_client_base.streaming_responses = [
[
ChatResponseUpdate(
contents=[Content.from_text("Hello Alice!")],
role="assistant",
response_id="resp_1",
finish_reason="stop",
),
]
]
agent = Agent(client=chat_client_base)
session = agent.create_session()
# Only iterate — do NOT call get_final_response()
async for _ in agent.run("My name is Alice", session=session, stream=True):
pass
chat_messages: list[Message] = session.state.get(InMemoryHistoryProvider.DEFAULT_SOURCE_ID, {}).get("messages", [])
assert len(chat_messages) == 2
assert chat_messages[0].text == "My name is Alice"
assert chat_messages[1].text == "Hello Alice!"
async def test_chat_client_agent_update_session_messages(
client: SupportsChatGetResponse,
) -> None:
from agent_framework._sessions import InMemoryHistoryProvider
agent = Agent(client=client)
@@ -376,7 +436,9 @@ async def test_chat_client_agent_update_session_messages(client: SupportsChatGet
assert chat_messages[1].text == "test response"
async def test_chat_client_agent_update_session_conversation_id_missing(client: SupportsChatGetResponse) -> None:
async def test_chat_client_agent_update_session_conversation_id_missing(
client: SupportsChatGetResponse,
) -> None:
agent = Agent(client=client)
session = agent.get_session(service_session_id="123")
@@ -384,7 +446,9 @@ async def test_chat_client_agent_update_session_conversation_id_missing(client:
assert session.service_session_id == "123"
async def test_chat_client_agent_default_author_name(client: SupportsChatGetResponse) -> None:
async def test_chat_client_agent_default_author_name(
client: SupportsChatGetResponse,
) -> None:
# Name is not specified here, so default name should be used
agent = Agent(client=client)
@@ -393,7 +457,9 @@ async def test_chat_client_agent_default_author_name(client: SupportsChatGetResp
assert result.messages[0].author_name == "UnnamedAgent"
async def test_chat_client_agent_author_name_as_agent_name(client: SupportsChatGetResponse) -> None:
async def test_chat_client_agent_author_name_as_agent_name(
client: SupportsChatGetResponse,
) -> None:
# Name is specified here, so it should be used as author name
agent = Agent(client=client, name="TestAgent")
@@ -402,11 +468,17 @@ async def test_chat_client_agent_author_name_as_agent_name(client: SupportsChatG
assert result.messages[0].author_name == "TestAgent"
async def test_chat_client_agent_author_name_is_used_from_response(chat_client_base: SupportsChatGetResponse) -> None:
async def test_chat_client_agent_author_name_is_used_from_response(
chat_client_base: SupportsChatGetResponse,
) -> None:
chat_client_base.run_responses = [
ChatResponse(
messages=[
Message(role="assistant", contents=[Content.from_text("test response")], author_name="TestAuthor")
Message(
role="assistant",
contents=[Content.from_text("test response")],
author_name="TestAuthor",
)
]
)
]
@@ -442,7 +514,9 @@ class MockContextProvider(BaseContextProvider):
self.new_messages.extend(context.response.messages)
async def test_chat_agent_context_providers_model_before_run(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_context_providers_model_before_run(
client: SupportsChatGetResponse,
) -> None:
"""Test that context providers' before_run is called during agent run."""
mock_provider = MockContextProvider(messages=[Message(role="system", text="Test context instructions")])
agent = Agent(client=client, context_providers=[mock_provider])
@@ -452,7 +526,9 @@ async def test_chat_agent_context_providers_model_before_run(client: SupportsCha
assert mock_provider.before_run_called
async def test_chat_agent_context_providers_after_run(chat_client_base: SupportsChatGetResponse) -> None:
async def test_chat_agent_context_providers_after_run(
chat_client_base: SupportsChatGetResponse,
) -> None:
"""Test that context providers' after_run is called during agent run."""
mock_provider = MockContextProvider()
chat_client_base.run_responses = [
@@ -471,7 +547,9 @@ async def test_chat_agent_context_providers_after_run(chat_client_base: Supports
assert mock_provider.last_service_session_id == "test-thread-id"
async def test_chat_agent_context_providers_messages_adding(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_context_providers_messages_adding(
client: SupportsChatGetResponse,
) -> None:
"""Test that context providers' after_run is called during agent run."""
mock_provider = MockContextProvider()
agent = Agent(client=client, context_providers=[mock_provider])
@@ -483,10 +561,16 @@ async def test_chat_agent_context_providers_messages_adding(client: SupportsChat
assert len(mock_provider.new_messages) >= 2
async def test_chat_agent_context_instructions_in_messages(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_context_instructions_in_messages(
client: SupportsChatGetResponse,
) -> None:
"""Test that AI context instructions are included in messages."""
mock_provider = MockContextProvider(messages=[Message(role="system", text="Context-specific instructions")])
agent = Agent(client=client, instructions="Agent instructions", context_providers=[mock_provider])
agent = Agent(
client=client,
instructions="Agent instructions",
context_providers=[mock_provider],
)
# We need to test the _prepare_session_and_messages method directly
session_context, _ = await agent._prepare_session_and_messages( # type: ignore[reportPrivateUsage]
@@ -503,10 +587,16 @@ async def test_chat_agent_context_instructions_in_messages(client: SupportsChatG
# instructions system message is added by a client
async def test_chat_agent_no_context_instructions(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_no_context_instructions(
client: SupportsChatGetResponse,
) -> None:
"""Test behavior when AI context has no instructions."""
mock_provider = MockContextProvider()
agent = Agent(client=client, instructions="Agent instructions", context_providers=[mock_provider])
agent = Agent(
client=client,
instructions="Agent instructions",
context_providers=[mock_provider],
)
session_context, _ = await agent._prepare_session_and_messages( # type: ignore[reportPrivateUsage]
session=None, input_messages=[Message(role="user", text="Hello")]
@@ -519,7 +609,9 @@ async def test_chat_agent_no_context_instructions(client: SupportsChatGetRespons
assert messages[0].text == "Hello"
async def test_chat_agent_run_stream_context_providers(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_run_stream_context_providers(
client: SupportsChatGetResponse,
) -> None:
"""Test that context providers work with run method."""
mock_provider = MockContextProvider(messages=[Message(role="system", text="Stream context instructions")])
agent = Agent(client=client, context_providers=[mock_provider])
@@ -537,7 +629,9 @@ async def test_chat_agent_run_stream_context_providers(client: SupportsChatGetRe
assert mock_provider.after_run_called
async def test_chat_agent_context_providers_with_service_session_id(chat_client_base: SupportsChatGetResponse) -> None:
async def test_chat_agent_context_providers_with_service_session_id(
chat_client_base: SupportsChatGetResponse,
) -> None:
"""Test context providers with service-managed session."""
mock_provider = MockContextProvider()
chat_client_base.run_responses = [
@@ -570,7 +664,9 @@ async def test_chat_agent_as_tool_basic(client: SupportsChatGetResponse) -> None
assert hasattr(tool, "input_model")
async def test_chat_agent_as_tool_custom_parameters(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_as_tool_custom_parameters(
client: SupportsChatGetResponse,
) -> None:
"""Test as_tool with custom parameters."""
agent = Agent(client=client, name="TestAgent", description="Original description")
@@ -618,7 +714,9 @@ async def test_chat_agent_as_tool_no_name(client: SupportsChatGetResponse) -> No
agent.as_tool()
async def test_chat_agent_as_tool_function_execution(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_as_tool_function_execution(
client: SupportsChatGetResponse,
) -> None:
"""Test that the generated FunctionTool can be executed."""
agent = Agent(client=client, name="TestAgent", description="Test agent")
@@ -632,7 +730,9 @@ async def test_chat_agent_as_tool_function_execution(client: SupportsChatGetResp
assert result == "test response" # From mock chat client
async def test_chat_agent_as_tool_with_stream_callback(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_as_tool_with_stream_callback(
client: SupportsChatGetResponse,
) -> None:
"""Test as_tool with stream callback functionality."""
agent = Agent(client=client, name="StreamingAgent")
@@ -655,7 +755,9 @@ async def test_chat_agent_as_tool_with_stream_callback(client: SupportsChatGetRe
assert result == expected_text
async def test_chat_agent_as_tool_with_custom_arg_name(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_as_tool_with_custom_arg_name(
client: SupportsChatGetResponse,
) -> None:
"""Test as_tool with custom argument name."""
agent = Agent(client=client, name="CustomArgAgent")
@@ -666,7 +768,9 @@ async def test_chat_agent_as_tool_with_custom_arg_name(client: SupportsChatGetRe
assert result == "test response"
async def test_chat_agent_as_tool_with_async_stream_callback(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_as_tool_with_async_stream_callback(
client: SupportsChatGetResponse,
) -> None:
"""Test as_tool with async stream callback functionality."""
agent = Agent(client=client, name="AsyncStreamingAgent")
@@ -689,7 +793,9 @@ async def test_chat_agent_as_tool_with_async_stream_callback(client: SupportsCha
assert result == expected_text
async def test_chat_agent_as_tool_name_sanitization(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_as_tool_name_sanitization(
client: SupportsChatGetResponse,
) -> None:
"""Test as_tool name sanitization."""
test_cases = [
("Invoice & Billing Agent", "Invoice_Billing_Agent"),
@@ -707,7 +813,9 @@ async def test_chat_agent_as_tool_name_sanitization(client: SupportsChatGetRespo
assert tool.name == expected_tool_name, f"Expected {expected_tool_name}, got {tool.name} for input {agent_name}"
async def test_chat_agent_as_tool_propagate_session_true(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_as_tool_propagate_session_true(
client: SupportsChatGetResponse,
) -> None:
"""Test that propagate_session=True forwards the parent's session to the sub-agent."""
agent = Agent(client=client, name="SubAgent", description="Sub agent")
tool = agent.as_tool(propagate_session=True)
@@ -733,7 +841,9 @@ async def test_chat_agent_as_tool_propagate_session_true(client: SupportsChatGet
assert captured_session.state["shared_key"] == "shared_value"
async def test_chat_agent_as_tool_propagate_session_false_by_default(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_as_tool_propagate_session_false_by_default(
client: SupportsChatGetResponse,
) -> None:
"""Test that propagate_session defaults to False and does not forward the session."""
agent = Agent(client=client, name="SubAgent", description="Sub agent")
tool = agent.as_tool() # default: propagate_session=False
@@ -755,7 +865,9 @@ async def test_chat_agent_as_tool_propagate_session_false_by_default(client: Sup
assert captured_session is None
async def test_chat_agent_as_tool_propagate_session_shares_state(client: SupportsChatGetResponse) -> None:
async def test_chat_agent_as_tool_propagate_session_shares_state(
client: SupportsChatGetResponse,
) -> None:
"""Test that shared session allows the sub-agent to read and write parent's state."""
agent = Agent(client=client, name="SubAgent", description="Sub agent")
tool = agent.as_tool(propagate_session=True)
@@ -824,13 +936,20 @@ async def test_chat_agent_with_local_mcp_tools(client: SupportsChatGetResponse)
# Test agent with MCP tools in constructor
with contextlib.suppress(Exception):
agent = Agent(client=client, name="TestAgent", description="Test agent", tools=[mock_mcp_tool])
agent = Agent(
client=client,
name="TestAgent",
description="Test agent",
tools=[mock_mcp_tool],
)
# Test async context manager with MCP tools
async with agent:
pass
async def test_mcp_tools_not_duplicated_when_passed_as_runtime_tools(chat_client_base: Any) -> None:
async def test_mcp_tools_not_duplicated_when_passed_as_runtime_tools(
chat_client_base: Any,
) -> None:
"""Test that MCP tool functions from self.mcp_tools are not duplicated when already present in runtime tools."""
captured_options: list[dict[str, Any]] = []
@@ -891,7 +1010,11 @@ async def test_agent_tool_receives_session_in_kwargs(chat_client_base: Any) -> N
messages=Message(
role="assistant",
contents=[
Content.from_function_call(call_id="1", name="echo_session_info", arguments='{"text": "hello"}')
Content.from_function_call(
call_id="1",
name="echo_session_info",
arguments='{"text": "hello"}',
)
],
)
),
@@ -901,7 +1024,11 @@ async def test_agent_tool_receives_session_in_kwargs(chat_client_base: Any) -> N
agent = Agent(client=chat_client_base, tools=[echo_session_info])
session = agent.create_session()
result = await agent.run("hello", session=session, options={"additional_function_arguments": {"session": session}})
result = await agent.run(
"hello",
session=session,
options={"additional_function_arguments": {"session": session}},
)
assert result.text == "done"
assert captured.get("has_session") is True
@@ -1343,7 +1470,9 @@ def test_chat_agent_calls_update_agent_name_on_client():
@pytest.mark.asyncio
async def test_chat_agent_context_provider_adds_tools_when_agent_has_none(chat_client_base: SupportsChatGetResponse):
async def test_chat_agent_context_provider_adds_tools_when_agent_has_none(
chat_client_base: SupportsChatGetResponse,
):
"""Test that context provider tools are used when agent has no default tools."""
@tool
@@ -1405,7 +1534,9 @@ async def test_chat_agent_context_provider_adds_instructions_when_agent_has_none
# region STORES_BY_DEFAULT tests
async def test_stores_by_default_skips_inmemory_injection(client: SupportsChatGetResponse) -> None:
async def test_stores_by_default_skips_inmemory_injection(
client: SupportsChatGetResponse,
) -> None:
"""Client with STORES_BY_DEFAULT=True should not auto-inject InMemoryHistoryProvider."""
from agent_framework._sessions import InMemoryHistoryProvider
@@ -1421,7 +1552,9 @@ async def test_stores_by_default_skips_inmemory_injection(client: SupportsChatGe
assert not any(isinstance(p, InMemoryHistoryProvider) for p in agent.context_providers)
async def test_stores_by_default_false_injects_inmemory(client: SupportsChatGetResponse) -> None:
async def test_stores_by_default_false_injects_inmemory(
client: SupportsChatGetResponse,
) -> None:
"""Client with STORES_BY_DEFAULT=False (default) should auto-inject InMemoryHistoryProvider."""
from agent_framework._sessions import InMemoryHistoryProvider
@@ -1434,7 +1567,9 @@ async def test_stores_by_default_false_injects_inmemory(client: SupportsChatGetR
assert any(isinstance(p, InMemoryHistoryProvider) for p in agent.context_providers)
async def test_stores_by_default_with_store_false_injects_inmemory(client: SupportsChatGetResponse) -> None:
async def test_stores_by_default_with_store_false_injects_inmemory(
client: SupportsChatGetResponse,
) -> None:
"""Client with STORES_BY_DEFAULT=True but store=False should still inject InMemoryHistoryProvider."""
from agent_framework._sessions import InMemoryHistoryProvider
@@ -1449,7 +1584,42 @@ async def test_stores_by_default_with_store_false_injects_inmemory(client: Suppo
assert any(isinstance(p, InMemoryHistoryProvider) for p in agent.context_providers)
# endregion
async def test_store_true_skips_inmemory_injection(
client: SupportsChatGetResponse,
) -> None:
"""Explicitly setting store=True should not auto-inject InMemoryHistoryProvider."""
from agent_framework._sessions import InMemoryHistoryProvider
agent = Agent(client=client)
session = agent.create_session()
await agent.run("Hello", session=session, options={"store": True})
# User explicitly enabled server storage, so InMemoryHistoryProvider should not be injected
assert not any(isinstance(p, InMemoryHistoryProvider) for p in agent.context_providers)
async def test_stores_by_default_with_store_false_in_default_options_injects_inmemory(
client: SupportsChatGetResponse,
) -> None:
"""Client with STORES_BY_DEFAULT=True but store=False in default_options should inject InMemoryHistoryProvider.
This covers the regression where store=False is set via Agent(..., default_options={"store": False})
with no per-run override while the client has STORES_BY_DEFAULT=True.
"""
from agent_framework._sessions import InMemoryHistoryProvider
client.STORES_BY_DEFAULT = True # type: ignore[attr-defined]
# Set store=False at agent initialization via default_options, not at run-time
agent = Agent(client=client, default_options={"store": False})
session = agent.create_session()
# Run without any per-run options override
await agent.run("Hello", session=session)
# User explicitly disabled server storage in default_options, so InMemoryHistoryProvider should be injected
assert any(isinstance(p, InMemoryHistoryProvider) for p in agent.context_providers)
# endregion
@@ -2666,6 +2666,58 @@ class TestResponseStreamBasicIteration:
assert stream.updates[0].text == "update_0"
assert stream.updates[1].text == "update_1"
async def test_auto_finalize_on_iteration_completion(self) -> None:
"""Stream auto-finalizes when async iteration completes."""
stream = ResponseStream(_generate_updates(2), finalizer=_combine_updates)
async for _ in stream:
pass
assert stream._finalized is True
assert stream._final_result is not None
assert stream._final_result.text == "update_0update_1"
async def test_auto_finalize_runs_result_hooks(self) -> None:
"""Result hooks run automatically when iteration completes."""
hook_called = {"value": False}
def tracking_hook(response: ChatResponse) -> ChatResponse:
hook_called["value"] = True
response.additional_properties["auto_finalized"] = True
return response
stream = ResponseStream(
_generate_updates(2),
finalizer=_combine_updates,
result_hooks=[tracking_hook],
)
async for _ in stream:
pass
assert hook_called["value"] is True
final = await stream.get_final_response()
assert final.additional_properties["auto_finalized"] is True
async def test_get_final_response_idempotent_after_auto_finalize(self) -> None:
"""get_final_response returns cached result after auto-finalization."""
call_count = {"value": 0}
def counting_finalizer(updates: list[ChatResponseUpdate]) -> ChatResponse:
call_count["value"] += 1
return _combine_updates(updates)
stream = ResponseStream(_generate_updates(2), finalizer=counting_finalizer)
async for _ in stream:
pass
final1 = await stream.get_final_response()
final2 = await stream.get_final_response()
assert call_count["value"] == 1
assert final1.text == final2.text
class TestResponseStreamTransformHooks:
"""Tests for transform hooks (per-update processing)."""
+1 -1
View File
@@ -22,7 +22,7 @@ export AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME="gpt-4o" # optional, defaults to
|---|------|-------------------|
| 1 | [01_hello_agent.py](01_hello_agent.py) | Create your first agent and run it (streaming and non-streaming). |
| 2 | [02_add_tools.py](02_add_tools.py) | Define a function tool with `@tool` and attach it to an agent. |
| 3 | [03_multi_turn.py](03_multi_turn.py) | Keep conversation history across turns with `AgentThread`. |
| 3 | [03_multi_turn.py](03_multi_turn.py) | Keep conversation history across turns with `AgentSession`. |
| 4 | [04_memory.py](04_memory.py) | Add dynamic context with a custom `ContextProvider`. |
| 5 | [05_first_workflow.py](05_first_workflow.py) | Chain executors into a workflow with edges. |
| 6 | [06_host_your_agent.py](06_host_your_agent.py) | Host a single agent with Azure Functions. |
@@ -2,6 +2,7 @@
import asyncio
from agent_framework import Message
from agent_framework.openai import OpenAIChatClient
from dotenv import load_dotenv
@@ -28,7 +29,7 @@ async def main() -> None:
client = OpenAIChatClient()
try:
task = asyncio.create_task(client.get_response(messages=["Tell me a fantasy story."]))
task = asyncio.create_task(client.get_response(messages=[Message(role="user", text="Tell me a fantasy story.")]))
await asyncio.sleep(1)
task.cancel()
await task
+39 -16
View File
@@ -1,34 +1,57 @@
# A2A Agent Examples
This folder contains examples demonstrating how to create and use agents with the A2A (Agent2Agent) protocol from the `agent_framework` package to communicate with remote A2A agents.
This sample demonstrates how to host and consume agents using the [A2A (Agent2Agent) protocol](https://a2a-protocol.org/latest/) with the `agent_framework` package. There are two runnable entry points:
By default the A2AAgent waits for the remote agent to finish before returning (`background=False`), so long-running A2A tasks are handled transparently. For advanced scenarios where you need to poll or resubscribe to in-progress tasks using continuation tokens, see the [background responses sample](../../02-agents/background_responses.py).
| Run this file | To... |
|---------------|-------|
| **[`a2a_server.py`](a2a_server.py)** | Host an Agent Framework agent as an A2A-compliant server. |
| **[`agent_with_a2a.py`](agent_with_a2a.py)** | Connect to an A2A server and send requests (non-streaming and streaming). |
For more information about the A2A protocol specification, visit: https://a2a-protocol.org/latest/
## Examples
The remaining files are supporting modules used by the server:
| File | Description |
|------|-------------|
| [`agent_with_a2a.py`](agent_with_a2a.py) | Demonstrates agent discovery, non-streaming and streaming responses using the A2A protocol. |
| [`agent_definitions.py`](agent_definitions.py) | Agent and AgentCard factory definitions for invoice, policy, and logistics agents. |
| [`agent_executor.py`](agent_executor.py) | Bridges the a2a-sdk `AgentExecutor` interface to Agent Framework agents. |
| [`invoice_data.py`](invoice_data.py) | Mock invoice data and tool functions for the invoice agent. |
| [`a2a_server.http`](a2a_server.http) | REST Client requests for testing the server directly from VS Code. |
## Environment Variables
Make sure to set the following environment variables before running the example:
Make sure to set the following environment variables before running the examples:
### Required
- `A2A_AGENT_HOST`: URL of a single A2A agent (for simple sample, e.g., `http://localhost:5001/`)
### Required (Server)
- `AZURE_AI_PROJECT_ENDPOINT` — Your Azure AI Foundry project endpoint
- `AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME` — Model deployment name (e.g. `gpt-4o`)
### Required (Client)
- `A2A_AGENT_HOST` — URL of the A2A server (e.g. `http://localhost:5001/`)
## Quick Testing with .NET A2A Servers
## Quick Start
For quick testing and demonstration, you can use the pre-built .NET A2A servers from this repository:
All commands below should be run from this directory:
**Quick Testing Reference**: Use the .NET A2A Client Server sample at:
`..\agent-framework\dotnet\samples\05-end-to-end\A2AClientServer`
### Run Python A2A Sample
```powershell
# Simple A2A sample (single agent)
cd python/samples/04-hosting/a2a
```
### 1. Start the A2A Server
Pick an agent type and start the server (each in its own terminal):
```powershell
uv run python a2a_server.py --agent-type invoice --port 5000
uv run python a2a_server.py --agent-type policy --port 5001
uv run python a2a_server.py --agent-type logistics --port 5002
```
You can run one agent or all three — each listens on its own port.
### 2. Run the A2A Client
In a separate terminal (from the same directory), point the client at a running server:
```powershell
$env:A2A_AGENT_HOST = "http://localhost:5001/"
uv run python agent_with_a2a.py
```
@@ -0,0 +1,82 @@
### Each A2A agent is available at a different host address
@hostInvoice = http://localhost:5000
@hostPolicy = http://localhost:5001
@hostLogistics = http://localhost:5002
### Query agent card for the invoice agent
GET {{hostInvoice}}/.well-known/agent.json
### Send a message to the invoice agent
POST {{hostInvoice}}
Content-Type: application/json
{
"id": "1",
"jsonrpc": "2.0",
"method": "message/send",
"params": {
"message": {
"kind": "message",
"role": "user",
"messageId": "msg_1",
"parts": [
{
"kind": "text",
"text": "Show me all invoices for Contoso"
}
]
}
}
}
### Query agent card for the policy agent
GET {{hostPolicy}}/.well-known/agent.json
### Send a message to the policy agent
POST {{hostPolicy}}
Content-Type: application/json
{
"id": "2",
"jsonrpc": "2.0",
"method": "message/send",
"params": {
"message": {
"kind": "message",
"role": "user",
"messageId": "msg_2",
"parts": [
{
"kind": "text",
"text": "What is the policy for short shipments?"
}
]
}
}
}
### Query agent card for the logistics agent
GET {{hostLogistics}}/.well-known/agent.json
### Send a message to the logistics agent
POST {{hostLogistics}}
Content-Type: application/json
{
"id": "3",
"jsonrpc": "2.0",
"method": "message/send",
"params": {
"message": {
"kind": "message",
"role": "user",
"messageId": "msg_3",
"parts": [
{
"kind": "text",
"text": "What is the status for SHPMT-SAP-001?"
}
]
}
}
}
+120
View File
@@ -0,0 +1,120 @@
# Copyright (c) Microsoft. All rights reserved.
import argparse
import os
import sys
import uvicorn
from a2a.server.apps.jsonrpc.starlette_app import A2AStarletteApplication
from a2a.server.request_handlers.default_request_handler import DefaultRequestHandler
from a2a.server.tasks.inmemory_task_store import InMemoryTaskStore
from agent_definitions import AGENT_CARD_FACTORIES, AGENT_FACTORIES
from agent_executor import AgentFrameworkExecutor
from agent_framework.azure import AzureOpenAIResponsesClient
from azure.identity import AzureCliCredential
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
"""
A2A Server Sample Host an Agent Framework agent as an A2A endpoint
This sample creates a Python-based A2A-compliant server that wraps an Agent
Framework agent. The server uses the a2a-sdk's Starlette application to handle
JSON-RPC requests and serves the AgentCard at /.well-known/agent.json.
Three agent types are available:
- invoice Answers invoice queries using mock data and function tools.
- policy Returns a fixed policy response.
- logistics Returns a fixed logistics response.
Usage:
uv run python a2a_server.py --agent-type policy --port 5001
uv run python a2a_server.py --agent-type invoice --port 5000
uv run python a2a_server.py --agent-type logistics --port 5002
Environment variables:
AZURE_AI_PROJECT_ENDPOINT Your Azure AI Foundry project endpoint
AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME Model deployment name (e.g. gpt-4o)
"""
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="A2A Agent Server")
parser.add_argument(
"--agent-type",
choices=["invoice", "policy", "logistics"],
default="policy",
help="Type of agent to host (default: policy)",
)
parser.add_argument(
"--host",
default="localhost",
help="Host to bind to (default: localhost)",
)
parser.add_argument(
"--port",
type=int,
default=5001,
help="Port to listen on (default: 5001)",
)
return parser.parse_args()
def main() -> None:
args = parse_args()
# Validate environment
project_endpoint = os.getenv("AZURE_AI_PROJECT_ENDPOINT")
deployment_name = os.getenv("AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME")
if not project_endpoint:
print("Error: AZURE_AI_PROJECT_ENDPOINT environment variable is not set.")
sys.exit(1)
if not deployment_name:
print("Error: AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME environment variable is not set.")
sys.exit(1)
# Create the LLM client
credential = AzureCliCredential()
client = AzureOpenAIResponsesClient(
project_endpoint=project_endpoint,
deployment_name=deployment_name,
credential=credential,
)
# Create the Agent Framework agent for the chosen type
agent_factory = AGENT_FACTORIES[args.agent_type]
agent = agent_factory(client)
# Build the A2A server components
url = f"http://{args.host}:{args.port}/"
agent_card = AGENT_CARD_FACTORIES[args.agent_type](url)
executor = AgentFrameworkExecutor(agent)
task_store = InMemoryTaskStore()
request_handler = DefaultRequestHandler(
agent_executor=executor,
task_store=task_store,
)
a2a_app = A2AStarletteApplication(
agent_card=agent_card,
http_handler=request_handler,
)
print(f"Starting A2A server: {agent_card.name}")
print(f" Agent type : {args.agent_type}")
print(f" Listening : {url}")
print(f" Agent card : {url}.well-known/agent.json")
print()
uvicorn.run(
a2a_app.build(),
host=args.host,
port=args.port,
)
if __name__ == "__main__":
main()
@@ -0,0 +1,169 @@
# Copyright (c) Microsoft. All rights reserved.
"""Agent definitions and AgentCard factories for the A2A server sample.
Provides factory functions to create Agent Framework agents and A2A
AgentCards for the invoice, policy, and logistics agent types.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
from a2a.types import AgentCapabilities, AgentCard, AgentSkill
from invoice_data import query_by_invoice_id, query_by_transaction_id, query_invoices
if TYPE_CHECKING:
from agent_framework import Agent
from agent_framework.azure import AzureOpenAIResponsesClient
# ---------------------------------------------------------------------------
# Agent instructions
# ---------------------------------------------------------------------------
INVOICE_INSTRUCTIONS = "You specialize in handling queries related to invoices."
POLICY_INSTRUCTIONS = """\
You specialize in handling queries related to policies and customer communications.
Always reply with exactly this text:
Policy: Short Shipment Dispute Handling Policy V2.1
Summary: "For short shipments reported by customers, first verify internal shipment records
(SAP) and physical logistics scan data (BigQuery). If discrepancy is confirmed and logistics data
shows fewer items packed than invoiced, issue a credit for the missing items. Document the
resolution in SAP CRM and notify the customer via email within 2 business days, referencing the
original invoice and the credit memo number. Use the 'Formal Credit Notification' email
template."
"""
LOGISTICS_INSTRUCTIONS = """\
You specialize in handling queries related to logistics.
Always reply with exactly:
Shipment number: SHPMT-SAP-001
Item: TSHIRT-RED-L
Quantity: 900
"""
# ---------------------------------------------------------------------------
# Agent factories
# ---------------------------------------------------------------------------
def create_invoice_agent(client: AzureOpenAIResponsesClient) -> Agent:
"""Create an invoice agent backed by the given client with query tools."""
return client.as_agent(
name="InvoiceAgent",
instructions=INVOICE_INSTRUCTIONS,
tools=[query_invoices, query_by_transaction_id, query_by_invoice_id],
)
def create_policy_agent(client: AzureOpenAIResponsesClient) -> Agent:
"""Create a policy agent backed by the given client."""
return client.as_agent(
name="PolicyAgent",
instructions=POLICY_INSTRUCTIONS,
)
def create_logistics_agent(client: AzureOpenAIResponsesClient) -> Agent:
"""Create a logistics agent backed by the given client."""
return client.as_agent(
name="LogisticsAgent",
instructions=LOGISTICS_INSTRUCTIONS,
)
# ---------------------------------------------------------------------------
# AgentCard factories
# ---------------------------------------------------------------------------
_CAPABILITIES = AgentCapabilities(streaming=True, push_notifications=False)
def get_invoice_agent_card(url: str) -> AgentCard:
"""Return an A2A AgentCard for the invoice agent."""
return AgentCard(
name="InvoiceAgent",
description="Handles requests relating to invoices.",
url=url,
version="1.0.0",
default_input_modes=["text"],
default_output_modes=["text"],
capabilities=_CAPABILITIES,
skills=[
AgentSkill(
id="id_invoice_agent",
name="InvoiceQuery",
description="Handles requests relating to invoices.",
tags=["invoice", "agent-framework"],
examples=["List the latest invoices for Contoso."],
),
],
)
def get_policy_agent_card(url: str) -> AgentCard:
"""Return an A2A AgentCard for the policy agent."""
return AgentCard(
name="PolicyAgent",
description="Handles requests relating to policies and customer communications.",
url=url,
version="1.0.0",
default_input_modes=["text"],
default_output_modes=["text"],
capabilities=_CAPABILITIES,
skills=[
AgentSkill(
id="id_policy_agent",
name="PolicyAgent",
description="Handles requests relating to policies and customer communications.",
tags=["policy", "agent-framework"],
examples=["What is the policy for short shipments?"],
),
],
)
def get_logistics_agent_card(url: str) -> AgentCard:
"""Return an A2A AgentCard for the logistics agent."""
return AgentCard(
name="LogisticsAgent",
description="Handles requests relating to logistics.",
url=url,
version="1.0.0",
default_input_modes=["text"],
default_output_modes=["text"],
capabilities=_CAPABILITIES,
skills=[
AgentSkill(
id="id_logistics_agent",
name="LogisticsQuery",
description="Handles requests relating to logistics.",
tags=["logistics", "agent-framework"],
examples=["What is the status for SHPMT-SAP-001"],
),
],
)
# ---------------------------------------------------------------------------
# Lookup helpers
# ---------------------------------------------------------------------------
AGENT_FACTORIES = {
"invoice": create_invoice_agent,
"policy": create_policy_agent,
"logistics": create_logistics_agent,
}
AGENT_CARD_FACTORIES = {
"invoice": get_invoice_agent_card,
"policy": get_policy_agent_card,
"logistics": get_logistics_agent_card,
}
@@ -0,0 +1,123 @@
# Copyright (c) Microsoft. All rights reserved.
"""AgentExecutor bridge between the a2a-sdk server and Agent Framework agents.
Implements the a2a-sdk ``AgentExecutor`` interface so that incoming A2A
requests are forwarded to an Agent Framework agent and the response is
published back through the a2a-sdk event queue.
"""
from __future__ import annotations
import asyncio
import uuid
from typing import TYPE_CHECKING
from a2a.server.agent_execution.agent_executor import AgentExecutor
from a2a.types import (
Message,
Part,
Role,
TaskState,
TaskStatus,
TaskStatusUpdateEvent,
TextPart,
)
if TYPE_CHECKING:
from a2a.server.agent_execution.context import RequestContext
from a2a.server.events.event_queue import EventQueue
from agent_framework import Agent
class AgentFrameworkExecutor(AgentExecutor):
"""Bridges A2A protocol requests to an Agent Framework agent.
For each incoming ``execute`` call the executor:
1. Extracts the user's text from the A2A ``RequestContext``.
2. Runs the Agent Framework agent (non-streaming).
3. Publishes the result as an A2A ``Message`` to the ``EventQueue``.
"""
def __init__(self, agent: Agent) -> None:
self.agent = agent
async def execute(self, context: RequestContext, event_queue: EventQueue) -> None:
"""Run the agent and publish the response."""
user_text = context.get_user_input()
if not user_text:
user_text = "Hello"
task_id = context.task_id or str(uuid.uuid4())
context_id = context.context_id or str(uuid.uuid4())
# Signal that the agent is working
await event_queue.enqueue_event(
TaskStatusUpdateEvent(
task_id=task_id,
context_id=context_id,
status=TaskStatus(state=TaskState.working),
final=False,
)
)
try:
response = await self.agent.run(user_text)
# Build response text from agent messages
response_parts: list[Part] = []
for msg in response.messages:
if msg.text:
response_parts.append(TextPart(text=msg.text))
if not response_parts:
response_parts.append(TextPart(text=str(response)))
# Publish the agent's response as a completed message
await event_queue.enqueue_event(
TaskStatusUpdateEvent(
task_id=task_id,
context_id=context_id,
status=TaskStatus(
state=TaskState.completed,
message=Message(
message_id=str(uuid.uuid4()),
role=Role.agent,
parts=response_parts,
),
),
final=True,
)
)
except asyncio.CancelledError:
raise
except Exception as e:
await event_queue.enqueue_event(
TaskStatusUpdateEvent(
task_id=task_id,
context_id=context_id,
status=TaskStatus(
state=TaskState.failed,
message=Message(
message_id=str(uuid.uuid4()),
role=Role.agent,
parts=[TextPart(text=f"Agent error: {e}")],
),
),
final=True,
)
)
async def cancel(self, context: RequestContext, event_queue: EventQueue) -> None:
"""Handle cancellation by publishing a canceled status."""
task_id = context.task_id or str(uuid.uuid4())
context_id = context.context_id or str(uuid.uuid4())
await event_queue.enqueue_event(
TaskStatusUpdateEvent(
task_id=task_id,
context_id=context_id,
status=TaskStatus(state=TaskState.canceled),
final=True,
)
)
@@ -78,16 +78,16 @@ async def main():
# Updates arrive as Server-Sent Events, letting you observe
# progress in real time as the remote agent works.
print("\n--- Streaming response ---")
async with agent.run("Tell me about yourself", stream=True) as stream:
async for update in stream:
for content in update.contents:
if content.text:
print(f" {content.text}")
stream = agent.run("Tell me about yourself", stream=True)
async for update in stream:
for content in update.contents:
if content.text:
print(f" {content.text}")
response = await stream.get_final_response()
print(f"\nFinal response ({len(response.messages)} message(s)):")
for message in response.messages:
print(f" {message.text}")
response = await stream.get_final_response()
print(f"\nFinal response ({len(response.messages)} message(s)):")
for message in response.messages:
print(f" {message.text}")
if __name__ == "__main__":
@@ -0,0 +1,167 @@
# Copyright (c) Microsoft. All rights reserved.
"""Mock invoice data and tool functions for the A2A server sample.
Provides mock invoice data and query tools for the A2A server sample,
enabling invoice-related queries through the A2A protocol.
"""
import json
import random
from dataclasses import dataclass, field
from datetime import datetime, timedelta, timezone
from typing import Annotated
from agent_framework import tool
from pydantic import Field
@dataclass
class Product:
"""A product line item on an invoice."""
name: str
quantity: int
price_per_unit: float
@property
def total_price(self) -> float:
return self.quantity * self.price_per_unit
def to_dict(self) -> dict:
return {
"name": self.name,
"quantity": self.quantity,
"price_per_unit": self.price_per_unit,
"total_price": self.total_price,
}
@dataclass
class Invoice:
"""An invoice record with products."""
transaction_id: str
invoice_id: str
company_name: str
invoice_date: datetime
products: list[Product] = field(default_factory=list)
@property
def total_invoice_price(self) -> float:
return sum(p.total_price for p in self.products)
def to_dict(self) -> dict:
return {
"transaction_id": self.transaction_id,
"invoice_id": self.invoice_id,
"company_name": self.company_name,
"invoice_date": self.invoice_date.strftime("%Y-%m-%d"),
"products": [p.to_dict() for p in self.products],
"total_invoice_price": self.total_invoice_price,
}
def _random_date_within_last_two_months() -> datetime:
end_date = datetime.now(timezone.utc)
start_date = end_date - timedelta(days=60)
random_days = random.randint(0, 60)
return start_date + timedelta(days=random_days)
def _build_invoices() -> list[Invoice]:
"""Build 10 mock invoices."""
return [
Invoice("TICKET-XYZ987", "INV789", "Contoso", _random_date_within_last_two_months(), [
Product("T-Shirts", 150, 10.00),
Product("Hats", 200, 15.00),
Product("Glasses", 300, 5.00),
]),
Invoice("TICKET-XYZ111", "INV111", "XStore", _random_date_within_last_two_months(), [
Product("T-Shirts", 2500, 12.00),
Product("Hats", 1500, 8.00),
Product("Glasses", 200, 20.00),
]),
Invoice("TICKET-XYZ222", "INV222", "Cymbal Direct", _random_date_within_last_two_months(), [
Product("T-Shirts", 1200, 14.00),
Product("Hats", 800, 7.00),
Product("Glasses", 500, 25.00),
]),
Invoice("TICKET-XYZ333", "INV333", "Contoso", _random_date_within_last_two_months(), [
Product("T-Shirts", 400, 11.00),
Product("Hats", 600, 15.00),
Product("Glasses", 700, 5.00),
]),
Invoice("TICKET-XYZ444", "INV444", "XStore", _random_date_within_last_two_months(), [
Product("T-Shirts", 800, 10.00),
Product("Hats", 500, 18.00),
Product("Glasses", 300, 22.00),
]),
Invoice("TICKET-XYZ555", "INV555", "Cymbal Direct", _random_date_within_last_two_months(), [
Product("T-Shirts", 1100, 9.00),
Product("Hats", 900, 12.00),
Product("Glasses", 1200, 15.00),
]),
Invoice("TICKET-XYZ666", "INV666", "Contoso", _random_date_within_last_two_months(), [
Product("T-Shirts", 2500, 8.00),
Product("Hats", 1200, 10.00),
Product("Glasses", 1000, 6.00),
]),
Invoice("TICKET-XYZ777", "INV777", "XStore", _random_date_within_last_two_months(), [
Product("T-Shirts", 1900, 13.00),
Product("Hats", 1300, 16.00),
Product("Glasses", 800, 19.00),
]),
Invoice("TICKET-XYZ888", "INV888", "Cymbal Direct", _random_date_within_last_two_months(), [
Product("T-Shirts", 2200, 11.00),
Product("Hats", 1700, 8.50),
Product("Glasses", 600, 21.00),
]),
Invoice("TICKET-XYZ999", "INV999", "Contoso", _random_date_within_last_two_months(), [
Product("T-Shirts", 1400, 10.50),
Product("Hats", 1100, 9.00),
Product("Glasses", 950, 12.00),
]),
]
# Module-level singleton so dates are stable for the lifetime of the server
INVOICES = _build_invoices()
@tool(approval_mode="never_require")
def query_invoices(
company_name: Annotated[str, Field(description="The company name to filter invoices by.")],
start_date: Annotated[str | None, Field(description="Optional start date (YYYY-MM-DD) to filter invoices.")] = None,
end_date: Annotated[str | None, Field(description="Optional end date (YYYY-MM-DD) to filter invoices.")] = None,
) -> str:
"""Retrieves invoices for the specified company and optionally within the specified time range."""
results = [i for i in INVOICES if i.company_name.lower() == company_name.lower()]
if start_date:
start = datetime.strptime(start_date, "%Y-%m-%d").replace(tzinfo=timezone.utc)
results = [i for i in results if i.invoice_date >= start]
if end_date:
end = datetime.strptime(end_date, "%Y-%m-%d").replace(tzinfo=timezone.utc) + timedelta(days=1)
results = [i for i in results if i.invoice_date < end]
return json.dumps([i.to_dict() for i in results], indent=2)
@tool(approval_mode="never_require")
def query_by_transaction_id(
transaction_id: Annotated[str, Field(description="The transaction ID to look up (e.g. TICKET-XYZ987).")],
) -> str:
"""Retrieves invoice using the transaction id."""
results = [i for i in INVOICES if i.transaction_id.lower() == transaction_id.lower()]
return json.dumps([i.to_dict() for i in results], indent=2)
@tool(approval_mode="never_require")
def query_by_invoice_id(
invoice_id: Annotated[str, Field(description="The invoice ID to look up (e.g. INV789).")],
) -> str:
"""Retrieves invoice using the invoice id."""
results = [i for i in INVOICES if i.invoice_id.lower() == invoice_id.lower()]
return json.dumps([i.to_dict() for i in results], indent=2)
+1 -1
View File
@@ -18,7 +18,7 @@ Start with `01-get-started/` and work through the numbered files:
1. **[01_hello_agent.py](./01-get-started/01_hello_agent.py)** — Create and run your first agent
2. **[02_add_tools.py](./01-get-started/02_add_tools.py)** — Add function tools with `@tool`
3. **[03_multi_turn.py](./01-get-started/03_multi_turn.py)** — Multi-turn conversations with `AgentThread`
3. **[03_multi_turn.py](./01-get-started/03_multi_turn.py)** — Multi-turn conversations with `AgentSession`
4. **[04_memory.py](./01-get-started/04_memory.py)** — Agent memory with `ContextProvider`
5. **[05_first_workflow.py](./01-get-started/05_first_workflow.py)** — Build a workflow with executors and edges
6. **[06_host_your_agent.py](./01-get-started/06_host_your_agent.py)** — Host your agent via Azure Functions
+170 -154
View File
@@ -1813,11 +1813,11 @@ wheels = [
[[package]]
name = "filelock"
version = "3.25.0"
version = "3.25.1"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/77/18/a1fd2231c679dcb9726204645721b12498aeac28e1ad0601038f94b42556/filelock-3.25.0.tar.gz", hash = "sha256:8f00faf3abf9dc730a1ffe9c354ae5c04e079ab7d3a683b7c32da5dd05f26af3", size = 40158, upload-time = "2026-03-01T15:08:45.916Z" }
sdist = { url = "https://files.pythonhosted.org/packages/b3/8b/4c32ecde6bea6486a2a5d05340e695174351ff6b06cf651a74c005f9df00/filelock-3.25.1.tar.gz", hash = "sha256:b9a2e977f794ef94d77cdf7d27129ac648a61f585bff3ca24630c1629f701aa9", size = 40319, upload-time = "2026-03-09T19:38:47.309Z" }
wheels = [
{ url = "https://files.pythonhosted.org/packages/f9/0b/de6f54d4a8bedfe8645c41497f3c18d749f0bd3218170c667bf4b81d0cdd/filelock-3.25.0-py3-none-any.whl", hash = "sha256:5ccf8069f7948f494968fc0713c10e5c182a9c9d9eef3a636307a20c2490f047", size = 26427, upload-time = "2026-03-01T15:08:44.593Z" },
{ url = "https://files.pythonhosted.org/packages/a9/b8/2f664b56a3b4b32d28d3d106c71783073f712ba43ff6d34b9ea0ce36dc7b/filelock-3.25.1-py3-none-any.whl", hash = "sha256:18972df45473c4aa2c7921b609ee9ca4925910cc3a0fb226c96b92fc224ef7bf", size = 26720, upload-time = "2026-03-09T19:38:45.718Z" },
]
[[package]]
@@ -1864,51 +1864,51 @@ wheels = [
[[package]]
name = "fonttools"
version = "4.61.1"
version = "4.62.0"
source = { registry = "https://pypi.org/simple" }
sdist = { url = "https://files.pythonhosted.org/packages/ec/ca/cf17b88a8df95691275a3d77dc0a5ad9907f328ae53acbe6795da1b2f5ed/fonttools-4.61.1.tar.gz", hash = "sha256:6675329885c44657f826ef01d9e4fb33b9158e9d93c537d84ad8399539bc6f69", size = 3565756, upload-time = "2025-12-12T17:31:24.246Z" }
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