Roger Barreto ca02146ee4 .NET: BugFix #3433 ChatClientAgent streaming responses missing messageid (#4615)
* Changes

* Fix ChatClientAgent streaming responses missing MessageId

Generate fallback MessageId in ChatClientAgent.RunCoreStreamingAsync when
the underlying LLM provider does not set ChatResponseUpdate.MessageId.
Without a MessageId the AGUI converter's null==null check silently drops
all text content, causing CopilotKit Zod validation errors.

Changes:
- ChatClientAgent: generate msg_{Guid} fallback via ??= in streaming loop
- AgentResponseExtensions: sync wrapper MessageId back to RawRepresentation
  in AsChatResponseUpdate() so downstream consumers see the value
- Add unit tests for both fixes and AGUI streaming MessageId scenarios

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

* Address PR #4615 review comments

- Fix MessageId seeding: use first-seen provider MessageId (or generate
  fallback) and apply consistently to all chunks in the stream, preventing
  message splitting when providers set MessageId only on the first chunk
- Add test for mixed MessageId scenario (first chunk only)
- Fix skipped TextStreaming test: assert Empty (not NotEmpty) to match
  actual null==null behavior
- Fix skipped ToolCalls test: assert empty ParentMessageId to match
  actual empty-string passthrough behavior

* Handle empty MessageId in AsChatResponseUpdate sync

Treat empty/whitespace MessageId the same as null when syncing from
the AgentResponseUpdate wrapper back to RawRepresentation. Providers
that return empty string MessageId (e.g. tool call responses) now get
the wrapper value recovered correctly.

Add test for empty string MessageId recovery scenario.

* Move MessageId fallback generation to AGUI layer

Move fallback MessageId generation from ChatClientAgent to
AsAGUIEventStreamAsync, addressing the architectural concern that
MessageId is nullable in the AIAgent abstraction and the requirement
for non-null values is specific to the AGUI protocol.

The AGUI layer now generates a fallback MessageId for null or
empty/whitespace values, covering all agent types (not just
ChatClientAgent) including external implementations.

Changes:
- Revert MessageId generation from ChatClientAgent.RunCoreStreamingAsync
- Add fallback MessageId generation in AsAGUIEventStreamAsync for
  null/empty MessageId values (handles both null and whitespace)
- Unskip and update AGUI tests to verify fallback generation
- Update ChatClientAgent tests to reflect passthrough behavior

* Revert AsChatResponseUpdate MessageId sync-back

Remove the MessageId sync-back logic from AsChatResponseUpdate() as it
is no longer needed. With fallback generation moved to the AGUI layer,
the abstraction layer should not mutate the RawRepresentation object.

Revert to the original passthrough behavior for AsChatResponseUpdate()
and update tests accordingly.

---------

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
ca02146ee4 · 2026-03-30 11:55:30 +00:00
1,768 Commits
2025-10-30 20:29:01 +00:00
2025-04-28 12:54:43 -07:00
2025-04-28 12:54:42 -07:00

Microsoft Agent Framework

Welcome to Microsoft Agent Framework!

Microsoft Azure AI Foundry Discord MS Learn Documentation PyPI NuGet

Welcome to Microsoft's comprehensive multi-language framework for building, orchestrating, and deploying AI agents with support for both .NET and Python implementations. This framework provides everything from simple chat agents to complex multi-agent workflows with graph-based orchestration.

Watch the full Agent Framework introduction (30 min)

Watch the full Agent Framework introduction (30 min)

📋 Getting Started

📦 Installation

Python

pip install agent-framework --pre
# This will install all sub-packages, see `python/packages` for individual packages.
# It may take a minute on first install on Windows.

.NET

dotnet add package Microsoft.Agents.AI

📚 Documentation

Still have questions? Join our weekly office hours or ask questions in our Discord channel to get help from the team and other users.

Highlights

  • Graph-based Workflows: Connect agents and deterministic functions using data flows with streaming, checkpointing, human-in-the-loop, and time-travel capabilities
  • AF Labs: Experimental packages for cutting-edge features including benchmarking, reinforcement learning, and research initiatives
  • DevUI: Interactive developer UI for agent development, testing, and debugging workflows

See the DevUI in action

See the DevUI in action (1 min)

💬 We want your feedback!

Quickstart

Basic Agent - Python

Create a simple Azure Responses Agent that writes a haiku about the Microsoft Agent Framework

# pip install agent-framework --pre
# Use `az login` to authenticate with Azure CLI
import os
import asyncio
from agent_framework.azure import AzureOpenAIResponsesClient
from azure.identity import AzureCliCredential


async def main():
    # Initialize a chat agent with Azure OpenAI Responses
    # the endpoint, deployment name, and api version can be set via environment variables
    # or they can be passed in directly to the AzureOpenAIResponsesClient constructor
    agent = AzureOpenAIResponsesClient(
        # endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
        # deployment_name=os.environ["AZURE_OPENAI_RESPONSES_DEPLOYMENT_NAME"],
        # api_version=os.environ["AZURE_OPENAI_API_VERSION"],
        # api_key=os.environ["AZURE_OPENAI_API_KEY"],  # Optional if using AzureCliCredential
        credential=AzureCliCredential(), # Optional, if using api_key
    ).as_agent(
        name="HaikuBot",
        instructions="You are an upbeat assistant that writes beautifully.",
    )

    print(await agent.run("Write a haiku about Microsoft Agent Framework."))

if __name__ == "__main__":
    asyncio.run(main())

Basic Agent - .NET

Create a simple Agent, using OpenAI Responses, that writes a haiku about the Microsoft Agent Framework

// dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
using Microsoft.Agents.AI;
using OpenAI;
using OpenAI.Responses;

// Replace the <apikey> with your OpenAI API key.
var agent = new OpenAIClient("<apikey>")
    .GetResponsesClient("gpt-4o-mini")
    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");

Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));

Create a simple Agent, using Azure OpenAI Responses with token based auth, that writes a haiku about the Microsoft Agent Framework

// dotnet add package Microsoft.Agents.AI.OpenAI --prerelease
// dotnet add package Azure.Identity
// Use `az login` to authenticate with Azure CLI
using System.ClientModel.Primitives;
using Azure.Identity;
using Microsoft.Agents.AI;
using OpenAI;
using OpenAI.Responses;

// Replace <resource> and gpt-4o-mini with your Azure OpenAI resource name and deployment name.
var agent = new OpenAIClient(
    new BearerTokenPolicy(new AzureCliCredential(), "https://ai.azure.com/.default"),
    new OpenAIClientOptions() { Endpoint = new Uri("https://<resource>.openai.azure.com/openai/v1") })
    .GetResponsesClient("gpt-4o-mini")
    .AsAIAgent(name: "HaikuBot", instructions: "You are an upbeat assistant that writes beautifully.");

Console.WriteLine(await agent.RunAsync("Write a haiku about Microsoft Agent Framework."));

More Examples & Samples

Python

.NET

Contributor Resources

Important Notes

If you use the Microsoft Agent Framework to build applications that operate with third-party servers or agents, you do so at your own risk. We recommend reviewing all data being shared with third-party servers or agents and being cognizant of third-party practices for retention and location of data. It is your responsibility to manage whether your data will flow outside of your organization's Azure compliance and geographic boundaries and any related implications.

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