Evan Mattson 4afc088f01 Python: Emit AG-UI events for MCP tool calls, results, and text reasoning (#4760)
* Python: Emit AG-UI events for MCP tool calls, results, and text reasoning

Fixes #4213 — `_emit_content()` in the AG-UI layer only handled `text`,
`function_call`, `function_result`, `function_approval_request`, `usage`,
and `oauth_consent_request` content types. Foundry MCP content types
(`mcp_server_tool_call`, `mcp_server_tool_result`) and `text_reasoning`
fell through unhandled, producing no SSE events for AG-UI consumers.

Added three new handler functions wired into `_emit_content()`:

- `_emit_mcp_tool_call`: emits TOOL_CALL_START + TOOL_CALL_ARGS and
  tracks in FlowState for MESSAGES_SNAPSHOT inclusion
- `_emit_mcp_tool_result`: emits TOOL_CALL_END + TOOL_CALL_RESULT with
  full FlowState cleanup mirroring `_emit_tool_result`
- `_emit_text_reasoning`: emits the protocol-defined reasoning event
  sequence (ReasoningStart → MessageStart → MessageContent → MessageEnd
  → ReasoningEnd) with ReasoningEncryptedValueEvent for protected_data

* Add HTTP round-trip tests for MCP tool and reasoning SSE events

Exercises the full POST → SSE bytes → parse → validate pipeline for
mcp_server_tool_call, mcp_server_tool_result, text_reasoning, and
ReasoningEncryptedValueEvent content through FastAPI TestClient.

* Fix _emit_mcp_tool_result missing predictive_handler support (#4213)

- Add predictive_handler parameter to _emit_mcp_tool_result and mirror
  the apply_pending_updates + StateSnapshotEvent block from _emit_tool_result
- Forward predictive_handler from _emit_content to _emit_mcp_tool_result
- Add assertion for stored arguments in MCP tool call test
- Add test for predictive handler state snapshot after MCP tool result

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

* Apply pre-commit auto-fixes

* Refactor MCP tool emit functions and add missing tests (#4213)

- Extract _emit_tool_result_common shared helper to eliminate duplication
  between _emit_tool_result and _emit_mcp_tool_result
- Remove server_name prefix from tool_call_name in _emit_mcp_tool_call;
  display_name now equals tool_name directly
- Add test for tool_name fallback to 'mcp_tool' when tool_name is None
- Add test for output=None fallback to empty string in _emit_mcp_tool_result

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

* Address review feedback for #4213: review comment fixes

---------

Co-authored-by: Copilot <copilot@github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
4afc088f01 · 2026-03-20 00:41:37 +00:00
1,714 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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