* feat(python): allow @tool functions to return rich content (images, audio) Add support for tool functions to return Content objects that the model can perceive natively. Closes #4272 Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Anthropic logging + mypy fix * Address PR review: fix MCP ordering, fold helper into from_function_result, fix Chat client - Preserve original content order in MCP tool results instead of text-first - Move _build_function_result logic into Content.from_function_result() - Chat Completions: inject user message for rich items (API only supports string tool content) - Update tests for ordering and new from_function_result behavior Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Use native Responses API multi-part output, warn+omit for Chat client - Responses client: put rich items directly in function_call_output's output field as list (native API support) instead of user message injection - Chat client: warn and omit rich items (API doesn't support multi-part tool results), matching Ollama/Bedrock pattern - Unify test image: use sample_image.jpg across all integration tests - Add Azure OpenAI Responses integration test - Assert model describes house image to verify perception Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix lint: remove print statement, wrap long line Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address review feedback: bug fixes, single-pass MCP, unit tests - Add isinstance guard in from_function_result for non-Content lists - Fix Anthropic empty tool_content fallback to string result - Fix Content(type='text', text=None) edge case in parse_result - Rewrite MCP _parse_tool_result_from_mcp as single-pass (no index counters) - Add Anthropic unit tests: data image, uri image, unsupported media, all-unsupported - Add OpenAI Chat unit test: rich items warning and omission - Add OpenAI Responses unit tests: function_result with/without items - Add test_types tests: only-rich-items list, non-Content list fallback Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix pyright errors: add type ignore comments for Any list iteration Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix mypy/pyright: ensure ToolExecutionException receives str Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix lint: remove duplicate test_prepare_options_excludes_conversation_id Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * refactor: unify all tool results into Content items * addressed copilot comments * pyright fix * small fix * comments * fix: address Copilot review - warnings, blob safety, dedup - Add warning logs when rich content is dropped in Claude agent and MCP server handlers (matching Chat/Bedrock/Ollama pattern) - Defensive blob URI construction: wrap plain base64 in data: prefix - Simplify Chat client _prepare_content_for_openai to use content.result - Simplify Responses client text-only path, remove redundant nesting - Add test for plain base64 blob without data: prefix Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix token double-counting in compaction and address review comments - Exclude items from _serialize_content() to prevent double-counting tokens when items mirrors result in function_result content - Add rich content warning in GitHub Copilot agent tool handler - Replace raw Content debug log with concise item count/type summary - Update stale test comments about FunctionTool.invoke return type Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Welcome to Microsoft Agent Framework!
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)
📋 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
- Overview - High level overview of the framework
- Quick Start - Get started with a simple agent
- Tutorials - Step by step tutorials
- User Guide - In-depth user guide for building agents and workflows
- Migration from Semantic Kernel - Guide to migrate from Semantic Kernel
- Migration from AutoGen - Guide to migrate from AutoGen
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 (1 min)
- Python and C#/.NET Support: Full framework support for both Python and C#/.NET implementations with consistent APIs
- Observability: Built-in OpenTelemetry integration for distributed tracing, monitoring, and debugging
- Multiple Agent Provider Support: Support for various LLM providers with more being added continuously
- Middleware: Flexible middleware system for request/response processing, exception handling, and custom pipelines
💬 We want your feedback!
- For bugs, please file a GitHub issue.
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
- Getting Started with Agents: progressive tutorial from hello-world to hosting
- Agent Concepts: deep-dive samples by topic (tools, middleware, providers, etc.)
- Getting Started with Workflows: workflow creation and integration with agents
.NET
- Getting Started with Agents: basic agent creation and tool usage
- Agent Provider Samples: samples showing different agent providers
- Workflow Samples: advanced multi-agent patterns and workflow orchestration
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.
