* Support MCP sampling tools capability (#4625) Forward systemPrompt, tools, and toolChoice from MCP sampling requests to the chat client's get_response() call. Also advertise the sampling.tools capability to MCP servers when a client is configured. - Pass SamplingCapability with tools support to ClientSession - Convert systemPrompt to instructions in options - Convert MCP Tool objects to FunctionTool instances for options - Map MCP ToolChoice.mode to tool_choice in options - Add tests for all new behaviors and update existing sampling tests Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix #4625: Support MCP sampling tool with proper typing and structured content - Fix mypy error by typing sampling callback options as ChatOptions[None] instead of dict[str, Any], and importing ChatOptions from _types - Handle structuredContent from CallToolResult in _parse_tool_result_from_mcp, serializing it as JSON text Content when present - Add tests for structuredContent parsing (with and without regular content) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix lint: add author to TODO comment Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address review feedback for #4625: remove default=str, add edge-case tests - Remove default=str from json.dumps for structuredContent to fail fast on non-JSON-serializable values instead of silently converting - Add test for non-JSON-serializable structuredContent (TypeError) - Add tests for empty systemPrompt ('') and empty tools list ([]) edge cases in sampling callback - Expand TODO comment noting list[Content] return type constraint for future result_type support Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Sanitize sampling callback error to avoid leaking internals (#4625) Log exception details at DEBUG level instead of including them in the ErrorData message returned to the MCP server, which may be untrusted. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address review feedback for #4625: move params to options, restore error info - Remove stale TODO comment about response_format (ChatOptions already has it) - Restore {ex} in sampling callback error message for useful debugging info - Set structuredContent as additional_property on Content for structured access - Move temperature, max_tokens, stop into options dict (not top-level kwargs) - Only set temperature when provided (not all models support it) - Add tests for generation params in options and temperature omission Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix MCP sampling callback and structured content error handling (#4625) - Guard max_tokens like temperature: only set when not None, so options can properly evaluate to None when all params are absent - Wrap json.dumps of structuredContent in try/except to fall back to str() for non-serializable values instead of propagating TypeError - Extract test_connect_sampling_capabilities_with_client into its own test function so pytest can discover it independently - Add test for max_tokens=None omission from options - Update structured content non-serializable test to expect fallback Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address review feedback for #4625: review comment fixes * Fix MCP and Azure validation regressions Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> --------- Co-authored-by: Copilot <copilot@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.
