* Use deepcopy for state snapshot to detect nested mutations (#4500) Replace dict() shallow copy with copy.deepcopy() when snapshotting workflow state before activity execution. The shallow copy shared references to nested objects (dicts, lists), so in-place mutations by executors were reflected in both the snapshot and live state, producing an empty diff and preventing state updates from propagating to downstream activities. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Python: Fix state snapshot to use deepcopy so nested mutations are detected in durable workflow activities Fixes #4500 * Address PR review: remove report, extract testable helpers (#4500) - Delete REPRODUCTION_REPORT.md (debugging artifact with local paths and raw LLM output) - Extract _create_state_snapshot() and _compute_state_updates() as module-level helpers in _app.py so tests exercise the production code path - Update TestStateSnapshotDiff to import and use production helpers instead of reimplementing snapshot/diff logic locally Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Apply pre-commit auto-fixes * Add regression tests proving shallow copy bug and deep copy isolation (#4500) Add two additional tests to TestStateSnapshotDiff: - test_shallow_copy_would_miss_nested_mutations: reproduces the original bug by demonstrating that dict() (shallow copy) misses nested mutations - test_create_state_snapshot_isolates_nested_objects: verifies the production _create_state_snapshot helper creates a true deep copy These tests ensure a regression back to shallow copy would be caught. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Add integration test exercising full activity code path (#4500) Address PR review comment: add test_executor_activity_detects_nested_state_mutations that captures the actual executor_activity function from _setup_executor_activity and verifies it detects in-place nested mutations. This test would fail if _app.py line 314 regressed from _create_state_snapshot() back to dict(). Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address review feedback for #4518: review comment fixes * Address PR review feedback for state snapshot diff - Inline _compute_state_updates logic at call site to reuse precomputed original_keys/current_keys sets, avoiding redundant set allocations - Fix test docstring to describe behavioral regression instead of hard-coding a specific line number - Use SOURCE_ORCHESTRATOR constant in integration test instead of literal string Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Apply pre-commit auto-fixes * fix: remove unused _compute_state_updates from _app.py (#4518) The function was inlined per review comment, making the module-level helper unused and triggering a pyright reportUnusedFunction error. Move the helper into the test file where it is still needed for unit testing the diffing logic. 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.
