* Python: Add OpenTelemetry instrumentation to ClaudeAgent (#4278) Add inline telemetry to ClaudeAgent.run() so that enable_instrumentation() emits invoke_agent spans and metrics. Covers both streaming and non-streaming paths using the same observability helpers as AgentTelemetryLayer. Adds 5 unit tests for telemetry behavior. Co-Authored-By: amitmukh <amimukherjee@microsoft.com> * Address PR review feedback for ClaudeAgent telemetry - Add justification comment for private observability API imports - Pass system_instructions to capture_messages for system prompt capture - Use monkeypatch instead of try/finally for test global state isolation Co-Authored-By: amitmukh <amitmukh@users.noreply.github.com> Co-Authored-By: Claude <noreply@anthropic.com> * Adopt AgentTelemetryLayer instead of inline telemetry Restructure ClaudeAgent to inherit from AgentTelemetryLayer via a _ClaudeAgentRunImpl mixin, eliminating duplicated telemetry code and private API imports. MRO: ClaudeAgent → AgentTelemetryLayer → _ClaudeAgentRunImpl → BaseAgent - Remove inline _run_with_telemetry / _run_with_telemetry_stream methods - Remove private observability helper imports (_capture_messages, etc.) - Add default_options property mapping system_prompt → instructions - Net -105 lines by reusing core telemetry layer Co-Authored-By: amitmukh <amitmukh@users.noreply.github.com> Co-Authored-By: Claude <noreply@anthropic.com> * Fix mypy: align _ClaudeAgentRunImpl.run() signature with AgentTelemetryLayer.run() Remove explicit `options` parameter from mixin's run() signature and extract it from **kwargs to match AgentTelemetryLayer's signature. Also align overload return types (ResponseStream, Awaitable) to match. Co-Authored-By: Claude <noreply@anthropic.com> * Introduce RawClaudeAgent following framework's RawAgent/Agent pattern Replace private _ClaudeAgentRunImpl mixin with public RawClaudeAgent class that contains all core logic (init, run, lifecycle, tools). ClaudeAgent becomes a thin wrapper that adds AgentTelemetryLayer. - RawClaudeAgent(BaseAgent): full implementation without telemetry - ClaudeAgent(AgentTelemetryLayer, RawClaudeAgent): adds OTel tracing - Export RawClaudeAgent from package __init__.py Users who want to skip telemetry or provide their own can use RawClaudeAgent directly. Co-Authored-By: Claude <noreply@anthropic.com> * Address review nits: trim RawClaudeAgent docstring, fix import paths - Simplify RawClaudeAgent docstring to a single basic example (not the primary entry point for most users) - Use agent_framework.anthropic import path in docstrings instead of direct agent_framework_claude path - Add RawClaudeAgent to agent_framework.anthropic lazy re-exports Co-Authored-By: Claude <noreply@anthropic.com> --------- Co-authored-by: Amit Mukherjee <amimukherjee@microsoft.com> Co-authored-by: amitmukh <amitmukh@users.noreply.github.com> Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Dmytro Struk <13853051+dmytrostruk@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.
