* Python: Enhance Azure AI Search citations with document URLs in Foundry V2 (Responses API) Override _parse_response_from_openai and _parse_chunk_from_openai in RawAzureAIClient to extract get_urls from azure_ai_search_call_output items and enrich url_citation annotations with document-specific URLs. - Non-streaming: first pass collects get_urls, post-processes annotations - Streaming: captures search output state, enriches url_citation events (also handles url_citation annotation type not handled by base class) - Updated V2 sample to demonstrate citation URL extraction - Added 14 unit tests covering extraction, enrichment, and edge cases Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * refactor: rework search citation enrichment to override _inner_get_response - Remove all direct openai/pydantic imports from _client.py - Override _inner_get_response instead of _parse_response_from_openai/_parse_chunk_from_openai - Use closure-local state for streaming instead of instance-level _streaming_search_get_urls - Add _build_url_citation_content helper for streaming url_citation handling - Fix mypy errors by using str(value or '') for Annotation TypedDict fields - Fix docstring to say 'citation' instead of 'url_citation' - Update tests to match new approach Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: handle streaming search citations from output_item.done events The azure_ai_search_call_output item only has populated output data (including get_urls) in the response.output_item.done event, not in the response.output_item.added event. Also removed the search_get_urls guard on url_citation handling so annotations are always produced even if get_urls haven't been captured yet. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * addressed comments * refactor: address PR review - eliminate type: ignore[assignment] pattern Call super()._inner_get_response() independently in each branch instead of once at the top with union type reassignment. Non-streaming uses two-arg super() in the closure; streaming uses cast() for type narrowing. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * refactor: remove defensive patterns per PR review - Replace all getattr() with direct attribute access - Remove cast() for streaming branch, use type: ignore[assignment] - Simplify _build_url_citation_content to use dict access directly - Simplify _extract_azure_search_urls to use item.type/item.output - Handle empty list output from streaming 'added' events - Update tests to match actual runtime types (objects, not dicts) Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * mypy fix * small fixes --------- 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.
