Giles Odigwe
3b6a4574eb
* Fix OpenAIResponsesAgentClient endpoint to include agentName in path (#5324) The sample OpenAIResponsesAgentClient used '/v1/' as the endpoint, which routes to the multi-agent endpoint requiring agent.name in the request body. However, AsIChatClient(agentName) maps agentName to the model field, not agent.name, causing HTTP 400 errors on OpenAI-compatible endpoints. Changed the endpoint to '/{agentName}/v1/' to match the pattern used by OpenAIChatCompletionsAgentClient, routing to the single-agent endpoint where no agent.name body field is needed. Added regression test verifying that the model field alone is insufficient for agent resolution on the multi-agent endpoint. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address review feedback for #5324 - URL-escape agentName in OpenAIResponsesAgentClient endpoint path to handle reserved characters safely - Add per-agent MapOpenAIResponses() calls in AgentHost so the sample host serves the /{agentName}/v1/responses routes the client now targets - Replace brittle Assert.Contains("agent.name") assertions with stable machine-readable error code assertion ("missing_required_parameter") Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Address additional review feedback for #5324 - Apply Uri.EscapeDataString to OpenAIChatCompletionsAgentClient endpoint for consistency with OpenAIResponsesAgentClient - Map OpenAI Responses and ChatCompletions endpoints for all builder-based agents (chemist, mathematician, literator, science workflows) so every discoverable agent is reachable via the single-agent endpoint path 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>
Agent Framework Samples
The agent framework samples are designed to help you get started with building AI-powered agents from various providers.
The Agent Framework supports building agents using various inference and inference-style services.
All these are supported using the single ChatClientAgent class.
The Agent Framework also supports creating proxy agents, that allow accessing remote agents as if they
were local agents. These are supported using various AIAgent subclasses.
Sample Structure
| Folder | Description |
|---|---|
01-get-started/ |
Progressive tutorial: hello agent → hosting |
02-agents/ |
Deep-dive by concept: tools, middleware, providers, orchestrations |
03-workflows/ |
Workflow patterns: sequential, concurrent, state, declarative |
04-hosting/ |
Deployment: Azure Functions, Durable Tasks |
05-end-to-end/ |
Full applications, evaluation, demos |
Getting Started
Start with 01-get-started/ and work through the numbered files:
- 01_hello_agent — Create and run your first agent
- 02_add_tools — Add function tools
- 03_multi_turn — Multi-turn conversations with
AgentSession - 04_memory — Agent memory with
AIContextProvider - 05_first_workflow — Build a workflow with executors and edges
- 06_host_your_agent — Host your agent via Azure Functions
Additional Samples
Some additional samples of note include:
- Agents: Basic steps to get started with the agent framework.
These samples demonstrate the fundamental concepts and functionalities of the agent framework when using the
AIAgentand can be used with any underlying service that provides anAIAgentimplementation. - Agent Providers: Shows how to create an AIAgent instance for a selection of providers.
- Agent Telemetry: Demo which showcases the integration of OpenTelemetry with the Microsoft Agent Framework using Azure OpenAI and .NET Aspire Dashboard for telemetry visualization.
- Durable Agents - Azure Functions: Samples for using the Microsoft Agent Framework with Azure Functions via the durable task extension.
- Durable Agents - Console Apps: Samples demonstrating durable agents in console applications.
Migration from Semantic Kernel
If you are migrating from Semantic Kernel to the Microsoft Agent Framework, the following resources provide guidance and side-by-side examples to help you transition your existing agents, tools, and orchestration patterns.
The migration samples map Semantic Kernel primitives (such as ChatCompletionAgent and Team orchestrations) to their Agent Framework equivalents (such as ChatClientAgent and workflow builders).
For an in-depth migration guide, see the official migration documentation.
Prerequisites
For prerequisites see each set of samples for their specific requirements.