Add more type supports

python/packages/foundry_hosting/agent_framework_foundry_hosting/_responses.py

@@ -42,15 +42,34 @@ from azure.ai.agentserver.responses.models import (
     MessageContentOutputTextContent,
     MessageContentReasoningTextContent,
     MessageContentRefusalContent,
+    OAuthConsentRequestOutputItem,
     OutputItem,
+    OutputItemApplyPatchToolCall,
+    OutputItemApplyPatchToolCallOutput,
+    OutputItemCodeInterpreterToolCall,
+    OutputItemComputerToolCall,
+    OutputItemComputerToolCallOutputResource,
+    OutputItemCustomToolCall,
+    OutputItemCustomToolCallOutput,
+    OutputItemFileSearchToolCall,
+    OutputItemFunctionShellCall,
+    OutputItemFunctionShellCallOutput,
     OutputItemFunctionToolCall,
+    OutputItemImageGenToolCall,
+    OutputItemLocalShellToolCall,
+    OutputItemLocalShellToolCallOutput,
+    OutputItemMcpApprovalRequest,
+    OutputItemMcpApprovalResponseResource,
+    OutputItemMcpToolCall,
     OutputItemMessage,
     OutputItemOutputMessage,
     OutputItemReasoningItem,
+    OutputItemWebSearchToolCall,
     OutputMessageContent,
     OutputMessageContentOutputTextContent,
     OutputMessageContentRefusalContent,
     ResponseStreamEvent,
+    StructuredOutputsOutputItem,
     SummaryTextContent,
     TextContent,
 )
@@ -572,6 +591,203 @@ def _to_message(item: OutputItem) -> Message:
                 contents.append(Content.from_text(summary.text))
         return Message(role="assistant", contents=contents)
 
+    if item.type == "mcp_call":
+        mcp = cast(OutputItemMcpToolCall, item)
+        return Message(
+            role="assistant",
+            contents=[
+                Content.from_mcp_server_tool_call(
+                    mcp.id,
+                    mcp.name,
+                    server_name=mcp.server_label,
+                    arguments=mcp.arguments,
+                )
+            ],
+        )
+
+    if item.type == "mcp_approval_request":
+        mcp_req = cast(OutputItemMcpApprovalRequest, item)
+        fc = Content.from_mcp_server_tool_call(
+            mcp_req.id,
+            mcp_req.name,
+            server_name=mcp_req.server_label,
+            arguments=mcp_req.arguments,
+        )
+        return Message(
+            role="assistant",
+            contents=[Content.from_function_approval_request(mcp_req.id, fc)],
+        )
+
+    if item.type == "mcp_approval_response":
+        mcp_resp = cast(OutputItemMcpApprovalResponseResource, item)
+        # Build a placeholder function_call Content since the original call details are not available
+        fc = Content.from_function_call(mcp_resp.approval_request_id, "mcp_approval")
+        return Message(
+            role="user",
+            contents=[Content.from_function_approval_response(mcp_resp.approve, mcp_resp.id, fc)],
+        )
+
+    if item.type == "code_interpreter_call":
+        ci = cast(OutputItemCodeInterpreterToolCall, item)
+        return Message(
+            role="assistant",
+            contents=[Content.from_code_interpreter_tool_call(call_id=ci.id)],
+        )
+
+    if item.type == "image_generation_call":
+        ig = cast(OutputItemImageGenToolCall, item)
+        return Message(
+            role="assistant",
+            contents=[Content.from_image_generation_tool_call(image_id=ig.id)],
+        )
+
+    if item.type == "shell_call":
+        sc = cast(OutputItemFunctionShellCall, item)
+        return Message(
+            role="assistant",
+            contents=[
+                Content.from_shell_tool_call(
+                    call_id=sc.call_id,
+                    commands=sc.action.commands,
+                    status=str(sc.status),
+                )
+            ],
+        )
+
+    if item.type == "shell_call_output":
+        sco = cast(OutputItemFunctionShellCallOutput, item)
+        outputs = [
+            Content.from_shell_command_output(
+                stdout=out.stdout or "",
+                stderr=out.stderr or "",
+                exit_code=getattr(out.outcome, "exit_code", None) if hasattr(out, "outcome") else None,
+            )
+            for out in (sco.output or [])
+        ]
+        return Message(
+            role="tool",
+            contents=[
+                Content.from_shell_tool_result(
+                    call_id=sco.call_id,
+                    outputs=outputs,
+                    max_output_length=sco.max_output_length,
+                )
+            ],
+        )
+
+    if item.type == "local_shell_call":
+        lsc = cast(OutputItemLocalShellToolCall, item)
+        commands = lsc.action.command if hasattr(lsc.action, "command") and lsc.action.command else []
+        return Message(
+            role="assistant",
+            contents=[
+                Content.from_shell_tool_call(
+                    call_id=lsc.call_id,
+                    commands=commands,
+                    status=str(lsc.status),
+                )
+            ],
+        )
+
+    if item.type == "local_shell_call_output":
+        lsco = cast(OutputItemLocalShellToolCallOutput, item)
+        return Message(
+            role="tool",
+            contents=[
+                Content.from_shell_tool_result(
+                    call_id=lsco.id,
+                    outputs=[Content.from_shell_command_output(stdout=lsco.output)],
+                )
+            ],
+        )
+
+    if item.type == "file_search_call":
+        fs = cast(OutputItemFileSearchToolCall, item)
+        return Message(
+            role="assistant",
+            contents=[
+                Content.from_function_call(
+                    fs.id,
+                    "file_search",
+                    arguments=json.dumps({"queries": fs.queries}),
+                )
+            ],
+        )
+
+    if item.type == "web_search_call":
+        ws = cast(OutputItemWebSearchToolCall, item)
+        return Message(
+            role="assistant",
+            contents=[Content.from_function_call(ws.id, "web_search")],
+        )
+
+    if item.type == "computer_call":
+        cc = cast(OutputItemComputerToolCall, item)
+        return Message(
+            role="assistant",
+            contents=[
+                Content.from_function_call(
+                    cc.call_id,
+                    "computer_use",
+                    arguments=str(cc.action),
+                )
+            ],
+        )
+
+    if item.type == "computer_call_output":
+        cco = cast(OutputItemComputerToolCallOutputResource, item)
+        return Message(
+            role="tool",
+            contents=[Content.from_function_result(cco.call_id, result=str(cco.output))],
+        )
+
+    if item.type == "custom_tool_call":
+        ct = cast(OutputItemCustomToolCall, item)
+        return Message(
+            role="assistant",
+            contents=[Content.from_function_call(ct.call_id, ct.name, arguments=ct.input)],
+        )
+
+    if item.type == "custom_tool_call_output":
+        cto = cast(OutputItemCustomToolCallOutput, item)
+        output = cto.output if isinstance(cto.output, str) else str(cto.output)
+        return Message(
+            role="tool",
+            contents=[Content.from_function_result(cto.call_id, result=output)],
+        )
+
+    if item.type == "apply_patch_call":
+        ap = cast(OutputItemApplyPatchToolCall, item)
+        return Message(
+            role="assistant",
+            contents=[
+                Content.from_function_call(
+                    ap.call_id,
+                    "apply_patch",
+                    arguments=str(ap.operation),
+                )
+            ],
+        )
+
+    if item.type == "apply_patch_call_output":
+        apo = cast(OutputItemApplyPatchToolCallOutput, item)
+        return Message(
+            role="tool",
+            contents=[Content.from_function_result(apo.call_id, result=apo.output or "")],
+        )
+
+    if item.type == "oauth_consent_request":
+        oauth = cast(OAuthConsentRequestOutputItem, item)
+        return Message(
+            role="assistant",
+            contents=[Content.from_oauth_consent_request(oauth.consent_link)],
+        )
+
+    if item.type == "structured_outputs":
+        so = cast(StructuredOutputsOutputItem, item)
+        text = json.dumps(so.output) if not isinstance(so.output, str) else so.output
+        return Message(role="assistant", contents=[Content.from_text(text)])
+
     raise ValueError(f"Unsupported OutputItem type: {item.type}")
 
 
@@ -752,7 +968,7 @@ async def _to_outputs(stream: ResponseEventStream, content: Content) -> AsyncIte
             yield event
     else:
         # Log a warning for unsupported content types instead of raising an error to avoid breaking the response stream.
-        logger.warning(f"Content type '{content.type}' is not supported yet.")
+        logger.warning(f"Content type '{content.type}' is not supported yet. This is usually safe to ignore.")
 
 
 # endregion

python/packages/foundry_hosting/tests/test_responses.py

@@ -29,6 +29,7 @@ from azure.ai.agentserver.responses import InMemoryResponseProvider
 from typing_extensions import Any
 
 from agent_framework_foundry_hosting import ResponsesHostServer
+from agent_framework_foundry_hosting._responses import _to_message  # pyright: ignore[reportPrivateUsage]
 
 # region Helpers
 
@@ -522,3 +523,395 @@ class TestStreaming:
 
 
 # endregion
+
+
+# region _to_message conversion
+
+
+class TestToMessage:
+    """Tests for _to_message covering all supported OutputItem types."""
+
+    def test_output_message(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemOutputMessage, OutputMessageContentOutputTextContent
+
+        item = OutputItemOutputMessage({
+            "type": "output_message",
+            "role": "assistant",
+            "content": [OutputMessageContentOutputTextContent({"type": "output_text", "text": "hello"})],
+            "status": "completed",
+            "id": "msg-1",
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert len(msg.contents) == 1
+        assert msg.contents[0].type == "text"
+        assert msg.contents[0].text == "hello"
+
+    def test_message(self) -> None:
+        from azure.ai.agentserver.responses.models import MessageContentInputTextContent, OutputItemMessage
+
+        item = OutputItemMessage({
+            "type": "message",
+            "role": "user",
+            "content": [MessageContentInputTextContent({"type": "input_text", "text": "hi"})],
+        })
+        msg = _to_message(item)
+        assert msg.role == "user"
+        assert len(msg.contents) == 1
+        assert msg.contents[0].text == "hi"
+
+    def test_function_call(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemFunctionToolCall
+
+        item = OutputItemFunctionToolCall({
+            "type": "function_call",
+            "call_id": "call_1",
+            "name": "get_weather",
+            "arguments": '{"city": "NYC"}',
+            "status": "completed",
+            "id": "fc-1",
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "function_call"
+        assert msg.contents[0].call_id == "call_1"
+        assert msg.contents[0].name == "get_weather"
+
+    def test_function_call_output(self) -> None:
+        from azure.ai.agentserver.responses.models import FunctionCallOutputItemParam
+
+        item = FunctionCallOutputItemParam({"type": "function_call_output", "call_id": "call_1", "output": "sunny"})
+        msg = _to_message(item)  # type: ignore[arg-type]
+        assert msg.role == "tool"
+        assert msg.contents[0].type == "function_result"
+        assert msg.contents[0].call_id == "call_1"
+        assert msg.contents[0].result == "sunny"
+
+    def test_reasoning(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemReasoningItem, SummaryTextContent
+
+        item = OutputItemReasoningItem({
+            "type": "reasoning",
+            "id": "r-1",
+            "summary": [SummaryTextContent({"type": "summary_text", "text": "thinking hard"})],
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert len(msg.contents) == 1
+        assert msg.contents[0].text == "thinking hard"
+
+    def test_reasoning_no_summary(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemReasoningItem
+
+        item = OutputItemReasoningItem({"type": "reasoning", "id": "r-2"})
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents == []
+
+    def test_mcp_call(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemMcpToolCall
+
+        item = OutputItemMcpToolCall({
+            "type": "mcp_call",
+            "id": "mcp-1",
+            "server_label": "my_server",
+            "name": "search",
+            "arguments": '{"q": "test"}',
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "mcp_server_tool_call"
+        assert msg.contents[0].server_name == "my_server"
+        assert msg.contents[0].tool_name == "search"
+
+    def test_mcp_approval_request(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemMcpApprovalRequest
+
+        item = OutputItemMcpApprovalRequest({
+            "type": "mcp_approval_request",
+            "id": "apr-1",
+            "server_label": "srv",
+            "name": "dangerous_tool",
+            "arguments": "{}",
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "function_approval_request"
+
+    def test_mcp_approval_response(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemMcpApprovalResponseResource
+
+        item = OutputItemMcpApprovalResponseResource({
+            "type": "mcp_approval_response",
+            "id": "resp-1",
+            "approval_request_id": "apr-1",
+            "approve": True,
+        })
+        msg = _to_message(item)
+        assert msg.role == "user"
+        assert msg.contents[0].type == "function_approval_response"
+        assert msg.contents[0].approved is True
+
+    def test_code_interpreter_call(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemCodeInterpreterToolCall
+
+        item = OutputItemCodeInterpreterToolCall({
+            "type": "code_interpreter_call",
+            "id": "ci-1",
+            "status": "completed",
+            "container_id": "c-1",
+            "code": "print('hi')",
+            "outputs": [],
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "code_interpreter_tool_call"
+
+    def test_image_generation_call(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemImageGenToolCall
+
+        item = OutputItemImageGenToolCall({"type": "image_generation_call", "id": "ig-1", "status": "completed"})
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "image_generation_tool_call"
+
+    def test_shell_call(self) -> None:
+        from azure.ai.agentserver.responses.models import (
+            FunctionShellAction,
+            FunctionShellCallEnvironment,
+            OutputItemFunctionShellCall,
+        )
+
+        item = OutputItemFunctionShellCall({
+            "type": "shell_call",
+            "id": "sc-1",
+            "call_id": "call_sc",
+            "action": FunctionShellAction({"commands": ["ls", "-la"], "timeout_ms": 5000, "max_output_length": 1024}),
+            "status": "completed",
+            "environment": FunctionShellCallEnvironment({"type": "local"}),
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "shell_tool_call"
+        assert msg.contents[0].commands == ["ls", "-la"]
+        assert msg.contents[0].call_id == "call_sc"
+
+    def test_shell_call_output(self) -> None:
+        from azure.ai.agentserver.responses.models import (
+            FunctionShellCallOutputContent,
+            FunctionShellCallOutputExitOutcome,
+            OutputItemFunctionShellCallOutput,
+        )
+
+        item = OutputItemFunctionShellCallOutput({
+            "type": "shell_call_output",
+            "id": "sco-1",
+            "call_id": "call_sc",
+            "status": "completed",
+            "output": [
+                FunctionShellCallOutputContent({
+                    "stdout": "file.txt",
+                    "stderr": "",
+                    "outcome": FunctionShellCallOutputExitOutcome({"exit_code": 0}),
+                })
+            ],
+            "max_output_length": 1024,
+        })
+        msg = _to_message(item)
+        assert msg.role == "tool"
+        assert msg.contents[0].type == "shell_tool_result"
+        assert msg.contents[0].call_id == "call_sc"
+
+    def test_local_shell_call(self) -> None:
+        from azure.ai.agentserver.responses.models import LocalShellExecAction, OutputItemLocalShellToolCall
+
+        item = OutputItemLocalShellToolCall({
+            "type": "local_shell_call",
+            "id": "lsc-1",
+            "call_id": "call_lsc",
+            "action": LocalShellExecAction({"type": "exec", "command": ["echo", "hello"], "env": {}}),
+            "status": "completed",
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "shell_tool_call"
+        assert msg.contents[0].commands == ["echo", "hello"]
+
+    def test_local_shell_call_output(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemLocalShellToolCallOutput
+
+        item = OutputItemLocalShellToolCallOutput({
+            "type": "local_shell_call_output",
+            "id": "lsco-1",
+            "output": "hello\n",
+        })
+        msg = _to_message(item)
+        assert msg.role == "tool"
+        assert msg.contents[0].type == "shell_tool_result"
+
+    def test_file_search_call(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemFileSearchToolCall
+
+        item = OutputItemFileSearchToolCall({
+            "type": "file_search_call",
+            "id": "fs-1",
+            "status": "completed",
+            "queries": ["what is AI"],
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "function_call"
+        assert msg.contents[0].name == "file_search"
+        assert '"what is AI"' in (msg.contents[0].arguments or "")
+
+    def test_web_search_call(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemWebSearchToolCall, WebSearchActionSearch
+
+        item = OutputItemWebSearchToolCall({
+            "type": "web_search_call",
+            "id": "ws-1",
+            "status": "completed",
+            "action": WebSearchActionSearch({"type": "search", "query": "test"}),
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "function_call"
+        assert msg.contents[0].name == "web_search"
+
+    def test_computer_call(self) -> None:
+        from azure.ai.agentserver.responses.models import ComputerAction, OutputItemComputerToolCall
+
+        item = OutputItemComputerToolCall({
+            "type": "computer_call",
+            "id": "cc-1",
+            "call_id": "call_cc",
+            "action": ComputerAction({"type": "click"}),
+            "pending_safety_checks": [],
+            "status": "completed",
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "function_call"
+        assert msg.contents[0].name == "computer_use"
+
+    def test_computer_call_output(self) -> None:
+        from azure.ai.agentserver.responses.models import (
+            ComputerScreenshotImage,
+            OutputItemComputerToolCallOutputResource,
+        )
+
+        item = OutputItemComputerToolCallOutputResource({
+            "type": "computer_call_output",
+            "call_id": "call_cc",
+            "output": ComputerScreenshotImage({
+                "type": "computer_screenshot",
+                "image_url": "data:image/png;base64,abc",
+            }),
+        })
+        msg = _to_message(item)
+        assert msg.role == "tool"
+        assert msg.contents[0].type == "function_result"
+        assert msg.contents[0].call_id == "call_cc"
+
+    def test_custom_tool_call(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemCustomToolCall
+
+        item = OutputItemCustomToolCall({
+            "type": "custom_tool_call",
+            "call_id": "call_ct",
+            "name": "my_tool",
+            "input": '{"key": "value"}',
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "function_call"
+        assert msg.contents[0].name == "my_tool"
+        assert msg.contents[0].arguments == '{"key": "value"}'
+
+    def test_custom_tool_call_output(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemCustomToolCallOutput
+
+        item = OutputItemCustomToolCallOutput({
+            "type": "custom_tool_call_output",
+            "call_id": "call_ct",
+            "output": "result text",
+        })
+        msg = _to_message(item)
+        assert msg.role == "tool"
+        assert msg.contents[0].type == "function_result"
+        assert msg.contents[0].result == "result text"
+
+    def test_apply_patch_call(self) -> None:
+        from azure.ai.agentserver.responses.models import ApplyPatchUpdateFileOperation, OutputItemApplyPatchToolCall
+
+        item = OutputItemApplyPatchToolCall({
+            "type": "apply_patch_call",
+            "id": "ap-1",
+            "call_id": "call_ap",
+            "status": "completed",
+            "operation": ApplyPatchUpdateFileOperation({
+                "type": "update_file",
+                "path": "file.py",
+                "diff": "+ new line",
+            }),
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "function_call"
+        assert msg.contents[0].name == "apply_patch"
+
+    def test_apply_patch_call_output(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItemApplyPatchToolCallOutput
+
+        item = OutputItemApplyPatchToolCallOutput({
+            "type": "apply_patch_call_output",
+            "id": "apo-1",
+            "call_id": "call_ap",
+            "status": "completed",
+            "output": "patch applied",
+        })
+        msg = _to_message(item)
+        assert msg.role == "tool"
+        assert msg.contents[0].type == "function_result"
+        assert msg.contents[0].result == "patch applied"
+
+    def test_oauth_consent_request(self) -> None:
+        from azure.ai.agentserver.responses.models import OAuthConsentRequestOutputItem
+
+        item = OAuthConsentRequestOutputItem({
+            "type": "oauth_consent_request",
+            "id": "oauth-1",
+            "consent_link": "https://example.com/consent",
+            "server_label": "my_server",
+        })
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "oauth_consent_request"
+        assert msg.contents[0].consent_link == "https://example.com/consent"
+
+    def test_structured_outputs_dict(self) -> None:
+        from azure.ai.agentserver.responses.models import StructuredOutputsOutputItem
+
+        item = StructuredOutputsOutputItem({"type": "structured_outputs", "id": "so-1", "output": {"answer": 42}})
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].type == "text"
+        assert json.loads(msg.contents[0].text or "") == {"answer": 42}
+
+    def test_structured_outputs_string(self) -> None:
+        from azure.ai.agentserver.responses.models import StructuredOutputsOutputItem
+
+        item = StructuredOutputsOutputItem({"type": "structured_outputs", "id": "so-2", "output": "plain text"})
+        msg = _to_message(item)
+        assert msg.role == "assistant"
+        assert msg.contents[0].text == "plain text"
+
+    def test_unsupported_type_raises(self) -> None:
+        from azure.ai.agentserver.responses.models import OutputItem
+
+        item = OutputItem({"type": "some_unknown_type"})
+        with pytest.raises(ValueError, match="Unsupported OutputItem type: some_unknown_type"):
+            _to_message(item)
+
+
+# endregion

python/samples/04-hosting/foundry-hosted-agents/README.md

@@ -0,0 +1,56 @@
+# Foundry Hosted Agents Samples
+
+This directory contains samples that demonstrate how to use the Agent Framework to host agents on Foundry with different capabilities and configurations. Each sample includes a README with instructions on how to set up, run, and interact with the agent.
+
+Read more about Foundry Hosted Agents [here](https://learn.microsoft.com/en-us/azure/foundry/agents/concepts/hosted-agents).
+
+## Environment setup
+
+1. Navigate to the sample directory you want to run. For example:
+
+   ```bash
+   python -m venv .venv
+
+   # Windows
+   .venv\Scripts\Activate
+
+   # macOS/Linux
+   source .venv/bin/activate
+   ```
+
+2. Install dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Create a `.env` file with your Foundry configuration following the `env.example` file in the sample.
+
+4. Make sure you are logged in with the Azure CLI:
+
+   ```bash
+   az login
+   ```
+
+## Deploying to a Docker container
+
+Navigate to the sample directory and build the Docker image:
+
+```bash
+docker build -t hosted-agent-sample .
+```
+
+Run the container, passing in the required environment variables:
+
+```bash
+docker run -p 8088:8088 \
+  -e FOUNDRY_PROJECT_ENDPOINT=<your-endpoint> \
+  -e FOUNDRY_MODEL=<your-model> \
+  hosted-agent-sample
+```
+
+The server will be available at `http://localhost:8088`. You can send requests using the same `curl` command shown above.
+
+## Deploying to Foundry
+
+Follow this [guide](https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/deploy-hosted-agent?tabs=bash#configure-your-agent) to deploy your agent to Foundry.

python/samples/04-hosting/foundry-hosted-agents/invocations/01-basic/README.md

@@ -1,13 +0,0 @@
-# Basic example of hosting an agent with the `invocations` API
-
-Run the following command to start the server:
-
-```bash
-python main.py
-```
-
-Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
-
-```bash
-curl -X POST http://localhost:8088/invocations -H "Content-Type: application/json" -d '{"message": "Hi!"}'
-```

python/samples/04-hosting/foundry-hosted-agents/invocations/01_basic/.dockerignore

@@ -0,0 +1,6 @@
+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python

python/samples/04-hosting/foundry-hosted-agents/invocations/01_basic/.env.example

@@ -0,0 +1,2 @@
+FOUNDRY_PROJECT_ENDPOINT= "..."
+MODEL_DEPLOYMENT_NAME="..."

python/samples/04-hosting/foundry-hosted-agents/invocations/01_basic/Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+
+COPY . user_agent/
+WORKDIR /app/user_agent
+
+RUN if [ -f requirements.txt ]; then \
+        pip install -r requirements.txt; \
+    else \
+        echo "No requirements.txt found"; \
+    fi
+
+EXPOSE 8088
+
+CMD ["python", "main.py"]

python/samples/04-hosting/foundry-hosted-agents/invocations/01_basic/README.md

@@ -0,0 +1,44 @@
+# Basic example of hosting an agent with the `invocations` API
+
+## Running the server locally
+
+### Environment setup
+
+Follow the instructions in the [Environment setup](../../README.md#environment-setup) section of the README in the parent directory to set up your environment and install dependencies.
+
+Run the following command to start the server:
+
+```bash
+python main.py
+```
+
+### Interacting with the agent
+
+Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
+
+```bash
+curl -X POST http://localhost:8088/invocations -i -H "Content-Type: application/json" -d '{"message": "Hi"}'
+```
+
+The server will respond with a JSON object containing the response text. The `-i` flag in the `curl` command includes the HTTP response headers in the output, which includes the session ID that can be used for multi-turn conversations. Here is an example of the response:
+
+```
+HTTP/1.1 200 
+content-length: 34
+content-type: application/json
+x-agent-invocation-id: ec04d020-a0e7-441e-ae83-db75635a9f83
+x-agent-session-id: 9370b9d4-cd13-4436-a57f-03b843ac0e17
+x-platform-server: azure-ai-agentserver-core/2.0.0a20260410006 (python/3.12)
+date: Fri, 17 Apr 2026 23:46:44 GMT
+server: hypercorn-h11
+
+{"response":"Hi! How can I help?"}
+```
+
+### Multi-turn conversation
+
+To have a multi-turn conversation with the agent, take the session ID from the response headers of the previous request and include it in URL parameters for the next request. For example:
+
+```bash
+curl -X POST http://localhost:8088/invocations?agent_session_id=9370b9d4-cd13-4436-a57f-03b843ac0e17 -i -H "Content-Type: application/json" -d '{"message": "How are you?"}'
+```

python/samples/04-hosting/foundry-hosted-agents/invocations/01_basic/agent.manifest.yaml

@@ -0,0 +1,23 @@
+name: agent-framework-agent-basic-invocations
+description: >
+  A basic Agent Framework agent hosted by Foundry.
+metadata:
+  tags:
+    - Agent Framework
+    - AI Agent Hosting
+    - Azure AI AgentServer
+    - Invocations Protocol
+    - Streaming
+template:
+  name: agent-framework-agent-basic-invocations
+  kind: hosted
+  protocols:
+    - protocol: invocations
+      version: 1.0.0
+  environment_variables:
+    - name: MODEL_DEPLOYMENT_NAME
+      value: "{{MODEL_DEPLOYMENT_NAME}}"
+resources:
+  - kind: model
+    id: gpt-4.1-mini
+    name: MODEL_DEPLOYMENT_NAME

python/samples/04-hosting/foundry-hosted-agents/invocations/01_basic/agent.yaml

@@ -0,0 +1,9 @@
+# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
+kind: hosted
+name: agent-framework-agent-basic-invocations
+protocols:
+  - protocol: invocations
+    version: 1.0.0
+resources:
+  cpu: '0.25'
+  memory: '0.5Gi'

python/samples/04-hosting/foundry-hosted-agents/invocations/01_basic/main.py

@@ -15,7 +15,7 @@ load_dotenv()
 def main():
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
-        model=os.environ["FOUNDRY_MODEL"],
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
         credential=AzureCliCredential(),
     )
 

python/samples/04-hosting/foundry-hosted-agents/invocations/02_break_glass/.dockerignore

@@ -0,0 +1,6 @@
+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python

python/samples/04-hosting/foundry-hosted-agents/invocations/02_break_glass/.env.example

@@ -0,0 +1,2 @@
+FOUNDRY_PROJECT_ENDPOINT= "..."
+MODEL_DEPLOYMENT_NAME="..."

python/samples/04-hosting/foundry-hosted-agents/invocations/02_break_glass/Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.12-slim
+
+WORKDIR /app
+
+COPY . user_agent/
+WORKDIR /app/user_agent
+
+RUN if [ -f requirements.txt ]; then \
+        pip install -r requirements.txt; \
+    else \
+        echo "No requirements.txt found"; \
+    fi
+
+EXPOSE 8088
+
+CMD ["python", "main.py"]

python/samples/04-hosting/foundry-hosted-agents/invocations/02_break_glass/README.md

@@ -0,0 +1,44 @@
+# Basic example of hosting an agent with the `invocations` API
+
+## Running the server locally
+
+### Environment setup
+
+Follow the instructions in the [Environment setup](../../README.md#environment-setup) section of the README in the parent directory to set up your environment and install dependencies.
+
+Run the following command to start the server:
+
+```bash
+python main.py
+```
+
+### Interacting with the agent
+
+Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
+
+```bash
+curl -X POST http://localhost:8088/invocations -i -H "Content-Type: application/json" -d '{"message": "Hi"}'
+```
+
+The server will respond with a JSON object containing the response text. The `-i` flag in the `curl` command includes the HTTP response headers in the output, which includes the session ID that can be used for multi-turn conversations. Here is an example of the response:
+
+```
+HTTP/1.1 200 
+content-length: 34
+content-type: application/json
+x-agent-invocation-id: ec04d020-a0e7-441e-ae83-db75635a9f83
+x-agent-session-id: 9370b9d4-cd13-4436-a57f-03b843ac0e17
+x-platform-server: azure-ai-agentserver-core/2.0.0a20260410006 (python/3.12)
+date: Fri, 17 Apr 2026 23:46:44 GMT
+server: hypercorn-h11
+
+{"response":"Hi! How can I help?"}
+```
+
+### Multi-turn conversation
+
+To have a multi-turn conversation with the agent, take the session ID from the response headers of the previous request and include it in URL parameters for the next request. For example:
+
+```bash
+curl -X POST http://localhost:8088/invocations?agent_session_id=9370b9d4-cd13-4436-a57f-03b843ac0e17 -i -H "Content-Type: application/json" -d '{"message": "How are you?"}'
+```

python/samples/04-hosting/foundry-hosted-agents/invocations/02_break_glass/agent.manifest.yaml

@@ -0,0 +1,23 @@
+name: agent-framework-agent-basic-invocations
+description: >
+  A basic Agent Framework agent hosted by Foundry.
+metadata:
+  tags:
+    - Agent Framework
+    - AI Agent Hosting
+    - Azure AI AgentServer
+    - Invocations Protocol
+    - Streaming
+template:
+  name: agent-framework-agent-basic-invocations
+  kind: hosted
+  protocols:
+    - protocol: invocations
+      version: 1.0.0
+  environment_variables:
+    - name: MODEL_DEPLOYMENT_NAME
+      value: "{{MODEL_DEPLOYMENT_NAME}}"
+resources:
+  - kind: model
+    id: gpt-4.1-mini
+    name: MODEL_DEPLOYMENT_NAME

python/samples/04-hosting/foundry-hosted-agents/invocations/02_break_glass/agent.yaml

@@ -0,0 +1,9 @@
+# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
+kind: hosted
+name: agent-framework-agent-basic-invocations
+protocols:
+  - protocol: invocations
+    version: 1.0.0
+resources:
+  cpu: '0.25'
+  memory: '0.5Gi'

python/samples/04-hosting/foundry-hosted-agents/invocations/02_break_glass/main.py

@@ -0,0 +1,74 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import os
+from collections.abc import AsyncGenerator
+
+from agent_framework import Agent, AgentSession
+from agent_framework.foundry import FoundryChatClient
+from azure.ai.agentserver.invocations import InvocationAgentServerHost
+from azure.identity import DefaultAzureCredential
+from dotenv import load_dotenv
+from starlette.requests import Request
+from starlette.responses import JSONResponse, Response, StreamingResponse
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+# In-memory session store — keyed by session ID.
+# WARNING: This is lost on restart. Use durable storage in production.
+_sessions: dict[str, AgentSession] = {}
+
+# Create the agent
+client = FoundryChatClient(
+    project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+    model=os.environ["MODEL_DEPLOYMENT_NAME"],
+    credential=DefaultAzureCredential(),
+)
+
+agent = Agent(
+    client=client,
+    instructions="You are a friendly assistant. Keep your answers brief.",
+    # History will be managed by the hosting infrastructure, thus there
+    # is no need to store history by the service. Learn more at:
+    # https://developers.openai.com/api/reference/resources/responses/methods/create
+    default_options={"store": False},
+)
+
+app = InvocationAgentServerHost()
+
+
+@app.invoke_handler
+async def handle_invoke(request: Request):
+    """Handle streaming multi-turn chat with Azure OpenAI via SSE."""
+    data = await request.json()
+    session_id = request.state.session_id
+
+    stream = data.get("stream", False)
+    user_message = data.get("message", None)
+    if user_message is None:
+        error = "Missing 'message' in request"
+        if stream:
+            return StreamingResponse(content=error, status_code=400)
+        return Response(content=error, status_code=400)
+
+    session = _sessions.setdefault(session_id, AgentSession(session_id=session_id))
+
+    if stream:
+
+        async def stream_response() -> AsyncGenerator[str]:
+            async for update in agent.run(user_message, session=session, stream=True):
+                yield update.text
+
+        return StreamingResponse(
+            stream_response(),
+            media_type="text/event-stream",
+            headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
+        )
+
+    response = await agent.run([user_message], session=session, stream=stream)
+    return JSONResponse({"response": response.text})
+
+
+if __name__ == "__main__":
+    app.run()

python/samples/04-hosting/foundry-hosted-agents/invocations/02_break_glass/requirements.txt

@@ -0,0 +1,2 @@
+agent-framework
+azure-ai-agentserver-invocations

python/samples/04-hosting/foundry-hosted-agents/invocations/README.md

@@ -0,0 +1,8 @@
+# Hosting agents with Foundry Hosting and the `invocations` API
+
+This folder contains a list of samples that show how to host agents using the `invocations` API and deploy them to Foundry Hosting.
+
+| Sample | Description |
+| --- | --- |
+| [01_basic](./01_basic) | A basic example of hosting an agent with the `invocations` API and carrying on a multi-turn conversation. |
+| [02_break_glass](./02-break-glass) | An example of hosting an agent with the `invocations` API and a "break glass" scenario where you can create your own `invoke_handler` to handle specific types of invocations. |

python/samples/04-hosting/foundry-hosted-agents/responses/01_basic/README.md

@@ -2,6 +2,18 @@
 
 This agent only contains an instruction (personal). It's the most basic agent with an LLM and no tools.
 
+## Running the server locally
+
+### Environment setup
+
+Follow the instructions in the [Environment setup](../../README.md#environment-setup) section of the README in the parent directory to set up your environment and install dependencies.
+
+Run the following command to start the server:
+
+```bash
+python main.py
+```
+
 ## Interacting with the agent
 
 Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
@@ -10,12 +22,6 @@ Send a POST request to the server with a JSON body containing a "message" field
 curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "Hi"}'
 ```
 
-### Invoke with `azd`
-
-```bash
-azd ai agent invoke --local "Hi"
-```
-
 ## Multi-turn conversation
 
 To have a multi-turn conversation with the agent, include the previous response id in the request body. For example:
@@ -23,11 +29,3 @@ To have a multi-turn conversation with the agent, include the previous response
 ```bash
 curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "How are you?", "previous_response_id": "REPLACE_WITH_PREVIOUS_RESPONSE_ID"}'
 ```
-
-Invoke with `azd`:
-
-```bash
-azd ai agent invoke --local "Hi!" --conversation-id "my_conv"
-
-azd ai agent invoke --local "How are you?" --conversation-id "my_conv"
-```

python/samples/04-hosting/foundry-hosted-agents/responses/02_local_tools/README.md

@@ -4,6 +4,18 @@ This agent is equipped with with a function tool and a local shell tool.
 
 > We recommend deploying this sample on a local container or to Foundry Hosting because the agent has access to a local shell tool, which can run arbitrary commands on the machine.
 
+## Running the server locally
+
+### Environment setup
+
+Follow the instructions in the [Environment setup](../../README.md#environment-setup) section of the README in the parent directory to set up your environment and install dependencies.
+
+Run the following command to start the server:
+
+```bash
+python main.py
+```
+
 ## Interacting with the agent
 
 Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
@@ -13,11 +25,3 @@ curl -X POST http://localhost:8088/responses -H "Content-Type: application/json"
 
 curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "List the files in the current directory."}'
 ```
-
-Invoke with `azd`:
-
-```bash
-azd ai agent invoke --local "What is the weather in Seattle?"
-
-azd ai agent invoke --local "List the files in the current directory."
-```

python/samples/04-hosting/foundry-hosted-agents/responses/03_remote_mcp/.env.example

@@ -1,4 +1,4 @@
 FOUNDRY_PROJECT_ENDPOINT="..."
 MODEL_DEPLOYMENT_NAME="..."
-FOUNDRY_AGENT_TOOLBOX_NAME="..."
+TOOLBOX_NAME="..."
 GITHUB_PAT="..."

python/samples/04-hosting/foundry-hosted-agents/responses/03_remote_mcp/README.md

@@ -4,6 +4,18 @@ This agent is equipped with a GitHub MCP server and a Foundry Toolbox, which are
 
 > Note that there are other ways to interact with Foundry toolboxes. Using it as a MCP is just one of the options.
 
+## Running the server locally
+
+### Environment setup
+
+Follow the instructions in the [Environment setup](../../README.md#environment-setup) section of the README in the parent directory to set up your environment and install dependencies.
+
+Run the following command to start the server:
+
+```bash
+python main.py
+```
+
 ## Interacting with the agent
 
 Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
@@ -11,9 +23,3 @@ Send a POST request to the server with a JSON body containing a "message" field
 ```bash
 curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "List all the repositories I own on GitHub."}'
 ```
-
-Invoke with `azd`:
-
-```bash
-azd ai agent invoke --local "List all the repositories I own on GitHub."
-```

python/samples/04-hosting/foundry-hosted-agents/responses/03_remote_mcp/main.py

@@ -32,7 +32,7 @@ def main():
 
     # Foundry Toolbox as a MCP tool
     project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
-    toolbox_name = os.environ["FOUNDRY_AGENT_TOOLBOX_NAME"]
+    toolbox_name = os.environ["TOOLBOX_NAME"]
     toolbox_endpoint = f"{project_endpoint.rstrip('/')}/toolboxes/{toolbox_name}/mcp?api-version=v1"
     http_client = httpx.AsyncClient(auth=ToolboxAuth(), headers={"Foundry-Features": "Toolboxes=V1Preview"})
     foundry_mcp_tool = MCPStreamableHTTPTool(

python/samples/04-hosting/foundry-hosted-agents/responses/04_workflows/README.md

@@ -2,6 +2,18 @@
 
 This sample demonstrates how to host a workflow using the `responses` API.
 
+## Running the server locally
+
+### Environment setup
+
+Follow the instructions in the [Environment setup](../../README.md#environment-setup) section of the README in the parent directory to set up your environment and install dependencies.
+
+Run the following command to start the server:
+
+```bash
+python main.py
+```
+
 ## Interacting with the agent
 
 Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
@@ -9,9 +21,3 @@ Send a POST request to the server with a JSON body containing a "message" field
 ```bash
 curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "Create a slogan for a new electric SUV that is affordable and fun to drive."}'
 ```
-
-Invoke with `azd`:
-
-```bash
-azd ai agent invoke --local "Create a slogan for a new electric SUV that is affordable and fun to drive."
-```

python/samples/04-hosting/foundry-hosted-agents/responses/04_workflows/main.py

@@ -4,7 +4,6 @@ import os
 
 from agent_framework import Agent, AgentExecutor, WorkflowBuilder
 from agent_framework.foundry import FoundryChatClient
-from agent_framework.orchestrations import GroupChatState
 from agent_framework_foundry_hosting import ResponsesHostServer
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -13,13 +12,6 @@ from dotenv import load_dotenv
 load_dotenv()
 
 
-def round_robin_selector(state: GroupChatState) -> str:
-    """A round-robin selector function that picks the next speaker based on the current round index."""
-
-    participant_names = list(state.participants.keys())
-    return participant_names[state.current_round % len(participant_names)]
-
-
 def main():
     client = FoundryChatClient(
         project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],

python/samples/04-hosting/foundry-hosted-agents/responses/README.md

@@ -8,58 +8,4 @@ This folder contains a list of samples that show how to host agents using the `r
 | [02_local_tools](./02_local_tools) | An example of hosting an agent with the `responses` API and local tools including a function tool and a local shell tool. |
 | [03_remote_mcp](./03_remote_mcp) | An example of hosting an agent with the `responses` API and remote MCPs, including a GitHub MCP server and a Foundry Toolboox. |
 | [04_workflows](./04_workflows) | An example of hosting a workflow with the `responses` API. |
-
-## Running the server locally
-
-Navigate to the sample directory and run the following command to start the server:
-
-```bash
-python main.py
-```
-
-## Interacting with the agent
-
-There two ways to interact with the agent: sending HTTP requests to the server or using the `azd` CLI:
-
-### Invoke with `azd`
-
-```bash
-azd ai agent invoke --local "Hi"
-```
-
-### Sending HTTP requests
-
-Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
-
-```bash
-curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "Hi"}'
-```
-
-> See the individual samples for more examples of interacting with the agent.
-
-## Deploying to a Docker container
-
-Navigate to the sample directory and build the Docker image:
-
-```bash
-docker build -t hosted-agent-sample .
-```
-
-Run the container, passing in the required environment variables:
-
-```bash
-docker run -p 8088:8088 \
-  -e FOUNDRY_PROJECT_ENDPOINT=<your-endpoint> \
-  -e FOUNDRY_MODEL=<your-model> \
-  hosted-agent-sample
-```
-
-The server will be available at `http://localhost:8088`. You can send requests using the same `curl` command shown above.
-
-## Deploying to Foundry
-
-TODO
-
-## Using the deployed agent in Agent Framework
-
-After deploying the agent, you can also try to use the agent in Agent Framework. Refer to the [using_deployed_agent.py](./using_deployed_agent.py) sample for an example of how to do this.
+| [using_deployed_agent.py](./using_deployed_agent.py) | An example of how to use the deployed agent in Agent Framework. |