test

python/samples/demos/hosted_agents/agent_with_hosted_mcp/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/demos/hosted_agents/agent_with_hosted_mcp/agent.yaml

@@ -0,0 +1,30 @@
+# Unique identifier/name for this agent
+name: agent-with-hosted-mcp
+# Brief description of what this agent does
+description: >
+  An AI agent that uses Azure OpenAI with a Hosted Model Context Protocol (MCP) server.
+  The agent answers questions by searching Microsoft Learn documentation using MCP tools.
+metadata:
+  # Categorization tags for organizing and discovering agents
+  authors:
+    - Microsoft Agent Framework Team
+  tags:
+    - Azure AI AgentServer
+    - Microsoft Agent Framework
+    - Model Context Protocol
+    - MCP
+template:
+  name: agent-with-hosted-mcp
+  # The type of agent - "hosted" for HOBO, "container" for COBO
+  kind: hosted
+  protocols:
+    - protocol: responses
+  environment_variables:
+    - name: AZURE_OPENAI_ENDPOINT
+      value: ${AZURE_OPENAI_ENDPOINT}
+    - name: AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
+      value: "{{chat}}"
+resources:
+  - kind: model
+    id: gpt-4o-mini
+    name: chat

python/samples/demos/hosted_agents/agent_with_hosted_mcp/main.py

@@ -0,0 +1,25 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from agent_framework import HostedMCPTool
+from agent_framework.azure import AzureOpenAIChatClient
+from azure.ai.agentserver.agentframework import from_agent_framework  # pyright: ignore[reportUnknownVariableType]
+from azure.identity import DefaultAzureCredential
+
+
+def main():
+    # Create an Agent using the Azure OpenAI Chat Client with a MCP Tool that connects to Microsoft Learn MCP
+    agent = AzureOpenAIChatClient(credential=DefaultAzureCredential()).as_agent(
+        name="DocsAgent",
+        instructions="You are a helpful assistant that can help with microsoft documentation questions.",
+        tools=HostedMCPTool(
+            name="Microsoft Learn MCP",
+            url="https://learn.microsoft.com/api/mcp",
+        ),
+    )
+
+    # Run the agent as a hosted agent
+    from_agent_framework(agent).run()
+
+
+if __name__ == "__main__":
+    main()

python/samples/demos/hosted_agents/agent_with_hosted_mcp/requirements.txt

@@ -0,0 +1,2 @@
+azure-ai-agentserver-agentframework==1.0.0b3
+agent-framework

python/samples/demos/hosted_agents/agent_with_text_search_rag/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/demos/hosted_agents/agent_with_text_search_rag/agent.yaml

@@ -0,0 +1,33 @@
+# Unique identifier/name for this agent
+name: agent-with-text-search-rag
+# Brief description of what this agent does
+description: >
+  An AI agent that uses a ContextProvider for retrieval augmented generation (RAG) capabilities.
+  The agent runs searches against an external knowledge base before each model invocation and
+  injects the results into the model context. It can answer questions about Contoso Outdoors
+  policies and products, including return policies, refunds, shipping options, and product care
+  instructions such as tent maintenance.
+metadata:
+  # Categorization tags for organizing and discovering agents
+  authors:
+    - Microsoft Agent Framework Team
+  tags:
+    - Azure AI AgentServer
+    - Microsoft Agent Framework
+    - Retrieval-Augmented Generation
+    - RAG
+template:
+  name: agent-with-text-search-rag
+  # The type of agent - "hosted" for HOBO, "container" for COBO
+  kind: hosted
+  protocols:
+    - protocol: responses
+  environment_variables:
+    - name: AZURE_OPENAI_ENDPOINT
+      value: ${AZURE_OPENAI_ENDPOINT}
+    - name: AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
+      value: "{{chat}}"
+resources:
+  - kind: model
+    id: gpt-4o-mini
+    name: chat

python/samples/demos/hosted_agents/agent_with_text_search_rag/main.py

@@ -0,0 +1,110 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import json
+import sys
+from collections.abc import MutableSequence
+from dataclasses import dataclass
+from typing import Any
+
+from agent_framework import ChatMessage, Context, ContextProvider, Role
+from agent_framework.azure import AzureOpenAIChatClient
+from azure.ai.agentserver.agentframework import from_agent_framework  # pyright: ignore[reportUnknownVariableType]
+from azure.identity import DefaultAzureCredential
+
+if sys.version_info >= (3, 12):
+    from typing import override
+else:
+    from typing_extensions import override
+
+
+@dataclass
+class TextSearchResult:
+    source_name: str
+    source_link: str
+    text: str
+
+
+class TextSearchContextProvider(ContextProvider):
+    """A simple context provider that simulates text search results based on keywords in the user's message."""
+
+    def _get_most_recent_message(self, messages: ChatMessage | MutableSequence[ChatMessage]) -> ChatMessage:
+        """Helper method to extract the most recent message from the input."""
+        if isinstance(messages, ChatMessage):
+            return messages
+        if messages:
+            return messages[-1]
+        raise ValueError("No messages provided")
+
+    @override
+    async def invoking(self, messages: ChatMessage | MutableSequence[ChatMessage], **kwargs: Any) -> Context:
+        message = self._get_most_recent_message(messages)
+        query = message.text.lower()
+
+        results: list[TextSearchResult] = []
+        if "return" in query and "refund" in query:
+            results.append(
+                TextSearchResult(
+                    source_name="Contoso Outdoors Return Policy",
+                    source_link="https://contoso.com/policies/returns",
+                    text=(
+                        "Customers may return any item within 30 days of delivery. "
+                        "Items should be unused and include original packaging. "
+                        "Refunds are issued to the original payment method within 5 business days of inspection."
+                    ),
+                )
+            )
+
+        if "shipping" in query:
+            results.append(
+                TextSearchResult(
+                    source_name="Contoso Outdoors Shipping Guide",
+                    source_link="https://contoso.com/help/shipping",
+                    text=(
+                        "Standard shipping is free on orders over $50 and typically arrives in 3-5 business days "
+                        "within the continental United States. Expedited options are available at checkout."
+                    ),
+                )
+            )
+
+        if "tent" in query or "fabric" in query:
+            results.append(
+                TextSearchResult(
+                    source_name="TrailRunner Tent Care Instructions",
+                    source_link="https://contoso.com/manuals/trailrunner-tent",
+                    text=(
+                        "Clean the tent fabric with lukewarm water and a non-detergent soap. "
+                        "Allow it to air dry completely before storage and avoid prolonged UV "
+                        "exposure to extend the lifespan of the waterproof coating."
+                    ),
+                )
+            )
+
+        if not results:
+            return Context()
+
+        return Context(
+            messages=[
+                ChatMessage(
+                    role=Role.USER, text="\n\n".join(json.dumps(result.__dict__, indent=2) for result in results)
+                )
+            ]
+        )
+
+
+def main():
+    # Create an Agent using the Azure OpenAI Chat Client
+    agent = AzureOpenAIChatClient(credential=DefaultAzureCredential()).as_agent(
+        name="SupportSpecialist",
+        instructions=(
+            "You are a helpful support specialist for Contoso Outdoors. "
+            "Answer questions using the provided context and cite the source document when available."
+        ),
+        context_provider=TextSearchContextProvider(),
+    )
+
+    # Run the agent as a hosted agent
+    from_agent_framework(agent).run()
+
+
+if __name__ == "__main__":
+    main()

python/samples/demos/hosted_agents/agent_with_text_search_rag/requirements.txt

@@ -0,0 +1,2 @@
+azure-ai-agentserver-agentframework==1.0.0b3
+agent-framework

python/samples/demos/hosted_agents/agents_in_workflow/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/demos/hosted_agents/agents_in_workflow/agent.yaml

@@ -0,0 +1,28 @@
+# Unique identifier/name for this agent
+name: agents-in-workflow
+# Brief description of what this agent does
+description: >
+  A workflow agent that responds to product launch strategy inquiries by concurrently leveraging insights from three specialized agents.
+metadata:
+  # Categorization tags for organizing and discovering agents
+  authors:
+    - Microsoft Agent Framework Team
+  tags:
+    - Azure AI AgentServer
+    - Microsoft Agent Framework
+    - Workflows
+template:
+  name: agents-in-workflow
+  # The type of agent - "hosted" for HOBO, "container" for COBO
+  kind: hosted
+  protocols:
+    - protocol: responses
+  environment_variables:
+    - name: AZURE_OPENAI_ENDPOINT
+      value: ${AZURE_OPENAI_ENDPOINT}
+    - name: AZURE_OPENAI_CHAT_DEPLOYMENT_NAME
+      value: "{{chat}}"
+resources:
+  - kind: model
+    id: gpt-4o-mini
+    name: chat

python/samples/demos/hosted_agents/agents_in_workflow/main.py

@@ -0,0 +1,44 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+from agent_framework import ConcurrentBuilder
+from agent_framework.azure import AzureOpenAIChatClient
+from azure.ai.agentserver.agentframework import from_agent_framework
+from azure.identity import DefaultAzureCredential  # pyright: ignore[reportUnknownVariableType]
+
+
+def main():
+    # Create agents
+    researcher = AzureOpenAIChatClient(credential=DefaultAzureCredential()).as_agent(
+        instructions=(
+            "You're an expert market and product researcher. "
+            "Given a prompt, provide concise, factual insights, opportunities, and risks."
+        ),
+        name="researcher",
+    )
+    marketer = AzureOpenAIChatClient(credential=DefaultAzureCredential()).as_agent(
+        instructions=(
+            "You're a creative marketing strategist. "
+            "Craft compelling value propositions and target messaging aligned to the prompt."
+        ),
+        name="marketer",
+    )
+    legal = AzureOpenAIChatClient(credential=DefaultAzureCredential()).as_agent(
+        instructions=(
+            "You're a cautious legal/compliance reviewer. "
+            "Highlight constraints, disclaimers, and policy concerns based on the prompt."
+        ),
+        name="legal",
+    )
+
+    # Build a concurrent workflow
+    workflow = ConcurrentBuilder().participants([researcher, marketer, legal]).build()
+
+    # Convert the workflow to an agent
+    workflow_agent = workflow.as_agent()
+
+    # Run the agent as a hosted agent
+    from_agent_framework(workflow_agent).run()
+
+
+if __name__ == "__main__":
+    main()

python/samples/demos/hosted_agents/agents_in_workflow/requirements.txt

@@ -0,0 +1,2 @@
+azure-ai-agentserver-agentframework==1.0.0b3
+agent-framework