Move samples (#5281)

2026-06-16 21:04:09 +08:00 · 2026-04-15 11:33:15 -07:00
parent 383a2afca2
commit 9e3983e547
64 changed files with 0 additions and 1243 deletions
@@ -0,0 +1,13 @@
+# 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!"}'
+```
@@ -0,0 +1,36 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import os
+
+from agent_framework import Agent
+from agent_framework.foundry import FoundryChatClient
+from agent_framework_foundry_hosting import InvocationsHostServer
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+def main():
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["FOUNDRY_MODEL"],
+        credential=AzureCliCredential(),
+    )
+
+    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},
+    )
+
+    server = InvocationsHostServer(agent)
+    server.run()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,2 @@
+agent-framework
+agent-framework-foundry-hosting
@@ -0,0 +1,6 @@
+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
@@ -0,0 +1,2 @@
+FOUNDRY_PROJECT_ENDPOINT="..."
+MODEL_DEPLOYMENT_NAME="..."
@@ -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"]
@@ -0,0 +1,33 @@
+# Basic example of hosting an agent with the `responses` API
+
+This agent only contains an instruction (personal). It's the most basic agent with an LLM and no tools.
+
+## 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/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:
+
+```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"
+```
@@ -0,0 +1,23 @@
+name: agent-framework-agent-basic
+description: >
+  A basic Agent Framework agent hosted by Foundry.
+metadata:
+  tags:
+    - Agent Framework
+    - AI Agent Hosting
+    - Azure AI AgentServer
+    - Responses Protocol
+    - Streaming
+template:
+  name: agent-framework-agent-basic
+  kind: hosted
+  protocols:
+    - protocol: responses
+      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
@@ -0,0 +1,8 @@
+kind: hosted
+name: agent-framework-agent-basic
+protocols:
+  - protocol: responses
+    version: v0.1.0
+resources:
+  cpu: "0.25"
+  memory: 0.5Gi
@@ -0,0 +1,37 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import os
+
+from agent_framework import Agent
+from agent_framework.foundry import FoundryChatClient
+from agent_framework_foundry_hosting import ResponsesHostServer
+from azure.ai.agentserver.responses import InMemoryResponseProvider
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+def main():
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
+
+    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},
+    )
+
+    server = ResponsesHostServer(agent, store=InMemoryResponseProvider())
+    server.run()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,2 @@
+agent-framework
+agent-framework-foundry-hosting
@@ -0,0 +1,6 @@
+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
@@ -0,0 +1,2 @@
+FOUNDRY_PROJECT_ENDPOINT="..."
+MODEL_DEPLOYMENT_NAME="..."
@@ -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"]
@@ -0,0 +1,23 @@
+# Basic example of hosting an agent with the `responses` API and local tools
+
+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.
+
+## 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/responses -H "Content-Type: application/json" -d '{"input": "What is the weather in Seattle?"}'
+
+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."
+```
@@ -0,0 +1,23 @@
+name: agent-framework-agent-with-local-tools
+description: >
+  An Agent Framework agent with local tools hosted by Foundry.
+metadata:
+  tags:
+    - Agent Framework
+    - AI Agent Hosting
+    - Azure AI AgentServer
+    - Responses Protocol
+    - Streaming
+template:
+  name: agent-framework-agent-with-local-tools
+  kind: hosted
+  protocols:
+    - protocol: responses
+      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
@@ -0,0 +1,8 @@
+kind: hosted
+name: agent-framework-agent-with-local-tools
+protocols:
+  - protocol: responses
+    version: v0.1.0
+resources:
+  cpu: "0.25"
+  memory: 0.5Gi
@@ -0,0 +1,75 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import os
+import subprocess
+from random import randint
+
+from agent_framework import Agent, tool
+from agent_framework.foundry import FoundryChatClient
+from agent_framework_foundry_hosting import ResponsesHostServer
+from azure.ai.agentserver.responses import InMemoryResponseProvider
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+from pydantic import Field
+from typing_extensions import Annotated
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+@tool(approval_mode="never_require")
+def get_weather(
+    location: Annotated[str, Field(description="The location to get the weather for.")],
+) -> str:
+    """Get the weather for a given location."""
+    conditions = ["sunny", "cloudy", "rainy", "stormy"]
+    return f"The weather in {location} is {conditions[randint(0, 3)]} with a high of {randint(10, 30)}°C."
+
+
+@tool(approval_mode="always_require")
+def run_bash(command: str) -> str:
+    """Execute a shell command locally and return stdout, stderr, and exit code."""
+    try:
+        result = subprocess.run(
+            command,
+            shell=True,
+            capture_output=True,
+            text=True,
+            timeout=30,
+        )
+        parts: list[str] = []
+        if result.stdout:
+            parts.append(result.stdout)
+        if result.stderr:
+            parts.append(f"stderr: {result.stderr}")
+        parts.append(f"exit_code: {result.returncode}")
+        return "\n".join(parts)
+    except subprocess.TimeoutExpired:
+        return "Command timed out after 30 seconds"
+    except Exception as e:
+        return f"Error executing command: {e}"
+
+
+def main():
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
+
+    agent = Agent(
+        client=client,
+        instructions="You are a friendly assistant. Keep your answers brief.",
+        tools=[get_weather, run_bash],
+        # 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},
+    )
+
+    server = ResponsesHostServer(agent, store=InMemoryResponseProvider())
+    server.run()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,2 @@
+agent-framework
+agent-framework-foundry-hosting
@@ -0,0 +1,6 @@
+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
@@ -0,0 +1,4 @@
+FOUNDRY_PROJECT_ENDPOINT="..."
+MODEL_DEPLOYMENT_NAME="..."
+FOUNDRY_AGENT_TOOLBOX_NAME="..."
+GITHUB_PAT="..."
@@ -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"]
@@ -0,0 +1,19 @@
+# Basic example of hosting an agent with the `responses` API and a remote MCP
+
+This agent is equipped with a GitHub MCP server and a Foundry Toolbox, which are both remote MCPs.
+
+> Note that there are other ways to interact with Foundry toolboxes. Using it as a MCP is just one of the options.
+
+## 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/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."
+```
@@ -0,0 +1,27 @@
+name: agent-framework-agent-with-remote-mcp-tools
+description: >
+  An Agent Framework agent with remote MCP tools hosted by Foundry.
+metadata:
+  tags:
+    - Agent Framework
+    - AI Agent Hosting
+    - Azure AI AgentServer
+    - Responses Protocol
+    - Streaming
+template:
+  name: agent-framework-agent-with-remote-mcp-tools
+  kind: hosted
+  protocols:
+    - protocol: responses
+      version: 1.0.0
+  environment_variables:
+    - name: MODEL_DEPLOYMENT_NAME
+      value: "{{MODEL_DEPLOYMENT_NAME}}"
+    - name: GITHUB_PAT
+      value: ${GITHUB_PAT}
+    - name: FOUNDRY_AGENT_TOOLBOX_NAME
+      value: ${FOUNDRY_AGENT_TOOLBOX_NAME}
+resources:
+  - kind: model
+    id: gpt-4.1-mini
+    name: MODEL_DEPLOYMENT_NAME
@@ -0,0 +1,8 @@
+kind: hosted
+name: agent-framework-agent-with-remote-mcp-tools
+protocols:
+  - protocol: responses
+    version: v0.1.0
+resources:
+  cpu: "0.25"
+  memory: 0.5Gi
@@ -0,0 +1,77 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import os
+
+import httpx
+from agent_framework import Agent, MCPStreamableHTTPTool
+from agent_framework.foundry import FoundryChatClient
+from agent_framework_foundry_hosting import ResponsesHostServer
+from azure.ai.agentserver.responses import InMemoryResponseProvider
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+class ToolboxAuth(httpx.Auth):
+    """httpx Auth that injects a fresh bearer token on every request."""
+
+    def auth_flow(self, request: httpx.Request):
+        credential = AzureCliCredential()
+        token = credential.get_token("https://ai.azure.com/.default").token
+        request.headers["Authorization"] = f"Bearer {token}"
+        yield request
+
+
+def main():
+    client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
+
+    # Foundry Toolbox as a MCP tool
+    project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+    toolbox_name = os.environ["FOUNDRY_AGENT_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(
+        name="toolbox",
+        url=toolbox_endpoint,
+        http_client=http_client,
+        load_prompts=False,
+    )
+
+    # GitHub MCP server
+    github_pat = os.environ["GITHUB_PAT"]
+    if not github_pat:
+        raise ValueError(
+            "GITHUB_PAT environment variable must be set. Create a token at https://github.com/settings/tokens"
+        )
+
+    github_mcp_tool = client.get_mcp_tool(
+        name="GitHub",
+        url="https://api.githubcopilot.com/mcp/",
+        headers={
+            "Authorization": f"Bearer {github_pat}",
+        },
+        approval_mode="never_require",
+    )
+
+    agent = Agent(
+        client=client,
+        instructions="You are a friendly assistant. Keep your answers brief.",
+        tools=[foundry_mcp_tool, github_mcp_tool],
+        # 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},
+    )
+
+    server = ResponsesHostServer(agent, store=InMemoryResponseProvider())
+    server.run()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,2 @@
+agent-framework
+agent-framework-foundry-hosting
@@ -0,0 +1,6 @@
+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
@@ -0,0 +1,2 @@
+FOUNDRY_PROJECT_ENDPOINT="..."
+MODEL_DEPLOYMENT_NAME="..."
@@ -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"]
@@ -0,0 +1,17 @@
+# Basic example of hosting an agent with the `responses` API and a workflow
+
+This sample demonstrates how to host a workflow using the `responses` API.
+
+## 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/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 "List all the repositories I own on GitHub."
+```
@@ -0,0 +1,23 @@
+name: agent-framework-workflows
+description: >
+  An Agent Framework workflow hosted by Foundry.
+metadata:
+  tags:
+    - Agent Framework
+    - AI Agent Hosting
+    - Azure AI AgentServer
+    - Responses Protocol
+    - Streaming
+template:
+  name: agent-framework-workflows
+  kind: hosted
+  protocols:
+    - protocol: responses
+      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
@@ -0,0 +1,8 @@
+kind: hosted
+name: agent-framework-workflows
+protocols:
+  - protocol: responses
+    version: v0.1.0
+resources:
+  cpu: "0.25"
+  memory: 0.5Gi
@@ -0,0 +1,74 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import os
+
+from agent_framework import Agent
+from agent_framework.foundry import FoundryChatClient
+from agent_framework.orchestrations import GroupChatBuilder, GroupChatState
+from agent_framework_foundry_hosting import ResponsesHostServer
+from azure.ai.agentserver.responses import InMemoryResponseProvider
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+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"],
+        model=os.environ["MODEL_DEPLOYMENT_NAME"],
+        credential=AzureCliCredential(),
+    )
+
+    writer_agent = Agent(
+        client=client,
+        instructions=(
+            "You are an excellent content writer. You create new content and edit contents based on the feedback."
+        ),
+        name="writer",
+        # 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},
+    )
+
+    reviewer_agent = Agent(
+        client=client,
+        instructions=(
+            "You are an excellent content reviewer."
+            "Provide actionable feedback to the writer about the provided content."
+            "Provide the feedback in the most concise manner possible."
+        ),
+        name="reviewer",
+        # 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},
+    )
+
+    workflow_agent = (
+        GroupChatBuilder(
+            participants=[writer_agent, reviewer_agent],
+            # Set a hard termination condition to stop after 4 messages:
+            # User message + writer message + reviewer message + writer message
+            termination_condition=lambda conversation: len(conversation) >= 4,
+            selection_func=round_robin_selector,
+        )
+        .build()
+        .as_agent()
+    )
+
+    server = ResponsesHostServer(workflow_agent, store=InMemoryResponseProvider())
+    server.run()
+
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,2 @@
+agent-framework
+agent-framework-foundry-hosting
@@ -0,0 +1,65 @@
+# Hosting agents with Foundry Hosting and the `responses` API
+
+This folder contains a list of samples that show how to host agents using the `responses` API and deploy them to Foundry Hosting.
+
+| Sample | Description |
+| --- | --- |
+| [01_basic](./01_basic) | A basic example of hosting an agent with the `responses` API and carrying on a multi-turn conversation. |
+| [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.
@@ -0,0 +1,50 @@
+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+
+from agent_framework import Agent, AgentResponse, AgentResponseUpdate, ResponseStream
+from agent_framework.openai import OpenAIChatClient
+from typing_extensions import Any
+
+"""
+This script demonstrates how to talk to a deployed agent using the OpenAIChatClient.
+
+Depending on where you have deployed your agent (local or Foundry Hosting), you may
+need to change the base_url when initializing the OpenAIChatClient.
+"""
+
+
+async def print_streaming_response(streaming_response: ResponseStream[AgentResponseUpdate, AgentResponse[Any]]) -> None:
+    async for chunk in streaming_response:
+        if chunk.text:
+            print(chunk.text, end="", flush=True)
+
+
+async def main() -> None:
+    agent = Agent(client=OpenAIChatClient(base_url="http://localhost:8088"))
+    session = agent.create_session()
+
+    # First turn
+    query = "Hi!"
+    print(f"User: {query}")
+    print("Agent: ", end="", flush=True)
+    streaming_response = agent.run(query, session=session, stream=True)
+    await print_streaming_response(streaming_response)
+
+    # Second turn
+    query = "You name is Javis. What can you do?"
+    print(f"\nUser: {query}")
+    print("Agent: ", end="", flush=True)
+    streaming_response = agent.run(query, session=session, stream=True)
+    await print_streaming_response(streaming_response)
+
+    # Third turn
+    query = "What is your name?"
+    print(f"\nUser: {query}")
+    print("Agent: ", end="", flush=True)
+    streaming_response = agent.run(query, session=session, stream=True)
+    await print_streaming_response(streaming_response)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())