mirror of
https://github.com/microsoft/agent-framework.git
synced 2026-06-16 21:04:09 +08:00
Improve samples
This commit is contained in:
@@ -1,13 +0,0 @@
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# Basic example of hosting an agent with the `invocations` API
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Run the following command to start the server:
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```bash
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python main.py
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```
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Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
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```bash
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curl -X POST http://localhost:8088/invocations -H "Content-Type: application/json" -d '{"message": "Hi!"}'
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```
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@@ -0,0 +1,6 @@
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.venv
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__pycache__
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*.pyc
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*.pyo
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*.pyd
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.Python
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@@ -0,0 +1,2 @@
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FOUNDRY_PROJECT_ENDPOINT= "..."
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MODEL_DEPLOYMENT_NAME="..."
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@@ -0,0 +1,16 @@
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FROM python:3.12-slim
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WORKDIR /app
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COPY . user_agent/
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WORKDIR /app/user_agent
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RUN if [ -f requirements.txt ]; then \
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pip install -r requirements.txt; \
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else \
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echo "No requirements.txt found"; \
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fi
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EXPOSE 8088
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CMD ["python", "main.py"]
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@@ -0,0 +1,44 @@
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# Basic example of hosting an agent with the `invocations` API
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## Running the server locally
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### Environment setup
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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.
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Run the following command to start the server:
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```bash
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python main.py
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```
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### Interacting with the agent
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Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
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```bash
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curl -X POST http://localhost:8088/invocations -i -H "Content-Type: application/json" -d '{"message": "Hi"}'
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```
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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:
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```
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HTTP/1.1 200
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content-length: 34
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content-type: application/json
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x-agent-invocation-id: ec04d020-a0e7-441e-ae83-db75635a9f83
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x-agent-session-id: 9370b9d4-cd13-4436-a57f-03b843ac0e17
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x-platform-server: azure-ai-agentserver-core/2.0.0a20260410006 (python/3.12)
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date: Fri, 17 Apr 2026 23:46:44 GMT
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server: hypercorn-h11
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{"response":"Hi! How can I help?"}
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```
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### Multi-turn conversation
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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:
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```bash
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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?"}'
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```
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+23
@@ -0,0 +1,23 @@
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name: agent-framework-agent-basic-invocations
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description: >
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A basic Agent Framework agent hosted by Foundry.
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metadata:
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tags:
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- Agent Framework
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- AI Agent Hosting
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- Azure AI AgentServer
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- Invocations Protocol
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- Streaming
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template:
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name: agent-framework-agent-basic-invocations
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kind: hosted
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protocols:
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- protocol: invocations
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version: 1.0.0
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environment_variables:
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- name: MODEL_DEPLOYMENT_NAME
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value: "{{MODEL_DEPLOYMENT_NAME}}"
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resources:
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- kind: model
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id: gpt-4.1-mini
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name: MODEL_DEPLOYMENT_NAME
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@@ -0,0 +1,9 @@
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# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
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kind: hosted
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name: agent-framework-agent-basic-invocations
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protocols:
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- protocol: invocations
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version: 1.0.0
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resources:
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cpu: '0.25'
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memory: '0.5Gi'
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+1
-1
@@ -15,7 +15,7 @@ load_dotenv()
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def main():
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["FOUNDRY_MODEL"],
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model=os.environ["MODEL_DEPLOYMENT_NAME"],
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credential=AzureCliCredential(),
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)
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+6
@@ -0,0 +1,6 @@
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.venv
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__pycache__
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*.pyc
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*.pyo
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*.pyd
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.Python
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+2
@@ -0,0 +1,2 @@
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FOUNDRY_PROJECT_ENDPOINT= "..."
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MODEL_DEPLOYMENT_NAME="..."
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@@ -0,0 +1,16 @@
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FROM python:3.12-slim
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WORKDIR /app
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COPY . user_agent/
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WORKDIR /app/user_agent
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RUN if [ -f requirements.txt ]; then \
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pip install -r requirements.txt; \
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else \
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echo "No requirements.txt found"; \
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fi
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EXPOSE 8088
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CMD ["python", "main.py"]
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@@ -0,0 +1,44 @@
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# Basic example of hosting an agent with the `invocations` API
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## Running the server locally
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### Environment setup
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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.
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Run the following command to start the server:
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```bash
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python main.py
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```
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### Interacting with the agent
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Send a POST request to the server with a JSON body containing a "message" field to interact with the agent. For example:
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```bash
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curl -X POST http://localhost:8088/invocations -i -H "Content-Type: application/json" -d '{"message": "Hi"}'
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```
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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:
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```
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HTTP/1.1 200
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content-length: 34
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content-type: application/json
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x-agent-invocation-id: ec04d020-a0e7-441e-ae83-db75635a9f83
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x-agent-session-id: 9370b9d4-cd13-4436-a57f-03b843ac0e17
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x-platform-server: azure-ai-agentserver-core/2.0.0a20260410006 (python/3.12)
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date: Fri, 17 Apr 2026 23:46:44 GMT
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server: hypercorn-h11
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{"response":"Hi! How can I help?"}
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```
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### Multi-turn conversation
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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:
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```bash
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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?"}'
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```
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+23
@@ -0,0 +1,23 @@
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name: agent-framework-agent-basic-invocations
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description: >
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A basic Agent Framework agent hosted by Foundry.
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metadata:
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tags:
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- Agent Framework
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- AI Agent Hosting
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- Azure AI AgentServer
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- Invocations Protocol
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- Streaming
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template:
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name: agent-framework-agent-basic-invocations
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kind: hosted
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protocols:
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- protocol: invocations
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version: 1.0.0
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environment_variables:
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- name: MODEL_DEPLOYMENT_NAME
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value: "{{MODEL_DEPLOYMENT_NAME}}"
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resources:
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- kind: model
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id: gpt-4.1-mini
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name: MODEL_DEPLOYMENT_NAME
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@@ -0,0 +1,9 @@
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# yaml-language-server: $schema=https://raw.githubusercontent.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
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kind: hosted
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name: agent-framework-agent-basic-invocations
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protocols:
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- protocol: invocations
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version: 1.0.0
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resources:
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cpu: '0.25'
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memory: '0.5Gi'
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@@ -0,0 +1,74 @@
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# Copyright (c) Microsoft. All rights reserved.
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import os
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from collections.abc import AsyncGenerator
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from agent_framework import Agent, AgentSession
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from agent_framework.foundry import FoundryChatClient
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from azure.ai.agentserver.invocations import InvocationAgentServerHost
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from azure.identity import DefaultAzureCredential
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from dotenv import load_dotenv
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from starlette.requests import Request
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from starlette.responses import JSONResponse, Response, StreamingResponse
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# Load environment variables from .env file
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load_dotenv()
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# In-memory session store — keyed by session ID.
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# WARNING: This is lost on restart. Use durable storage in production.
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_sessions: dict[str, AgentSession] = {}
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# Create the agent
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client = FoundryChatClient(
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project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
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model=os.environ["MODEL_DEPLOYMENT_NAME"],
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credential=DefaultAzureCredential(),
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)
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agent = Agent(
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client=client,
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instructions="You are a friendly assistant. Keep your answers brief.",
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# History will be managed by the hosting infrastructure, thus there
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# is no need to store history by the service. Learn more at:
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# https://developers.openai.com/api/reference/resources/responses/methods/create
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default_options={"store": False},
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)
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app = InvocationAgentServerHost()
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@app.invoke_handler
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async def handle_invoke(request: Request):
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"""Handle streaming multi-turn chat with Azure OpenAI via SSE."""
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data = await request.json()
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session_id = request.state.session_id
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stream = data.get("stream", False)
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user_message = data.get("message", None)
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if user_message is None:
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error = "Missing 'message' in request"
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if stream:
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return StreamingResponse(content=error, status_code=400)
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return Response(content=error, status_code=400)
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session = _sessions.setdefault(session_id, AgentSession(session_id=session_id))
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if stream:
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async def stream_response() -> AsyncGenerator[str]:
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async for update in agent.run(user_message, session=session, stream=True):
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yield update.text
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return StreamingResponse(
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stream_response(),
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media_type="text/event-stream",
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headers={"Cache-Control": "no-cache", "Connection": "keep-alive"},
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)
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response = await agent.run([user_message], session=session, stream=stream)
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return JSONResponse({"response": response.text})
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if __name__ == "__main__":
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app.run()
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+2
@@ -0,0 +1,2 @@
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agent-framework
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azure-ai-agentserver-invocations
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@@ -0,0 +1,8 @@
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# Hosting agents with Foundry Hosting and the `invocations` API
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This folder contains a list of samples that show how to host agents using the `invocations` API and deploy them to Foundry Hosting.
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| Sample | Description |
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| --- | --- |
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| [01_basic](./01_basic) | A basic example of hosting an agent with the `invocations` API and carrying on a multi-turn conversation. |
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| [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. |
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