Python OpenAI Agent Sample

Docs/Reference/Samples/Python OpenAI Agent Sample

Python OpenAI Agent Sample

Docs Reference Samples Python OpenAI Agent Sample

Aspire sample

TypeScript AppHost

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AppHost

The entry point that composes every resource and dependency in this sample's distributed application.

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apphost.mts

import { createBuilder } from "./.aspire/modules/aspire.mjs";

const builder = await createBuilder();

await builder.addDockerComposeEnvironment("dc");

const openAiApiKey = await builder.addParameter("openai-api-key", { secret: true });

await builder.addOpenAI("openai")
    .withApiKey(openAiApiKey);

await builder.addUvicornApp("ai-agent", "./agent", "main:app")
    .withUv()
    .withExternalHttpEndpoints()
    .withEnvironment("OPENAI_API_KEY", openAiApiKey);

await builder.build().run();

Note

The screenshots above show a deterministic, stubbed conversation captured for documentation. Live OpenAI responses require your own API key (see below) and were not exercised when these images were generated.

Python FastAPI AI agent with OpenAI integration demonstrating AI workloads with Aspire.

This sample demonstrates Aspire 13's Python support combined with AI workloads, showcasing a Python-based AI chat agent powered by OpenAI.

Quick start

Prerequisites

• Aspire CLI

• Docker

• Python 3.8+

• uv

• OpenAI API key

Commands

aspire run      # Run locally
aspire deploy   # Deploy to Docker Compose
aspire do docker-compose-down-dc  # Teardown deployment

When you run the app for the first time, Aspire will prompt you for your OpenAI API key.

Overview

The application consists of:

• Aspire AppHost - Orchestrates the Python AI agent

• Python AI Agent - FastAPI service with web UI and REST API powered by OpenAI

• Chat UI - Simple web interface for chatting with the AI agent

Key code

The apphost.mts configuration demonstrates AI workloads with Aspire:

import { createBuilder } from "./.aspire/modules/aspire.mjs";

const builder = await createBuilder();

await builder.addDockerComposeEnvironment("dc");

const openAiApiKey = await builder.addParameter("openai-api-key", { secret: true });

await builder.addOpenAI("openai")
    .withApiKey(openAiApiKey);

await builder.addUvicornApp("ai-agent", "./agent", "main:app")
    .withUv()
    .withExternalHttpEndpoints()
    .withEnvironment("OPENAI_API_KEY", openAiApiKey);

await builder.build().run();

Key features:

• Python AI Integration: Uses addUvicornApp to run FastAPI with OpenAI SDK

• OpenAI Integration: addOpenAI prompts for API key on first run and securely stores it

• Direct Key Injection: The OpenAI API key is passed directly to the Python app as OPENAI_API_KEY

• uv Package Manager: Uses .withUv() for fast dependency installation from pyproject.toml

• Web UI: Clean, modern chat interface for interacting with the AI

• Automatic Virtual Environment: Aspire creates .venv and installs dependencies with uv

• External HTTP Endpoints: AI agent accessible externally for testing

• Session Management: Maintains conversation history per session for context-aware responses

Accessing the application

Once you run aspire run, you can access the AI agent in two ways:

1. web UI (recommended)

Open your browser and navigate to the AI agent's endpoint shown in the Aspire Dashboard. You'll see a clean chat interface where you can:

• Type messages and get AI responses

• See conversation history

• Watch typing indicators while the AI thinks

• Get automatic session management

2. REST API

The Python AI agent also provides REST endpoints:

• GET / - Serves the chat UI

• GET /api - API information

• GET /health - Health check (shows OpenAI availability)

• POST /chat - Send a message and get AI response

• GET /sessions - List active conversation sessions

• DELETE /sessions/{id} - Clear a conversation session

Security notes

This sample is instructional and is not production-ready; see the repository's security disclaimer. By default, /chat is anonymous. If you configure AGENT_API_KEY, /chat, GET /sessions, and DELETE /sessions/{id} require the same value in the X-API-Key header.

The code includes basic OpenAI quota and cost controls: message length, session/history caps, response token caps, per-client rate limiting, and a model allow-list. Production deployments should add real authentication and authorization (see FastAPI security), abuse monitoring, persistent session storage if conversations must survive process restarts, and prompt/content safety controls aligned with OpenAI production best practices, OpenAI safety best practices, and OWASP LLM Prompt Injection guidance.

Example API usage

You can also interact with the AI agent programmatically via its REST API:

# Send a chat message
curl -X POST http://localhost:<port>/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "What is the fastest land animal?", "session_id": "my-session"}'

# Check health
curl http://localhost:<port>/health

# List active sessions
curl http://localhost:<port>/sessions

How it works

1. Virtual Environment: Aspire automatically creates a .venv directory for the Python agent

2. Fast Dependency Installation: uv installs dependencies from pyproject.toml (much faster than pip)

3. AI Agent Startup: The Python FastAPI app initializes the OpenAI client with the provided API key

4. Web UI: The root endpoint (/) serves a static HTML chat interface

5. REST API: Additional endpoints for programmatic access

6. Session Management: The agent maintains conversation history per session for context-aware responses

VS code integration

This sample includes VS Code configuration for Python development:

• .vscode/settings.json: Configures the Python interpreter to use the Aspire-created virtual environment

• After running aspire run, open the sample in VS Code for full IntelliSense and debugging support

• The virtual environment at agent/.venv will be automatically detected

Deployment

Deploy to Docker Compose:

aspire deploy

This will:

1. Generate a Dockerfile for the Python application

2. Install Python dependencies and build container image

3. Generate Docker Compose configuration

4. Deploy the application stack

Development tips

• The Python app uses automatic dependency installation via pyproject.toml with uv (much faster than pip)

• Hot reload is enabled for local development

• OpenAI responses are stored in session history for contextual conversations

• Use the Aspire Dashboard to view logs and monitor the agent

• uv automatically creates a uv.lock file for reproducible builds

Preview

Sample screenshots

Select the image to zoom in.

Screenshot of the Ember AI chat assistant UI for the Python OpenAI Agent sample (light theme)