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pydantic-ai-demos

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Pydantic AI Demos with Temporal's durable execution

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# Pydantic AI Temporal Demos

This repository contains five standalone demos showcasing Pydantic AI integrated with Temporal's durable execution.

Inspired by the [OpenAI cookbook](https://cookbook.openai.com/examples/deep_research_api/introduction_to_deep_research_api) examples, these demos show different patterns of agent orchestration using OpenAI models with Pydantic AI, from simple single-agent workflows to complex multi-agent research systems with interactive user clarifications.

For detailed information about the research agents in this repo, see [pydantic_demos/workflows/research_agents/README.md](pydantic_demos/workflows/research_agents/README.md)

More Temporal Pydantic AI samples can be found in Temporal's [samples-python repository](https://github.com/temporalio/samples-python/tree/main/pydantic_ai).

## Prerequisites

1. **Python 3.10+** - Required for the demos
2. **Temporal Server** - Must be running locally on `localhost:7233`
3. **OpenAI API Key** - Set as environment variable `OPENAI_API_KEY` (note, you will need enough quota on in your [OpenAI account](https://platform.openai.com/api-keys) to run this demo)
4. **PDF Generation Dependencies** - Required for PDF output (optional)

### Starting Temporal Server

```bash
# Install Temporal CLI
curl -sSf https://temporal.download/cli.sh | sh

# Start Temporal server
temporal server start-dev
```

## Setup

1. Clone this repository
2. Install dependencies:
   ```bash
   uv sync
   ```
3. Set your OpenAI API key:
   ```bash
   export OPENAI_API_KEY="your-api-key-here"
   ```

### PDF Generation (optional)

Only used in `Demo 4: Multi-Agent Interactive Research Workflow`

For PDF generation functionality, you'll need WeasyPrint and its system dependencies:

#### macOS (using Homebrew)
```bash
brew install weasyprint
# OR install system dependencies for pip installation:
brew install pango glib gtk+3 libffi
```

#### Linux (Ubuntu/Debian)
```bash
# For package installation:
sudo apt install weasyprint

# OR for pip installation:
sudo apt install python3-pip libpango-1.0-0 libpangoft2-1.0-0 libharfbuzz-subset0
```

#### Linux (Fedora)
```bash
# For package installation:
sudo dnf install weasyprint

# OR for pip installation:
sudo dnf install python-pip pango
```

#### Windows
1. Install Python from Microsoft Store
2. Install MSYS2 from https://www.msys2.org/
3. In MSYS2 shell: `pacman -S mingw-w64-x86_64-pango`
4. Set environment variable: `WEASYPRINT_DLL_DIRECTORIES=C:\msys64\mingw64\bin`

**Note:** PDF generation gracefully degrades when dependencies are unavailable - workflows will still generate markdown reports.

## Running the Demos

### Step 1: Start the Worker

In one terminal, start the worker that will handle all workflows:

```bash
uv run pydantic_demos/run_worker.py
```

Keep this running throughout your demo sessions. The worker registers all available workflows and activities.

### Step 2: Run Any Demo

In a separate terminal, run any of the demo scripts:

### Demo 1: Hello World Workflow

A simple agent that responds only in haikus.

**Files:**
- `pydantic_demos/workflows/hello_world_workflow.py` - Simple haiku-generating agent
- `pydantic_demos/run_hello_world_workflow.py` - Client runner

**To run:**
```bash
uv run pydantic_demos/run_hello_world_workflow.py
```

### Demo 2: Tools Workflow

An agent that uses a weather function as a tool.

**Files:**
- `pydantic_demos/workflows/tools_workflow.py` - Workflow using weather tool
- `pydantic_demos/run_tools_workflow.py` - Client runner

**To run:**
```bash
uv run pydantic_demos/run_tools_workflow.py
```

### Demo 3: Basic Research Workflow

A research system that processes queries and generates comprehensive markdown reports.

**Files:**
- `pydantic_demos/workflows/research_bot_workflow.py` - Main research workflow
- `pydantic_demos/workflows/simple_research_manager.py` - Simple research orchestrator
- `pydantic_demos/workflows/research_agents/` - Research agent components
- `pydantic_demos/run_research_workflow.py` - Research client

**Agents:**
- **Planner Agent**: Plans web searches based on the query
- **Search Agent**: Performs searches to gather information
- **Writer Agent**: Compiles the final research report

**To run:**
```bash
uv run pydantic_demos/run_research_workflow.py "Tell me about quantum computing"
```

**Output:**
- `pydantic_research_report.md` - Comprehensive markdown report

**Note:** The research workflow may take 1-2 minutes to complete due to web searches and report generation.

### Demo 4: Multi-Agent Interactive Research Workflow

An enhanced version of the research workflow with interactive clarifying questions to refine research parameters before execution and optional PDF generation.

This example is inspired by the [OpenAI Deep Research API cookbook](https://cookbook.openai.com/examples/deep_research_api/introduction_to_deep_research_api).

**Files:**
- `pydantic_demos/workflows/interactive_research_workflow.py` - Interactive research workflow
- `pydantic_demos/workflows/research_agents/` - All research agent components
- `pydantic_demos/run_interactive_research_workflow.py` - Interactive research client
- `pydantic_demos/workflows/pdf_generation_activity.py` - PDF generation activity
- `pydantic_demos/workflows/research_agents/pdf_generator_agent.py` - PDF generation agent

**Agents:**
- **Triage Agent**: Analyzes research queries and determines if clarifications are needed
- **Clarifying Agent**: Generates follow-up questions for better research parameters
- **Planner Agent**: Creates web search plans
- **Search Agent**: Performs web searches
- **Writer Agent**: Compiles final research reports
- **PDF Generator Agent**: Converts markdown reports to professionally formatted PDFs

**To run:**
```bash
uv run pydantic_demos/run_interactive_research_workflow.py "Tell me about quantum computing"
```

**Additional options:**
- `--non-interactive`: Skip clarifying questions and do direct research

**Output:**
- `research_report.md` - Comprehensive markdown report
- `pdf_output/research_report.pdf` - Professionally formatted PDF (if PDF generation is available)

**Note:** The interactive workflow may take 2-3 minutes to complete due to web searches and report generation.

### Demo 5: Streaming Model Response

A streaming agent using Pydantic AI's event_stream_handler for real-time responses via WebSocket.

**Files:**
- `pydantic_demos/run_streaming_workflow.py` - All-in-one websocket server, workflow, and client runner

**To run:**
```bash
uv run pydantic_demos/run_streaming_workflow.py
```

**Features:**
- Real-time model response streaming via WebSocket using Pydantic AI's event_stream_handler

## Project Structure

```
pydantic-ai-demos/
├── README.md                           # This file
├── pyproject.toml                      # Project dependencies
├── pydantic_demos/
│   ├── __init__.py
│   ├── run_worker.py                   # Worker that registers all workflows
│   ├── run_hello_world_workflow.py     # Hello World demo runner
│   ├── run_tools_workflow.py           # Tools demo runner
│   ├── run_research_workflow.py        # Research demo runner
│   ├── run_interactive_research_workflow.py     # Interactive research demo runner
│   ├── run_streaming_workflow.py       # Streaming demo runner
│   └── workflows/
│       ├── __init__.py
│       ├── hello_world_workflow.py     # Simple haiku agent
│       ├── tools_workflow.py           # Weather tool demo
│       ├── research_bot_workflow.py    # Main research workflow
│       ├── interactive_research_workflow.py  # Interactive research workflow
│       ├── simple_research_manager.py  # Simple research orchestrator
│       ├── interactive_research_manager.py  # Interactive research orchestrator
│       ├── pdf_generation_activity.py  # PDF generation activity
│       └── research_agents/            # Research agent components
│           ├── __init__.py
│           ├── research_models.py      # Data models
│           ├── triage_agent.py         # Query analysis agent
│           ├── clarifying_agent.py     # Question generation agent
│           ├── planner_agent.py        # Research planning agent
│           ├── search_agent.py         # Web search agent
│           ├── writer_agent.py         # Report writing agent
│           └── pdf_generator_agent.py  # PDF generation agent
```

## Development

### Code Quality Tools

```bash
# Format code
uv run -m black .
uv run -m isort .

# Type checking
uv run -m mypy --check-untyped-defs --namespace-packages .
uv run pyright .
```

## Key Features

- **Temporal Workflows**: All demos use Temporal for reliable workflow orchestration
- **Pydantic AI**: Powered by Pydantic AI for natural language processing with OpenAI models
- **Multi-Agent Systems**: The research demo showcases complex multi-agent coordination
- **Interactive Workflows**: Research demo supports real-time user interaction
- **Tool Integration**: Tools demo shows how to integrate external functions as tools
- **PDF Generation**: Interactive research workflow generates professional PDF reports alongside markdown
- **Streaming Responses**: WebSocket-based real-time model response streaming

## License

MIT License - see the original project for full license details.

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