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# Alfred Media Organizer 🎬 # Agent Media 🎬
An AI-powered agent for managing your local media library with natural language. Search, download, and organize movies and TV shows effortlessly through a conversational interface. An AI-powered agent for managing your local media library with natural language. Search, download, and organize movies and TV shows effortlessly.
[![Python 3.14](https://img.shields.io/badge/python-3.14-blue.svg)](https://www.python.org/downloads/) ## Features
[![Poetry](https://img.shields.io/badge/dependency%20manager-poetry-blue)](https://python-poetry.org/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff)
## ✨ Features - 🤖 **Natural Language Interface**: Talk to your media library in plain language
- 🔍 **Smart Search**: Find movies and TV shows via TMDB
- 📥 **Torrent Integration**: Search and download via qBittorrent
- 🧠 **Contextual Memory**: Remembers your preferences and conversation history
- 📁 **Auto-Organization**: Keeps your media library tidy
- 🌐 **API Compatible**: OpenAI-compatible API for easy integration
- 🤖 **Natural Language Interface** — Talk to your media library in plain language ## Architecture
- 🔍 **Smart Search** — Find movies and TV shows via TMDB with rich metadata
- 📥 **Torrent Integration** — Search and download via qBittorrent
- 🧠 **Contextual Memory** — Remembers your preferences and conversation history
- 📁 **Auto-Organization** — Keeps your media library tidy and well-structured
- 🌐 **OpenAI-Compatible API** — Works with any OpenAI-compatible client
- 🖥️ **LibreChat Frontend** — Beautiful web UI included out of the box
- 🔒 **Secure by Default** — Auto-generated secrets and encrypted credentials
## 🏗️ Architecture Built with **Domain-Driven Design (DDD)** principles:
Built with **Domain-Driven Design (DDD)** principles for clean separation of concerns:
``` ```
alfred/ agent_media/
├── agent/ # AI agent orchestration ├── agent/ # AI agent orchestration
│ ├── llm/ # LLM clients (Ollama, DeepSeek) ├── application/ # Use cases & DTOs
│ └── tools/ # Tool implementations ├── domain/ # Business logic & entities
── application/ # Use cases & DTOs ── infrastructure/ # External services & persistence
│ ├── movies/ # Movie search use cases
│ ├── torrents/ # Torrent management
│ └── filesystem/ # File operations
├── domain/ # Business logic & entities
│ ├── movies/ # Movie entities
│ ├── tv_shows/ # TV show entities
│ └── subtitles/ # Subtitle entities
└── infrastructure/ # External services & persistence
├── api/ # External API clients (TMDB, qBittorrent)
├── filesystem/ # File system operations
└── persistence/ # Memory & repositories
``` ```
See [docs/architecture_diagram.md](docs/architecture_diagram.md) for detailed architectural diagrams. See [architecture_diagram.md](docs/architecture_diagram.md) for architectural details.
## 🚀 Quick Start ## Quick Start
### Prerequisites ### Prerequisites
- **Python 3.14+** (required) - Python 3.12+
- **Poetry** (dependency manager) - Poetry
- **Docker & Docker Compose** (recommended for full stack) - qBittorrent (optional, for downloads)
- **API Keys:** - API Keys:
- TMDB API key ([get one here](https://www.themoviedb.org/settings/api)) - DeepSeek API key (or Ollama for local LLM)
- Optional: DeepSeek, OpenAI, Anthropic, or other LLM provider keys - TMDB API key
### Installation ### Installation
```bash ```bash
# Clone the repository # Clone the repository
git clone https://github.com/francwa/alfred_media_organizer.git git clone https://github.com/your-username/agent-media.git
cd alfred_media_organizer cd agent-media
# Install dependencies # Install dependencies
make install poetry install
# Bootstrap environment (generates .env with secure secrets) # Copy environment template
make bootstrap cp .env.example .env
# Edit .env with your API keys # Edit .env with your API keys
nano .env nano .env
``` ```
### Running with Docker (Recommended) ### Configuration
Edit `.env`:
```bash ```bash
# Start all services (LibreChat + Alfred + MongoDB + Ollama) # LLM Provider (deepseek or ollama)
make up LLM_PROVIDER=deepseek
DEEPSEEK_API_KEY=your-api-key-here
# Or start with specific profiles # TMDB (for movie/TV show metadata)
make up p=rag,meili # Include RAG and Meilisearch TMDB_API_KEY=your-tmdb-key-here
make up p=qbittorrent # Include qBittorrent
make up p=full # Everything
# View logs # qBittorrent (optional)
make logs QBITTORRENT_HOST=http://localhost:8080
QBITTORRENT_USERNAME=admin
# Stop all services QBITTORRENT_PASSWORD=adminadmin
make down
``` ```
The web interface will be available at **http://localhost:3080** ### Run
### Running Locally (Development)
```bash ```bash
# Install dependencies
poetry install
# Start the API server # Start the API server
poetry run uvicorn alfred.app:app --reload --port 8000 poetry run uvicorn app:app --reload
# Or with Docker
docker-compose up
``` ```
## ⚙️ Configuration The API will be available at `http://localhost:8000`
### Environment Bootstrap ## Usage
Alfred uses a smart bootstrap system that:
1. **Generates secure secrets** automatically (JWT tokens, database passwords, encryption keys)
2. **Syncs build variables** from `pyproject.toml` (versions, image names)
3. **Preserves existing secrets** when re-running (never overwrites your API keys)
4. **Computes database URIs** automatically from individual components
```bash
# First time setup
make bootstrap
# Re-run after updating pyproject.toml (secrets are preserved)
make bootstrap
```
### Configuration File (.env)
The `.env` file is generated from `.env.example` with secure defaults:
```bash
# --- CORE SETTINGS ---
HOST=0.0.0.0
PORT=3080
MAX_HISTORY_MESSAGES=10
MAX_TOOL_ITERATIONS=10
# --- LLM CONFIGURATION ---
# Providers: 'local' (Ollama), 'deepseek', 'openai', 'anthropic', 'google'
DEFAULT_LLM_PROVIDER=local
# Local LLM (Ollama - included in Docker stack)
OLLAMA_BASE_URL=http://ollama:11434
OLLAMA_MODEL=llama3.3:latest
LLM_TEMPERATURE=0.2
# --- API KEYS (fill only what you need) ---
TMDB_API_KEY=your-tmdb-key-here # Required for movie search
DEEPSEEK_API_KEY= # Optional
OPENAI_API_KEY= # Optional
ANTHROPIC_API_KEY= # Optional
# --- SECURITY (auto-generated, don't modify) ---
JWT_SECRET=<auto-generated>
JWT_REFRESH_SECRET=<auto-generated>
CREDS_KEY=<auto-generated>
CREDS_IV=<auto-generated>
# --- DATABASES (auto-generated passwords) ---
MONGO_PASSWORD=<auto-generated>
POSTGRES_PASSWORD=<auto-generated>
```
### Security Keys
Security keys are defined in `pyproject.toml` and generated automatically:
```toml
[tool.alfred.security]
jwt_secret = "32:b64" # 32 bytes, base64 URL-safe
jwt_refresh_secret = "32:b64"
creds_key = "32:hex" # 32 bytes, hexadecimal (AES-256)
creds_iv = "16:hex" # 16 bytes, hexadecimal (AES IV)
mongo_password = "16:hex"
postgres_password = "16:hex"
```
**Formats:**
- `b64` — Base64 URL-safe (for JWT tokens)
- `hex` — Hexadecimal (for encryption keys, passwords)
## 🐳 Docker Services
### Service Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ alfred-net (bridge) │
├─────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ LibreChat │───▶│ Alfred │───▶│ MongoDB │ │
│ │ :3080 │ │ (core) │ │ :27017 │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │
│ │ ▼ │
│ │ ┌──────────────┐ │
│ │ │ Ollama │ │
│ │ │ (local) │ │
│ │ └──────────────┘ │
│ │ │
│ ┌──────┴───────────────────────────────────────────────┐ │
│ │ Optional Services (profiles) │ │
│ ├──────────────┬──────────────┬──────────────┬─────────┤ │
│ │ Meilisearch │ RAG API │ VectorDB │qBittor- │ │
│ │ :7700 │ :8000 │ :5432 │ rent │ │
│ │ [meili] │ [rag] │ [rag] │[qbit..] │ │
│ └──────────────┴──────────────┴──────────────┴─────────┘ │
│ │
└─────────────────────────────────────────────────────────────┘
```
### Docker Profiles
| Profile | Services | Use Case |
|---------|----------|----------|
| (default) | LibreChat, Alfred, MongoDB, Ollama | Basic setup |
| `meili` | + Meilisearch | Fast search |
| `rag` | + RAG API, VectorDB | Document retrieval |
| `qbittorrent` | + qBittorrent | Torrent downloads |
| `full` | All services | Complete setup |
```bash
# Start with specific profiles
make up p=rag,meili
make up p=full
```
### Docker Commands
```bash
make up # Start containers (default profile)
make up p=full # Start with all services
make down # Stop all containers
make restart # Restart containers
make logs # Follow logs
make ps # Show container status
make shell # Open bash in Alfred container
make build # Build production image
make build-test # Build test image
```
## 🛠️ Available Tools
The agent has access to these tools for interacting with your media library:
| Tool | Description |
|------|-------------|
| `find_media_imdb_id` | Search for movies/TV shows on TMDB by title |
| `find_torrent` | Search for torrents across multiple indexers |
| `get_torrent_by_index` | Get detailed info about a specific torrent result |
| `add_torrent_by_index` | Download a torrent by its index in search results |
| `add_torrent_to_qbittorrent` | Add a torrent via magnet link directly |
| `set_path_for_folder` | Configure folder paths for media organization |
| `list_folder` | List contents of a folder |
| `set_language` | Set preferred language for searches |
## 💬 Usage Examples
### Via Web Interface (LibreChat)
Navigate to **http://localhost:3080** and start chatting:
```
You: Find Inception in 1080p
Alfred: I found 3 torrents for Inception (2010):
1. Inception.2010.1080p.BluRay.x264 (150 seeders) - 2.1 GB
2. Inception.2010.1080p.WEB-DL.x265 (80 seeders) - 1.8 GB
3. Inception.2010.1080p.REMUX (45 seeders) - 25 GB
You: Download the first one
Alfred: ✓ Added to qBittorrent! Download started.
Saving to: /downloads/Movies/Inception (2010)/
You: What's downloading right now?
Alfred: You have 1 active download:
- Inception.2010.1080p.BluRay.x264 (45% complete, ETA: 12 min)
```
### Via API ### Via API
@@ -279,177 +91,219 @@ Alfred: You have 1 active download:
# Health check # Health check
curl http://localhost:8000/health curl http://localhost:8000/health
# Chat with the agent (OpenAI-compatible) # Chat with the agent
curl -X POST http://localhost:8000/v1/chat/completions \ curl -X POST http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \ -H "Content-Type: application/json" \
-d '{ -d '{
"model": "alfred", "model": "agent-media",
"messages": [ "messages": [
{"role": "user", "content": "Find The Matrix 4K"} {"role": "user", "content": "Find Inception 1080p"}
] ]
}' }'
# List available models
curl http://localhost:8000/v1/models
# View memory state (debug)
curl http://localhost:8000/memory/state
# Clear session memory
curl -X POST http://localhost:8000/memory/clear-session
``` ```
### Via OpenWebUI or Other Clients ### Via OpenWebUI
Alfred is compatible with any OpenAI-compatible client: Agent Media is compatible with [OpenWebUI](https://github.com/open-webui/open-webui):
1. Add as OpenAI-compatible endpoint: `http://localhost:8000/v1` 1. Add as OpenAI-compatible endpoint: `http://localhost:8000/v1`
2. Model name: `alfred` 2. Model name: `agent-media`
3. No API key required (or use any placeholder) 3. Start chatting!
## 🧠 Memory System ### Example Conversations
Alfred uses a three-tier memory system for context management: ```
You: Find Inception in 1080p
Agent: I found 3 torrents for Inception:
1. Inception.2010.1080p.BluRay.x264 (150 seeders)
2. Inception.2010.1080p.WEB-DL.x265 (80 seeders)
3. Inception.2010.720p.BluRay (45 seeders)
You: Download the first one
Agent: Added to qBittorrent! Download started.
You: List my downloads
Agent: You have 1 active download:
- Inception.2010.1080p.BluRay.x264 (45% complete)
```
## Available Tools
The agent has access to these tools:
| Tool | Description |
|------|-------------|
| `find_media_imdb_id` | Search for movies/TV shows on TMDB |
| `find_torrents` | Search for torrents |
| `get_torrent_by_index` | Get torrent details by index |
| `add_torrent_by_index` | Download torrent by index |
| `add_torrent_to_qbittorrent` | Add torrent via magnet link |
| `set_path_for_folder` | Configure folder paths |
| `list_folder` | List folder contents |
## Memory System
Agent Media uses a three-tier memory system:
### Long-Term Memory (LTM) ### Long-Term Memory (LTM)
- **Persistent** — Saved to JSON files - **Persistent** (saved to JSON)
- **Contents:** Configuration, user preferences, media library state - Configuration, preferences, media library
- **Survives:** Application restarts - Survives restarts
### Short-Term Memory (STM) ### Short-Term Memory (STM)
- **Session-based** — Stored in RAM - **Session-based** (RAM only)
- **Contents:** Conversation history, current workflow state - Conversation history, current workflow
- **Cleared:** On session end or restart - Cleared on restart
### Episodic Memory ### Episodic Memory
- **Transient** — Stored in RAM - **Transient** (RAM only)
- **Contents:** Search results, active downloads, recent errors - Search results, active downloads, recent errors
- **Cleared:** Frequently, after task completion - Cleared frequently
## 🧪 Development ## Development
### Project Setup ### Project Structure
```bash ```
# Install all dependencies (including dev) agent_media/
poetry install ├── agent/
│ ├── agent.py # Main agent orchestrator
# Install pre-commit hooks │ ├── prompts.py # System prompt builder
make install-hooks │ ├── registry.py # Tool registration
│ ├── tools/ # Tool implementations
# Run the development server │ └── llm/ # LLM clients (DeepSeek, Ollama)
poetry run uvicorn alfred.app:app --reload ├── application/
│ ├── movies/ # Movie use cases
│ ├── torrents/ # Torrent use cases
│ └── filesystem/ # Filesystem use cases
├── domain/
│ ├── movies/ # Movie entities & value objects
│ ├── tv_shows/ # TV show entities
│ ├── subtitles/ # Subtitle entities
│ └── shared/ # Shared value objects
├── infrastructure/
│ ├── api/ # External API clients
│ │ ├── tmdb/ # TMDB client
│ │ ├── knaben/ # Torrent search
│ │ └── qbittorrent/ # qBittorrent client
│ ├── filesystem/ # File operations
│ └── persistence/ # Memory & repositories
├── tests/ # Test suite (~500 tests)
└── docs/ # Documentation
``` ```
### Running Tests ### Running Tests
```bash ```bash
# Run all tests (parallel execution) # Run all tests
make test poetry run pytest
# Run with coverage report # Run with coverage
make coverage poetry run pytest --cov
# Run specific test file # Run specific test file
poetry run pytest tests/test_agent.py -v poetry run pytest tests/test_agent.py
# Run specific test # Run specific test
poetry run pytest tests/test_config_loader.py::TestBootstrapEnv -v poetry run pytest tests/test_agent.py::TestAgent::test_step
``` ```
### Code Quality ### Code Quality
```bash ```bash
# Lint and auto-fix # Linting
make lint poetry run ruff check .
# Format code # Formatting
make format poetry run black .
# Clean build artifacts # Type checking (if mypy is installed)
make clean poetry run mypy .
``` ```
### Adding a New Tool ### Adding a New Tool
1. **Create the tool function** in `alfred/agent/tools/`: Quick example:
```python ```python
# alfred/agent/tools/api.py # 1. Create the tool function in agent/tools/api.py
def my_new_tool(param: str) -> dict[str, Any]: def my_new_tool(param: str) -> Dict[str, Any]:
""" """Tool description."""
Short description of what this tool does.
This will be shown to the LLM to help it decide when to use this tool.
"""
memory = get_memory() memory = get_memory()
# Implementation
# Your implementation here return {"status": "ok", "data": "result"}
result = do_something(param)
# 2. Register in agent/registry.py
return { Tool(
"status": "success", name="my_new_tool",
"data": result description="What this tool does",
} func=api_tools.my_new_tool,
parameters={
"type": "object",
"properties": {
"param": {"type": "string", "description": "Parameter description"},
},
"required": ["param"],
},
),
``` ```
2. **Register in the registry** (`alfred/agent/registry.py`): ## Docker
```python ### Build
tool_functions = [
# ... existing tools ...
api_tools.my_new_tool, # Add your tool here
]
```
The tool will be automatically registered with its parameters extracted from the function signature.
### Version Management
```bash ```bash
# Bump version (must be on main branch) docker build -t agent-media .
make patch # 0.1.7 -> 0.1.8
make minor # 0.1.7 -> 0.2.0
make major # 0.1.7 -> 1.0.0
``` ```
## 📚 API Reference ### Run
```bash
docker run -p 8000:8000 \
-e DEEPSEEK_API_KEY=your-key \
-e TMDB_API_KEY=your-key \
-v $(pwd)/memory_data:/app/memory_data \
agent-media
```
### Docker Compose
```bash
# Start all services (agent + qBittorrent)
docker-compose up -d
# View logs
docker-compose logs -f
# Stop
docker-compose down
```
## API Documentation
### Endpoints ### Endpoints
#### `GET /health` #### `GET /health`
Health check endpoint. Health check endpoint.
**Response:**
```json ```json
{ {
"status": "healthy", "status": "healthy",
"version": "0.1.7" "version": "0.2.0"
} }
``` ```
#### `GET /v1/models` #### `GET /v1/models`
List available models (OpenAI-compatible). List available models (OpenAI-compatible).
```json
{
"object": "list",
"data": [
{
"id": "alfred",
"object": "model",
"owned_by": "alfred"
}
]
}
```
#### `POST /v1/chat/completions` #### `POST /v1/chat/completions`
Chat with the agent (OpenAI-compatible). Chat with the agent (OpenAI-compatible).
**Request:** **Request:**
```json ```json
{ {
"model": "alfred", "model": "agent-media",
"messages": [ "messages": [
{"role": "user", "content": "Find Inception"} {"role": "user", "content": "Find Inception"}
], ],
@@ -463,7 +317,7 @@ Chat with the agent (OpenAI-compatible).
"id": "chatcmpl-xxx", "id": "chatcmpl-xxx",
"object": "chat.completion", "object": "chat.completion",
"created": 1234567890, "created": 1234567890,
"model": "alfred", "model": "agent-media",
"choices": [{ "choices": [{
"index": 0, "index": 0,
"message": { "message": {
@@ -476,120 +330,71 @@ Chat with the agent (OpenAI-compatible).
``` ```
#### `GET /memory/state` #### `GET /memory/state`
View full memory state (debug endpoint). View full memory state (debug).
#### `POST /memory/clear-session` #### `POST /memory/clear-session`
Clear session memories (STM + Episodic). Clear session memories (STM + Episodic).
## 🔧 Troubleshooting ## Troubleshooting
### Agent doesn't respond ### Agent doesn't respond
- Check API keys in `.env`
1. Check API keys in `.env` - Verify LLM provider is running (Ollama) or accessible (DeepSeek)
2. Verify LLM provider is running: - Check logs: `docker-compose logs agent-media`
```bash
# For Ollama
docker logs alfred-ollama
# Check if model is pulled
docker exec alfred-ollama ollama list
```
3. Check Alfred logs: `docker logs alfred-core`
### qBittorrent connection failed ### qBittorrent connection failed
- Verify qBittorrent is running
1. Verify qBittorrent is running: `docker ps | grep qbittorrent` - Check `QBITTORRENT_HOST` in `.env`
2. Check Web UI is enabled in qBittorrent settings - Ensure Web UI is enabled in qBittorrent settings
3. Verify credentials in `.env`:
```bash
QBITTORRENT_URL=http://qbittorrent:16140
QBITTORRENT_USERNAME=admin
QBITTORRENT_PASSWORD=<check-your-env>
```
### Database connection issues
1. Check MongoDB is healthy: `docker logs alfred-mongodb`
2. Verify credentials match in `.env`
3. Try restarting: `make restart`
### Memory not persisting ### Memory not persisting
- Check `memory_data/` directory exists and is writable
1. Check `data/` directory exists and is writable - Verify volume mounts in Docker
2. Verify volume mounts in `docker-compose.yaml`
3. Check file permissions: `ls -la data/`
### Bootstrap fails
1. Ensure `.env.example` exists
2. Check `pyproject.toml` has required sections:
```toml
[tool.alfred.settings]
[tool.alfred.security]
```
3. Run manually: `python scripts/bootstrap.py`
### Tests failing ### Tests failing
- Run `poetry install` to ensure dependencies are up to date
- Check logs for specific error messages
1. Update dependencies: `poetry install` ## Contributing
2. Check Python version: `python --version` (needs 3.14+)
3. Run specific failing test with verbose output:
```bash
poetry run pytest tests/test_failing.py -v --tb=long
```
## 🤝 Contributing Contributions are welcome!
Contributions are welcome! Please follow these steps: ### Development Workflow
1. **Fork** the repository 1. Fork the repository
2. **Create** a feature branch: `git checkout -b feature/my-feature` 2. Create a feature branch: `git checkout -b feature/my-feature`
3. **Make** your changes 3. Make your changes
4. **Run** tests: `make test` 4. Run tests: `poetry run pytest`
5. **Run** linting: `make lint && make format` 5. Run linting: `poetry run ruff check . && poetry run black .`
6. **Commit**: `git commit -m "feat: add my feature"` 6. Commit: `git commit -m "Add my feature"`
7. **Push**: `git push origin feature/my-feature` 7. Push: `git push origin feature/my-feature`
8. **Create** a Pull Request 8. Create a Pull Request
### Commit Convention ## Documentation
We use [Conventional Commits](https://www.conventionalcommits.org/): - [Architecture Diagram](docs/architecture_diagram.md) - System architecture overview
- [Class Diagram](docs/class_diagram.md) - Class structure and relationships
- [Component Diagram](docs/component_diagram.md) - Component interactions
- [Sequence Diagram](docs/sequence_diagram.md) - Sequence flows
- [Flowchart](docs/flowchart.md) - System flowcharts
- `feat:` New feature ## License
- `fix:` Bug fix
- `docs:` Documentation
- `refactor:` Code refactoring
- `test:` Adding tests
- `chore:` Maintenance
## 📖 Documentation MIT License - see [LICENSE](LICENSE) file for details.
- [Architecture Diagram](docs/architecture_diagram.md) — System architecture overview ## Acknowledgments
- [Class Diagram](docs/class_diagram.md) — Class structure and relationships
- [Component Diagram](docs/component_diagram.md) — Component interactions
- [Sequence Diagram](docs/sequence_diagram.md) — Sequence flows
- [Flowchart](docs/flowchart.md) — System flowcharts
## 📄 License - [DeepSeek](https://www.deepseek.com/) - LLM provider
- [TMDB](https://www.themoviedb.org/) - Movie database
- [qBittorrent](https://www.qbittorrent.org/) - Torrent client
- [FastAPI](https://fastapi.tiangolo.com/) - Web framework
MIT License — see [LICENSE](LICENSE) file for details. ## Support
## 🙏 Acknowledgments
- [LibreChat](https://github.com/danny-avila/LibreChat) — Beautiful chat interface
- [Ollama](https://ollama.ai/) — Local LLM runtime
- [DeepSeek](https://www.deepseek.com/) — LLM provider
- [TMDB](https://www.themoviedb.org/) — Movie database
- [qBittorrent](https://www.qbittorrent.org/) — Torrent client
- [FastAPI](https://fastapi.tiangolo.com/) — Web framework
- [Pydantic](https://docs.pydantic.dev/) — Data validation
## 📬 Support
- 📧 Email: francois.hodiaumont@gmail.com - 📧 Email: francois.hodiaumont@gmail.com
- 🐛 Issues: [GitHub Issues](https://github.com/francwa/alfred_media_organizer/issues) - 🐛 Issues: [GitHub Issues](https://github.com/your-username/agent-media/issues)
- 💬 Discussions: [GitHub Discussions](https://github.com/francwa/alfred_media_organizer/discussions) - 💬 Discussions: [GitHub Discussions](https://github.com/your-username/agent-media/discussions)
--- ---
<p align="center">Made with ❤️ by <a href="https://github.com/francwa">Francwa</a></p> Made with ❤️ by Francwa
+1 -1
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@@ -27,7 +27,7 @@ qbittorrent_password = "16:hex"
python = "==3.14.2" python = "==3.14.2"
python-dotenv = "^1.0.0" python-dotenv = "^1.0.0"
requests = "^2.32.5" requests = "^2.32.5"
fastapi = "^0.136.0" fastapi = "^0.127.1"
pydantic = "^2.12.4" pydantic = "^2.12.4"
uvicorn = "^0.40.0" uvicorn = "^0.40.0"
pytest-xdist = "^3.8.0" pytest-xdist = "^3.8.0"