Trakt.tv Integration

Track and recommend TV shows and movies using Trakt.tv. Use when the user asks for show/movie recommendations, wants to track what they're watching, check their watchlist, or get personalized suggestions based on their viewing history. Requires Trakt.tv account with Pro subscription for full functionality.

Trakt.tv Integration for OpenClaw

Integrate with Trakt.tv to track watch history and provide personalized show/movie recommendations.

📚 Trakt API Documentation: https://trakt.docs.apiary.io/

First-Time Setup Required

Before using this skill, run the interactive setup:

Automated Setup (Recommended)

python3 scripts/setup.py

This will guide you through:

1. Installing dependencies
2. Creating a Trakt application
3. Configuring credentials
4. Authenticating with PIN
5. Testing the integration

Manual Setup

If automated setup doesn't work, follow the manual steps in the Setup section below.

Interactive Setup for OpenClaw

When a user asks to "install Trakt" or "set up Trakt integration," OpenClaw should:

1. Read INSTALL.md for detailed interactive flow
2. Or run python3 scripts/setup.py and guide user through prompts

---

Features

• Track watch history (automatically synced by Trakt from streaming services)
• Get personalized recommendations based on viewing habits
• Access user watchlists and collections
• Search for shows and movies
• View trending content

Prerequisites

1. Python dependencies:

   # Install via pip (with --break-system-packages if needed)
   pip3 install requests
   
   # OR use a virtual environment (recommended)
   python3 -m venv ~/.openclaw-venv
   source ~/.openclaw-venv/bin/activate
   pip install requests
   

   Alternatively, install via Homebrew if available:

   brew install python-requests
   

2. Trakt.tv account with Pro subscription (required for automatic watch tracking)

3. Trakt API application - Create at https://trakt.tv/oauth/applications

4. Configuration file: ~/.openclaw/trakt_config.json (see setup below)

Setup

1. Create Trakt Application

1. Visit https://trakt.tv/oauth/applications
2. Click "New Application"
3. Fill in the form:
   • Name: "OpenClaw Assistant"
   • Description: "Personal AI assistant integration"
   • Redirect URI: urn:ietf:wg:oauth:2.0:oob (for PIN auth)
   • Permissions: Check all that apply
4. Save and note your Client ID and Client Secret

2. Create Configuration File

Create ~/.openclaw/trakt_config.json with your credentials:

{
  "client_id": "YOUR_CLIENT_ID_HERE",
  "client_secret": "YOUR_CLIENT_SECRET_HERE",
  "access_token": "",
  "refresh_token": ""
}

Replace YOUR_CLIENT_ID_HERE and YOUR_CLIENT_SECRET_HERE with your actual values from step 1.

Note: Leave access_token and refresh_token empty - they'll be filled automatically after authentication.

3. Authenticate

Run the authentication script:

python3 scripts/trakt_client.py auth

This will output a PIN URL. Visit it, authorize the app, and run:

python3 scripts/trakt_client.py auth <PIN>

Authentication tokens are saved to ~/.openclaw/trakt_config.json

Usage

Get Recommendations

When a user asks for show/movie recommendations:

python3 scripts/trakt_client.py recommend

This returns personalized recommendations based on the user's watch history and ratings.

Check Watch History

python3 scripts/trakt_client.py history

Returns the user's recent watch history.

View Watchlist

python3 scripts/trakt_client.py watchlist

Shows content the user has saved to watch later.

Search

python3 scripts/trakt_client.py search "Breaking Bad"

Search for specific shows or movies.

Trending Content

python3 scripts/trakt_client.py trending

Get currently trending shows and movies.

Recommendation Workflow

When a user asks "What should I watch?" or similar:

1. Get personalized recommendations:

   python3 scripts/trakt_client.py recommend
   

2. Parse the results and present them naturally:

   • Show title, year, rating
   • Brief description/genre
   • Why it's recommended (if available)

3. Optionally check watchlist to avoid suggesting shows they already plan to watch

4. Consider recent history to avoid re-suggesting recently watched content

API Reference

See references/api.md for detailed Trakt API endpoint documentation.

Common Use Cases

"What should I watch tonight?"

• Get recommendations, filter by mood/genre if specified
• Check trending if user wants something popular

"Add [show] to my watchlist"

• Search for the show
• Add to Trakt watchlist (requires additional endpoint implementation)

"What have I been watching lately?"

• Get watch history
• Summarize recent shows/movies

"Is [show] trending?"

• Get trending list
• Search for specific show

Limitations

• Trakt Pro subscription required for automatic watch tracking from streaming services
• Recommendations improve over time as watch history grows
• API rate limits apply: 1000 requests per 5 minutes (authenticated)
• Full API documentation: https://trakt.docs.apiary.io/

Troubleshooting

"Authentication failed"

• Verify CLIENT_ID and CLIENT_SECRET are set correctly in ~/.openclaw/trakt_config.json
• Ensure PIN is copied accurately (case-sensitive)
• Check that your Trakt application has proper permissions

"No recommendations returned"

• User may not have enough watch history yet
• Try falling back to trending content
• Ensure user has rated some content on Trakt

"API request failed"

• Check authentication token hasn't expired
• Verify network connectivity
• Check Trakt API status: https://status.trakt.tv