Reddit Parser: - Uses Reddit's native .json URL suffix (zero dependencies) - Extracts post title, body, author, score, flair - Top comments sorted by score (up to 15) - Nested reply threads (up to 3 levels deep) - Media detection (images, galleries, Reddit video) - Proper error handling (429 rate limit, 404, 403) Core: - Added is_reddit_url() to utils.py - Registered RedditParser in router.py - Added reddit_reading capability to manifest.json README: - Updated all 7 language versions with Reddit section (EN, 中文, Español, 한국어, 日本語, العربية, Français)
🦞 OpenClaw DeepReeder
Autonomous web content ingestion engine for AI agents.
DeepReeder intercepts URLs from user messages, scrapes content intelligently using specialized parsers, formats it into clean Markdown with YAML frontmatter, and saves it to the agent's long-term memory.
🌍 Translations: 中文 · Español · 한국어 · 日本語 · العربية · Français
✨ Features
| Parser | Sources | Method |
|---|---|---|
| 🌐 Generic | Blogs, articles, docs | Trafilatura with BeautifulSoup fallback |
| 🐦 Twitter / X | Tweets, threads, X Articles | FxTwitter API (primary) + Nitter (fallback) |
| Posts + comment threads | Reddit .json API (zero-config) | |
| 🎬 YouTube | Video transcripts | youtube-transcript-api |
🐦 Twitter / X — Deep Integration
Powered by FxTwitter API with Nitter fallback. Inspired by x-tweet-fetcher.
| Content Type | Support |
|---|---|
| Regular tweets | ✅ Full text + engagement stats |
| Long tweets (Twitter Blue) | ✅ Full text |
| X Articles (long-form) | ✅ Complete article text + word count |
| Quoted tweets | ✅ Nested content included |
| Media (images, video, GIF) | ✅ URLs extracted |
| Reply threads | ✅ Via Nitter fallback (first 5) |
| Engagement stats | ✅ ❤️ likes, 🔁 RTs, 👁️ views, 🔖 bookmarks |
🟠 Reddit — Native JSON Integration
Uses Reddit's built-in .json URL suffix — no API keys, no OAuth, no registration.
| Content Type | Support |
|---|---|
| Self posts (text) | ✅ Full markdown body |
| Link posts | ✅ URL + metadata |
| Top comments (sorted by score) | ✅ Up to 15 comments |
| Nested reply threads | ✅ Up to 3 levels deep |
| Media (images, galleries, video) | ✅ URLs extracted |
| Post stats | ✅ ⬆️ score, 💬 comment count, upvote ratio |
| Flair tags | ✅ Included |
No API keys. No login. No rate limits.
Output Format
Every piece of content is saved as a .md file with structured YAML frontmatter:
---
title: "[r/python] How I built an AI agent framework"
source_url: "https://www.reddit.com/r/python/comments/abc123/..."
domain: "reddit.com"
parser: "reddit"
ingested_at: "2026-02-16T12:00:00Z"
content_hash: "sha256:abc123..."
word_count: 2500
---
# How I built an AI agent framework
**r/python** · u/developer123 · 2026-02-16 12:00 UTC
📊 ⬆️ 847 (96% upvoted) · 💬 234 comments · 🏷️ Discussion
---
Post body goes here...
---
### 💬 Top Comments
**u/expert_dev** (⬆️ 342):
> This is a really well-structured approach...
📦 Installation
# Clone the repository
git clone https://github.com/astonysh/OpenClaw-DeepReeder.git
cd OpenClaw-DeepReeder
# Create a virtual environment
python3 -m venv .venv
source .venv/bin/activate
# Install dependencies
pip install -e .
🚀 Quick Start
from deepreader_skill import run
# Process a single URL
result = run("Check out this article: https://example.com/blog/post")
print(result)
# Process a tweet (uses FxTwitter API automatically)
result = run("Interesting thread: https://x.com/elonmusk/status/123456")
print(result)
# Process a Reddit post (uses .json API automatically)
result = run("Great discussion: https://www.reddit.com/r/python/comments/abc123/my_post/")
print(result)
# Process multiple URLs at once
result = run("""
Here are some links:
https://example.com/article
https://youtube.com/watch?v=dQw4w9WgXcQ
https://x.com/user/status/123456
https://www.reddit.com/r/MachineLearning/comments/xyz789/new_paper/
""")
print(result)
🏗️ Architecture
deepreader_skill/
├── __init__.py # Entry point — run() function
├── manifest.json # Skill metadata & trigger config
├── requirements.txt # Dependencies
├── core/
│ ├── router.py # URL → Parser routing logic
│ ├── storage.py # Markdown file generation & saving
│ └── utils.py # URL extraction & helper utilities
└── parsers/
├── base.py # Abstract base parser & ParseResult model
├── generic.py # Generic article/blog parser (Trafilatura)
├── twitter.py # Twitter/X parser (FxTwitter + Nitter)
├── reddit.py # Reddit parser (.json API)
└── youtube.py # YouTube transcript parser
Parser Selection Strategy
URL detected → is Twitter/X? → FxTwitter API → Nitter fallback
→ is Reddit? → .json suffix API
→ is YouTube? → youtube-transcript-api
→ otherwise → Trafilatura (generic)
🔧 Configuration
DeepReeder uses sensible defaults out of the box. Configuration can be customized via environment variables:
| Variable | Default | Description |
|---|---|---|
DEEPREEDER_MEMORY_PATH |
../../memory/inbox/ |
Where to save ingested content |
DEEPREEDER_LOG_LEVEL |
INFO |
Logging verbosity |
🙏 Credits
- FxTwitter / FixTweet — Public API for fetching Twitter/X content
- x-tweet-fetcher — Inspiration for the FxTwitter integration approach
- Trafilatura — Robust web content extraction
- youtube-transcript-api — YouTube transcript fetching
🤝 Contributing
Contributions are welcome! Feel free to:
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-parser) - Commit your changes (
git commit -m 'Add amazing parser') - Push to the branch (
git push origin feature/amazing-parser) - Open a Pull Request
📄 License
This project is licensed under the MIT License — see the LICENSE file for details.
Built with 🦞 by OpenClaw