OpenMaven monitors Reddit comment streams in real-time and turns raw community noise into actionable intelligence. Built in Rust for speed and reliability, it's designed for traders, analysts, and anyone who needs to identify emerging trends before they hit the mainstream.
It goes beyond basic sentiment scraping—OpenMaven generates analyst-style summaries that explain why the mood is shifting and what that likely means for the market.
- 🎯 Live Sentiment Scoring — Every incoming comment is classified (Positive / Negative / Neutral) as it arrives via the Sylvia stream.
- 🧠 Mood Shift Detection — Automatically triggers when sentiment changes significantly, generating a concise brief on what's driving it.
- 🔮 Predictive Signals — Produces forward-looking predictions with confidence scores, so you know how much weight to put on them.
- 📚 Contextual Memory — Uses a vector store to retain historical data, allowing it to connect current events to past patterns.
- ⚡ High Performance — Rust keeps things fast and memory-safe, handling large data volumes without issue.
- 🔔 Telegram/Discord Notifications - Uses telegram bot token or discord webhook to send formatted notificaitons
- ⏲️ Low latency - Due to sylvia, we get access to data at sub 7 second latency from the moment it is posted, but granted with higher polling rates prices increase as well, and its often not needed for normal usecase unless youre monitoring a lot of subreddits.
- Language: Rust (1.75+)
- Database: SQLite — stored at
~/.openmaven/data.db - Data Source: Sylvia API
- AI Providers: Groq, Anthropic, OpenAI, HuggingFace, Ollama (your choice)
Prerequisites:
- Rust — Install from rust-lang.org
- Sylvia API key — Get one at sylvia-api.com
- AI provider key — OpenAI, Groq, or similar
git clone https://github.com/glassheadclown/openmaven
cd openmaven
cargo runOn first run, a setup wizard walks you through configuration. You have two options:
- Easy setup — Pick a major provider (Groq, OpenAI, etc.) and let the wizard configure defaults.
- Manual setup — Set your own API URLs, rate limits, and polling windows.
The wizard also shows an estimated daily cost for the Sylvia API before you commit.
| Command | Description |
|---|---|
cargo run |
Start OpenMaven (or launch the setup wizard on first run) |
cargo run -- dashboard |
Open the TUI dashboard |
cargo run -- setup |
Re-run configuration |
cargo run -- backfill |
Fetch historical data (e.g., --subreddit wallstreetbets --limit 10) |
cargo run -- export |
Export your data (e.g., --format markdown) |
cargo run -- stats |
View database stats |
Settings are stored at ~/.openmaven/config.toml and can be edited directly if you prefer not to use the wizard.
[sylvia]
api_key = "syl_..."
max_comments_per_poll = 100
[ai]
provider_type = "openai"
sentiment_model = "llama-3.1-8b-instant" # Fast/cheap for high-volume classification
narrative_model = "llama-3.3-70b-versatile" # More capable model for analysis
tpm_limit = 6000
[[tracking.subreddits]]
name = "wallstreetbets"
keywords = ["GME", "puts", "calls"]
poll_interval_secs = 60Signal: Sentiment around [Topic] in r/worldnews is shifting rapidly. 🔮 High Confidence — 85% | Timeframe: next 2–4 hours Historically, this pattern precedes an official government statement. Watch for movement from NATO.
| Poll Interval | Est. Daily Cost(24H) |
|---|---|
| 15 seconds | ~$864 |
| 60 seconds | ~$216 |
| 5 minutes | ~$43 |
| 15 minutes | ~$14 |
AI inference costs vary by provider. OpenMaven batches requests to stay within rate limits.
- Implement global keywords that work across all selected subreddits
- Implement a chat feature for web/tui dashboard
- Build a better web dashboard
- Implement trend reporting in Tui dashboard
- Implement setting daily budget setup
- Fork the repo
- Create a branch (
git checkout -b feature/YourFeature) - Run
cargo fmtandcargo clippybefore submitting - Open a pull request
MIT — see the LICENSE file for details.
⭐ If this is useful, a star goes a long way!
Made with ❤️ by glassheadclown with help from claude code