Intelligent Log Root Cause Analysis — one command opens a full React dashboard.
Project description
CausaLog 🔍
Intelligent Log Root Cause Analysis — one command, instant dashboard.
Quick start
pip install causalog
python -m spacy download en_core_web_sm
causalog run # auto-discovers .log/.json in current directory
causalog run /var/log/app.log # explicit file
causalog run logs/ # scan a directory
Opens an interactive React dashboard at http://localhost:8000 showing:
- Dashboard — metric cards, system health timeline, AI insights, root cause hypotheses
- Analysis — full paginated log stream with filtering by level/service/keyword
- Clusters — grouped log clusters with severity, keywords, sparklines
- Timelines — area chart of errors/anomalies over time with spike markers and debug runbooks
Options
causalog run app.log --port 9000 # custom port
causalog run app.log --no-browser # server only, no browser popup
causalog run app.log --no-server # terminal report only
causalog run app.log --report out.json # also save a JSON report
causalog version
How it works
Log file upload
↓
Preprocessing (Pandas + Regex) — parse timestamps, levels, services
↓
NLP Extraction (spaCy) — extract keywords from messages
↓
Clustering (TF-IDF+KMeans) — group similar log entries
↓
Anomaly Detection(IsolationForest)— flag statistically unusual entries
↓
Ontology Mapping (keyword→cause) — map to root cause categories
↓
RCA Engine (rule-based) — frequency + per-error details
↓
Intelligence Layer — hypotheses, confidence scores,
temporal trend analysis,
actionable debug suggestions
↓
React Dashboard (FastAPI + CDN) — live, connected, no npm needed
Requirements
- Python ≥ 3.9
- spaCy English model:
python -m spacy download en_core_web_sm
Stack
| Layer | Technology |
|---|---|
| Backend API | FastAPI + Uvicorn |
| Frontend | React 18 + Recharts (CDN, no npm) |
| ML/NLP | scikit-learn, spaCy |
| Data | Pandas, NumPy |
License
MIT
Project details
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