calebevans / cordon
Reduce logs to their semantic anomalies
Why this job: works in the areas we cover, but does not say which job it does
What it does
- Semantic Analysis. Uses transformer models to understand log content meaning, not just keyword matching
- k-NN Density Scoring. Identifies anomalies using k-NN distance in embedding space
- Noise Reduction. Filters out repetitive logs, keeping only unusual patterns
- Multiple Backends. sentence-transformers (default), llama.cpp for containers, or remote APIs (OpenAI, Gemini, etc.)
- Multiple Output Formats. XML (default) or JSON output with --format json
Get started
make container-build- Maintenance
- Active
- Last commit 5 months ago
- Stars
- 165
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v1.1.1
- Released 5 months ago
- Built with
- Python
- 99% of the code
Is it looked after?
- Contributors
- 0
- Last 90 days
- Issues
- 100% closed
- 0 still open
- Pull requests
- 39 merged
- 1 waiting
- Releases
- 15
- 15 in the last year
- Contributing guide (opens in new tab)
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Python 99%
- Makefile 1%
- Dockerfile <1%
Topics
- anomaly-detection
- context-engineering
- devops
- embeddings
- log-aggregation
- log-analysis
- machine-learning
- nlp
- observability
- preprocessing
- pytorch
- semantic-analysis
Project record
- JobOther AI tools
- StatusListed
- Commits158
- Forks6
- Watching3
- Stars in 28 days+2
- CreatedNov 21, 2025
- First seen by usNov 7, 2026
- CheckedNov 7, 2026