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Running on Zero
| title: Cordon - Log Anomaly Detection | |
| emoji: 🔍 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 6.2.0 | |
| app_file: app.py | |
| pinned: false | |
| license: apache-2.0 | |
| short_description: Find anomalies in logs using AI. Paste logs, get insights. | |
| hardware: zero-gpu | |
| tags: | |
| - log-analysis | |
| - anomaly-detection | |
| - semantic-search | |
| - embeddings | |
| - transformers | |
| - devops | |
| - sre | |
| - observability | |
| - debugging | |
| - nlp | |
| - text-analysis | |
| - machine-learning | |
| - ai-tools | |
| # Cordon | |
| **Reduce logs to their semantically anomalous parts.** | |
| Cordon uses AI to understand the *meaning* of your logs and find the parts that are semantically unusual—reducing massive log files to just the anomalous sections worth investigating. | |
| ## How It Works | |
| 1. **Segmentation** — Logs are split into non-overlapping windows | |
| 2. **Embedding** — Each window is vectorized using transformers | |
| 3. **Scoring** — Distance to nearest neighbors identifies unusual patterns | |
| 4. **Thresholding** — Top anomalies are selected | |
| 5. **Output** — XML-tagged blocks ready for LLM analysis | |
| ## Use Cases | |
| - **Debug faster** — Find the needle in the haystack | |
| - **LLM pre-processing** — Reduce logs to fit in context windows | |
| - **Incident response** — Surface unusual events quickly | |
| - **Exploratory analysis** — Discover patterns you didn't know to search for | |
| ## Key Insight | |
| > Repetitive patterns (even errors) are filtered out. Cordon surfaces the *rare* and *unusual* events that stand out from the bulk of your logs. | |
| ## Links | |
| - [GitHub Repository](https://github.com/calebevans/cordon) | |
| - [PyPI Package](https://pypi.org/project/cordon/) | |
| - [Red Hat Developer Article](https://developers.redhat.com/articles/2025/12/09/semantic-anomaly-detection-log-files-cordon) |