logsleuth 0.4.0

Local AI root-cause analysis for production logs. Nothing leaves your machine.

logsleuth

Local AI root-cause analysis for production logs. Nothing leaves your machine.

Feed it an incident's worth of logs — gigabytes really are fine: 183MB scans in 3 seconds across your cores using 21MB of RAM — and get back a structured root-cause report: symptom, timeline, hypothesis with cited evidence, ruled-out red herrings, next steps. All inference runs locally via Ollama, so you can use it on logs you'd never paste into a cloud AI: they're full of PII, tokens and internal hostnames, and your security team knows it.

$ logsleuth incident.log

## Root cause hypothesis
PostgreSQL connection pool size reduction (from 50 to 10) during deployment.
Confidence: High.
- Deployment log explicitly sets PG_POOL_MAX=10 (line 1316)
- "pg pool timeout" errors start immediately after the deployment
- Latency grew from 56.9ms to 217.9ms (x3.8), idle connections dropped to 0

## Ruled out
- "healthz" noise present before the incident — baseline, not cause
...

Why

• Your logs never leave the machine. No API keys, no cloud, no data processing agreements, no argument with the CISO. Works air-gapped.
• Built for real log sizes. Streaming, parallel, bounded memory: the file is read once in byte-range chunks across CPU cores and only aggregates are kept, so a 2GB log costs the same RAM as a 2MB one. (Measured: 2M lines in 3.1s / 21MB on an M1 Pro; a naive in-memory pass took 29s and 788MB.)
• No keyword lists to maintain. State changes are found structurally: a deploy, an eviction or a leader switch is a near-unique line in a file where normal operation repeats thousands of times. Rare + shortly-before-the-first-error = prime suspect, in any stack, any vendor, any phrasing.
• It reads all of it. A deterministic preprocessor crunches the full file — deduplicates error patterns, computes numeric trends (heap, latency, disk, queue depths), finds deploys/migrations/flag flips, detects error concentration by node/pod/build — and hands the model a dense evidence pack. No more guessing which 10KB of a 2GB log to paste into a chatbot.
• It distrusts loud noise. Patterns present since the start of the file are flagged as baseline; the report explicitly lists red herrings it didn't blame.
• Honest by design. The model is instructed to cite only real lines and to say "insufficient evidence" rather than invent a story.

Benchmarks

Two blind benchmark sets, both in bench/ with generators and ground truth. Every scenario was written after the code was frozen, so nothing is tuned to the answers. Reproduce with python bench/run_bench.py.

Set 1 — 10 common failures (Kafka rebalance storms, expired TLS certs, clock skew, cache evictions after a config change, backup-window IO saturation, connection leaks, third-party rate limiting, NFS-stalled thread pools, bad canaries, flapping health checks): 10/10 correct root causes.

Set 2 — 9 harder failures, most requiring a 2–3 hop causal chain (a poison-pill message stuck at one offset, inode exhaustion with 412GB free, a DST-repeated cron run, an upstream contract change, a hot shard, CPU throttling after a pod reschedule, humongous-allocation GC thrash, split brain, config drift): 6/9 correct. The 3 misses name the right component but stop one causal hop short of the trigger.

Measured with qwen3:8b on a MacBook M1 Pro (16GB), ~80s per analysis. Bigger models do better; that's a one-flag change.

Install

pipx install logsleuth       # or: pip install logsleuth   (installs the `logsleuth` command)
ollama pull qwen3:8b          # one-time, ~5GB
logsleuth /var/log/app/incident.log

No other dependencies — pure stdlib.

Output

In a terminal you get a visual report: an incident map (error density across the file, with deploy/config markers), trend sparklines (latency, memory, queue depths), and color-coded sections with confidence badges. Piped or with --plain it degrades to clean markdown; --json for machines.

 INCIDENT MAP (error density across the file)
   ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▃▅▄▅▅▄▄▅▆▇▅▆▆▆▆▆▆▆▆▆▆▆▇▆▇▆▆███
                 ▼                               ▼ = deploy/config/migration

 TRENDS
   ▼ idle 28→0                    ██████▄▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
   ▲ latency 57→218ms             ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁██▇███▁▁▁▁▁

Usage

logsleuth demo                        # try it right now on a bundled sample incident
logsleuth incident.log                # analyze a file
logsleuth api.log db.log gateway.log  # merge services chronologically; source becomes a dimension
kubectl logs deploy/api | logsleuth -  # or pipe anything into it
logsleuth incident.log --json         # machine-readable output
logsleuth incident.log --dry-run      # show the evidence pack, prove nothing else is sent
logsleuth incident.log --model qwen3:14b  # bigger machine, smarter analysis

--dry-run prints exactly what would be passed to the local model — audit it, then grep your favorite secret to confirm it's not there.

Choosing a model

Your machine	Model	Notes
8GB RAM	qwen3:4b	fast, decent
16GB RAM	qwen3:8b	default, benchmark numbers above
32GB+ RAM	qwen3:14b / qwen3:32b	noticeably deeper analysis
On-prem server	anything Ollama serves	point LOGSLEUTH_OLLAMA_URL at it

How it works

raw logs ──► deterministic preprocessor ──► evidence pack ──► local LLM ──► report
             (dedup, trends, changes,        (~25KB max)       (Ollama)
              dimensions, context windows)

The preprocessor is the point: local 8B models are good analysts but bad readers of 2GB files. logsleuth does the reading with boring, auditable code and saves the model for the part it's actually good at — causal reasoning over dense evidence.

Roadmap

• Deeper causal-chain analysis (the 2/10 benchmark misses)
• logsleuth-8b: a fine-tuned model distilled from thousands of incident analyses
• Watch mode / webhook: auto-analyze on alert
• Team features: shared incident history, PagerDuty/Opsgenie integration

License

MIT