Summarize massive logs locally. Stop burning token budgets on noise.
Get Started FreeYou hit a production error. You paste a 30KB stack trace into Claude for debugging. The AI spends 35% of the response tokens regurgitating your own logs back at you. Your token budget burns. Your cost per debug cycle doubles.
The raw logs contain massive noise: duplicate timestamps, framework boilerplate, sanitized redactions, memory addresses. The signal you need (what failed, where, why) is buried in walls of text.
You need local log compression. Run before pasting into Claude, ChatGPT, or any LLM. No uploads. No privacy risks. Just smarter summaries.
Dump your full stack trace, kubectl output, database logs, or application crash. Size doesn't matter.
Our rules extract signal, strip noise. Format agnostic: JSON, syslog, Go panics, Python tracebacks, raw text.
70-85% smaller. Same signal. Now Claude spends tokens on analysis, not repetition.
No API calls. No cloud uploads. No privacy concerns. Your logs never leave your machine.
Strip boilerplate, deduplicate timestamps, remove redactions. Keep what the AI needs to understand your error.
Works with JSON, syslog, Python tracebacks, Go panics, Node stack traces, Rust backtraces, raw text.
Define noise patterns for your stack. Update rules without re-release. Share rule sets with your team.
Paste raw log. Get condensed output. Paste into Claude. No CLI flags, no config files, no friction.
Run anywhere. Audit the rules. Contribute improvements. No vendor lock-in.
Every token costs money and latency. A 30KB error log parsed by Claude wastes 5-10K tokens on echo. Condenser cuts it to 1-2K. On a team doing 50 debug cycles per week, that's 20,000 wasted tokens per week. At $0.03/1K, that's $600/month of pure noise.
Beyond cost: faster feedback loops. Fewer tokens in context means faster response times. Fewer page-downs reading regurgitation. Debugging becomes sharp, not tedious.
Error Log Condenser is free to use locally. No registration, no limits, no trials.
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