A pluggable CLI filter that strips verbose output before it hits Claude, ChatGPT, or any LLM API. Save hundreds per month on token costs.
Get Started on GitHubReal CLI output filtering in action
Every time you pipe a CLI command to Claude or ChatGPT, you pay for every character. When you run kubectl get -o yaml, you get 10,000 lines of output. Your LLM needs about 50 lines to make a decision. You're paying 200x for noise.
Error traces with 500 lines of stack unwinding. Git logs with 1000 commits. Build logs with duplicated status lines. Deploy manifests with hundreds of unchanged fields. All of it hits your API, all of it costs tokens, and 95% of it never influences the LLM's answer.
kubectl get pods output
Or: ~$20 per month for 100 queries
Filtered output
Or: ~$2 per month for 100 queries
Lowfat sits between your CLI and your LLM. It understands command patterns and strips the noise: timestamps, repeated lines, empty fields, verbose headers, debug metadata. What's left is signal.
One single binary. Works as an agent hook. Works as a shell wrapper. Works with custom filter plugins. Drop it in and it just works.
Two months of real-world use: 91.8% token reduction across common DevOps and deployment workflows. Compounded across a team, that's hundreds of dollars per month saved.
Lowfat is a Go binary that compiles to a single executable. Drop it in your PATH, add it to your agent hooks, and it works. No runtime, no dependencies, no config hell.
Use it as an agent hook, a shell wrapper, a piped filter, or integrated into your scripts. Works with Claude, ChatGPT, Anthropic SDK, or any LLM API that accepts text input.
Out of the box, Lowfat knows kubectl, docker, git, npm, and common Unix tools. Add your own filters via a simple plugin system. Customize per command.
lowfat history --all shows exactly what you're saving. Track cost savings over time, per command, per team member. Know your ROI.
Runs locally. Runs offline. Open source licensing. No cloud lock-in. No telemetry. No vendor dependencies. Your data stays yours.
Not theoretical. 91.8% token reduction verified over 2 months of personal use on real DevOps workflows, agent integrations, and deployment pipelines.
DevOps and Infrastructure Teams: Before piping kubectl, docker, or helm output to Claude for debugging and suggestions, run it through Lowfat. Reduce token costs by 80-95%. Same advice, 1/10th the price.
Autonomous Agents: Agents that make decisions from system logs, error traces, deployment output, or cluster state can now run 10x more queries for the same API budget. Or run the same queries at 1/10th the cost.
CI/CD Pipelines: Agent-driven CI/CD (tests, deployments, rollbacks) generate massive logs. Lowfat filters them before feeding to the LLM. Faster decisions, lower token spend.
Log Analysis and Monitoring: Feeding raw application logs to Claude or ChatGPT for analysis? Lowfat extracts the signal, removes duplicates and timestamps, keeps the error messages and stack traces that matter.
Git and Code Analysis: Large git logs, diff output, or code analysis results become manageable. Lowfat strips the boilerplate, keeps the semantics your LLM needs.
Installation: Download the binary from GitHub releases or build from source.
As a shell wrapper:
As an agent hook (Anthropic SDK):
Custom plugin for your tool:
Then run lowfat my-verbose-tool args and it automatically applies your filters.
Lowfat is free and open source under the MIT license. Use it anywhere, customize it, redistribute it. No commercial license required.
Commercial Support: For teams needing dedicated support, custom filters, or SLA commitments, contact us on GitHub for commercial licensing options.
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