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OSS2026-07-28 · source-backed
62,876 stars, ~313/day. Compresses tool outputs, logs, RAG chunks and files before they hit the model, and unusually publishes the other side of the trade: GSM8K holds at 0.870 (±0.000), TruthfulQA improves 0.530 → 0.560, SQuAD v2 and BFCL retain 97%. Real workloads: 92% savings on code search (17,765 → 1,408 tokens), 92% on SRE debugging, 73% on GitHub issue triage. Three-stage pipeline routes by content type into SmartCrusher (JSON), an AST-aware CodeCompressor covering eight languages, or a trained Kompress-v2-base for prose. The detail that shows they've actually run this in production: a CacheAligner deliberately preserves provider KV-cache prefixes so compression doesn't destroy prompt caching. Library, proxy (headroom proxy --port 8787) or MCP server. (GitHub)
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headroomlabs-ai/headroom at 62.5K stars sits between an agent and its LLM compressing tool outputs, logs, files, and RAG chunks in flight, claiming 20% fewer tokens for coding agents and 60-95% fewer for JSON at unchanged answer quality (GitHub). Six content-type-specific comp...
Headroom compresses tool outputs, logs, RAG chunks, and files before they ever reach the model. It deploys as a library, a proxy server, or an MCP server, and the benchmarks are blunt: 92% token reduction on code search (17,765 down to 1,408) and SRE debugging (65,694 down to...
1. Use claude agents --json to build session dashboards. Claude Code v2.1.145 outputs all live agent sessions as structured JSON with status, model, elapsed time, and parent relationships. Pipe it into a tmux status bar widget or session picker script for switching between bac...
If the margin collapse is happening to the providers, the demand-side version is happening in your own usage graph. Practitioners are now stacking independent token-reduction layers and compounding the savings past 90% total. (Codepointer) The recipe that's making the rounds:...
They work at different layers. RTK sits between the agent and the shell, compressing per-command output 60-90% across 100-plus commands. Headroom sits as a local API proxy compressing everything bound for the model: JSON, AST-aware code, logs, RAG results, conversation history...
Headroom compresses what enters the model (tool results, logs, RAG chunks) via AST- and schema-aware compressors; Caveman compresses what leaves it (narration) via prompt-level style constraints. Their own benchmarks show output-side compression collapsing to single digits on...
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