fast-jev-compaction vs headroom
Claude Code plugin and npm library that replaces compaction summaries with Jev decisions: every tool call and result is scored, stale ones dropped or truncated, everything kept stays verbatim. — versus — Context compression layer for agents: squeezes tool outputs, logs, files and RAG chunks 20-95% before the LLM — reversible, local-first; library, proxy, one-command agent wrap, or MCP.
Same goal, opposite method: headroom squeezes tool output, logs and chunks 20-95% before the model sees them, this drops entire calls and keeps survivors byte-for-byte - lossy-but-smaller versus exact-but-fewer.
| fast-jev-compaction | headroom | |
|---|---|---|
| Stars | 4.2k | 73k |
| Forks | 221 | 5.6k |
| Language | TypeScript | Python |
| License | MIT | Apache-2.0 |
| Last activity | yesterday | today |
| Topics | coding, agents | coding, gateway |
| Curated connections | 5 | 8 |
fast-jev-compaction — the curator's take
The sharpest idea in agent context management right now: never rewrite, only delete. A summary can silently lose the exact error, path or constraint you need twenty turns later; this scores each tool call and result with two noul questions against the whole conversation and then removes rather than paraphrases, so user and assistant text survives byte-for-byte. Fitting the state into 25k tokens is staged and documented, and it throws instead of guessing when history won't fit. Use it when losing a literal detail is the failure you fear. Don't use it if your bloat is prose rather than tool output - text is never shortened in the output - and note the honest limitation in its own README: a keep probability is not a proof that a result is safe to delete. Needs a hosted TypeSafe key on every compaction.
headroom — the curator's take
The dedicated answer to context bloat: content-aware compressors route JSON, logs and code differently (60-95% on JSON, 15-20% on real coding sessions), originals stay cached so the model can retrieve what compression dropped — the reversibility is what makes aggressive ratios safe. Adoption cost is near zero: `headroom wrap claude` and you're running, or use it as proxy/library/MCP. Prompt-cache-aware alignment avoids torching your cache hit rate. NOT free lunch: a lossy-in-context layer between agent and model is another thing to debug when the model 'misses' something — budget for retrieval round-trips — and the 20% coding figure is the honest number, not the 95% headline.