headroomContext 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.
Why switchSame goal, different reach. Headroom is a reversible compression layer for tool outputs, logs and RAG chunks, and injects instructions to do it; Token Optimizer compresses more surfaces (search, re-read diffs, structure maps) with zero baseline context overhead, and adds compaction survival and structural audits Headroom does not attempt.
Full comparison → rtkRust CLI proxy compressing dev-command output 60-90% before your agent reads it — git, tests, linters, docker, 100+ commands; hooks auto-rewrite bash calls. Single binary, <10ms overhead.
Why switchRTK is one Rust binary that compresses shell output pre-shell in <10ms and stops there. Token Optimizer reaches bash plus grep, file re-reads and compaction loss, and reports dollars — at the cost of a much larger installed surface. Pick RTK for minimal, boring and fast.
Full comparison → fast-jev-compactionClaude 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.
Why switchBoth fight context waste in Claude Code with hooks; token-optimizer compresses reads, bash and search output on the way in, fast-jev-compaction leaves everything verbatim and instead deletes whole tool calls a decision model judges stale.
Full comparison → SoL-PiNVIDIA's Pi extension with four auto-research-discovered efficiency mechanisms - action fusion, observation packing, evidence-preserving log reduction, online context compaction - all opt-in.
Why switchtoken-optimizer uses hooks to compress reads/bash/search output and checkpoint before compaction for Claude Code; SoL-Pi does the equivalent inside Pi.
Full comparison →