headroom vs SoL-Pi
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. — versus — NVIDIA's Pi extension with four auto-research-discovered efficiency mechanisms - action fusion, observation packing, evidence-preserving log reduction, online context compaction - all opt-in.
Both compress tool outputs and context before the model sees them; headroom is a harness-agnostic proxy layer, SoL-Pi is Pi-native with evidence-preservation guarantees.
| headroom | SoL-Pi | |
|---|---|---|
| Stars | 73k | 2.3k |
| Forks | 5.6k | 181 |
| Language | Python | TypeScript |
| License | Apache-2.0 | MIT |
| Last activity | 2 days ago | yesterday |
| Topics | coding, gateway | coding, agents |
| Curated connections | 6 | 6 |
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.
SoL-Pi — the curator's take
Install SoL-Pi if you run Pi as your coding agent and want fewer turns and less context replay without the agent skipping work: Action Fusion runs an edit's validation command in the same tool call, ObservationPack turns repeated big tool results into paged handles, the Reducer only compacts a log when every retained quote still matches the archive. It is an extension over unmodified Pi 0.85.1 - no patches, everything disabled until you write sol-pi.json. Not for Claude Code/Codex users (it uses Pi's extension APIs); headroom, rtk and token-optimizer are the harness-agnostic ways to get the same effect. The paper is the interesting part: the mechanisms were found by scaled auto-research loops over the harness itself, which is autoresearch/evo pointed at agent efficiency.