{"slug": "headroom", "name": "headroom", "category": "ai", "category_name": "AI models and assistants", "description": "", "recommenders": 1, "commenters": 4, "warners": 1, "score": 0.392, "rising": -0.33, "first": "2026-04-26", "latest": "2026-08-11", "why": "", "why_is_quote": false, "has_verdict": false, "sentiment": {"recommend": 1, "neutral": 1, "compare": 2, "warn": 1}, "spark": [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], "listed": false, "loved": [], "caveat": "", "short_quote": "", "icon": false, "domain": null, "github_repo": "github.com/chopratejas/headroom", "official_url": "https://github.com/chopratejas/headroom", "pricing": "", "aliases": ["headroom", "Headroom"], "verdict": null, "praise": [{"label": "", "summary": "", "quotes": [{"id": 777501728, "item_id": 48593858, "url": "https://news.ycombinator.com/item?id=48593858", "by": "jvican", "date": "2026-06-18", "stance": "recommend", "quote": "Headroom, a much more legit alternative to RTK", "excerpt": "An ex colleague is working on Headroom, a much more legit alternative to RTK. They provide accuracy benchmarks in the repo and are transparent about the compression algorithms used for the different output types. I liked their approach a lot better than RTK and thought it might be relevant for you. https://github.com/chopratejas/headroom", "context": "", "use_case": "LLM token compression", "switched_from": "", "story": "The Token Compression Illusion: Why I'm Skeptical of RTK"}]}], "caveats": [{"label": "", "summary": "", "quotes": [{"id": 770792801, "item_id": 48174550, "url": "https://news.ycombinator.com/item?id=48174550", "by": "esperent", "date": "2026-05-17", "stance": "warn", "quote": "I got clearly better results with both off", "excerpt": "I did some evals with pi and GPT 5.5. I tested RTK on / headroom on / both on / both off (all with the standard pi system instructions and no AGENTS.md). I forget the exact tests I used (a couple of the standard agent evals that people use, one python and one typescript because those are what I use). I don't claim it was an exhaustive test, or even a good one. It's possible I could have spent a day or so tuning my AG", "context": "ran own agent evals", "use_case": "reducing agent token use", "switched_from": "", "story": "Show HN: Semble \u2013 Code search for agents that uses 98% fewer tokens than grep"}]}], "who": [], "tags": [], "use_cases": ["reducing tool tokens", "token saving", "llm token compression", "reducing agent token use", "reducing token usage"], "compared": [{"slug": "rtk", "name": "RTK", "co": 4, "switched_from": 0, "switched_to": 0}, {"slug": "pi", "name": "Pi", "co": 2, "switched_from": 0, "switched_to": 0}], "makers": [{"id": 746052624, "item_id": 46628289, "url": "https://news.ycombinator.com/item?id=46628289", "by": "chopratejas", "date": "2026-01-14", "stance": "recommend", "quote": "I built Headroom because I was spending $200/day running agents with tool calls.", "excerpt": "Author here. I built Headroom because I was spending $200/day running agents with tool calls. The problem: tools return huge JSON (search results, DB queries, file listings). Each response bloats context. By turn 10, you're paying for 100k+ tokens on every LLM call. Existing solutions have a fundamental tradeoff: - Truncation: fast but you might cut data the model needs - Summarization: slow (~500ms) and still lossy ", "context": "", "use_case": "LLM context compression", "switched_from": "", "story": "Show HN: Headroom \u2013 Reversible context compression for LLMs(~60% cost reduction)"}], "threads": [{"url": "https://news.ycombinator.com/item?id=49253721", "title": "Show HN: Mcptoon \u2013 MCP CLI client that cuts tool discovery tokens by 97%", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=48606997", "title": "Ask HN: Will we start seeing tools for LLM use?", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=48588755", "title": "The Token Compression Illusion: Why I'm Skeptical of RTK", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=48169874", "title": "Show HN: Semble \u2013 Code search for agents that uses 98% fewer tokens than grep", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=47914963", "title": "8v: One CLI for you and your AI agent. Up to 66% fewer tokens", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=46628278", "title": "Show HN: Headroom \u2013 Reversible context compression for LLMs(~60% cost reduction)", "mentions": 1}], "sentiment_by_month": {"2025-11": {}, "2025-12": {}, "2026-01": {}, "2026-02": {}, "2026-03": {}, "2026-04": {"neutral": 1}, "2026-05": {"warn": 1}, "2026-06": {"recommend": 1, "compare": 1}, "2026-07": {}, "2026-08": {"compare": 1}, "2026-09": {}, "2026-10": {}}, "quotes": [{"id": 788098896, "item_id": 49256181, "url": "https://news.ycombinator.com/item?id=49256181", "by": "swedishagentic", "date": "2026-08-11", "stance": "compare", "quote": "How is this different from headroom?", "excerpt": "How is this different from headroom? Mcptoon seems like it's specific to tool calls. https://github.com/headroomlabs-ai/headroom", "context": "", "use_case": "reducing tool tokens", "switched_from": "", "story": "Show HN: Mcptoon \u2013 MCP CLI client that cuts tool discovery tokens by 97%"}, {"id": 777712689, "item_id": 48607043, "url": "https://news.ycombinator.com/item?id=48607043", "by": "bonigv", "date": "2026-06-20", "stance": "compare", "quote": "It runs elaborate subsystems towards a noble objective of saving tokens.", "excerpt": "Clarifying - I am not suggesting headroom and lean-ctx are only doing the tool compression. It runs elaborate subsystems towards a noble objective of saving tokens. RTK on the otherhand is focused purely on the compression. At the sametime, platforms - the providers - especially Anthropic and and Open AI have transparently absorbed many of the big ticket token saving items these tools are cleverly managing. That is a", "context": "", "use_case": "token saving", "switched_from": "", "story": "Ask HN: Will we start seeing tools for LLM use?"}, {"id": 777501728, "item_id": 48593858, "url": "https://news.ycombinator.com/item?id=48593858", "by": "jvican", "date": "2026-06-18", "stance": "recommend", "quote": "Headroom, a much more legit alternative to RTK", "excerpt": "An ex colleague is working on Headroom, a much more legit alternative to RTK. They provide accuracy benchmarks in the repo and are transparent about the compression algorithms used for the different output types. I liked their approach a lot better than RTK and thought it might be relevant for you. https://github.com/chopratejas/headroom", "context": "", "use_case": "LLM token compression", "switched_from": "", "story": "The Token Compression Illusion: Why I'm Skeptical of RTK"}, {"id": 770792801, "item_id": 48174550, "url": "https://news.ycombinator.com/item?id=48174550", "by": "esperent", "date": "2026-05-17", "stance": "warn", "quote": "I got clearly better results with both off", "excerpt": "I did some evals with pi and GPT 5.5. I tested RTK on / headroom on / both on / both off (all with the standard pi system instructions and no AGENTS.md). I forget the exact tests I used (a couple of the standard agent evals that people use, one python and one typescript because those are what I use). I don't claim it was an exhaustive test, or even a good one. It's possible I could have spent a day or so tuning my AG", "context": "ran own agent evals", "use_case": "reducing agent token use", "switched_from": "", "story": "Show HN: Semble \u2013 Code search for agents that uses 98% fewer tokens than grep"}, {"id": 766662369, "item_id": 47916398, "url": "https://news.ycombinator.com/item?id=47916398", "by": "esperent", "date": "2026-04-26", "stance": "neutral", "quote": "I have tried both of these and I have to say, I didn't notice any difference.", "excerpt": "Similar tools in the space: RTK which I guess is a close equivalent. https://github.com/rtk-ai/rtk Headroom which is complementary. https://github.com/chopratejas/headroom I have tried both of these and I have to say, I didn't notice any difference. That's not to say there wasn't a difference (rtk logs were telling me enthusiastically how many tokens I'd saved), it's just that pretty much all context is coming from r", "context": "", "use_case": "reducing token usage", "switched_from": "", "story": "8v: One CLI for you and your AI agent. Up to 66% fewer tokens"}]}