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  1. Home
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  3. /headroomlabs-ai/headroom
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repoGitHubTrust 82 · PrimaryPublished 23h agoLive · 20h ago

headroomlabs-ai/headroom

Compress tool outputs, logs, files, and RAG chunks before they reach the LLM. 60-95% fewer tokens, same answers. Library, proxy, MCP server.

Lineage graph

Paper → model → repo connections mined from source citations (Tier-1 exact match).

Covers

newsI spent ~4.5 months building a free, self-hosted AI gateway: one endpoint for 237 providers (90+ free), auto-fallback, and a token-compression pipeline (MIT)news[R] Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost

Implements

paperTraceLab: Characterizing Coding Agent Workloads for LLM Serving

Related across the graph

paperTraceLab: Characterizing Coding Agent Workloads for LLM ServingnewsI spent ~4.5 months building a free, self-hosted AI gateway: one endpoint for 237 providers (90+ free), auto-fallback, and a token-compression pipeline (MIT)news[R] Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost
Knowledge path·PTraceLab: Characterizing Coding Agent Workloads for LLM Serving→NI spent ~4.5 months building a free, self-hosted AI gateway: one endpoint for 237 providers (90+ free), auto-fallback, and a token-compression pipeline (MIT)→N[R] Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost→Rheadroomlabs-ai/headroom

Topics

agentaianthropicclaude-codecompressioncontext-engineeringcontext-windowcursorfastapilangchain

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RIS65estimated
Graph trust82Primary
Graph score55829