MCP memory server with dual-timeline conflict arbitration. Not just store & search — resolve contradictions.
View on GitHub ↗mcp-name: io.github.billy12151/memory-arbiter-mcp
A lightweight, fully local AI enhancement middleware — not another memory tool, but a layer that makes every AI client you already use noticeably smarter, by fixing the one thing they all get wrong: context quality.
From planning (OpenClaw) to design (OpenDesign) to code (ZCode, Codex, Cursor, Claude Code) — Memory Arbiter connects your entire AI toolchain through one shared SQLite database.
Your AI client loads MEMORY.md + memory/.md into the system prompt every turn. As knowledge grows, 5K–20K tokens burn before the model even reads your question — and worse, the model drowns in noise, losing track of what's current, what's confirmed, and what's stale.
Memory Arbiter replaces this with a SQLite-backed search: only the relevant entries come back, everything else stays on disk.
The result isn't just cheaper — it's sharper. When the model receives precise, de-duplicated, trust-ranked context, the same model produces noticeably better output. Most AI execution errors aren't the model being dumb — they're the model acting on stale, contradictory, or diluted context. Fix the input, and your existing model jumps a level.
Token savings are easy to measure. But the deeper, more impactful change is output quality — because Memory Arbiter fixes what the model sees before it even starts thinking.
What pollutes AI context How it degrades output How Memory Arbiter fixes it --------- Key info buried in a 20K-token blob Model attention is spread thin; it grabs the wrong detail or hallucinates. memorysearch returns 3–5 laser-relevan
From the project README.
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An OKF-backed Model Context Protocol (MCP) server delivering persistent long-term memory and SQLite FTS5 search for AI agents.
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