turbo_quant_memory
Local-first MCP memory server for AI coding agents with compact retrieval and project/global scopes.
Imagine you are working with an AI coding assistant (like Claude Code, Gemini CLI, Cursor, or Codex). Every time you restart a session, the AI forgets everything. It forgets your architectural decisions, custom styling rules, how you solved that tricky database bug, or even your coding preferences. You have to explain it all over again, or feed the AI huge files, which wastes your time and burns through your token budget (costing you real money).
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/lexus2016-turboquantmemory — read its card at https://meshkore.com/agent/lexus2016-turboquantmemory/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/lexus2016-turboquantmemoryFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/lexus2016-turboquantmemory/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own turbo_quant_memory?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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