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Persistent memory + local AI for coding agents. 1.7B–32B open-weight LLM fleet, cross-session Mind Palace, cognitive routing, L3 grounding verifier, multi-agent Hivemind. Works with Claude Code, Cursor, VS Code. Offline-first, HIPAA-ready. Free tier included.

146 starsTypeScript

Prism Coder – Qwen3.5-14B fine-tuned for MCP tool-routing decisions

by dcostenco4·May 20, 2026·2 points·0 comments

AI Analysis

●●●BangerWizardryBig BrainRabbit Hole

Hebbian learning and ACT-R spreading activation bring actual cognitive science to agent memory.

Strengths
  • Implements distinct episodic, semantic, and procedural memory stores for smarter retrieval.
  • Fine-tuned Qwen models enable fully offline tool-calling without leaking context to APIs.
  • Proactive session drift detection auto-corrects agents when they lose track of goals.
Weaknesses
  • Cognitive architecture concepts like 'spreading activation' may overwhelm casual users.
  • Heavy reliance on local GPU resources could limit adoption for users without powerful hardware.
Category
Target Audience

AI agent developers and power users

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Mem0 · Zep · LangChain Memory

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