Mnemosyne – Cognitive memory OS for AI agents (zero LLM calls)
Claims brain-like cognition with zero LLM calls, but zero evidence of actual learning.

This is a practical play: an external memory server that exposes APIs for storing/retrieving agent memories and keeps embeddings/persistent context out of your request path, which directly cuts repeated compute. It's squarely in the vector-store/RAG land so it's useful for multi-agent setups, but the space is crowded — clear adapter support (Chroma/Milvus/Weaviate), latency/cost benchmarks, and migration docs will determine whether it becomes a default piece of infra or just another niche tool.
AI/ML engineers and backend developers building agentic systems or multi-step LLM workflows
Claims brain-like cognition with zero LLM calls, but zero evidence of actual learning.
Versioned code artifacts replace static LangGraph definitions for persistent runnable agent workflows.
Audit-ready semantic memory for legal AI, billing via x402 on Base.
AI Work OS with persistent memory, but core orchestration of docs+chat+tasks exists in Slack, Notion, Linear.
Scatter-gather retrieval with prompt caching beats naive vector search for Claude Code memory.
x402 pay-per-invocation billing is a genuinely novel approach to agent economics.