YantrikDB – persistent memory for AI agents
Bundled 7MB embedder means zero network calls or model downloads for agent memory.

Bi-temporal versioning with LLM-driven auto-supersede solves agent memory rot elegantly.
AI agent developers and backend engineers
Mem0 · Zep · LangChain Memory
Bundled 7MB embedder means zero network calls or model downloads for agent memory.
Predict-calibrate extraction reduces noise, but Zep and Mem0 already dominate the agent memory space.
Path-scoped memory beats project-wide rules files for team context sharing.
Yet another agent framework competing with LangChain and CrewAI in a saturated market.
LLM-as-strategy replaces rigid backtesting, remembers trades, no code rewrites needed.
Hooks into MCP (Claude Desktop, Ollama, etc.) and keeps everything on disk — auto-saved chats, Slack/Notion imports, and file ingestion make it useful right away for local-agent workflows. The hybrid retrieval combo (graph + vector + keyword) without requiring an external vector DB is an interesting engineering choice, but the space is crowded and I want benchmarks and failure-mode details before recommending it for production.