Show HN: Mengram – AI agent memory with facts, events, and evolving workflows (github.com)
Hi HN, I built Mengram because every AI memory tool I tried only stored facts. My agents kept making the same mistakes — forgetting what happened, losing workflows.
Mengram stores 3 types: semantic (facts), episodic (events/decisions), and procedural (workflows). The key difference: procedures evolve when they fail.
Week 1: deploy → build → push (fails: forgot migrations)
Week 2: deploy v2 → build → migrate → push (fails: OOM)
Week 3: deploy v3 → build → migrate → check memory → push
This happens automatically from conversations — report a failure, the procedure evolves.
Stack: Python, PostgreSQL + pgvector, FastAPI. Free cloud API, self-hostable, SDKs for Python/JS, integrations with LangChain, CrewAI, MCP.
Honest limitations: extraction quality depends on LLM, procedural evolution needs clear failure descriptions, no real-time streaming yet.
Would love feedback on the memory model — is 3 types the right abstraction, or too complex?
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