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Show HN: Mengram – AI agent memory with facts, events, and evolving workflows

2 points by mengram-ai 7mo ago

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?

mengram-ai 7mo ago
Hi HN, I'm Ali. I've been building Mengram for the past year. The problem: Every AI memory tool stores facts — "user likes dark mode." But when my agents failed at a task, they'd fail the exact same way next time. They had no memory of what happened or how to do things better. What Mengram does: It stores 3 types of memory, modeled after how human cognition works: - Semantic — facts and preferences (like Mem0, Zep) - Episodic — events, decisions, outcomes (what happened and when) - Procedural — learned workflows that evolve when they fail The procedural part is what I'm most excited about. When an agent reports a failure, Mengram automatically evolves the procedure — adds a new step, changes the order, removes what didn't work. Your agent literally gets better at its job over time. Example: Week 1, "Deploy" = build → push → deploy. Agent forgets migrations, DB crashes. Week 2, Mengram evolves it to build → run migrations → push → deploy. Agent hits OOM. Week 3, adds memory check step. This happens automatically. Technical details: - Python & JS SDKs: pip install mengram-ai - Free cloud API (no credit card) or fully self-hostable - MCP server for Claude Desktop / Cursor (21 tools) - LangChain, CrewAI, OpenClaw integrations - Knowledge graph + vector search + reranking - Cognitive Profile: one API call generates a system prompt from all memories What it's NOT good at (yet): - Smaller community than Mem0 (they have 25K stars, I'm just starting) - No SOC2/HIPAA yet (Zep has this) - No agent-controlled memory like Letta/MemGPT I'd love feedback on the API design and the procedural memory concept. Is this something you'd actually use in production? GitHub: https://github.com/alibaizhanov/mengram Docs: https://mengram.io/docs Get a free key: https://mengram.io

2 points by mengram-ai 7mo ago

mengram-ai 7mo ago

Hi HN, I'm Ali. I've been building Mengram for the past year. The problem: Every AI memory tool stores facts — "user likes dark mode." But when my agents failed at a task, they'd fail the exact same way next time. They had no memory of what happened or how to do things better. What Mengram does: It stores 3 types of memory, modeled after how human cognition works: - Semantic — facts and preferences (like Mem0, Zep) - Episodic — events, decisions, outcomes (what happened and when) - Procedural — learned workflows that evolve when they fail The procedural part is what I'm most excited about. When an agent reports a failure, Mengram automatically evolves the procedure — adds a new step, changes the order, removes what didn't work. Your agent literally gets better at its job over time. Example: Week 1, "Deploy" = build → push → deploy. Agent forgets migrations, DB crashes. Week 2, Mengram evolves it to build → run migrations → push → deploy. Agent hits OOM. Week 3, adds memory check step. This happens automatically. Technical details: - Python & JS SDKs: pip install mengram-ai - Free cloud API (no credit card) or fully self-hostable - MCP server for Claude Desktop / Cursor (21 tools) - LangChain, CrewAI, OpenClaw integrations - Knowledge graph + vector search + reranking - Cognitive Profile: one API call generates a system prompt from all memories What it's NOT good at (yet): - Smaller community than Mem0 (they have 25K stars, I'm just starting) - No SOC2/HIPAA yet (Zep has this) - No agent-controlled memory like Letta/MemGPT I'd love feedback on the API design and the procedural memory concept. Is this something you'd actually use in production? GitHub: https://github.com/alibaizhanov/mengram Docs: https://mengram.io/docs Get a free key: https://mengram.io