Engram
Global, shared and reviewable memory layer for AI agents
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Engram - Global, shared and reviewable memory layer for AI agents

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Engram is peer-verified procedural memory for AI agents — a shared catalog of signed, AI-reviewed procedures any MCP client can search and execute. Ranked by attested runs, not votes.

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Hey Product Hunt! 👋 I’m the maker of Engram. We’ve all watched modern coding agents get stuck on the simplest setups. You ask an agent to configure an older C++ library, set up audio inference, or build a tool on linux/windows, and it falls into a multi-turn failure loop: guessing outdated CLI flags, triggering missing-header errors, scraping bloated web docs, and burning through your context tokens. Frontier models are brilliant at reasoning, but they lack deterministic, version-accurate procedural memory. We built Engram to fix that. Engram is a decentralized procedural memory layer for developer AI agents (via MCP): Deterministic, 1-Turn Executions: Instead of letting the agent guess through trial-and-error, Engram injects exact, verified operational runbooks directly into the context window. Sub-50ms Shard Retrieval: Replaces 10k-token web scrapes and slow search queries with lightweight, 200-token shards that execute cleanly on Attempt #1. Zero Host Pollution: Keeps workflows isolated, fast, and repeatable across teams and IDEs like Cursor, Claude Code, and Windsurf. We’re open-sourcing our core MCP implementation and offering free early access to our community runbook index for the Product Hunt community today. We’d love your honest feedback: 💬 What is the single most annoying setup or compilation loop your AI coding agent constantly gets stuck in?