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set up audio inference, or build a tool on linux/windows, and it falls into a multi-turn failure loop: guessing outdated CLI flags, triggering missing-header errors, scraping bloated web docs, and burning through your context tokens.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?

Engram

Global, shared and reviewable memory layer for AI agents

Global, shared and reviewable memory layer for AI agents