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Engram
Global, shared and reviewable memory layer for AI agents
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.
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set up audio inference, or build a tool on linux/windows, and it falls into a multi-turn failure loop: guessing outdated CLI flags, triggering missing-header errors, scraping bloated web docs, and burning through your context tokens.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?
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About Engram on Product Hunt
“Global, shared and reviewable memory layer for AI agents”
Engram was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #70 on the daily leaderboard. 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.
Engram was featured in Developer Tools (518.8k followers), Artificial Intelligence (477.9k followers), GitHub (41.4k followers) and Tech (631.7k followers) on Product Hunt. Together, these topics include over 398.2k products, making this a competitive space to launch in.
Who hunted Engram?
Engram was hunted by Yashwant Sandey. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Engram stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Yashwant Sandey