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Engram

The memory layer your AI agent is missing

Engram gives AI agents persistent memory that actually works. Instead of dumping entire conversation histories into context (expensive) or maintaining files by hand (tedious), Engram automatically extracts, connects, and surfaces the right memories at recall time. Scores 79.6% on LOCOMO vs Mem0's 66.9%. Uses 30x fewer tokens than full context. Two commands to set up. Works with Claude Code, Cursor, and any MCP client. Open source, TypeScript, zero dependencies.

About Engram on Product Hunt

The memory layer your AI agent is missing

Engram was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #46 on the daily leaderboard. Engram gives AI agents persistent memory that actually works. Instead of dumping entire conversation histories into context (expensive) or maintaining files by hand (tedious), Engram automatically extracts, connects, and surfaces the right memories at recall time. Scores 79.6% on LOCOMO vs Mem0's 66.9%. Uses 30x fewer tokens than full context. Two commands to set up. Works with Claude Code, Cursor, and any MCP client. Open source, TypeScript, zero dependencies.

Who hunted Engram?

Engram was hunted by Thomas Stockham. 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.

For a complete overview of Engram including community comment highlights and product details, visit the product overview.