Groundhog is a game agent that dies, remembers, and comes back smarter. An LLM agent crawls a little dungeon we rigged so that a memoryless run reliably fails: the shortest corridor hides lethal spikes, and the merchant at the fork cheerfully directs you into them. Life 1 dies in four steps. Death fires an aggregation pipeline that compresses the raw episode log into a handful of scored lessons ("never move east from the fork at 4,4"), embedded and indexed in Atlas. Then life 2 runs the exact same code with zero hints. Vector search recall reroutes it. You watch it walk around the ghost of its own corpse, dig a hidden key out from behind the crates, and leave through the vent, saying out loud which memory drove each choice (ElevenLabs does the voice). Kill the backend mid-run and it resumes mid-dungeon from per-step checkpoints. MongoDB is doing real work here, on camera: Atlas Vector Search plus Atlas Search for hybrid recall, aggregation pipelines for consolidation, TTL indexes decaying raw episodes while distilled lessons persist, change streams feeding the live hippocampus panel, checkpoints for crash recovery. Rip out the database and the agent is a goldfish. Built solo (with Claude) between 1:30 and 4:40 today; the commit history is the receipt.
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