mongoARC

mongoARC gives a Claude Code agent a long-term memory in MongoDB Atlas while it plays ARC-AGI-3, a benchmark of unfamiliar interactive games played level by level. Agents lose what they worked out when their context is compacted. Here the agent writes each lesson into Atlas in its own words, and searches that memory in plain language before it acts on the next level. Retrieval is MongoDB's own: Atlas Vector Search with automated embedding (Voyage) embeds every note and every question, so the harness sends only raw text. Notes are updated or deleted as later levels correct them, and memory is scoped to a single game, because ARC-AGI-3 knowledge transfers between levels, not between games. A live dashboard, read entirely from Atlas, replays every move frame by frame, shows which button or pixel the agent chose, its reasoning and code, and every memory write and search as it happens (via change streams).