Ledger is an evidence-driven memory system for AI agents. Every memory system in the category (Mem0, Letta, Zep, Cognee) is write-only and unfalsifiable. A lesson gets stored and then served forever, with no measurement of whether it ever helped, and the only eviction signal anyone has is recency. That matters because self-generated lessons are often bad, since the model writing the rule is the model that just failed. Ledger treats every memory as a hypothesis instead. When a run fails, a reflection step proposes a candidate lesson. Inserting it fires a MongoDB change stream that starts an experiment: vector search finds about 20 past situations the lesson claims to cover, each one is replayed from its LangGraph checkpoint with the lesson injected, and each is re-graded by the same execution-accuracy grader. The result is a lift score with a confidence interval and a verdict of promoted, probation, rejected, or harmful. The metric that makes this different is the regression rate, measured on tasks that already passed. It catches the lesson that fixes one thing and breaks three others. Nothing else in the category can produce it, because nothing else measures a memory after storing it. Proven memories then graduate out of the prompt and into behaviour. Task-space is split into regions clustered by query shape, and each region tracks a success posterior per model. A cheap model shadows the expensive one on every task, graded but never served. When the evidence shows it is not worse by more than a margin we are willing to trade, the region switches over and gets permanently cheaper. Regions that no off-the-shelf model can solve accumulate failures until another change stream fires a Fireworks LoRA, trained on that region's own successful runs and registered as a new option for that region. A memory therefore moves from text, to a routing decision, to weights, with vector search deciding which weights answer next. MongoDB Atlas acts as the control plane rather than the storage layer, since three separate vector search paths each change what the system does, and change streams trigger the work throughout, so writes drive everything and nothing polls. It runs on Spider text-to-SQL because execution gives exact ground truth, and is verified by 98 fully offline tests whose headline case is a negative control: a deliberately harmful lesson must be caught with a non-zero regression rate against the real grader.
Team
Placement
Participant
