Let’s build your Living Brain

Yes. You can connect the relevant brain to supported tools and agents, helping people work from accumulated knowledge without repeatedly rebuilding context.

Living Brain ranked #1 on LongMemEval-S and #1 on LoCoMo in our internal comparisons, ahead of the other systems tested.

Our latest internal LoCoMo run achieved 91.0% judged accuracy across 1,540 questions, using approximately 5.3K context tokens per question, compared with approximately 26K for the full-context baseline at 4.9× fewer context tokens.

In our internal LoCoMo run, Living Brain achieved 464 ms median memory retrieval, including search and page retrieval, and 3.7 seconds median total answer time, including retrieval and LLM generation.

Living Brain stores knowledge outside the model and retrieves relevant context for each task. This reduces the need to repeatedly send an entire conversation history or document collection to the model.

The compiler and memory policies guide what is retained, organised and resurfaced. The agent harness then uses the selected context during execution.

Savings depend on the workload and configuration. In our internal LongMemEval-S evaluation, Living Brain used 22.6× fewer tokens per question and achieved 95% lower cost per task against the tested full-context baseline.

Yes. Living Brain can support two complementary paths to AI adaptation.

Adaptation without changing model weights. Living Brain retains knowledge, corrections and decisions, then brings relevant context into future tasks. This helps connected models adapt to your work at inference time, including closed-weight models.

Memory as a foundation for post-training. Accumulated knowledge, feedback and task outcomes can be curated into training examples and evaluation datasets. These can support fine-tuning or other post-training workflows for open-weight models and closed-weight models where the provider offers training access.