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invisible_dots runs Dots: autonomous AI agents, each with its own Linux computer, that work for a person through chat and tasks. This wiki is where the work behind them is written up: what was measured, how, what came out, and what was chosen because of it. Every study has its numbers and its method, so a reader can check them or run them again.
The README is how to run invisible_dots; the architecture is how it is built.
- How a Dot remembers: past conversations as files, a profile in every prompt, and the pass that keeps it current. 7.8% to 90.4% on LongMemEval.
- Agent memory systems, measured on the same questions: Mastra, LangMem, Hindsight, an agentic "dream" pass and a single request, with their cost per pass.
- Token counts are not portable between models: tiktoken against five models' own counts, and why a fixed safety margin got a request refused.
- Compacting a thread at the model's own window: no token cap of our own, old tool results cleared before anything is summarized.
- Benchmarking an agent on real VMs: Harbor tasks, two-to-three-hour builds, and eight Dots on one host.