Building adaptive memory systems for software that needs context, history, and reliable recall.
WaveMind · PyPI · Quick Start · Benchmarks · Contribute
I work on systems that make memory usable: storage, retrieval, priority, decay, feedback, corrections, and benchmarks that show whether recall actually improves over time.
My current focus is WaveMind, a local-first adaptive memory layer. Vector search finds candidates; memory state decides what deserves attention now.
pip install wavemind
wavemind quickstart
wavemind studio| Track | Shipping direction |
|---|---|
| Memory quality | Hotness, decay, corrections, TTL, feedback signals, stale-fact suppression, and graph recall. |
| Developer experience | Python API, CLI, FastAPI server, Studio UI, imports, backups, and framework examples. |
| Scale path | SQLite/Postgres truth stores, ANN candidate indexes, sharding, cache layers, and reproducible scale evidence. |
| Evidence | Long-memory benchmarks, retrieval baselines, latency profiles, regression tests, and public result artifacts. |
| Project | Snapshot | Links |
|---|---|---|
| WaveMind | Adaptive memory infrastructure for software that must preserve, prioritize, and update context over time. | Docs / Benchmarks / Issues |
| focus-flow | Minimal desktop focus timer for deep-work sessions with themes and English/Russian UI. | Repository |
| CORECITY | Browser game experiment around a living market mechanic driven by players. | Repository |
| Collaboration | Useful contribution |
|---|---|
| Benchmarks | Long-memory evaluation, stale-fact suppression, retrieval quality, latency, agent-impact tests, and scale profiles. |
| Integrations | LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, notebooks, local apps, and production workflows. |
| Production feedback | Real systems where memory must evolve, forget, explain, or preserve user-specific context over time. |
Open an issue in WaveMind if you want to test the project, contribute an integration, add a benchmark, or discuss adaptive memory for production software.