zensation-ai/zenbrain
Updated • 5
🏗️ Building on HF
Alexander Bering
alexanderbering
AI & ML interests
Practitioner-Researcher in Agent Memory & Cognitive Architectures. ZenBrain: 7-layer neuroscience-inspired memory (arXiv:2604.23878, wins all nine head-to-head answer-quality comparisons on LongMemEval-500 at 1/106 token cost). Apache 2.0.
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We just put our money where our manifesto is.
Our position has been that the layer of an AI system that touches your data should be open and auditable, not something you rent on trust. So instead of asking you to believe that, we made it runnable.
The ZenBrain Playground is a static HF Space that executes our open-source memory library, @zensation /algorithms (Apache-2.0, zero-dependency), live in your browser. No install, no backend, no mockup: it runs the actual published code. Four panels drive real functions from the library — Ebbinghaus-style retention curves, Hebbian strengthening/decay, sleep-consolidation replay + pruning, and Bayesian retrievability with confidence intervals. The library is vendored into the page, so what you run is the same code that's published on npm.
ZenBrain is a neuroscience-inspired 7-layer memory architecture for AI agents. The open core is the 20-module algorithm library (FSRS, Hebbian, Ebbinghaus, sleep-consolidation, Bayesian confidence, and more). On LongMemEval-500 it reaches 91.3% of a long-context oracle's accuracy at 1/106 of the per-query token budget, and wins all nine head-to-head answer-quality comparisons (3 competitors x 3 LLM judges) against Letta, Mem0, and A-Mem. The open core ships with 528 automated tests (429 algorithms + 99 core); the ZenAI system it was extracted from has over 12,000 (as of August 2026). Full details are in the arXiv preprint.
If you build agent memory, we'd genuinely like your eyes on it — open the Space, poke the algorithms, read the code, tell us where it breaks.
Live demo: zensation-ai/zenbrain-playground
Model: zensation-ai/zenbrain
Paper: arXiv:2604.23878
Our position has been that the layer of an AI system that touches your data should be open and auditable, not something you rent on trust. So instead of asking you to believe that, we made it runnable.
The ZenBrain Playground is a static HF Space that executes our open-source memory library, @zensation /algorithms (Apache-2.0, zero-dependency), live in your browser. No install, no backend, no mockup: it runs the actual published code. Four panels drive real functions from the library — Ebbinghaus-style retention curves, Hebbian strengthening/decay, sleep-consolidation replay + pruning, and Bayesian retrievability with confidence intervals. The library is vendored into the page, so what you run is the same code that's published on npm.
ZenBrain is a neuroscience-inspired 7-layer memory architecture for AI agents. The open core is the 20-module algorithm library (FSRS, Hebbian, Ebbinghaus, sleep-consolidation, Bayesian confidence, and more). On LongMemEval-500 it reaches 91.3% of a long-context oracle's accuracy at 1/106 of the per-query token budget, and wins all nine head-to-head answer-quality comparisons (3 competitors x 3 LLM judges) against Letta, Mem0, and A-Mem. The open core ships with 528 automated tests (429 algorithms + 99 core); the ZenAI system it was extracted from has over 12,000 (as of August 2026). Full details are in the arXiv preprint.
If you build agent memory, we'd genuinely like your eyes on it — open the Space, poke the algorithms, read the code, tell us where it breaks.
Live demo: zensation-ai/zenbrain-playground
Model: zensation-ai/zenbrain
Paper: arXiv:2604.23878
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Post
4181
We just put our money where our manifesto is.
Our position has been that the layer of an AI system that touches your data should be open and auditable, not something you rent on trust. So instead of asking you to believe that, we made it runnable.
The ZenBrain Playground is a static HF Space that executes our open-source memory library, @zensation /algorithms (Apache-2.0, zero-dependency), live in your browser. No install, no backend, no mockup: it runs the actual published code. Four panels drive real functions from the library — Ebbinghaus-style retention curves, Hebbian strengthening/decay, sleep-consolidation replay + pruning, and Bayesian retrievability with confidence intervals. The library is vendored into the page, so what you run is the same code that's published on npm.
ZenBrain is a neuroscience-inspired 7-layer memory architecture for AI agents. The open core is the 20-module algorithm library (FSRS, Hebbian, Ebbinghaus, sleep-consolidation, Bayesian confidence, and more). On LongMemEval-500 it reaches 91.3% of a long-context oracle's accuracy at 1/106 of the per-query token budget, and wins all nine head-to-head answer-quality comparisons (3 competitors x 3 LLM judges) against Letta, Mem0, and A-Mem. The open core ships with 528 automated tests (429 algorithms + 99 core); the ZenAI system it was extracted from has over 12,000 (as of August 2026). Full details are in the arXiv preprint.
If you build agent memory, we'd genuinely like your eyes on it — open the Space, poke the algorithms, read the code, tell us where it breaks.
Live demo: zensation-ai/zenbrain-playground
Model: zensation-ai/zenbrain
Paper: arXiv:2604.23878
Our position has been that the layer of an AI system that touches your data should be open and auditable, not something you rent on trust. So instead of asking you to believe that, we made it runnable.
The ZenBrain Playground is a static HF Space that executes our open-source memory library, @zensation /algorithms (Apache-2.0, zero-dependency), live in your browser. No install, no backend, no mockup: it runs the actual published code. Four panels drive real functions from the library — Ebbinghaus-style retention curves, Hebbian strengthening/decay, sleep-consolidation replay + pruning, and Bayesian retrievability with confidence intervals. The library is vendored into the page, so what you run is the same code that's published on npm.
ZenBrain is a neuroscience-inspired 7-layer memory architecture for AI agents. The open core is the 20-module algorithm library (FSRS, Hebbian, Ebbinghaus, sleep-consolidation, Bayesian confidence, and more). On LongMemEval-500 it reaches 91.3% of a long-context oracle's accuracy at 1/106 of the per-query token budget, and wins all nine head-to-head answer-quality comparisons (3 competitors x 3 LLM judges) against Letta, Mem0, and A-Mem. The open core ships with 528 automated tests (429 algorithms + 99 core); the ZenAI system it was extracted from has over 12,000 (as of August 2026). Full details are in the arXiv preprint.
If you build agent memory, we'd genuinely like your eyes on it — open the Space, poke the algorithms, read the code, tell us where it breaks.
Live demo: zensation-ai/zenbrain-playground
Model: zensation-ai/zenbrain
Paper: arXiv:2604.23878
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