
Open source memory for agentic AI
Give AI systems memory that lasts.
HumemAI is an open source organization. We publish research, code, and benchmarks for a memory layer that stays persistent across sessions, adapts to mixed data types, and remains inspectable instead of turning into a black box.

Why memory
Most agents still behave like stateless interfaces with better wording.
HumemAI focuses on what should persist beyond a prompt: what happened, what matters now, and how structured knowledge should stay available over time. That means treating memory as a real system layer instead of a side effect hidden in context windows.
The result is a stack that can hold documents, tables, graphs, and traces in forms that remain inspectable, replayable, and useful to both people and agents.
Open source
Everything we make is free to use, modify, and redistribute.
The code, Python packages, benchmarks, and papers are published under open licenses.
Contribute
Built in the open, with anyone who wants to help.
Issues, pull requests, benchmark reports, and research collaborations are all welcome. Pick a project and start from its repository on GitHub.
Open source projects
Open source projects shape the work.

Audit-Ready Memory
A public-interest project building local-first, inspectable memory infrastructure for AI systems that need audit trails, deterministic replay, and explicit deletion.
Learn more
DBBench
An independent, open benchmark of database engines. The same tasks on the same machine, with published settings and every result tied to the exact engine version.
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ArcadeDB
A multi-model database engine that keeps documents, graphs, vectors, and time series in one transactional engine, plus the Python package that runs that engine inside your process, benchmarked against the comparators in each category.
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Machines With Human-Like Memory
A PhD research project on explicit memory architectures for AI, spanning benchmarks, handcrafted agents, learned policies, and temporal knowledge-graph memory.
Learn moreNews



