Good work deserves
to be remembered.
We are building Memora to help AI engineering teams carry useful decisions, lessons, and project context into the next task.
Our purpose
Less rediscovery.
More continuity.
An AI-assisted project accumulates more than code. It develops reasons for decisions, constraints that matter, and lessons from what did not work. That knowledge is useful long after a conversation ends.
Memora’s mission is to make those lessons available when they can help, with the context and evidence needed to judge them.
Built on an inspectable foundation
Memora’s engineering foundation is Accuracy Memory: a local-first memory system for AI agents. It keeps canonical project knowledge in provider-neutral Markdown, separates captured observations from reviewed knowledge, and connects to supported agent workflows through a CLI and MCP.
The approach is deliberate: capture useful experience, recall relevant context, and review proposed changes before they become durable knowledge. Memory should help reasoning while preserving uncertainty and provenance.
Who we are building for
Our initial focus is AI engineering teams working across projects, sessions, and tools. We are interested in the practical questions: which context is worth keeping, when it is useful, and how teams can update it responsibly.
Where we are today
Memora is early stage. The current foundation is a project-scoped, local-first runtime. Enterprise shared memory is a long-term product direction; a hosted, multi-tenant enterprise platform is not currently offered through this website.
We are looking to learn from teams working on persistent agent context and reliable AI workflows.
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