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Let AI agents keep persistent, searchable memory with editable history across sessions.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MemAI" yet — see the docs or source repo.
Use MemAI to record this meeting decision: the project will use a single database for context, because it reduces operational complexity; tag it with project name “Customer Profiling”, date, and owner for later retrieval.
Create a structured memory entry with decision details, time, and metadata that can be directly found in later searches.
Start a new session and first query MemAI for historical facts and decisions related to “Project A”, then provide continuation suggestions based on the results.
First return relevant memories, then generate next-step recommendations so the workflow continues without context break.
Search MemAI for existing records of “data cleaning rules,” update them with new rules when outdated, and note the reason for change while preserving full revision history.
Show old and new versions or change notes, and confirm the latest memory is saved with traceable history.
Developers building AI agents often switch contexts across sessions. MemAI stores facts and decisions so new sessions do not lose prior context, improving continuity.
Researchers can revisit prior conclusions over long projects by using MemAI retrieval and revision history to recover facts and verify updated versions.
MemAI is a long-term memory MCP tool for AI agents. It stores facts and decisions in a single SQLite database with hybrid retrieval and full edit history to keep sessions consistent.
The provided information does not include installation steps. Check the source repository documentation for setup instructions; see source repository.
Regular chat memory is often temporary within one session, while MemAI is designed for long-term persistence with database storage and edit history, suitable for cross-session continuity and traceability.
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