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Give AI assistants persistent memory with automatic retrieval and organization.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Long-Term Memory" yet — see the docs or source repo.
Save the following project background, tech stack, key decisions, and todos into long-term memory, and prioritize them in future responses: We are building a multi-tenant SaaS dashboard with Next.js on the frontend, Python FastAPI on the backend, and PostgreSQL as the database. This week we decided not to add Redis caching yet.
The tool stores the project details in persistent memory for accurate future recall and assistance.
Search long-term memory for last month’s discussions about the user permission model, summarize the conclusions as bullet points, and highlight decided approaches and open questions.
It returns a relevant memory summary with decisions made, rationale, and unresolved items.
Continuously ingest these product docs, meeting notes, and customer feedback into long-term memory, automatically link related topics, and enable semantic search for similar issues and past resolutions.
It creates a self-organizing knowledge memory that supports semantic search, relationship discovery, and long-term accumulation.
Enable AI assistants to store, search, and manage persistent semantic memories.
Persist long-term AI memory with semantic retrieval and knowledge graph context.
Give AI assistants persistent memory with tagged retrieval, links, and source references.
Give AI assistants persistent memory, adaptive recall, and graph-based knowledge retrieval.
Give AI assistants persistent knowledge graph memory across sessions and workflows.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.