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Provide AI assistants with persistent memory, full-text search, and knowledge graph storage.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "memdb" yet — see the docs or source repo.
Store the following user preferences and chat history in memdb as searchable long-term memory: the user prefers concise answers, is building a financial analysis bot, and often uses Python and SQLite.
A summary of stored memory records and a note that they can be retrieved later by keywords or relationships.
Search memdb for historical memories related to “financial analysis bot” and “SQLite”, extract key facts, and return them ranked by relevance.
Ranked search results with matching memories, relevant snippets, and extracted key facts.
Using existing memories, create entity relationships among “user”, “financial analysis bot”, “Python”, and “SQLite”, then output a relationship summary for later AI reasoning.
A knowledge graph summary of entities and relationships for future linked retrieval and contextual reasoning.
Persist conversational memory with SQLite and retrieve context using metadata.
Give AI agents vector memory to reuse past solutions for similar requests.
Give AI agents durable local memory, knowledge graph storage, and fast recall.
Provide long-term memory storage and fast semantic retrieval for AI applications.
Lightweight vector memory for AI agents to store, search, and delete memories.
Persistent knowledge-graph memory for MCP with semantic search and version tracking.