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Give AI assistants local-first graph memory with semantic search tools.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Memory Engine MCP" yet — see the docs or source repo.
Please write the following user preferences and project context into Memory Engine MCP as searchable memory: the user prefers concise replies; the project uses SQLite; the current goal is to build a local-first AI assistant.
The preferences and context are stored as structured memory for future retrieval and use.
Search Memory Engine MCP for past memories related to 'semantic search, SQLite, local-first' and return the most relevant context summary.
Returns semantically matched memory snippets or a summary to help the assistant continue with context.
Record this as graph memory: Project A is owned by product manager Li Hua, developer Wang Ming maintains the MCP integration, and the research goal is improving long-term assistant memory.
Creates a memory structure with entities and relationships for later retrieval by person, project, or goal.
Developers building a local-first AI assistant can use it to store user preferences, project context, and past facts. This lets the assistant retrieve and use long-term context in later interactions.
Researchers or product managers can use its semantic search capability to quickly find relevant information from past records. It is useful for recalling scattered context beyond simple keyword matches.
When a team wants an assistant to remember links among people, projects, and tasks, this tool can act as a graph memory layer. It is suitable for maintaining queryable relational context.
It is a local-first memory tool for AI assistants, focused on graph memory, SQLite storage, semantic search, and MCP tool integration. It can be used to save and query long-term context.
Based on the description, it uses SQLite as its storage foundation, so it appears suitable for local use. For exact schemas and runtime details, see the source repository.
The description explicitly mentions semantic search and graph memory, which suggests it goes beyond simple text lookup by retrieving context through meaning and relationships. For implementation details, see the source repository.
Give AI assistants a persistent, searchable memory layer and context management.
Provide local knowledge graph memory for AI assistants with linked context retrieval.
Give AI agents persistent knowledge-graph memory and cross-session retrieval.
Provide persistent local semantic memory for MCP tools to store and search notes.
Give AI assistants persistent knowledge graph memory across sessions and workflows.
Provide persistent graph memory, semantic search, and traversal for AI agents.