$ loading_
Give AI agents long-term memory with user-scoped storage, recall, and deletion.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Recall Select" yet — see the docs or source repo.
Please store this user's preferences as long-term memory: prefers concise answers, uses Chinese, and cares about product management topics.
The tool stores a memory scoped to that user for future conversations.
Recall the long-term memories previously stored for this user and summarize the key points to follow in future replies.
The tool returns relevant user memories, and the AI derives personalized response guidelines.
Delete this user's old memory about 'reply in English by default' because replies should now be in Chinese.
The tool removes the specified outdated memory so future responses do not use stale preferences.
Developers building AI assistants can use it to save each user's preferences, context, or recurring requirements. This helps the assistant respond more consistently across future conversations without asking for the same details again.
In automated agent workflows, it can serve as a long-term memory layer for storing, recalling, and deleting information. Its per-user scoping fits agent systems that serve multiple end users.
It is an MCP tool that gives AI agents long-term memory. It can store, recall, and delete memories, with per-user scoping.
Yes. The description explicitly mentions per-user scoping, which means memories can be isolated by user.
It is known to have usage limits. The current material does not provide installation steps, runtime details, or key requirements; see the source repository.
Give AI agents a memory layer with storage, recall, forgetting, and reinforcement.
Store and retrieve agent lessons to improve tasks and avoid repeated mistakes.
Add persistent local semantic long-term memory to AI agents.
Give AI agents persistent memory to log and recall conversation context via MCP.
Provide persistent, explainable, MCP-native memory for AI agents.
Store completed agent sessions as long-term memory and retrieve relevant past interactions through MCP.