$ loading_
Give AI agents a memory layer with storage, recall, forgetting, and reinforcement.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Deep Recall MCP Server" yet — see the docs or source repo.
Store the following in long-term memory: the user prefers concise answers, defaults to Chinese, and focuses on product analytics and automation topics. Prioritize these preferences in future conversations.
The tool stores these preferences as retrievable memories for future conversations.
Retrieve existing memories related to the current user, especially prior goals, confirmed constraints, and recently mentioned project context.
It returns relevant past memories so the agent can continue with context instead of re-asking.
The user previously said the budget was 50,000 RMB, but now says the maximum budget is 30,000 RMB. Update the relevant memory and flag or handle the contradiction between these facts.
The tool updates memory and detects the conflict, helping the agent avoid responding from outdated information.
Developers can connect this MCP tool to an AI agent so it remembers user preferences, background, and prior facts across sessions. This reduces repeated questions and improves conversational continuity.
When user goals, constraints, or project status keep changing, the agent can use it to update and manage memory. Forgetting, reinforcement, and contradiction detection help handle stale and conflicting information.
Research or analysis agents can use it to store important findings and contextual clues, then retrieve them when needed. This makes multi-step exploration more coherent and reduces missed conclusions.
It provides AI agents with a memory layer for storing, retrieving, and managing memories. The description also states that it includes biological-like properties such as forgetting, reinforcement, and contradiction detection.
The provided description says it offers these capabilities through two simple tools. For the exact tool names and invocation details, see the source repository.
The current material does not provide installation steps, runtime requirements, or API key information. Please see the source repository for prerequisites.
Add persistent local semantic long-term memory to AI agents.
Give AI coding agents persistent semantic memory with embedding-based retrieval.
Centralize memory, tools, and logic for leaner prompts and scalable AI agents.
Give AI chatbots autonomous long-term memory with monitoring and consolidation.
Provide persistent, categorized memory storage and retrieval for AI agents.
Store and retrieve agent lessons to improve tasks and avoid repeated mistakes.