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Store, search, and retrieve memories for local-first LLM agents.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ctrl-memory" yet — see the docs or source repo.
Use ctrl-memory to store this long-term memory: the user prefers Chinese responses, concise answers, and local deployment options.
The user preferences are stored in memory for later retrieval and reuse.
Use ctrl-memory to search for memories related to “API limits” and summarize the key constraints recorded earlier.
Relevant past memories are returned along with a summary of the recorded constraints.
Use ctrl-memory to retrieve memories about “Project Alpha” and extract its goal, owner, and latest decision.
Memory results related to the project are returned and distilled into key context.
Developers building MCP-based LLM agents can use it to store user preferences, task context, and past decisions, reducing repeated prompting. It fits local agent workflows that need persistent memory.
In research or product workflows, an agent can search saved memories to quickly recover prior discussions and constraints. This improves consistency and efficiency across ongoing tasks.
For teams that want to minimize reliance on outside services, this tool provides a lightweight, local-first memory server. It suits agent systems that value local operation and simple deployment.
It is a lightweight, local-first MCP memory server for LLM agents to store, search, and retrieve memories. Its role is to provide long-term memory capabilities for agents.
According to the description, it emphasizes zero external dependencies, meaning its core memory features do not require external services. For implementation details, see the source repository.
It is suited for local-first LLM agent scenarios, especially workflows that repeatedly save and recall past context. For installation steps or runtime requirements, see the source repository.
Give MCP-compatible AI agents persistent local memory across sessions.
Manage persistent agent memories across global or repository-specific scopes.
Store and query namespaced key-value memory for persistent agent context.
Give AI agents persistent memory, recall, and context management across sessions
Share, search, and reuse local memory across multiple AI coding agents.
Give AI agents searchable contextual memory across queries, errors, and solutions.