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Give AI coding agents persistent semantic memory with embedding-based retrieval.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "mcp-memory-server" yet — see the docs or source repo.
Store the following project conventions in semantic memory: backend uses TypeScript, API errors must return {code, message}, and new modules require unit tests.The tool persistently stores the text so coding agents can retrieve and reuse it later.
Find historical memories most relevant to 'API error response format' and return context useful for the current task.
It returns the most semantically relevant stored text to help the agent follow existing conventions.
Continuously record important constraints, naming rules, and confirmed decisions during this refactor for later tasks.
It creates a reusable persistent semantic memory layer across sessions, reducing repeated explanations.
Developers can store architecture choices, API conventions, and testing requirements in long-running projects. The agent can later retrieve them semantically, reducing repeated instruction.
When an AI coding agent handles work across multiple tasks, this tool can preserve important text from earlier steps. Later tasks can retrieve relevant memory and maintain continuity.
If a team has accumulated many project notes, decisions, or conventions, the agent can use vector-embedding-based semantic retrieval. This makes relevant context easier to find than keyword matching alone.
It is a persistent semantic memory layer for AI coding agents. It stores text through MCP tools and retrieves it using vector embeddings.
The provided information indicates that it stores and retrieves text. For support of other data types, see the source repository.
The provided material does not include installation steps or prerequisites. For runtime, configuration, or key requirements, see the source repository.
Give AI coding agents long-term memory, search, and organization across sessions.
Build persistent, semantically searchable memory for codebases via natural language queries.
Provides durable local memory for LLM coding workflows.
Give AI coding tools persistent memory across sessions, devices, and workflows.
Persistent memory for coding agents across sessions
Give AI coding agents searchable local project memory with safe structured updates.