What data does the server store?
It stores entities (with type and observations), relations between entities, and lessons (error patterns, solutions, metadata) in local JSON files (`memory.json` and `lesson.json`).
@T1nker-1220
基本信息
I enhance the existing memory mcp server from the official mcp github, so big thanks and credits for creating this
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"memories-with-lessons-mcp-server": {
"command": "docker",
"args": [
"build",
"-t",
"mcp/memory",
"-f",
"src/memory/Dockerfile",
"."
]
}
}
}Create multiple new entities in the knowledge graph
Create multiple new relations between entities
Add new observations to existing entities
Remove entities and their relations
Remove specific observations from entities
Remove specific relations from the graph
Read the entire knowledge graph
Search for nodes based on query
Retrieve specific nodes by name
A persistent memory implementation using a local knowledge graph, allowing Claude to remember user information across chats and learn from past errors through a lesson system.
Install via npx or Docker, configure the MCP client (e.g., Claude Desktop or Cursor), and add a system prompt for memory usage. The server exposes tools for managing entities, relations, observations, and lessons. Environment variable MEMORY_FILE_PATH can set a custom storage path.
It stores entities (with type and observations), relations between entities, and lessons (error patterns, solutions, metadata) in local JSON files (`memory.json` and `lesson.json`).
Lessons are special entities with structured error pattern information, solution steps, success rate tracking, and environmental context, designed for learning from errors.
Requires Node.js and npm/npx or Docker. The server uses local file storage; no external database is needed.
Files are automatically split if they exceed 1000 lines to maintain performance.
The server uses stdio transport (for Claude Desktop) and is intended for local use with no authentication mechanism described.

Persistent memory for AI agents. Hosted MCP server with capsule-scoped key/value storage, tags, TTL expiration, bulk ops, and RBAC.

Exact, shared memory for AI agents: a hierarchical database where totals always add up, every write is attributed to the agent and person behind it, and any figure traces back to its source file. Agents create databases,

Ask your Takibi Base collections anything; get answers with cited passages and a support score.
Person-owned, portable AI memory served over MCP. Your knowledge, preferences, and decisions — captured in one assistant, available in all of them.
Project context your AI can query through MCP — with source evidence.
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