zhiji-memory
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@zhiji-memoryFind my memories about the marketing campaign proposal"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
知己 (Zhiji) Memory — MCP Server
Chinese-first, brain-inspired long-term memory as an MCP server. Give any MCP client — Claude Desktop / Claude Code / Cursor / Cline / Cherry Studio / Coze — the ability to remember and understand your user across sessions.
中文用户:完整接入手册见
MCP-USAGE.md,或在线版 https://ai-know.me/mcp。
v0.4.0 · 9 tools / 2 resources / 1 prompt · stdio + Streamable HTTP · MCP protocol 2025-11-25
This repo is a thin bridge: it translates MCP tool calls into REST calls to the Zhiji backend (hosted at ai-know.me). The bridge stores nothing; all memory lives in the Zhiji service you connect to.
Why not just another vector-memory MCP?
Mem0 / Zep / LangMem expose add / search / delete over a vector store. 知己 exposes brain-inspired primitives:
11-stage hybrid retrieval — trigram full-text +
bge-large-zhsemantic + time-decay + access-reinforcement + dedup ranking.7-layer / 36-dimension evolving user profile — values, decision logic, behavior style, self-cognition, interoceptive & dynamic state.
Workspace assembly — one call returns profile + relevant memories + confirmed facts + behavioral inferences, budget-trimmed for the client context window.
Prospective reminders, multimodal ingest (audio / OCR / doc), and a self-evolving reward loop driven by user feedback.
All Chinese-optimized (trigram tokenizer + bge-large-zh); English is supported too. Memory is stored in the Zhiji backend you connect to — self-host it, or use the hosted ai-know.me service; either way it's your Zhiji instance, not a generic memory-SaaS middleman.
Related MCP server: Samantha
The 9 tools
Tool | What it does |
| Flagship — one-shot "everything needed to understand this user" for a query |
| 11-stage hybrid recall (FTS + semantic + time) |
| Write a conversation to long-term memory (fields must be |
| Multimodal ingest — audio / image / PDF / Word / Excel / video → memory |
| 7-layer / 36-dim user profile summary |
| Distilled atomic facts with confidence & conflict status |
| Due/upcoming intentions (todos, promises, plans) |
| Thumbs up/down → self-evolution reward + per-memory Q-value |
| Health & memory scale (call first to verify connectivity) |
Experimental capabilities (sleep consolidation / dream replay / emergence) are not exposed until their groundedness passes ablation.
Quick start
You need an agent Key (mb- prefix). Get one at https://ai-know.me/memory?tab=api (register + create a Key bound to your account).
Option A — remote, no install (recommended)
Point any Streamable-HTTP MCP client at the hosted endpoint — you don't need this repo at all:
claude mcp add zhiji-memory --transport http \
--url https://ai-know.me/mcp \
--header "Authorization: Bearer mb-yourKey"Or in a URL-style client config (Cursor / Cherry Studio / LobeChat):
{
"mcpServers": {
"zhiji-memory": {
"url": "https://ai-know.me/mcp",
"headers": { "Authorization": "Bearer mb-yourKey" }
}
}
}Option B — run this bridge locally (stdio)
Use this repo when you want the bridge as a local stdio process (e.g. a desktop client launches it for you), talking to the hosted Zhiji backend:
npm install # only @modelcontextprotocol/sdk + zodThen in your client config:
{
"mcpServers": {
"zhiji-memory": {
"command": "node",
"args": ["/absolute/path/to/zhiji-mcp/zhiji-mcp-server.mjs"],
"env": {
"MB_BASE_URL": "https://ai-know.me",
"MB_API_KEY": "mb-yourKey"
}
}
}
}Verify
# visual debugger — should list 9 tools / 2 resources / 1 prompt
npx @modelcontextprotocol/inspector node zhiji-mcp-server.mjsFull client matrix (LangChain, OpenAI Agents SDK, Coze…) is in MCP-USAGE.md.
Architecture — a stateless thin bridge
MCP client ──stdio / HTTP(JSON-RPC)──► zhiji-mcp-*.mjs ──REST──► Zhiji backend (ai-know.me)The MCP layer stores nothing; it translates MCP tool calls into REST calls to the Zhiji backend. Shared core zhiji-mcp-core.mjs backs both entry points:
File | Role |
| Tool/resource/prompt definitions (single source of truth) |
| stdio entry ( |
| Streamable HTTP entry ( |
Isolation & auth
All data is hard-isolated by
userEmail. The bridge treatsuserEmailas a namespace, not a credential.The hosted public endpoint requires an agent Key — requests without
Bearer mb-…get 401; the backend binds each Key to its userEmail and rejects cross-user access.Never hand a key-less, userEmail-swappable bridge to end users — front it with your own backend that injects the correct userEmail. See
MCP-USAGE.md §8.
Environment
Var | Default | Notes |
|
| Zhiji backend address; for the hosted service use |
| — | default user namespace (single-user local use) |
| — |
|
|
| slow workspace routes can take ~15s |
Status
Version | Highlights |
v0.4 |
|
v0.5 (planned) | inference-list tool, resource subscriptions, fine-grained key permissions |
Docs
MCP-USAGE.md— full guide (install, all clients, per-tool reference, troubleshooting, FAQ)demo-guide.md— demo video recording guideOnline: https://ai-know.me/mcp
License
MIT © 2026 知己 AI (ai-know.me)
知己 AI · MCP integration — Chinese-first brain-inspired memory. The bridge is open; the memory intelligence lives in the Zhiji backend.
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