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Chinese-first brain-inspired memory MCP — hybrid recall, 7-layer user profile, workspace assembly.
Chinese-first brain-inspired memory MCP — hybrid recall, 7-layer user profile, workspace assembly.
Remote endpoints: streamable-http: https://ai-know.me/mcp
Valid MCP server (4 strong, 4 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
Endpoint verified · Requires authentication · 1 issue found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
This plugin requests these system permissions. Most are normal for its category.
Set these up before or after installing:
Environment variable: MB_BASE_URL
Environment variable: MB_TIMEOUT_MS
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"me-ai-know-memory": {
"url": "https://ai-know.me/mcp"
}
}
}From the project's GitHub README.
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.
Mem0 / Zep / LangMem expose add / search / delete over a vector store. 知己 exposes brain-inspired primitives:
bge-large-zh semantic + time-decay + access-reinforcement + dedup ranking.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.
| Tool | What it does |
|---|---|
zhiji_workspace_assemble | Flagship — one-shot "everything needed to understand this user" for a query |
zhiji_memory_search | 11-stage hybrid recall (FTS + semantic + time) |
zhiji_memory_ingest | Write a conversation to long-term memory (fields must be author/text) |
zhiji_ingest_file | Multimodal ingest — audio / image / PDF / Word / Excel / video → memory |
zhiji_profile_get | 7-layer / 36-dim user profile summary |
zhiji_facts_get | Distilled atomic facts with confidence & conflict status |
zhiji_prospective_due | Due/upcoming intentions (todos, promises, plans) |
zhiji_feedback | Thumbs up/down → self-evolution reward + per-memory Q-value |
zhiji_status | Health & memory scale (call first to verify connectivity) |
Experimental capabilities (sleep consolidation / dream replay / emergence) are not exposed until their groundedness passes ablation.
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).
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" }
}
}
}
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 + zod
Then 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"
}
}
}
}
# visual debugger — should list 9 tools / 2 resources / 1 prompt
npx @modelcontextprotocol/inspector node zhiji-mcp-server.mjs
Full client matrix (LangChain, OpenAI Agents SDK, Coze…) is in MCP-USAGE.md.
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 |
|---|---|
zhiji-mcp-core.mjs | Tool/resource/prompt definitions (single source of truth) |
zhiji-mcp-server.mjs | stdio entry (npm start) |
zhiji-mcp-http.mjs | Streamable HTTP entry (npm run http) |
userEmail. The bridge treats userEmail as a namespace, not a credential.Bearer mb-… get 401; the backend binds each Key to its userEmail and rejects cross-user access.MCP-USAGE.md §8.| Var | Default | Notes |
|---|---|---|
MB_BASE_URL | http://localhost:3001 | Zhiji backend address; for the hosted service use https://ai-know.me |
MB_USER_EMAIL | — | default user namespace (single-user local use) |
MB_API_KEY | — | mb- agent Key; forwarded as Bearer |
MB_TIMEOUT_MS | 60000 | slow workspace routes can take ~15s |
| Version | Highlights |
|---|---|
| v0.4 | zhiji_ingest_file (9 tools) + public-release hardening (TLS, MCP_REQUIRE_KEY, JWT-only key issuance, cross-user isolation hard-test) |
| v0.5 (planned) | inference-list tool, resource subscriptions, fine-grained key permissions |
MCP-USAGE.md — full guide (install, all clients, per-tool reference, troubleshooting, FAQ)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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