Power your agent
पब्लिशर का अपना URL। PlugMCP एंडपॉइंट नहीं।
Host a copy with our keys and logs.
https://ai-know.me/mcpऊपर दिया Claude बटन नाम और URL भरकर Claude खोलता है। पुष्टि आपको वहीं करनी होती है।
claude mcp add --transport http me.ai-know-memory https://ai-know.me/mcpRun in a terminal.
{ "mcpServers": { "me.ai-know-memory": { "command": "npx", "args": [ "-y", "mcp-remote", "https://ai-know.me/mcp" ] } }}claude_desktop_config.json
रिमोट URL या आपकी मशीन पर कमांड? दोनों इंस्टॉल में क्या अंतर है
PlugMCP पर चलाएँ
A new server you create. It is not this listing's official endpoint. Use the publisher install when you want their URL and their credentials.
लॉन्चपब्लिशर से
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.
Chinese-first brain-inspired memory MCP — hybrid recall, 7-layer user profile, workspace assembly.
MCP-USAGE.mdv0.4.0 · 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.
9 tools, grouped by role. Every user-scoped tool takes userEmail (or falls back to MB_USER_EMAIL); only the notable extra params are listed.
2 resources — zhiji://schema/dimensions (authoritative 7-layer / 37-dim profile schema) · zhiji://server/status (live service status).
1 prompt — personal-context: weaves profile + memories + facts into a ready-to-prepend system prompt for a given query.
Experimental capabilities (sleep consolidation / dream replay / emergence) are not exposed until their groundedness passes ablation.
zhiji_status → verify connectivity
zhiji_memory_ingest → feed a conversation (or zhiji_ingest_file for audio/docs)
zhiji_workspace_assemble → in a *new* session, recall + understand the user
zhiji_feedback → thumbs up/down, closing the self-evolution loop
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:
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.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.
Chinese-first brain-inspired memory MCP — hybrid recall, 7-layer user profile, workspace assembly.
{ "mcpServers": { "me.ai-know-memory": { "url": "https://ai-know.me/mcp", "headers": {} } }}टूल
अभी टूल सूची नहीं है। रिमोट सर्वर को पहले हेल्थ चेक के बाद सूची मिलती है।
FAQ
इस पेज पर Cursor, Claude Desktop, Claude Code या VS Code के लिए http कॉन्फ़िग कॉपी करें। यह पब्लिशर का इंस्टॉल इस्तेमाल करता है, PlugMCP प्रॉक्सी नहीं।
इस रिकॉर्ड में कोई ज़रूरी एनवायरनमेंट वेरिएबल दर्ज नहीं है।
Memory तक HTTP से पहुँचा जाता है।
पिछली जाँच 2026-11-10 को हुई। Chinese-first brain-inspired memory MCP — hybrid recall, 7-layer user profile, workspace assembly.