mnemo-memory

name: mnemo-memory version: 0.3.0 description: "Cloud-persistent memory for AI agents. Stateless plugins + mnemo-server = cross-session recall, multi-agent sharing, and hybrid vector + keyword search. Works with OpenClaw, Claude Code, and OpenCode." author: qiffang keywords: [memory, agent-memory, persistent-memory, tidb, vector-search, hybrid-search, cloud-memory, multi-agent, cross-session, openclaw, claude-code, opencode, stateless, ai-agent, developer-tools] metadata: openclaw: emoji: "\U0001F9E0"

mnemo — Cloud-Persistent Memory for AI Agents 🧠

Your agents are stateless. Your memory shouldn't be.

Every AI agent session starts from zero. Context is lost, decisions are forgotten, and your agents keep rediscovering what they already knew. mnemo externalizes agent memory into a central server — so agents stay disposable, but memory persists forever.

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│ Claude Code  │     │  OpenCode   │     │  OpenClaw   │
│   Plugin     │     │   Plugin    │     │   Plugin    │
└──────┬───────┘     └──────┬──────┘     └──────┬──────┘
       │                    │                    │
       └────────────────────┼────────────────────┘
                            │
                    ┌───────┴────────┐
                    │  mnemo-server  │  ← Go REST API
                    └───────┬────────┘
                            │
                    ┌───────┴────────┐
                    │   TiDB / MySQL │  ← your database
                    │                │
                    │  • VECTOR type │
                    │  • Hybrid      │
                    │    search      │
                    └────────────────┘

What Problem Does This Solve?

Pain Point	Without mnemo	With mnemo
Session amnesia	Agent forgets everything on restart	Memory persists in the cloud
Machine-locked	Memory in local files, lost on device switch	Same memory from any machine
Agent silos	Claude can't see what OpenCode learned	All agents share one memory pool
Team isolation	Teammate's agent starts from scratch	Shared spaces with per-agent tokens
No semantic search	Grep through flat files	Hybrid vector + keyword search

Hybrid Search: Vector + Keyword

The server supports hybrid search when an embedding provider is configured:

Vector search — Semantic similarity via cosine distance
Keyword search — Full-text search with fallback to LIKE
RRF merge — Results from both legs are merged and ranked

No embedding config? Keyword search works immediately. Add vectors later — no migration needed.

Install for OpenClaw

npm install mnemo-openclaw

Add to openclaw.json:

{
  "plugins": {
    "slots": { "memory": "mnemo" },
    "entries": {
      "mnemo": {
        "enabled": true,
        "config": {
          "apiUrl": "http://your-server:8080",
          "userToken": "mnemo_your_token"
        }
      }
    }
  }
}

Also Works With

Platform	Install
Claude Code	`/plugin marketplace add qiffang/mnemos` → `/plugin install mnemo-memory@mnemos`
OpenCode	`"plugin": ["mnemo-opencode"]` in `opencode.json`
Any HTTP client	REST API via mnemo-server

5 Memory Tools

Tool	What it does
`memory_store`	Store a memory
`memory_search`	Hybrid vector + keyword search across all memories
`memory_get`	Retrieve a single memory by ID
`memory_update`	Update an existing memory
`memory_delete`	Delete a memory