Mnemo Memory

mnemo — Cloud-Persistent Memory for AI Agents \U0001F9E0

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 TiDB Cloud Serverless — so agents stay disposable, but memory persists forever.

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│ Claude Code  │     │  OpenCode   │     │  OpenClaw   │
│   Plugin     │     │   Plugin    │     │   Plugin    │
└──────┬───────┘     └──────┬──────┘     └──────┬──────┘
       │                    │                    │
       └────────────────────┼────────────────────┘
                            │
                    ┌───────┴────────┐
                    │  mnemo-server  │  ← optional (team mode)
                    └───────┬────────┘
                            │
                    ┌───────┴────────┐
                    │   TiDB Cloud   │  ← zero-ops, free tier
                    │   Serverless   │
                    │                │
                    │  • VECTOR type │
                    │  • EMBED_TEXT  │
                    │  • HTTP API    │
                    └────────────────┘

What Problem Does This Solve?

Why TiDB Cloud Serverless?

mnemo chose TiDB Cloud Serverless because it uniquely combines everything agent memory needs — in one service, at zero cost:

• Native VECTOR type — Semantic search in the same table as your metadata. No separate vector database.
• EMBED_TEXT() auto-embedding — TiDB generates embeddings server-side (e.g. tidbcloud_free/amazon/titan-embed-text-v2). No OpenAI API key required for semantic search.
• HTTP Data API — Agents talk to TiDB via fetch/curl. No database drivers, no connection pools.
• Free tier — 25 GiB storage, 250M Request Units/month. More than enough for individual use.
• MySQL compatible — Migrate to self-hosted TiDB or MySQL anytime.

One database gives you relational storage + vector search + auto-embedding + HTTP access. No glue code. No infra.

Hybrid Search: Vector + Keyword

              Embedding configured?
              ┌─────────┴─────────┐
             Yes                  No
              │                    │
        Hybrid search        Keyword only
        (vector + keyword)   (LIKE '%q%')
              │                    │
    ┌─────────┴─────────┐         │
 Vector results     Keyword       │
 (ANN cosine)       results       │
    └─────────┬─────────┘         │
         Merge & rank         Direct results

Three embedding options — pick one or none:

1. TiDB auto-embedding — EMBED_TEXT() generates vectors server-side. Zero config. Free.
2. OpenAI / compatible API — Set MNEMO_EMBED_API_KEY. Works with Ollama too.
3. No embedding — Keyword search works immediately. Add vectors later, no migration needed.

Multi-Agent Conflict Resolution (CRDT)

When multiple agents write to the same memory, mnemo uses vector clocks — no coordination required:

Agent A: clock {A:3, B:1}        Agent B: clock {A:2, B:2}
         \                                /
          └──── Server compares ─────────┘
                       │
               Neither dominates →
               Concurrent conflict!
                       │
            Deterministic tie-break
                       │
               Winner saved, clocks merged: {A:3, B:2}

Deletes are soft (tombstone + clock increment) — no ghost resurrection from agents that missed the delete.

Install for OpenClaw

npm install mnemo-openclaw

Add to openclaw.json:

{
  "plugins": {
    "slots": { "memory": "mnemo" },
    "entries": {
      "mnemo": {
        "enabled": true,
        "config": {
          "host": "<your-tidb-host>",
          "username": "<your-tidb-user>",
          "password": "<your-tidb-pass>"
        }
      }
    }
  }
}

Get a free cluster in 30 seconds at tidbcloud.com.

Optional — enable auto-embedding (no API key needed):

{
  "config": {
    "host": "...",
    "username": "...",
    "password": "...",
    "autoEmbedModel": "tidbcloud_free/amazon/titan-embed-text-v2",
    "autoEmbedDims": 1024
  }
}

Also Works With

5 Memory Tools

Two Modes, One Plugin

Mode is inferred from config. Start personal, scale to team — no code change.

Links

• GitHub: github.com/qiffang/mnemos
• Design Doc: docs/DESIGN.md
• CRDT Proposal: crdt-memory-proposal.md
• TiDB Cloud Serverless: tidbcloud.com (free tier)

Built for agents that need to remember. Powered by TiDB Cloud Serverless.