Persistent Memory for AI Agents.
Your agents forget everything between sessions. mem9 fixes that with persistent memory across sessions and machines, shared memory for multi-agent workflows, and hybrid recall with a visual dashboard.

For OpenClaw and ClawHub installs, start here: mem9.ai/openclaw-memory

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🚀 Quick Start

Server-based memory via mem9-server.

1. Deploy mnemo-server.

   cd server && MNEMO_DSN="user:pass@tcp(host:4000)/mnemos?parseTime=true" go run ./cmd/mnemo-server

2. Install the plugin for your agent (pick one).

   Platform	Install
   Claude Code	/plugin marketplace add mem9-ai/mem9 then /plugin install mem9@mem9
   OpenCode	Add "plugin": ["@mem9/opencode"] to opencode.json
   OpenClaw	Add mnemo to openclaw.json plugins (see openclaw-plugin/README)

3. Provision a tenant and set credentials.

   curl -s -X POST localhost:8080/v1alpha1/mem9s
   # → {"id":"..."}
   
   export MEM9_API_URL="http://localhost:8080"
   export MEM9_API_KEY="..."

   All agents pointing at the same tenant ID share one memory pool.

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The Problem

AI coding agents — Claude Code, OpenCode, OpenClaw, and others — often maintain separate local memory files. As a result:

• 🧠 Amnesia — Agents forget everything when a session ends
• 🏝️ Silos — One agent can't access what another learned yesterday
• 📁 Local files — Memory is tied to a single machine, lost when you switch devices
• 🚫 No team sharing — Your teammate's agent can't benefit from your agent's discoveries

mnemos gives every agent a shared, cloud-persistent memory with hybrid vector + keyword search — powered by TiDB Cloud Starter.

Why TiDB Cloud Starter?

mnemos uses TiDB Cloud Starter (formerly TiDB Cloud Serverless) as the backing store for mnemo-server:

Feature	What it means for you
Free tier	25 GiB storage, 250M Request Units/month — enough for most individual and small team use
TiDB Cloud Zero	Instant database provisioning via API — no signup required for first 30 days
Native VECTOR type	Hybrid search (vector + keyword) without a separate vector database
Auto-embedding (EMBED_TEXT)	TiDB generates embeddings server-side — no OpenAI key needed for semantic search
Zero ops	No servers to manage, no scaling to worry about, automatic backups
MySQL compatible	Migrate to self-hosted TiDB or MySQL anytime

This architecture keeps agent plugins stateless — all state lives in mnemo-server, backed by TiDB.

Supported Agents

mnemos provides native plugins for major AI coding agent platforms:

Platform	Plugin	How It Works	Install Guide
Claude Code	Hooks + Skills	Auto-loads memories on session start, auto-saves on stop	claude-plugin/README.md
OpenCode	Plugin SDK	system.transform injects memories, session.idle auto-captures	opencode-plugin/README.md
OpenClaw	Memory Plugin	Replaces built-in memory slot (kind: "memory"), framework manages lifecycle	openclaw-plugin/README.md
Any HTTP client	REST API	curl to mnemo-server	API Reference

All plugins expose the same 5 tools: memory_store, memory_search, memory_get, memory_update, memory_delete.

Note

🤖 For AI Agents: Use the Quick Start above to deploy mnemo-server and provision an API key, then follow the platform-specific README for configuration details.

Stateless Agents, Cloud Memory

A key design principle: agent plugins carry zero state. All memory lives in mnemo-server, backed by TiDB/MySQL. This means:

• Agent plugins stay stateless — deploy any number of agent instances freely; they all share the same memory pool via mnemo-server
• Switch machines freely — your agent's memory follows you, not your laptop
• Multi-agent collaboration — Claude Code, OpenCode, OpenClaw, and any HTTP client share memories when pointed at the same server
• Centralized control — rate limits and audit live in one place

Related Repositories

The broader mem9 product surface is split across a few repositories and workstreams:

Repo / Name	Where	What it owns
mem9	current repo	Core Go memory API, agent plugins, CLI, website, docs site, dashboard frontend, benchmark harnesses
mem9-node	sibling repo, commonly ../mem9-node	Node/Nest backend for dashboard analysis workflows, async jobs, and workers
mem9-benchmark	benchmark-focused repo / workstream name you may hear internally	Benchmark-heavy evaluation work; the benchmark harnesses that currently ship with mem9 live under benchmark/ in this repo
mem9-tester	private repo, often checked out as ../mem9-tester	Automated OpenClaw install / verification harness for SKILL.md flows, smoke tests, and artifact capture

Notes:

• dashboard/app/ in this repo is the frontend half of the dashboard product. The backend half for async analysis lives in mem9-node, especially its apps/api and apps/worker.
• mem9-tester is not part of the production runtime. It exists to validate installation / reconnect behavior and reduce fragile manual testing.
• If someone says mem9-benchmark, they usually mean the benchmark work around mem9 evaluation. In this repo, that work currently starts in benchmark/.

API Reference

Agent identity: X-Mnemo-Agent-Id header.

Method	Path	Description
POST	/v1alpha1/mem9s	Provision tenant (no auth). Returns { "id" }.
POST	/v1alpha1/mem9s/{tenantID}/memories	Legacy unified write endpoint. Tenant key travels in the URL path.
GET	/v1alpha1/mem9s/{tenantID}/memories	Legacy search endpoint for tenantID-configured clients.
GET	/v1alpha1/mem9s/{tenantID}/memories/:id	Legacy get-by-id endpoint.
PUT	/v1alpha1/mem9s/{tenantID}/memories/:id	Legacy update endpoint. Optional If-Match for version check.
DELETE	/v1alpha1/mem9s/{tenantID}/memories/:id	Legacy delete endpoint.
POST	/v1alpha2/mem9s/memories	Preferred unified write endpoint. Requires X-API-Key header.
GET	/v1alpha2/mem9s/memories	Preferred search endpoint. Requires X-API-Key header.
GET	/v1alpha2/mem9s/memories/:id	Preferred get-by-id endpoint. Requires X-API-Key header.
PUT	/v1alpha2/mem9s/memories/:id	Preferred update endpoint. Requires X-API-Key header.
DELETE	/v1alpha2/mem9s/memories/:id	Preferred delete endpoint. Requires X-API-Key header.

Self-Hosting

Environment Variables

Variable	Required	Default	Description
MNEMO_DSN	Yes	—	Database connection string
MNEMO_PORT	No	8080	HTTP listen port
MNEMO_RATE_LIMIT	No	100	Requests/sec per IP
MNEMO_RATE_BURST	No	200	Burst size
MNEMO_EMBED_API_KEY	No	—	Embedding provider API key
MNEMO_EMBED_BASE_URL	No	OpenAI	Custom embedding endpoint
MNEMO_EMBED_MODEL	No	text-embedding-3-small	Model name
MNEMO_EMBED_DIMS	No	1536	Vector dimensions
MNEMO_ENCRYPT_TYPE	No	plain	Encryption type for tenant DB passwords: plain, md5, or kms. ⚠️ One-time deployment decision — cannot be changed without re-provisioning all tenants.
MNEMO_ENCRYPT_KEY	No	—	Encryption key (for md5) or KMS key ID (for kms). Required when MNEMO_ENCRYPT_TYPE is not plain.
MNEMO_DEBUG_LLM	No	false	Log raw LLM responses for debugging parse errors. ⚠️ Dev/test only — responses may contain user data.

Build & Run

make build
cd server
MNEMO_DSN="user:pass@tcp(host:4000)/mnemos?parseTime=true" ./bin/mnemo-server

Docker

docker build -t mnemo-server ./server
docker run -e MNEMO_DSN="..." -p 8080:8080 mnemo-server

Repository Map

Path	Role
server/	Core Go REST API and source of truth for spaces, memories, search, ingest, and tenant provisioning
cli/	Standalone Go CLI for exercising the mem9 API and import / ingest flows
openclaw-plugin/	OpenClaw memory plugin
opencode-plugin/	OpenCode plugin
claude-plugin/	Claude Code hooks + skills integration
site/	Public mem9.ai site. This includes the marketing site, docs page, and published onboarding documents like site/public/SKILL.md and site/public/beta/SKILL.md
dashboard/	Dedicated home for dashboard work: product docs plus the frontend app
dashboard/app/	Your Memory frontend SPA served under /your-memory
dashboard/app/src/pages/connect.tsx	Dashboard connect / onboarding page
dashboard/app/src/pages/space.tsx	Main Your Memory page for browsing, filtering, importing, exporting, and analyzing memories
dashboard/app/src/pages/pixel-farm.tsx	Memory Farm, the lab-style interactive memory experience exposed at /your-memory/labs/memory-farm
dashboard/app/src/pages/pixel-farm-editor.tsx	Dev-only editor for Memory Farm world / mask work
dashboard/app/src/lib/pixel-farm/	Memory Farm world generation, memory-to-world transforms, rendering, tiles, and runtime logic
dashboard/app/src/components/pixel-farm/	Memory Farm UI components
dashboard/docs/	Dashboard product specs, information architecture, data contract, and implementation plans
benchmark/	Benchmark harnesses and datasets for comparing OpenClaw native memory against mem9, including MR-NIAH and LoCoMo adapters
e2e/	Live end-to-end scripts against a running mem9 server
docs/	Architecture notes, design docs, and feature / experiment specs
docs/superpowers/specs/	Feature-level specs and experiments, including Pixel / Memory Farm design work
skills/	Shared setup / onboarding skills

Roadmap

Phase	What	Status
Phase 1	Core server + CRUD + auth + hybrid search + upsert + plugins	✅ Done
Phase 3	LLM-assisted conflict merge, auto-tagging	🔜 Planned
Phase 4	Web dashboard, bulk import/export, CLI wizard	📋 Planned

Vector Clock CRDT was deferred and removed from the roadmap.

Contributing

See CONTRIBUTING.md for development setup and guidelines.

License

Apache-2.0

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_{Built with TiDB Starter — zero-ops database with native vector search.}