Memoria

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name: Memoria for OpenClaw version: 3.34.0 description: "The most advanced memory system for AI agents. 24 cognitive layers, knowledge graph, procedural learning, dialectic queries, AI self-observation, auto skill creation, crash-safe WAL, async prefetch. Works with Claude, Cursor, Copilot, ChatGPT & any OpenClaw agent. 100% local-first (SQLite + Ollama), zero cloud cost, zero API keys required." author: Primo Studio (@Nieto42) license: Apache-2.0 homepage: https://github.com/Primo-Studio/openclaw-memoria repository: https://github.com/Primo-Studio/openclaw-memoria feedback: https://x.com/Nitix_ keywords: - memory - ai-agent - persistent-memory - long-term-memory - knowledge-graph - procedural-learning - vector-search - sqlite - ollama - lm-studio - local-first - cognitive - context - claude - cursor - copilot - chatgpt - openclaw - wal - vibe-coding - developer-tools - devtools - typescript - llm - automation - brain-inspired - multi-layer - fact-extraction - self-improving tags: - memory - ai-agent - persistent-memory - long-term-memory - knowledge-graph - procedural-learning - vector-search - sqlite - ollama - lm-studio - local-first - cognitive - context - claude - cursor - copilot - chatgpt - openclaw - wal - vibe-coding - developer-tools - devtools - typescript - llm - automation - brain-inspired - multi-layer - fact-extraction - self-improving - self-observation - dialectic - auto-skill - prefetch - crash-recovery - async env: - name: OPENAI_API_KEY required: false description: Optional — used as fallback for LLM extraction and embeddings when local models are unavailable. Never required for default operation. - name: OPENROUTER_API_KEY required: false description: Optional — used as fallback for remote LLM provider. Never required for default operation. - name: OPENCLAW_WORKSPACE required: false description: Auto-set by OpenClaw runtime — workspace path for memory files. Do not set manually. security: | ## Data & Privacy - Memoria runs 100% locally by default. No data leaves your machine unless you explicitly configure a remote LLM. - All memory is stored in a local SQLite database in your OpenClaw workspace. - API keys (OPENAI_API_KEY, OPENROUTER_API_KEY) are optional fallbacks — never required.

## What Memoria reads - Workspace files: USER.md, COMPANY.md, projects/* — used for identity-aware relevance scoring. These files may contain personal or business information. Review them before enabling Memoria. - Conversation content: assistant messages and tool call results, to extract durable facts. - No files outside the OpenClaw workspace are read.

## What Memoria writes - A single SQLite database (memoria.db) in your workspace/memory/ folder. - Optional markdown summaries in workspace/memory/ folder. - No network requests unless a remote LLM fallback is configured.

## Hooks used - before_prompt_build: injects recalled facts into context - after_tool_call: captures procedural knowledge (how-to steps) - agent_end: extracts facts from completed conversations - after_compaction: extracts facts from compacted summaries These are standard OpenClaw plugin hooks. They run locally within the plugin process. entrypoint: index.ts

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🧠 Memoria — Multi-Layer Persistent Memory for OpenClaw

The most complete memory system for OpenClaw. 20 layers of memory that work together, powered by YOUR choice of LLM.

Why Memoria?

🏗️ 20 Memory Layers (not just a fact store)

1. Facts — Durable knowledge extracted from every conversation 2. Procedures — HOW to do things, improves with repetition, learns from failures 3. Knowledge Graph — Entities + relations connecting your facts 4. Topics & Expertise — Tracks what you talk about most, specializes over time 5. Observations — Short-term working memory for active context 6. Error Detection 🔥 — Touch fire once, remember forever. Dangers captured on first occurrence 7. Lifecycle — Fresh → Settled → Dormant. Nothing deleted, priority shifts naturally

🔌 Bring Your Own LLM

Configure each layer independently. Mix and match:

Ollama — Run gemma3, qwen3.5, llama, or any model locally (recommended)

LM Studio — Use any GGUF model from your local server

Remote APIs — OpenAI, Anthropic, OpenRouter as primary or fallback

Fallback chains — Ollama → LM Studio → API. If one fails, the next takes over automatically

🏠 100% Local-First

SQLite + FTS5 — No external database needed

Local embeddings — nomic-embed-text via Ollama (zero API cost)

Zero cloud dependency — Works offline, your data stays on your machine

Fallback chain — Degrades gracefully if a provider goes down

🧬 What Makes Memoria Different

| Feature | Memoria | Basic memory plugins | |---------|---------|---------------------| | Memory layers | 20 specialized layers | Single fact store | | LLM choice | Any local or remote model | Usually hardcoded | | Per-layer LLM config | ✅ Different model per layer | ❌ | | Procedural learning | ✅ Learns HOW, not just WHAT | ❌ | | Error detection | ✅ Auto-captures dangers | ❌ | | Knowledge graph | ✅ Entities + relations | ❌ | | Lifecycle management | ✅ Smart aging, never forgets | ❌ or simple TTL | | Cost | $0 with local models | Varies |

Installation

As Plugin (recommended — one command)

openclaw plugins install clawhub:memoria-plugin

This installs Memoria from the ClawHub registry. No manual steps needed.

From source (for contributors / advanced users)

If you prefer to inspect the code first: 1. Browse the repository: github.com/Primo-Studio/openclaw-memoria 2. Review the source code, especially index.ts (main entrypoint) and openclaw.plugin.json (config schema) 3. Clone and install:

cd ~/.openclaw/extensions
git clone https://github.com/Primo-Studio/openclaw-memoria.git memoria
cd memoria && npm install

Then add to your openclaw.json under plugins.entries:

{
  "memoria": { "enabled": true },
  "memory-convex": { "enabled": false }
}

Configuration

Minimal (works out of the box with Ollama)

Just install and restart. Defaults: Ollama + gemma3:4b for extraction, nomic for embeddings.

Custom LLM per layer

"memoria": {
  "enabled": true,
  "config": {
    "llm": {
      "default": { "provider": "ollama", "model": "qwen3.5:4b" },
      "procedural": { "provider": "lmstudio", "model": "your-model" },
      "graph": { "provider": "openai", "model": "gpt-4o-mini" }
    }
  }
}

Source Code

The full source is available on GitHub: Primo-Studio/openclaw-memoria

Key files:

index.ts — Main plugin entrypoint (hooks, extraction, recall pipeline)

procedural.ts — Procedural memory (how-to learning)

lifecycle.ts — Lifecycle management (fresh/settled/dormant)

scoring.ts — Temporal scoring and relevance ranking

selective.ts — Dedup, contradiction detection, fact quality

openclaw.plugin.json — Configuration schema

Feedback & Community

We'd love your feedback! Tell us how Memoria works for you:

🐦 Tweet us @Nitix_ — share your setup, results, or ideas

⭐ Star the repo: github.com/Primo-Studio/openclaw-memoria

🐛 Issues: GitHub Issues

Built with ❤️ by Primo Studio 🇬🇫 — AI tooling from French Guiana.

Minimal (works out of the box with Ollama)

Just install and restart. Defaults: Ollama + gemma3:4b for extraction, nomic for embeddings.

Custom LLM per layer

"memoria": {
  "enabled": true,
  "config": {
    "llm": {
      "default": { "provider": "ollama", "model": "qwen3.5:4b" },
      "procedural": { "provider": "lmstudio", "model": "your-model" },
      "graph": { "provider": "openai", "model": "gpt-4o-mini" }
    }
  }
}