Cyber Memory

Five-layer memory system with automatic fact extraction via local LLM (Ollama). Processes session transcripts locally — no external API required.

Memory Architecture 🧠

A complete memory system for OpenClaw agents. Five layers of storage, automatic fact extraction via local LLM, hybrid search, and behavioral rules that prevent context loss.

🔒 Local-first by default — all fact extraction runs on your local Ollama instance. No data leaves your machine.

🔒 Privacy & Data Handling

This skill includes a hook (memory-flush) that:

• Reads session transcripts from disk (~/.openclaw/agents/*/sessions/*.jsonl)
• Processes content locally via Ollama (default) — no data leaves your machine
• No external API key required — works out of the box with local LLM

What is processed: Recent user/assistant messages (last 30 messages, each truncated to 500 chars). Where it runs: Local Ollama endpoint (http://localhost:11434/v1/chat/completions by default). What is saved locally: Extracted facts as Markdown files in workspace/memory/.

Optional: You can configure an external OpenAI-compatible API by setting baseUrl and apiKey in the hook config, but local Ollama is the default and recommended setup.

Architecture

text

🔥 Hot    → SESSION-STATE.md (WAL protocol, survives compaction)
🌤 Warm   → memory/YYYY-MM-DD.md (daily event summaries)
🧊 Cold   → MEMORY.md (decisions, preferences, rules — always loaded)
🕸 Graph  → memory/ontology/ (entity relationships)
📚 Learn  → .learnings/ (errors, best practices)

Automation

Mechanism	Trigger	What it does
session-memory (built-in)	/new /reset	Saves conversation to memory/
memory-flush (this skill)	Compaction + /new	LLM extracts structured facts (local Ollama)
command-logger (built-in)	Any command	Audit log
session indexing	Automatic	Historical sessions searchable

Search

• Vector: any OpenAI-compatible embedding provider (Ollama, OpenAI, etc.)
• Keyword: SQLite FTS5 (BM25)
• Hybrid: weighted vector + keyword fusion
• Scope: MEMORY.md + daily logs + session transcripts + SESSION-STATE.md

Setup

1. Prerequisites

Install Ollama and pull a chat model:

bash

# Install Ollama (https://ollama.ai)
ollama pull qwen2.5:7b   # or any chat model you prefer
ollama serve              # ensure Ollama is running on localhost:11434

2. Enable Built-in Hooks

bash

openclaw hooks enable session-memory
openclaw hooks enable command-logger

3. Install Memory-Flush Hook

Copy the hooks/memory-flush/ directory to ~/.openclaw/hooks/:

bash

cp -r hooks/memory-flush ~/.openclaw/hooks/
openclaw hooks enable memory-flush

4. Configure Fact Extraction (Optional)

By default, the hook uses local Ollama — no configuration needed. To customize:

json5

{
  hooks: {
    internal: {
      enabled: true,
      entries: {
        "memory-flush": {
          enabled: true,
          extractionModel: "qwen2.5:7b",           // Ollama model name
          baseUrl: "http://localhost:11434/v1/chat/completions"  // Ollama endpoint
        }
      }
    }
  }
}

Works with any OpenAI-compatible API (Ollama, LM Studio, vLLM, etc.). Set baseUrl and apiKey to use an external provider.

5. Enable Session Indexing

json5

{
  agents: {
    defaults: {
      memorySearch: {
        provider: "local",  // or "openai", "ollama", "gemini", "voyage", etc.
        experimental: {
          sessionMemory: true
        },
        sources: ["memory", "sessions"],
        extraPaths: ["SESSION-STATE.md"]
      }
    }
  }
}

6. Restart Gateway

bash

openclaw gateway restart

Agent Behavioral Rules

Add these rules to your AGENTS.md:

Memory Writing

• Important info → MEMORY.md immediately (decisions, preferences, rules)
• Daily summaries → memory/YYYY-MM-DD.md (event summaries, no raw tool output)
• Cron report details → skip (already delivered elsewhere)
• Critical info zero loss — important things must go to MEMORY.md, not just daily logs

Memory Searching

• Check MEMORY.md + today/yesterday logs at session start
• Use memory_search for historical queries
• Ontology queries (relationships, "who is responsible for X") → use ontology skill

Sub-agent Context Injection

When spawning sub-agents, inject relevant context from MEMORY.md:

text

[Key context from memory, max 500 words]

---

[Actual task]

File Structure

text

workspace/
├── AGENTS.md              # Behavioral rules (loaded every session)
├── SOUL.md                # Agent personality
├── USER.md                # User preferences
├── TOOLS.md               # Tool notes (keep lean, <2KB)
├── MEMORY.md              # Long-term curated memory
├── SESSION-STATE.md       # Hot working memory (WAL)
├── memory/
│   ├── YYYY-MM-DD.md      # Daily logs (summaries only)
│   ├── YYYY-MM-DD-facts-* # Auto-extracted facts
│   ├── YYYY-MM-DD-compact # Pre-compaction snapshots
│   └── ontology/
│       ├── graph.jsonl    # Knowledge graph
│       └── schema.yaml    # Entity type definitions
├── .learnings/
│   ├── LEARNINGS.md       # Best practices
│   ├── ERRORS.md          # Error log
│   └── FEATURE_REQUESTS.md
└── hooks/
    └── memory-flush/
        ├── HOOK.md
        └── handler.ts     # LLM fact extraction (local Ollama default)

What Gets Loaded When

File	When
AGENTS.md, SOUL.md, USER.md, TOOLS.md	Every session start
MEMORY.md	DM session start
memory/today + yesterday	Every session start
SESSION-STATE.md	Via memory_search (indexed)
Other memory files	Via memory_search on demand

Token Optimization

• Keep TOOLS.md lean (<2KB), move detailed configs to tools/ subdirectory
• Daily logs: event summaries, not raw tool output
• Fact extraction: 30 messages → ~10 facts (16:1 compression)

Troubleshooting

Facts not extracted? Check Ollama is running (ollama serve) and the model is available (ollama list).

Session search not working? Verify experimental.sessionMemory: true and sources: ["memory", "sessions"].

Hook not loading? Run openclaw hooks list --verbose and check for errors.

Want to use external API? Set baseUrl and apiKey in the hook config to use OpenAI or any compatible provider.

License

MIT