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Selig 4c966a3ad2 Initial commit: OpenClaw Skill Collection

6 custom skills (assign-task, dispatch-webhook, daily-briefing,
task-capture, qmd-brain, tts-voice) with technical documentation.
Compatible with Claude Code, OpenClaw, Codex CLI, and OpenCode.

2026-03-13 10:58:30 +08:00

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Memory

OpenClaw's memory system uses plain Markdown in the agent workspace as the foundational approach. Files serve as the authoritative source rather than RAM-based storage.

Memory File Structure

The system organizes information across two layers:

Daily logs (`memory/YYYY-MM-DD.md`)

Append-only daily entries, with today's and yesterday's files loaded at session start.

Long-term memory (`MEMORY.md`)

Curated persistent information, loaded only in private sessions.

Writing to Memory

Recommended storage patterns:

Decisions, preferences, and durable facts go to MEMORY.md
Ephemeral notes and contextual information in daily logs
Explicit requests to remember something should be written immediately

Automatic Memory Management

When sessions approach token limits, OpenClaw triggers a silent agentic turn prompting memory consolidation before context compaction occurs.

This flush mechanism can be configured via agents.defaults.compaction.memoryFlush settings:

{
  agents: {
    defaults: {
      compaction: {
        memoryFlush: {
          enabled: true,
          softThresholdTokens: 4000,
          prompt: "...",
          systemPrompt: "..."
        }
      }
    }
  }
}

Search Capabilities

The system supports vector-based semantic search across memory files, with configurable backends including:

Backend	Description
Built-in SQLite	Optional vector acceleration
QMD sidecar	Local-first search combining BM25 + vectors + reranking
Hybrid search	Merges both keyword and semantic signals

Tools

memory_search - Semantic queries across memory files
memory_get - Direct file retrieval

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