How OpenClaw Memory ACTUALLY Works (4 Memory Layers)
Summary
This video breaks down how OpenClaw's memory system actually works under the hood — and it's not one system, it's four distinct layers, each behaving differently. Understanding which layer does what is the key to fixing memory issues and building more reliable agents. The first layer is Bootstrap Files — your agent's permanent identity. These files are loaded fresh from disk at every session start, which means they survive compaction. However, they have hard limits: 20,000 characters per file and 150,000 characters total. If you exceed these limits, files get truncated silently. The practical advice here is to keep these files lean — especially soul.md, which should stay between 15 and 30 lines. Also note that parallel sub-agents only read agents.md and tools.md, so make sure those files contain everything sub-agents need to complete their tasks. The second layer is the Session Transcript — the full conversation history saved as a file on disk. It includes user messages, assistant messages, and tool calls. The problem is that when your context window approaches its limit (around 156,000 tokens with default settings, not 200,000), OpenClaw triggers auto-compaction. After compaction, old messages are replaced with a summary, and the model can no longer see the originals. Only the last 20,000 tokens stay intact. This is why instructions you give in chat get lost over time — if it's not saved to a file, it doesn't exist. The third layer is the Context Window — the active working memory where everything competes for space: system prompts, bootstrap files, conversation history, tool results, and the current message. Claude Opus and Sonnet have 200,000 tokens, which sounds like a lot but fills up fast. Tool results — file reads, web snapshots, API responses — are the biggest consumers. You can manually trigger compaction before the auto-threshold kicks in (recommended around 150,000 tokens) to maintain more control over what gets summarized. The fourth layer is the Retrieval Index — a searchable archive that indexes your memory files. The agent uses a two-step process: search with a keyword or semantic query, then retrieve the full context. This lets you offload large amounts of information outside the active context window and query it on demand, which is far more efficient for long-term storage like scraped news or past session data.
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