Technical Deep Dive

How OpenClaw Remembers Everything

A technical exploration of the OpenClaw memory architecture that enables AI agents to maintain persistent context across sessions through hybrid search, intelligent compaction, and human-readable storage.

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The Problem

AI Agents Forget Everything

Every time you start a new conversation with an AI, it has no memory of your past interactions. Context is lost, decisions are forgotten, and you have to re-explain everything.

Session-Bound Context

Traditional LLMs lose all context when a session ends

Limited Context Windows

Even within sessions, there's a hard limit on how much can be remembered

OpenClaw's Solution

Persistent memory that survives sessions and context compaction

Traditional AI

Session 1
Memory Lost
Session 2
Memory Lost
Session 3
Memory Lost

OpenClaw

Session 1
Memory Preserved
Session 2
Memory Preserved
Session 3
Memory Preserved

Each session builds on previous memories

Architecture

The Big Picture

OpenClaw acts as a central hub, routing messages between channels and your AI agent while managing persistent memory.

Loading diagram...
Multi-layer Storage
Memory stored in markdown files, daily logs, and SQLite database for redundancy.
Hybrid Search
Combines vector similarity with BM25 keyword search for accurate retrieval.
Pre-compaction Flush
Automatically saves important memories before context window compression.
Real-time Sync
Changes propagate instantly across all storage layers for consistency.
Memory Types

Three Layers of Memory

OpenClaw uses a layered approach to memory storage, combining human-readable files with a searchable database.

Click each layer to see example code

~/.openclaw/MEMORY.md
1# ~/.openclaw/MEMORY.md
2 
3## Project Context
4OpenClaw is a memory architecture for AI agents.
5 
6## Key Decisions
7- SQLite for local storage
8- Markdown for human readability
9- Hybrid search for retrieval
10 
11## User Preferences
12- Dark mode preferred
13- TypeScript enthusiast

File Structure

Explore the Memory Files

OpenClaw stores memories in human-readable files. Click on any file to see its contents.

Click files in the tree to preview their contents
Interactive
Explorer

Select a file to preview its contents

MEMORY.md

The main memory file containing curated long-term knowledge, preferences, and key decisions.

Daily Logs

Automatic daily markdown files capturing conversations, insights, and TODOs from each day.

memory.sqlite

SQLite database with vector embeddings for semantic search across all stored memories.

Pre-Compaction Flush

Save Before You Forget

When context windows fill up, OpenClaw ensures important memories are saved before the LLM compresses or forgets them.

The Problem: Context Window Limits

LLMs have finite context windows. When they fill up, older information gets compressed or dropped entirely. Without intervention, valuable context about decisions, preferences, and ongoing work can be permanently lost.

Click stages to explore
Interactive

Normal Operation

Agent is operating normally with plenty of context space available.

Healthy
Context Window50% Used
90%
Context Memory
API design decisions...
Persistent Storage
MEMORY.md - Core knowledge
memory.sqlite - Searchable archive
Stage 1 of 5

Implementation: Flush Trigger

lib/compaction.ts
1// Pre-compaction flush trigger
2async function checkContextUsage(agent: Agent) {
3  const usage = agent.getContextUsage();
4  const threshold = 0.9; // 90%
5 
6  if (usage >= threshold) {
7    console.log('Context threshold reached, initiating flush...');
8 
9    // Extract important memories from current context
10    const memories = await agent.extractImportantMemories();
11 
12    // Write to daily log before compaction
13    await writeToDailyLog(memories);
14 
15    // Signal that compaction can proceed safely
16    return { canCompact: true, memoriesSaved: memories.length };
17  }
18 
19  return { canCompact: false };
20}
21 
22// Runs before each context compaction
23agent.onBeforeCompaction(checkContextUsage);

How It Works

Step 1

Automatic Detection

Monitors context usage and triggers at 90% threshold

Step 2

Memory Extraction

Identifies and extracts important information from context

Step 3

Daily Log Storage

Writes memories to dated markdown files for persistence

Step 4

Safe Compaction

Only allows compaction after memories are safely stored

Summary

Key Takeaways

OpenClaw's memory architecture solves the fundamental problem of AI amnesia with a practical, transparent approach.

Files as Source of Truth
Human-readable markdown files store memories in a format you can inspect, edit, and version control. No black boxes.
Hybrid Search Works
Combining vector similarity with BM25 keyword search delivers more accurate and relevant memory retrieval.
Graceful Degradation
Pre-compaction flush ensures memories are preserved even when context windows fill up. Nothing important is lost.

Understanding AI Memory Systems

This technical deep dive demonstrates the key concepts behind OpenClaw's persistent AI memory: layered storage, hybrid retrieval, and intelligent context management. These patterns enable AI systems to maintain continuity across conversations and provide more contextual, personalized assistance.