Automated daily memory backfill for OpenClaw sessions

Scrape and analyze OpenClaw JSONL session logs to reconstruct and backfill agent memory files. Use when: (1) Memory appears incomplete after model switches, (2) Verifying memory coverage, (3) Reconstructing lost memory, (4) Automated daily memory sync via cron/heartbeat. Supports simple extraction and LLM-based narrative summaries with automatic secret sanitization.

Memory Sync

Tool for maintaining agent memory continuity across model switches with automatic secret sanitization.

Installation

Requires Python 3.11+ and click:

pip install click

# Optional: for direct API summarization (only if not using OpenClaw backend)
pip install openai

Quick Start

# Run directly from skill directory
python ~/.openclaw/skills/memory-sync/memory_sync.py compare

# Or create an alias for convenience
alias memory-sync="python ~/.openclaw/skills/memory-sync/memory_sync.py"

# Check for gaps
memory-sync compare

# Backfill today's memory (simple extraction - fast, no LLM)
memory-sync backfill --today

# Backfill with LLM narrative (uses OpenClaw's native model - no API key needed)
memory-sync backfill --today --summarize

# Backfill all missing
memory-sync backfill --all

Commands

Command	Description
compare	Find gaps between session logs and memory files
backfill --today	Generate memory for current day
backfill --since YYYY-MM-DD	Backfill from date to present
backfill --all	Backfill all missing dates
backfill --incremental	Backfill only changed dates since last run
extract	Extract conversations matching criteria
summarize --date YYYY-MM-DD	Generate LLM summary for a single day
transitions	List model transitions
validate	Check memory files for consistency issues
stats	Show coverage statistics

Simple Extraction vs LLM Summarization

The backfill command supports two modes:

Simple Extraction (default, without --summarize):

• Fast, no LLM or API calls needed
• Extracts topics via keyword frequency analysis
• Identifies key user questions and assistant responses
• Detects decision markers from text patterns
• Produces structured output with Topics, Key Exchanges, Decisions sections
• With --preserve: Hand-written content is appended to the end of the new file
• Best for: Quick backfills, initial setup, systems without LLM access

LLM Summarization (with --summarize) - Recommended:

• Uses LLM to generate narrative summaries
• Produces coherent 2-4 paragraph prose
• Better context and insight extraction
• With --preserve: Existing content is passed to the LLM with instructions to incorporate it into the new summary, maintaining temporal order and thematic structure
• Best for: Daily automation, high-quality memory files

Recommended for regular use:

# Best quality: LLM summary that incorporates any existing notes
memory-sync backfill --today --summarize --preserve

Both modes automatically sanitize secrets before writing.

Common Workflows

Initial Setup

# Check what's missing
memory-sync compare

# Backfill everything (may take time)
memory-sync backfill --all

Nightly Automation (Recommended)

# Best: LLM summary that incorporates any existing notes
memory-sync backfill --today --summarize --preserve

# Smart: Process only days changed since last run
memory-sync backfill --incremental --summarize --preserve

# Or use a specific backend if preferred
memory-sync backfill --today --summarize --preserve --summarize-backend anthropic

Catch-Up After Gaps

# Backfill from last week to present
memory-sync backfill --since 2026-01-28 --summarize

Regenerate with Preserved Content

# Keep hand-written notes when regenerating
memory-sync backfill --date 2026-02-05 --force --preserve --summarize

Secret Sanitization

All content is automatically sanitized to prevent secret leakage:

• 30+ explicit patterns: OpenAI, Anthropic, GitHub, AWS, Stripe, Discord, Slack, Notion, Google, Brave, Tavily, SerpAPI, etc.
• Structural detection: JWT tokens, SSH keys, database connection strings, high-entropy base64
• Generic patterns: API keys, tokens, passwords, environment variables
• Defense-in-depth: Secrets redacted at every stage (extraction, LLM processing, file writes, CLI display)

Secrets are replaced with [REDACTED-TYPE] placeholders.

See SECRET_PATTERNS.md for complete pattern list.

Summarization Backends

The --summarize flag supports multiple backends via --summarize-backend:

Backend	Description	API Key Required
openclaw (default)	Uses OpenClaw's sessions spawn with your configured model	No
anthropic	Direct Anthropic API via openai package	ANTHROPIC_API_KEY
openai	Direct OpenAI API via openai package	OPENAI_API_KEY

Examples

# Default: use OpenClaw's native model (no API key needed)
memory-sync backfill --today --summarize

# Explicit backend selection
memory-sync backfill --today --summarize --summarize-backend openclaw
memory-sync backfill --today --summarize --summarize-backend anthropic
memory-sync backfill --today --summarize --summarize-backend openai

# Override model for any backend
memory-sync backfill --today --summarize --model claude-sonnet-4-20250514
memory-sync backfill --today --summarize --summarize-backend openai --model gpt-4o

The openclaw backend is recommended as it:

• Uses your existing OpenClaw configuration
• Requires no separate API keys
• Leverages whatever model you have configured in OpenClaw

Automated Usage

Nightly Cron (3am)

Process today with LLM summary, preserving any existing notes:

0 3 * * * cd ~/.openclaw/skills/memory-sync && python memory_sync.py backfill --today --summarize --preserve >> ~/.memory-sync/cron.log 2>&1

Smart Incremental Mode

Automatically detects changes since last run:

# Initial backfill (run once, simple extraction for speed)
python memory_sync.py backfill --all

# Then set up nightly incremental with LLM summaries
0 3 * * * cd ~/.openclaw/skills/memory-sync && python memory_sync.py backfill --incremental --summarize --preserve >> ~/.memory-sync/cron.log 2>&1

State is tracked in ~/.memory-sync/state.json.

Configuration

Default paths:

• Session logs: ~/.openclaw/agents/main/sessions/*.jsonl
• Memory files: ~/.openclaw/workspace/memory/

Override with CLI flags:

• --sessions-dir /path/to/sessions
• --memory-dir /path/to/memory

Environment variables (only for direct API backends):

• ANTHROPIC_API_KEY - Required for --summarize-backend anthropic
• OPENAI_API_KEY - Required for --summarize-backend openai

The default openclaw backend requires no API keys - it uses your OpenClaw configuration.

# Only needed if using direct API backends
export ANTHROPIC_API_KEY=sk-ant-...
export OPENAI_API_KEY=sk-...

Content Preservation

The --preserve flag behavior depends on whether --summarize is used:

Without --summarize (simple extraction):

• Hand-written content (after footer marker) is appended verbatim to the end of the newly generated file
• The new extraction replaces the auto-generated portion, your notes are kept at the end

With --summarize (LLM mode):

• Existing hand-written content is passed to the LLM as context
• The LLM is instructed to incorporate your notes into the new summary
• Result: Your insights are woven into a coherent narrative, not just appended

Example:

# Regenerate with LLM, incorporating existing notes into the summary
memory-sync backfill --date 2026-02-05 --force --preserve --summarize

Auto-generated markers:

• Header: *Auto-generated from N session messages*
• Footer: *Review and edit this draft to capture what's actually important.*

Content after the footer marker is considered hand-written and will be preserved.

Backfill Options

Date selection (choose one):

• --date YYYY-MM-DD - Single specific date
• --today - Current date only (for nightly automation)
• --since YYYY-MM-DD - From date to present (for catch-up)
• --all - All missing dates (for initial setup)
• --incremental - Only dates changed since last run (smart automation)

Additional flags:

• --dry-run - Show what would be created without creating files
• --force - Overwrite existing files (required for regeneration)
• --preserve - Keep hand-written content when regenerating
• --summarize - Use LLM for narrative summaries
• --summarize-backend BACKEND - Backend for summarization: openclaw (default), anthropic, openai
• --model MODEL - Model override for summarization (default varies by backend)

Performance

Mode	Time per Day	Best For
--all	5-10 min × N days	Initial setup only
--since	5-10 min × N days	Recovery after gaps
--today	30-60 sec	Nightly automation
--incremental	30-60 sec × changed days	Smart automation