Session Persistence

What it does

OpenClaw stores session data in JSONL files that are difficult to search and easy to lose track of. Session Persistence imports every message into a local SQLite database with full-text search, making the agent's entire history queryable — across all sessions, all channels, all time.

Inspired by lossless-claw's SQLite message persistence layer, which stores every message with sequence numbers, token counts, and structured message parts.

When to invoke

• Automatically every 15 minutes (cron) — incremental import of new messages
• When the agent needs to search past conversations — use

  --search

• After a crash or session rollover — verify all messages are persisted
• For analytics — message counts, session timelines, activity patterns

How to use

python3 persist.py --import                       # Import new messages from session files
python3 persist.py --import --source <dir>        # Import from a specific directory
python3 persist.py --search "auth migration"      # FTS5 full-text search
python3 persist.py --search "deploy" --role user  # Search only user messages
python3 persist.py --recent --hours 24            # Messages from the last 24 hours
python3 persist.py --conversation <id>            # Dump a full conversation
python3 persist.py --stats                        # Database statistics
python3 persist.py --export --format jsonl        # Export back to JSONL
python3 persist.py --status                       # Last import summary
python3 persist.py --format json                  # Machine-readable output

Database schema

Stored at

~/.openclaw/lcm-db/messages.db

:

conversations   — id, channel, started_at, last_message_at, message_count
messages        — id, conversation_id, seq, role, content, token_estimate, created_at
messages_fts    — FTS5 virtual table over messages.content for fast search
import_log      — id, imported_at, conversations_added, messages_added, source

Cron wakeup behaviour

Every 15 minutes:

1. Scan session directory for JSONL files
2. For each file, check

   last_imported_seq

   to skip already-imported messages
3. Parse new messages and insert into SQLite
4. Update FTS5 index
5. Update import log and state

Procedure

Step 1 — Initial import

python3 persist.py --import

First run imports all existing session files. Subsequent runs are incremental — only new messages since last import.

Step 2 — Search your history

python3 persist.py --search "how did we handle the database migration"

FTS5 provides ranked results across all sessions and time periods. Results include conversation ID, timestamp, role, and content snippet.

Step 3 — Analyze patterns

python3 persist.py --stats

Shows total messages, conversations, date ranges, messages per role, and activity timeline.

Integration with other skills

• memory-dag-compactor: Can use SQLite messages as source data instead of MEMORY.md, bringing architecture closer to lossless-claw
• dag-recall: Search results feed into DAG expansion for detailed recall
• context-assembly-scorer: Uses message database to measure true coverage

State

Import tracking and database stats stored in

~/.openclaw/skill-state/session-persistence/state.yaml

. Database stored at

~/.openclaw/lcm-db/messages.db

.

Fields:

last_import_at

,

db_path

,

total_messages

,

total_conversations

,

import_history

.

Notes

• Uses Python's built-in

  sqlite3

  module — no external dependencies
• FTS5 used when available; falls back to LIKE queries otherwise
• Idempotent: safe to re-run; tracks per-conversation sequence numbers
• Import lag: up to 15 minutes behind real-time (cron interval)
• Database is local-only — never committed to the repo