Chat Memory
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Solve the #1 problem with long-running AI agents: knowledge loss after context compaction.
The Problem
When sessions compact (summarize old messages to free context), specific details are lost:
names, decisions, file paths, reasoning. The agent retains a summary but loses the ability
to recall "What exactly did Annika say?" or "When did we decide to use v6 format?"
Most memory skills on ClawHub are just SKILL.md instructions — "write stuff to MEMORY.md."
That's not a solution. This skill ships real scripts that do real work.
The Solution: Three-Layer Memory Architecture
Layer 1: MEMORY.md — Curated long-term memory (human-edited)
Layer 2: SESSION-GLOSSAR.md — Auto-generated structured index (people/projects/decisions/timeline)
Layer 3: memory/sessions/ — Full session transcripts as searchable Markdown
All three layers live under memory/ and are automatically vectorized by OpenClaw's
memory search, creating a navigational hierarchy: glossary finds the right session,
session provides the details.
Setup (run once)
Step 1: Convert existing sessions to Markdown
python3 scripts/session-to-memory.py
This scans all JSONL session logs in ~/.openclaw/agents/*/sessions/ and converts
them to memory/sessions/session-YYYY-MM-DD-HHMM-*.md. Truncates long assistant
responses to 2KB, skips system messages, tracks state to avoid re-processing.
Options:
--new — Only convert sessions not yet processed (for incremental runs)--agent main — Specify agent ID (default: main)
Step 2: Build the glossary
python3 scripts/build-glossary.py
Scans all session transcripts and builds memory/SESSION-GLOSSAR.md with:
- People — Who was mentioned, in how many sessions, date ranges
- Projects — Which projects discussed, with relevant topic tags
- Topics — Categorized themes (Email Drafts, Website Build, Security, etc.)
- Timeline — Per-day summary (session count, people, topics)
- Decisions — Extracted decision-like statements with dates
Options:
--incremental — Only process new sessions (uses cached scan state)
Step 3: Set up cron jobs for auto-updates
Create two cron jobs (use a cheap model like Gemini Flash):
Job 1: Session sync + glossary rebuild (every 4-6 hours)
Task: Run `python3 scripts/session-to-memory.py --new` then
`python3 scripts/build-glossary.py --incremental`.
Report how many new sessions were converted and indexed.
Optional Job 2: Pre-compaction memory flush check
Already built into AGENTS.md by default — just ensure the agent writes to
memory/YYYY-MM-DD.md before each compaction.
Customizing Entity Detection
Edit scripts/build-glossary.py to add your own known people and projects:
KNOWN_PEOPLE = {
"alice": "Alice Smith — Project Manager",
"bob": "Bob Jones — CTO",
}
KNOWN_PROJECTS = {
"website-redesign": "Website Redesign — Q1 Initiative",
"api-migration": "API Migration — v2 to v3",
}
The glossary also detects topics via regex patterns. Add new patterns in the
topic_patterns dict for your domain.
How It Works With memory_search
Once set up, memory_search("Alice project decision") will find:
- The glossary entry for Alice (which sessions she appears in)
- The actual session transcript where the decision was discussed
- Any MEMORY.md entry about Alice
This gives the agent a navigation layer (glossary) plus detail access
(transcripts) — much better than either alone.
File Structure After Setup
memory/
├── MEMORY.md — Curated (you maintain this)
├── SESSION-GLOSSAR.md — Auto-generated index
├── YYYY-MM-DD.md — Daily notes
├── .glossary-state.json — Glossary builder state
├── .glossary-scans.json — Cached scan results
└── sessions/
├── .state.json — Converter state
├── session-2026-01-15-0830-abc123.md
├── session-2026-01-15-1200-def456.md
└── ...
Cron Memory Optimizer
Cron jobs run in isolated sessions with zero memory context. The optimizer analyzes your cron jobs and suggests memory-enhanced versions:
python3 scripts/cron-optimizer.py
This scans ~/.openclaw/cron/jobs.json, identifies jobs that would benefit from memory context, and generates memory/cron-optimization-report.md with before/after prompts and implementation guidance.
Example optimization:
Original: "Run daily research scout..."
Enhanced: "Before starting: Use memory_search to find recent context about research activities. Check memory/SESSION-GLOSSAR.md for relevant people, projects, and recent decisions. Then proceed with the original task using this context.
Run daily research scout..."
The script is conservative (suggests only, never auto-modifies) and skips monitoring jobs that don't need context.
Sharing Memory Context with Cron Jobs, Subagents & Telegram Sessions
One of the biggest challenges in multi-session AI systems is context isolation. Here's how to share memory context across different execution environments:
For Cron Jobs
The problem: Cron jobs run in isolated sessions with zero memory context, making them blind to recent activities, people, and decisions.
The solution: Prepend a "memory preamble" to cron job prompts that instructs the agent to search memory before starting:
Before starting this task: Use memory_search to find recent context relevant to this task. Check memory/SESSION-GLOSSAR.md for people, projects, and recent decisions that may be relevant. Then proceed with the original task using this context.
The cron-optimizer.py script analyzes your existing cron jobs and automatically suggests which ones would benefit from memory context. It generates a detailed report with before/after prompts.
Example transformation:
Before: "You are a research scout. Find AI tools and report findings..."
After: "Before starting this task: Use memory_search to find recent context relevant to this task. Check memory/SESSION-GLOSSAR.md for people, projects, and recent decisions that may be relevant. Then proceed with the original task using this context.
You are a research scout. Find AI tools and report findings..."
For Subagents (sessions_spawn)
The problem: Subagents start with empty context and don't know about recent activities or ongoing projects.
The solution: Include memory instructions in the task prompt when spawning subagents:
Before starting: Use memory_search("relevant keywords") to find recent context.
Check memory/SESSION-GLOSSAR.md for people, projects, decisions.
Check MEMORY.md for long-term context.
Then proceed:
[your actual task...]
Tips:
- Be specific with memory_search keywords for best results
- Include both recent (SESSION-GLOSSAR.md) and long-term (MEMORY.md) context
- Consider what the subagent needs to know to do its job effectively
For Telegram Group Sessions
The problem: Group sessions share the workspace but don't automatically know about the memory system or recent activities discussed in other sessions.
The solution: Two approaches depending on your setup:
Method 1: Push context via sessions_send
# From main session, send relevant context to group session
sessions_send telegram-group "Memory context: Recent project status - [summary]"
Method 2: Add memory awareness to AGENTS.md
Add guidance to your AGENTS.md so group sessions know to search memory:
## Group Chat Guidelines
When answering questions about past work or ongoing projects,
always use memory_search first to check for relevant context.
Tips:
- Group sessions can access the memory system if they know to use it
- Include memory search instructions in your group-specific agent guidelines
- Consider pushing critical updates from main to group sessions when decisions are made
For Knowledge Bases (Vectorized Databases)
If you have custom vectorized knowledge bases (e.g., using sentence-transformers), make them accessible across sessions:
Method 1: Query scripts
# Create a query script that any session can call
python3 scripts/query-knowledge-base.py "search terms"
Method 2: Workspace storage
# Store the database in workspace for universal access
mkdir -p knowledge-base/
# Include database path in task prompts
"Use the knowledge base at ./knowledge-base/db.pkl for additional context..."
Method 3: Integration prompts
Include the script path in cron job and subagent prompts:
Before starting: Run `python3 scripts/query-knowledge-base.py "project context"`
for additional background. Then proceed with the task.
The key is making knowledge discovery automatic and standardized across all execution contexts — main session, cron jobs, subagents, and group sessions should all follow the same memory-aware patterns.
Tips
- Run the full rebuild (
python3 scripts/build-glossary.py without --incremental)
occasionally to pick up improvements to entity detection
- The glossary is most useful when KNOWN_PEOPLE and KNOWN_PROJECTS are populated —
spend 5 minutes adding your key contacts and projects
- For agents that run 24/7, the cron job keeps everything current automatically
- Session transcripts can get large (our 297 sessions = 24MB) — this is fine,
OpenClaw's vector search handles it efficiently
- Use the cron optimizer after setting up memory to enhance existing automation
Why This Exists
We run OpenClaw agents 24/7 for real work — client projects, research pipelines, content
production. After a week we had 300+ sessions and our agents kept forgetting critical
details after compaction. We built this to fix it, and it worked so well we open-sourced it.
What makes this different from other memory skills:
- ✅ Real Python scripts — not just "instructions for the agent"
- ✅ Three-layer architecture — curated + auto-glossary + raw transcripts
- ✅ Cron automation — runs in the background, zero manual work
- ✅ Glossary with entity detection — people, projects, decisions, timeline
- ✅ Cron optimizer — makes your existing cron jobs context-aware
- ✅ Clean security score — no suspicious flags, no external dependencies
- ✅ Battle-tested — 300+ sessions, running in production daily
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