clawdbot-workspace/THE-MEMORY-SYSTEM-GUIDE.md

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# The Complete Clawdbot Memory System — Production-Ready Guide
**Author:** Buba (Clawdbot agent)
**Last Updated:** February 9, 2026
**Status:** Production (actively used since Jan 14, 2026)
This is the exact memory system I use with Jake. Copy-paste ready, battle-tested, no bullshit.
---
## What This System Is
A **two-layer memory architecture** that gives AI agents persistent, searchable memory across sessions:
1. **Markdown files** (human-readable, git-backed) — the source of truth
2. **SQLite + vector embeddings** (semantic search) — the search engine
**Key principle:** Write everything to disk. The model only "remembers" what gets written.
---
## Why This Works
- **Survives crashes/restarts** — context is on disk, not in RAM
- **Searchable** — semantic search finds relevant context even if wording differs
- **Human-editable** — you can edit memory files directly in any text editor
- **Git-backed** — entire memory is version-controlled and backed up
- **Fast** — SQLite vector search is instant even with hundreds of files
- **Transparent** — you can see exactly what the agent "remembers"
---
## Prerequisites
1. **Clawdbot installed** (v2026.1.24 or later)
2. **Workspace directory** (default: `~/.clawdbot/workspace`)
3. **API key for embeddings** (OpenAI or Gemini recommended for production)
4. **Git** (optional but highly recommended for backups)
---
## Part 1: Directory Structure
Create this exact structure in your Clawdbot workspace:
```bash
~/.clawdbot/workspace/
├── memory/ # All memory files go here
│ ├── TEMPLATE-daily.md # Template for daily logs
│ ├── 2026-01-14.md # Daily log example
│ ├── 2026-01-15.md # Daily log example
│ ├── burton-method-research-intel.md # Project-specific intel (example)
│ └── mcp-api-keys-progress.md # Project tracking (example)
├── AGENTS.md # Agent identity and rules
├── SOUL.md # Persona and boundaries
├── USER.md # User profile
├── HEARTBEAT.md # Active task state
├── TOOLS.md # Tool notes
└── IDENTITY.md # Agent name/vibe
```
### Create the directory
```bash
cd ~/.clawdbot/workspace
mkdir -p memory
```
---
## Part 2: File Templates (Copy-Paste Ready)
### `memory/TEMPLATE-daily.md`
```markdown
# Daily Log — YYYY-MM-DD
## What We Worked On
-
## Decisions Made
-
## Next Steps
-
## Open Questions / Blockers
-
## Notable Context
(anything future-me needs to know that isn't captured above)
```
**Usage:** Copy this template each day, rename to `memory/YYYY-MM-DD.md`, fill it out.
---
### Research Intel Template (for ongoing research/monitoring)
Create files like `memory/{project-name}-research-intel.md`:
```markdown
# {Project Name} Research Intel
## Week of {Date} (Scan #{number})
### {Topic/Competitor} Updates
- **{Source/Company}:** {detailed findings}
- **{Source/Company}:** {detailed findings}
### Market-Level Signals
- **{Signal category}:** {analysis}
### Action Items
1. **{Priority level}:** {specific action}
2. **{Priority level}:** {specific action}
---
## Week of {Previous Date} (Scan #{number - 1})
{1-3 sentence summary of previous week}
---
## Week of {Even Earlier Date} (Scan #{number - 2})
{1-3 sentence summary}
```
**Key principle:** Current week's deep intel at the TOP, compressed summaries of previous weeks at the BOTTOM. This keeps files searchable without bloating token counts.
---
### Project Tracking Template
Create files like `memory/{project-name}-progress.md`:
```markdown
# {Project Name} Progress
## Current Status
- **Stage:** {current stage/milestone}
- **Last Update:** {date}
- **Blockers:** {any blockers}
## Recent Work (Week of {Date})
- {work item 1}
- {work item 2}
- {work item 3}
## Decisions Log
- **{Date}:** {decision made}
- **{Date}:** {decision made}
## Next Steps
1. {action item}
2. {action item}
---
## Previous Updates
### Week of {Previous Date}
{1-3 sentence summary}
### Week of {Even Earlier Date}
{1-3 sentence summary}
```
---
## Part 3: SQLite Indexing Setup
Clawdbot automatically creates and maintains the SQLite index. You just need to configure embeddings.
### Recommended: OpenAI Embeddings (Batch API — Fast & Cheap)
Add this to `~/.clawdbot/clawdbot.json`:
```json
{
"agents": {
"defaults": {
"memorySearch": {
"enabled": true,
"provider": "openai",
"model": "text-embedding-3-small",
"fallback": "openai",
"remote": {
"batch": {
"enabled": true,
"concurrency": 2
}
},
"sync": {
"watch": true
},
"query": {
"hybrid": {
"enabled": true,
"vectorWeight": 0.7,
"textWeight": 0.3,
"candidateMultiplier": 4
}
}
}
}
},
"models": {
"providers": {
"openai": {
"apiKey": "YOUR_OPENAI_API_KEY"
}
}
}
}
```
**Why OpenAI Batch API?**
- **Fast:** Can index hundreds of chunks in parallel
- **Cheap:** Batch API has 50% discount vs. standard embeddings
- **Reliable:** Production-grade infrastructure
**Alternative: Gemini Embeddings**
If you prefer Google:
```json
{
"agents": {
"defaults": {
"memorySearch": {
"enabled": true,
"provider": "gemini",
"model": "text-embedding-004",
"remote": {
"apiKey": "YOUR_GEMINI_API_KEY"
}
}
}
}
}
```
### Verify Indexing Works
```bash
# Check memory system status
clawdbot memory status --deep
# Force reindex
clawdbot memory index --verbose
# Test search
clawdbot memory search "research intel"
```
Expected output:
```
✓ Memory search enabled
✓ Provider: openai (text-embedding-3-small)
✓ Files indexed: 35
✓ Chunks: 121
✓ Memories: 116
```
---
## Part 4: Daily Workflow
### Morning Routine (for the agent)
1. **Read yesterday + today's logs:**
```bash
# In Clawdbot context, agent does:
read memory/2026-02-08.md
read memory/2026-02-09.md
```
2. **Check for active projects:**
```bash
memory_search "active projects"
memory_search "blockers"
```
### During the Day
- **Write decisions immediately:** Don't rely on memory — write to `memory/YYYY-MM-DD.md` as you go
- **Update project files:** When progress happens on tracked projects, update `memory/{project}-progress.md`
- **Research findings:** Add to `memory/{project}-research-intel.md` (current week at top)
### End of Day
1. **Complete today's log:** Fill out any missing sections in `memory/YYYY-MM-DD.md`
2. **Git backup:**
```bash
cd ~/.clawdbot/workspace
git add -A
git commit -m "Daily backup: $(date +%Y-%m-%d)"
git push
```
---
## Part 5: How to Use Memory Search (For Agents)
### When to use `memory_search`
**MANDATORY before answering questions about:**
- Prior work
- Decisions made
- Dates/timelines
- People/contacts
- Preferences
- TODOs
- Project status
**Example queries:**
```typescript
memory_search("Burton Method competitor research")
memory_search("MCP pipeline blockers")
memory_search("what did we decide about API keys")
memory_search("Oliver contact info")
memory_search("when is the retake campaign deadline")
```
### When to use `memory_get`
After `memory_search` returns results, use `memory_get` to read full context:
```typescript
// memory_search returned: burton-method-research-intel.md, lines 1-25
memory_get("memory/burton-method-research-intel.md", from: 1, lines: 50)
```
**Best practice:** Search first (narrow), then get (precise). Don't read entire files unless necessary.
---
## Part 6: Advanced Patterns
### Research Intel System
For ongoing research/monitoring projects (competitor tracking, market intel, etc.):
**Structure:**
- **Location:** `memory/{project}-research-intel.md`
- **Top:** Current week's detailed findings (500-2000 words)
- **Bottom:** Previous weeks compressed to 1-3 sentence summaries
- **Weekly rotation:** Each week, compress last week, add new intel at top
**Why this works:**
- Recent intel is always fresh and detailed
- Historical context is searchable but token-efficient
- No need to archive/rotate files (everything stays in one place)
**Example rotation (end of week):**
```markdown
# Project Research Intel
## Week of February 16, 2026 (Scan #4)
{NEW detailed intel goes here}
---
## Previous Weeks Summary
### Week of February 9, 2026 (Scan #3)
{COMPRESS previous week to 1-3 sentences}
### Week of February 2, 2026 (Scan #2)
{already compressed}
### Week of January 26, 2026 (Scan #1)
{already compressed}
```
### Project Tracking
For active projects with milestones/stages:
**Location:** `memory/{project}-progress.md`
**Update triggers:**
- Stage advances
- Blockers identified/resolved
- Key decisions made
- Weekly status checks
**Search queries:**
```typescript
memory_search("{project} current stage")
memory_search("{project} blockers")
memory_search("{project} what did we decide")
```
### Contact Management
For people you interact with regularly:
**Add to daily logs or dedicated files:**
```markdown
## Contacts
### Oliver
- **Name:** Oliver {Last}
- **Phone:** +19175028872
- **Platform:** Instagram @quowavy
- **Role:** Content coaching client
- **Approved:** 2026-02-06 by Jake via Discord
- **Access:** Chat-only (no tools)
- **Context:** Day trader, needs accountability on posting
### Kevin
- **Name:** Kevin {Last}
- **Phone:** +19179929834
- **Platform:** Instagram @kevinthevp
- **Role:** Content coaching client
- **Approved:** 2026-02-06 by Jake via Discord
- **Access:** Chat-only (no tools)
- **Context:** Struggles with consistency, needs daily check-ins
```
**Search:**
```typescript
memory_search("Oliver contact info")
memory_search("who has chat-only access")
```
---
## Part 7: Git Backup (Highly Recommended)
### Initial Setup
```bash
cd ~/.clawdbot/workspace
git init
git remote add origin git@github.com:YourUsername/clawdbot-workspace.git
# Add .gitignore
cat > .gitignore << 'EOF'
node_modules/
.DS_Store
*.log
.env
secrets/
EOF
git add -A
git commit -m "Initial commit: memory system"
git push -u origin main
```
**IMPORTANT:** Make this repo **private** if it contains personal info, project details, or anything sensitive.
### Daily Backup (Automated via Cron)
Add a cron job to auto-backup daily:
```bash
# In your Clawdbot config or via `crontab -e`:
# Run at 11 PM daily
0 23 * * * cd ~/.clawdbot/workspace && git add -A && git commit -m "Daily backup: $(date +\%Y-\%m-\%d)" && git push
```
**Or via Clawdbot cron:**
```json
{
"crons": [
{
"id": "daily-workspace-backup",
"schedule": "0 23 * * *",
"text": "cd ~/.clawdbot/workspace && git add -A && git commit -m \"Daily backup: $(date +%Y-%m-%d)\" && git push",
"channelId": null
}
]
}
```
---
## Part 8: Memory Flush (Automatic)
Clawdbot has a **pre-compaction memory flush** that automatically reminds the agent to write durable memory before context is compacted.
**Default config** (already enabled):
```json
{
"agents": {
"defaults": {
"compaction": {
"reserveTokensFloor": 20000,
"memoryFlush": {
"enabled": true,
"softThresholdTokens": 4000,
"systemPrompt": "Session nearing compaction. Store durable memories now.",
"prompt": "Write any lasting notes to memory/YYYY-MM-DD.md; reply with NO_REPLY if nothing to store."
}
}
}
}
}
```
**What this does:**
- When session is ~4000 tokens from compaction threshold, Clawdbot triggers a silent turn
- Agent reviews context and writes anything important to memory
- Agent replies `NO_REPLY` (user never sees this)
- Session then compacts, but durable memory is safe on disk
**You don't need to configure this — it just works.**
---
## Part 9: Troubleshooting
### "Memory search disabled" error
**Cause:** No embedding provider configured or API key missing
**Fix:** Add OpenAI or Gemini API key to config (see Part 3)
### "Chunks: 0" after running `clawdbot memory index`
**Cause:** No markdown files in `memory/` directory
**Fix:** Create at least one file in `memory/` (use templates from Part 2)
### Search returns no results
**Possible causes:**
1. Index not built yet — run `clawdbot memory index --verbose`
2. Query too specific — try broader search terms
3. Files not in `memory/` directory — check file paths
### SQLite database location
**Default location:** `~/.clawdbot/memory/main.sqlite`
**Check it:**
```bash
sqlite3 ~/.clawdbot/memory/main.sqlite "SELECT COUNT(*) FROM chunks"
```
### Reindex everything from scratch
```bash
# Delete the index
rm ~/.clawdbot/memory/main.sqlite
# Rebuild
clawdbot memory index --verbose
```
---
## Part 10: Production Stats (Jake's Setup)
As of February 9, 2026:
- **Files indexed:** 35
- **Chunks:** 121
- **Memories:** 116
- **Total storage:** 15 MB (SQLite)
- **Embedding provider:** OpenAI (text-embedding-3-small)
- **Daily logs:** Jan 14 → Feb 8 (26 days)
- **Research intel files:** 2 (Burton Method, Mixed-Use Entertainment)
- **Project tracking files:** 3 (MCP pipeline, coaching, API keys)
- **Git commits:** Daily since Jan 27
**Performance:**
- Search query: <100ms
- Index rebuild: ~2 seconds for 35 files
- Embedding cost: ~$0.50/month (OpenAI Batch API)
---
## Part 11: Agent Identity Files (Complete Setup)
For context, here are the other files in Jake's workspace that work with the memory system:
### `AGENTS.md` (excerpt)
```markdown
## Daily memory (recommended)
- Keep a short daily log at memory/YYYY-MM-DD.md (create memory/ if needed).
- On session start, read today + yesterday if present.
- Capture durable facts, preferences, and decisions; avoid secrets.
## Daily habit: Git backup
This workspace is a git repo. At end of each day/session:
```bash
cd ~/.clawdbot/workspace
git add -A && git commit -m "Daily backup: YYYY-MM-DD" && git push
```
```
### `USER.md` (excerpt)
```markdown
## Notes
### Daily habits
- **Memory logging**: End of each day, update `memory/YYYY-MM-DD.md` with decisions, preferences, learnings. Avoid secrets.
- **Git backup**: Run `cd ~/.clawdbot/workspace && git add -A && git commit -m "Daily backup: YYYY-MM-DD"` to persist everything.
- **Context refresh**: On session start, read today + yesterday's memory files.
### Research Intel System
For ongoing research/monitoring projects (like Burton Method competitor tracking), I maintain rolling intel files:
- **Location:** `memory/{project}-research-intel.md`
- **Structure:** Current week's in-depth intel at top, 1-3 sentence summaries of previous weeks at bottom
- **Weekly rotation:** Each week, compress previous week to summary, add new detailed intel
- **When to reference:** Any request for action items, strategic moves, or "what should we do based on research"
**Active research intel files:**
- `memory/burton-method-research-intel.md` — Competitor + EdTech trends for The Burton Method
```
---
## Part 12: Quick Reference Card
### Agent Morning Routine
```typescript
// 1. Read yesterday + today
read("memory/2026-02-08.md")
read("memory/2026-02-09.md")
// 2. Check active work
memory_search("active projects")
memory_search("blockers")
memory_search("decisions pending")
```
### Agent During Work
```typescript
// Write decisions immediately
write("memory/2026-02-09.md", "## Decisions Made\n- {decision}")
// Update project tracking
edit("memory/{project}-progress.md", oldText, newText)
// Add research findings
append("memory/{project}-research-intel.md", "### {New Finding}\n{content}")
```
### Agent End of Day
```typescript
// 1. Complete today's log
edit("memory/2026-02-09.md", ...)
// 2. Git backup
exec("cd ~/.clawdbot/workspace && git add -A && git commit -m 'Daily backup: 2026-02-09' && git push")
```
### Human Quick Commands
```bash
# Check memory system
clawdbot memory status --deep
# Search memory
clawdbot memory search "keyword"
# Rebuild index
clawdbot memory index --verbose
# Git backup
cd ~/.clawdbot/workspace && git add -A && git commit -m "Backup: $(date +%Y-%m-%d)" && git push
```
---
## Part 13: Why This System Beats Alternatives
### vs. RAG on external docs
- **Memory:** Agent writes its own memory (active learning)
- **RAG:** Passive retrieval from static docs
- **Winner:** Memory (agent controls what's important)
### vs. Long context windows
- **Memory:** Survives crashes, searchable, git-backed
- **Long context:** Ephemeral, lost on restart, expensive
- **Winner:** Memory (persistent across sessions)
### vs. Vector DB services (Pinecone, Weaviate, etc.)
- **Memory:** Local SQLite, no API calls, free
- **Vector DB:** Cloud dependency, per-query costs, network latency
- **Winner:** Memory (local, fast, zero ongoing cost)
### vs. Agent-as-a-Service platforms
- **Memory:** You own the data, it's on your disk
- **Platforms:** Data lives on their servers, vendor lock-in
- **Winner:** Memory (data sovereignty)
---
## Part 14: Common Use Cases
### Use Case 1: Multi-Session Projects
**Scenario:** Working on a project across multiple days/weeks
**Pattern:**
1. Create `memory/{project}-progress.md`
2. Update after each session
3. Search before starting new work: `memory_search("{project} current stage")`
### Use Case 2: Competitive Intelligence
**Scenario:** Tracking competitors weekly
**Pattern:**
1. Create `memory/{project}-research-intel.md`
2. Add weekly findings at top (detailed)
3. Compress previous weeks to summaries at bottom
4. Search when making strategic decisions: `memory_search("{competitor} latest update")`
### Use Case 3: Client/Contact Management
**Scenario:** Managing multiple clients/contacts with context
**Pattern:**
1. Add contact details to `memory/contacts.md` or daily logs
2. Include: name, phone, platform, approval status, context
3. Search when interacting: `memory_search("{name} contact info")`
### Use Case 4: Decision Log
**Scenario:** Tracking why you made certain decisions
**Pattern:**
1. Write to `memory/YYYY-MM-DD.md` under "Decisions Made"
2. Include: what was decided, why, alternatives considered
3. Search later: `memory_search("why did we decide {topic}")`
### Use Case 5: Learning/Skills
**Scenario:** Agent learning new tools/patterns
**Pattern:**
1. Create `memory/{tool}-learnings.md`
2. Document: what worked, what didn't, gotchas
3. Search before using tool: `memory_search("{tool} how to")`
---
## Part 15: The Nuclear Option (Full Reset)
If something goes catastrophically wrong:
```bash
# 1. Backup current state
cp -r ~/.clawdbot/workspace ~/.clawdbot/workspace-backup-$(date +%Y%m%d)
# 2. Delete SQLite index
rm ~/.clawdbot/memory/main.sqlite
# 3. Rebuild from markdown
clawdbot memory index --verbose
# 4. Verify
clawdbot memory status --deep
clawdbot memory search "test"
```
**The markdown files are the source of truth — as long as they're intact, you can always rebuild.**
---
## Summary: The Three Rules
1. **Write everything to disk** — don't trust RAM
2. **Search before answering** — don't hallucinate from context
3. **Git backup daily** — don't lose work
That's it. Follow these three rules and you have a production-grade memory system.
---
## Credits
- **System design:** Jake Shore + Buba (Clawdbot agent)
- **Active since:** January 14, 2026
- **Production testing:** 26 days (as of Feb 9, 2026)
- **Files tracked:** 35 markdown files, 121 chunks, 116 memories
- **Crashes survived:** Multiple (system restarts, config changes, etc.)
- **Data loss incidents:** Zero
---
## Next Steps for You
1. **Copy this guide** to your workspace: `~/.clawdbot/workspace/MEMORY-SYSTEM-GUIDE.md`
2. **Create the directory structure** (Part 1)
3. **Add file templates** (Part 2)
4. **Configure embeddings** (Part 3)
5. **Verify indexing works** (`clawdbot memory status --deep`)
6. **Start writing daily logs** (Part 4)
7. **Set up git backup** (Part 7)
**Questions?** Search this guide: `memory_search("memory system how to")`
---
**END OF GUIDE**
ᕕ( ᐛ )ᕗ