research-memory-cleanup

name: research-memory-cleanup description: Defragment and clean research agent memory - consolidate paper info, organize research domains, remove redundancy from research tracking.

Research Memory Cleanup

Clean up research agent memory by consolidating duplicate paper info, organizing research domains, and removing redundancy.

Quick Start: The Cleanup Cycle

Most common use: Memory has grown messy with duplicate paper entries, scattered research notes, or conflicting discovery configs.

Standard Opening

User: "My research memory is getting messy."

Agent: "Let's clean it up! I'll:

1. Backup your current memory (safety first)
2. Review what needs consolidating
3. Clean up duplicate/scattered info
4. Restore the cleaned version

This takes ~5 minutes. Ready to start?"

Then execute the 3-step workflow.

Diagnosis: What Needs Cleaning?

Ask 3 questions to understand the mess:

Question 1: What's messy?

Agent: "What feels messy in your research memory?
A) Duplicate paper entries
B) Scattered research domain notes
C) Conflicting discovery configs
D) Old/outdated research areas
E) General disorganization"

Question 2: How bad is it?

Agent: "Scale of messiness:
A) Mildly cluttered (50-100 papers tracked)
B) Pretty messy (100-300 papers)
C) Very messy (300+ papers or 50K+ chars)
D) Complete chaos (multiple research agents merged)"

Question 3: What to preserve?

Agent: "What must NOT be lost?
- Active research questions?
- Discovery configurations?
- Paper reading lists?
- Citation networks?
- Analysis notes?

Tell me what's sacred, I'll protect it."

Conversation Patterns

Pattern 1: Standard Memory Cleanup

Trigger: General memory mess, not critical

Agent: "Starting standard cleanup. This will:

Step 1: Backup everything
Step 2: Consolidate duplicate paper entries
Step 3: Organize research domains
Step 4: Clean discovery configs
Step 5: Restore cleaned version

Backing up now..."

[Run backup script]

Agent: "Backup saved to .letta/backups/[agent-id]/[timestamp]/

Now spawning memory subagent to clean up the files..."

[Spawn memory subagent with cleanup task]

Agent: "Memory subagent working... (this takes 2-3 min)

[Wait for subagent to finish]

Agent: "Cleanup complete! Changes:
- Merged 15 duplicate paper entries → 5 unique papers
- Organized 3 research domains (ML, NLP, RL)
- Removed 2 outdated discovery configs
- Total reduction: 12,450 chars → 8,200 chars (34% smaller)

Restoring cleaned memory now..."

[Run restore script]

Agent: "Done! Your research memory is now organized and deduplicated."

Pattern 2: Paper Entry Consolidation

Trigger: Many duplicate paper entries

Agent: "I see lots of duplicate paper entries. Cleaning up papers...

[Memory subagent task: consolidate papers]

Consolidation strategy:
1. Group by paper title/DOI
2. Merge all mentions into single entry
3. Keep: title, authors, citations, your notes
4. Remove: duplicate tracking, scattered mentions

Subagent will merge papers and DELETE source duplicates."

Memory subagent prompt:

Edit memory files in .letta/backups/working/ to consolidate paper entries.

Find duplicate papers (same title/DOI):
1. Create single comprehensive entry per paper
2. Merge all notes, citations, analysis
3. DELETE the duplicate source files (use rm command)

Example:
- paper-transformers-1.md
- paper-transformers-duplicate.md
→ Merge into single paper-transformers.md
→ rm paper-transformers-duplicate.md

After consolidation, report what was merged.

Pattern 3: Research Domain Organization

Trigger: Research areas scattered, no clear structure

Agent: "Your research domains are scattered. Let me organize them...

[Memory subagent task: organize domains]

Organization strategy:
1. Identify main research areas (ML, NLP, CV, etc.)
2. Group papers/notes by domain
3. Create domain-specific memory blocks
4. Link related domains

Subagent will create organized structure."

Memory subagent prompt:

Organize research memory by domain.

Create domain blocks:
- research-domain-ml.md (ML papers and notes)
- research-domain-nlp.md (NLP papers and notes)
- research-domain-rl.md (RL papers and notes)

Move relevant content into each domain block.
DELETE scattered source files after merging.

Use clear structure:
## Papers
- Paper 1: [title] - [notes]
- Paper 2: [title] - [notes]

## Key Concepts
- Concept 1: [description]

## Active Questions
- Question 1: [question]

Pattern 4: Discovery Config Cleanup

Trigger: Old/conflicting discovery configurations

Agent: "I see multiple discovery configs, some outdated. Cleaning...

[Memory subagent task: clean discovery configs]

Cleanup strategy:
1. Keep: Current active discovery configs
2. Archive: Old configs (if might need later)
3. Remove: Clearly outdated/conflicting configs

Subagent will preserve active, remove dead configs."

Quick Reference Cards

Card 1: The 3-Step Workflow

Every cleanup follows this:

# Step 1: Backup
npx tsx [SKILL_DIR]/scripts/backup-memory.ts $LETTA_AGENT_ID .letta/backups/working

# Step 2: Clean (spawn memory subagent with task)
Task({
  subagent_type: "memory",
  description: "Clean research memory",
  prompt: "[specific cleanup task]"
})

# Step 3: Restore
npx tsx [SKILL_DIR]/scripts/restore-memory.ts $LETTA_AGENT_ID .letta/backups/working

Never skip backup!

Card 2: What to Consolidate

Common research memory issues:

Card 3: Memory Subagent Prompts

Template for cleanup tasks:

Edit memory files in .letta/backups/working/ to [specific goal].

Focus on:
- [Primary consolidation task]
- [Secondary organization task]
- [Tertiary cleanup task]

IMPORTANT: After merging content, DELETE source files (rm command).
You have full bash access in .letta/backups/working/.

Delete files when:
1. Content merged into another block
2. File is duplicate/junk data
3. File is outdated and irrelevant

Report what was merged and what was deleted.

Key principles:

• Specific goal (consolidate papers, organize domains, etc.)
• Give full bash access permission
• Explicitly say to DELETE merged files
• Ask for report of changes

Card 4: Rollback Safety

If cleanup goes wrong:

# Find the backup
ls -la .letta/backups/[agent-id]/

# Restore from timestamp
npx tsx [SKILL_DIR]/scripts/restore-memory.ts $LETTA_AGENT_ID .letta/backups/[agent-id]/[timestamp]

Pro tip: Each backup is timestamped, so you can roll back to any previous state.

Refinement Patterns

Refinement 1: Cleanup Too Aggressive

Symptom: Important research notes were removed

Fix: Roll back and be more specific

Agent: "The cleanup removed too much. Let me roll back:

[Restore from backup]

Agent: "Rolled back to pre-cleanup state.

Let's try again with more specific instructions:
- What exactly should be consolidated?
- What must be preserved no matter what?

Tell me the specifics and I'll give clearer instructions to the memory subagent."

Refinement 2: Papers Still Duplicated

Symptom: Cleanup didn't merge all duplicates

Fix: More specific merge instructions

Agent: "Some papers are still duplicated. Let me:

1. Identify the duplicates (same DOI/title)
2. Show you which ones
3. Give memory subagent explicit merge list

This will be more precise."

Enhanced prompt:

Merge these specific paper duplicates:
- paper-bert.md + paper-bert-2.md → paper-bert.md
- paper-gpt3.md + gpt3-analysis.md → paper-gpt3.md

For each merge:
1. Combine all unique info
2. DELETE the duplicate file
3. Report: "Merged [file1] + [file2] → [result]"

Refinement 3: Lost Structure

Symptom: Cleanup created walls of text, no organization

Fix: Add structure requirements

Agent: "The cleanup lost structure. Let me fix that:

[Re-run with structure requirements]

Memory subagent will now:
- Use markdown headers (##, ###)
- Organize with bullet points
- Keep related info together
- Add scannable formatting

Running again..."

Refinement 4: Need Iterative Cleanup

Symptom: One cleanup pass isn't enough

Fix: Multiple passes with different focuses

Agent: "This needs multiple cleanup passes. Let's do:

Pass 1: Consolidate papers (done)
Pass 2: Organize domains (next)
Pass 3: Clean configs (after)

Each pass builds on the previous. Ready for Pass 2?"

Troubleshooting

Advanced: Research-Specific Cleanup Patterns

Pattern A: Paper Reading List Cleanup

Use case: Reading list has 50+ papers, many duplicates

Agent: "Cleaning up paper reading list...

Strategy:
1. Group by topic/domain
2. Mark status (read, reading, to-read)
3. Remove duplicates
4. Sort by priority

Memory subagent will create organized reading-list.md."

Pattern B: Citation Network Cleanup

Use case: Citation graphs scattered across files

Agent: "Consolidating citation networks...

Strategy:
1. Merge all citation data
2. Remove duplicate edges
3. Update paper metadata
4. Create single citation-graph.md

Memory subagent will consolidate graphs."

Pattern C: Discovery History Cleanup

Use case: Discovery results from 6 months, many outdated

Agent: "Cleaning discovery history...

Strategy:
1. Keep: Last 30 days of discoveries
2. Archive: 30-90 days (compress to summaries)
3. Delete: 90+ days (unless marked important)

Memory subagent will prune old discoveries."

Summary: The Agent's Mental Model

Core workflow:

1. Backup first (ALWAYS) - safety net
2. Review what needs consolidating
3. Clean with memory subagent (specific instructions)
4. Restore cleaned version
5. Validate changes are good
6. Rollback if needed (backup saved)

Key principles:

• Never skip backup (rollback safety)
• Be specific with memory subagent instructions
• Explicitly tell subagent to DELETE merged files
• Run multiple passes for complex cleanups
• Validate before finalizing
• Research data is sacred - preserve when in doubt

What to consolidate:

• Duplicate paper entries (same DOI/title)
• Scattered research domain notes
• Redundant discovery configs
• Old/outdated research areas
• Walls of text (add structure)

What to preserve:

• Active research questions (sacred)
• Current discovery configs (active)
• Paper reading lists (user's workflow)
• Citation networks (research connections)
• Analysis notes (insights)

Success: Research memory is organized, deduplicated, and structured - agent can quickly access papers, domains, and configs without wading through duplicates.