Neo4j Memory to Graphiti Migration Skill

When to Use This Skill

Invoke this skill when the user asks about:

• Migrating data from Neo4j Memory to Graphiti
• Converting entities to episodes
• Episode format templates
• Migration validation
• Group ID classification
• Rate limiting strategies

Core Concepts

Source System: Neo4j Memory (MCP_DOCKER)

• Entity-first architecture (direct node creation)
• Observations as array properties on entities
• Simple typed relationships
• No temporal tracking, no embeddings

Target System: Graphiti (graphiti-local)

• Episode-first architecture (LLM extraction)
• Temporal tracking (valid_at, invalid_at)
• group_id namespace isolation
• Auto-generated embeddings

Entity-to-Episode Conversion Templates

Person Entities (source: text)

{Name} is a {role/title} with {experience summary}.
{Key professional details}.

Key observations:
- {observation 1}
- {observation 2}
- {observation N}

Experience Entities (source: json)

{
  "name": "Role/Position Name",
  "type": "Experience",
  "years": "YYYY-YYYY",
  "duration": "N years",
  "company": "Company Name",
  "details": ["detail 1", "detail 2"]
}

Organization Entities (source: text)

{Organization Name} is a {type} organization.
{Description and context}.

Key divisions/teams:
- {division 1}
- {division 2}

Leadership: {key people and roles}

JobPosting Entities (source: json)

{
  "title": "Job Title",
  "company": "Company",
  "salary_range": "$XXK-$YYK",
  "requirements": ["req1", "req2"],
  "posted_date": "YYYY-MM"
}

Pattern/Architecture Entities (source: text)

{Pattern Name} Pattern

Description: {what it does}

When to use:
- {use case 1}
- {use case 2}

Trade-offs:
- Pros: {benefits}
- Cons: {limitations}

Related patterns: {related}

Group ID Classification Rules

Entity Type	group_id
Don Branson, Experience, Skills, Credentials	don_branson_career
Organization (Disney*), Team, BuyingCenter	disney_knowledge
JobPosting, Recruiter	career_opportunities
Pattern, Architecture	technical_patterns
research, Project (other)	ai_engineering_research

Rate Limiting

Sequential Mode (don_branson_career)

• 1 episode at a time
• 2 second pause between calls
• For CRITICAL priority data

Batch Mode (all others)

• Up to 10 episodes per batch
• 0.3 second delay within batch
• 3 second pause between batches

Exponential Backoff on 429 Errors

Base delay → 2x → 4x → 8x → 16x (max 60s)

Validation Queries

After each group_id migration, verify:

1. Episode count: get_episodes(group_ids=[group_id])

   • Pass threshold: ≥80% of expected

2. Entity discovery: search_nodes(query="entity name", group_ids=[group_id])

   • Pass threshold: ≥80% of key entities found

3. Relationship extraction: search_memory_facts(query="relationship query")

   • Pass threshold: ≥3 relevant facts per query

Migration State File

Location: migration/progress/migration_state.json

{
  "started_at": "ISO timestamp",
  "current_phase": 1,
  "completed_groups": ["group1"],
  "in_progress_group": "group2",
  "completed_episodes": {"group1": ["entity1", "entity2"]},
  "validation_results": {"group1": {...}},
  "last_checkpoint": "ISO timestamp"
}

Quick Reference Commands

# Dry run
uv run python migration/orchestrate_migration.py --dry-run

# Run specific phase
uv run python migration/orchestrate_migration.py --phase 1

# Resume from checkpoint
uv run python migration/orchestrate_migration.py --resume

# Verbose logging
uv run python migration/orchestrate_migration.py -v

Rollback

Per-group rollback (DANGEROUS - requires confirmation):

# Clear specific namespace
mcp__graphiti-local__clear_graph(group_ids=["group_id"])

Source Neo4j Memory remains unchanged as read-only reference.