tomevault-io Lindy Migration Deep Dive
Overview
Migrate existing automation workflows from Zapier, Make (Integromat), n8n, LangChain, or custom code to Lindy AI. Key insight: Lindy replaces rigid rule-based automations with AI agents that can reason, adapt, and handle ambiguity — so migration is a redesign opportunity, not a 1:1 translation.
Prerequisites
- Inventory of existing automations (source platform)
- Lindy workspace ready with required integrations authorized
- Migration timeline approved
- Rollback plan defined for customer-facing workflows
Migration Source Comparison
| Source Platform | Lindy Equivalent | Key Difference |
|---|---|---|
| Zapier Zap | Lindy Agent | AI reasoning replaces rigid if/then |
| Make Scenario | Lindy Agent | No-code builder instead of module chains |
| n8n Workflow | Lindy Agent | Managed infra, no self-hosting |
| LangChain Agent | Lindy Agent Step | No-code, managed, no Python needed |
| Custom code | HTTP Request + Run Code | Less code, AI fills gaps |
Instructions
Step 1: Inventory Source Automations
For each existing automation, document:
| Field | Example |
|---|---|
| Name | Support Email Triage |
| Trigger | New email in support@co.com |
| Steps | 1. Parse email 2. Classify 3. Route to channel |
| Integrations | Gmail, Slack, Sheets |
| Frequency | ~50 runs/day |
| Complexity | Medium (3 steps, 1 condition) |
Step 2: Classify Migration Complexity
| Complexity | Criteria | Migration Approach | Time |
|---|---|---|---|
| Simple | 1-3 steps, no conditions | Build from scratch in Lindy | 30 min |
| Medium | 4-8 steps, conditions | Natural language description to Agent Builder | 1-2 hours |
| Complex | 9+ steps, multi-branch, loops | Redesign as multi-agent society | 1-2 days |
| Custom code | Python/JS logic | Run Code action + HTTP Request | 2-4 hours |
Step 3: Migration Strategy by Source
From Zapier:
Zapier Pattern → Lindy Pattern
────────────────────────────────
Trigger (New Email) → Trigger (Email Received)
Filter Step → Trigger Filter (more efficient)
Formatter → AI Prompt field mode (AI does formatting)
Lookup → Knowledge Base search or HTTP Request
Multi-step Zap → Single agent with conditions
Paths → Conditions (natural language branching)
From Make (Integromat):
Make Pattern → Lindy Pattern
────────────────────────────────
Scenario → Agent workflow
Module → Action step
Router → Conditions
Iterator → Loop
Aggregator → Run Code action (consolidation logic)
Error Handler → Agent prompt error instructions
From n8n:
n8n Pattern → Lindy Pattern
────────────────────────────────
Trigger Node → Trigger
Function Node → Run Code (Python/JS)
HTTP Request Node → HTTP Request action
IF Node → Condition
Merge Node → Agent step (AI merges intelligently)
From LangChain/Custom Code:
LangChain Pattern → Lindy Pattern
────────────────────────────────
Agent → Agent Step with skills
Tool → Action or HTTP Request
Memory → Lindy Memory (persistent)
Chain → Workflow steps
Vector Store → Knowledge Base
Retrieval Chain → Knowledge Base + AI Prompt
Step 4: Execute Migration (Phased)
Phase 1: Internal-Only Agents (Days 1-3)
- Migrate non-customer-facing automations first
- Build in Lindy using natural language description
- Test with real data for 48 hours
- Compare output quality to source automation
- Decommission source automation after verification
Phase 2: Low-Risk Customer-Facing (Days 4-7)
- Build Lindy agent alongside existing automation (parallel run)
- Route 10% of traffic to Lindy agent
- Compare results for 48 hours
- Gradually increase to 50%, then 100%
- Monitor task success rate and response quality
Phase 3: Critical Workflows (Days 8-14)
- Build Lindy agent as exact replacement
- Test extensively with staging data
- Schedule cutover during low-traffic window
- Keep source automation pausable (not deleted) for 7 days
- Monitor closely for 48 hours post-cutover
Step 5: Redesign Opportunities
Migration is a chance to improve, not just replicate:
| Old Pattern | Lindy Improvement |
|---|---|
| Rigid if/then classification | AI classifies naturally, handles edge cases |
| Template-based email responses | AI generates contextual, personalized responses |
| Multiple automations for variations | Single agent with conditions handles all |
| Manual data transformation | Run Code action or AI handles transformation |
| No error handling | Agent prompt includes fallback behavior |
Step 6: Validate and Cutover
# Post-migration validation checklist
echo "=== Migration Validation ==="
# 1. Task completion rate
echo "Check: Agent Tasks tab - expect >95% success rate"
# 2. Response quality
echo "Check: Compare 10 agent outputs to old automation outputs"
# 3. Trigger coverage
echo "Check: All events triggering correctly (no missed events)"
# 4. Performance
echo "Check: Task duration within acceptable range"
# 5. Cost
echo "Check: Credit consumption within budget"
Migration Checklist
- Source system inventory complete
- Each automation classified by complexity
- Lindy integrations authorized
- Phase 1 (internal) agents migrated and verified
- Phase 2 (low-risk) agents running in parallel
- Phase 3 (critical) agents tested with staging data
- Cutover window scheduled
- Rollback procedure tested
- Source automations paused (not deleted)
- 7-day post-cutover monitoring complete
- Source automations decommissioned
Error Handling
| Issue | Cause | Solution |
|---|---|---|
| Output quality lower | AI prompt needs tuning | Add few-shot examples to agent prompt |
| Missing edge cases | Source had specific rules | Add condition branches or prompt instructions |
| Higher cost than expected | Overuse of large models | Right-size models per step |
| Integration auth fails | OAuth not set up in Lindy | Authorize integrations before migration |
| Data format mismatch | Different field names | Map fields in Run Code action |
Resources
Next Steps
This completes the Flagship tier. Review Standard and Pro skills for comprehensive Lindy mastery.
Converted and distributed by TomeVault — claim your Tome and manage your conversions.