lindy-migration-deep-dive

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 [email protected]
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)

1. Migrate non-customer-facing automations first
2. Build in Lindy using natural language description
3. Test with real data for 48 hours
4. Compare output quality to source automation
5. Decommission source automation after verification

Phase 2: Low-Risk Customer-Facing (Days 4-7)

1. Build Lindy agent alongside existing automation (parallel run)
2. Route 10% of traffic to Lindy agent
3. Compare results for 48 hours
4. Gradually increase to 50%, then 100%
5. Monitor task success rate and response quality

Phase 3: Critical Workflows (Days 8-14)

1. Build Lindy agent as exact replacement
2. Test extensively with staging data
3. Schedule cutover during low-traffic window
4. Keep source automation pausable (not deleted) for 7 days
5. 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

• Lindy Templates
• Lindy Agent Builder
• Lindy Documentation
• Lindy vs n8n

Next Steps

This completes the Flagship tier. Review Standard and Pro skills for comprehensive Lindy mastery.