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Advanced migration strategies for Lindy AI integrations. Use when migrating from other platforms, consolidating agents, or performing major architecture changes. Trigger with phrases like "lindy...
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 ā so migration is a redesign opportunity, not a 1:1 translation.
| 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 |
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) |
| 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 |
From Zapier:
Zapier Pattern ā Lindy Pattern
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Trigger (New Email) ā Trigger (Email Received)
Filter Step ā Trigger Filter (more efficient)
Formatter ā AI Prompt field mode (AI does formatting)
Lookup ā Knowledge Base search or HTTP Request
Multi-step Zap ā Single agent with conditions
Paths ā Conditions (natural language branching)
From Make (Integromat):
Make Pattern ā Lindy Pattern
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Scenario ā Agent workflow
Module ā Action step
Router ā Conditions
Iterator ā Loop
Aggregator ā Run Code action (consolidation logic)
Error Handler ā Agent prompt error instructions
From n8n:
n8n Pattern ā Lindy Pattern
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Trigger Node ā Trigger
Function Node ā Run Code (Python/JS)
HTTP Request Node ā HTTP Request action
IF Node ā Condition
Merge Node ā Agent step (AI merges intelligently)
From LangChain/Custom Code:
LangChain Pattern ā Lindy Pattern
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā
Agent ā Agent Step with skills
Tool ā Action or HTTP Request
Memory ā Lindy Memory (persistent)
Chain ā Workflow steps
Vector Store ā Knowledge Base
Retrieval Chain ā Knowledge Base + AI Prompt
Phase 1: Internal-Only Agents (Days 1-3)
Phase 2: Low-Risk Customer-Facing (Days 4-7)
Phase 3: Critical Workflows (Days 8-14)
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 |
# 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"
Create a migration record that maps each source automation to its proposed Lindy workflow, risk tier, owner, test corpus, success measures, rollback trigger, and disposition. For every customer-facing cutover, retain the parallel-run comparison and the evidence used to pause, proceed, or roll back; do not treat a configured agent as proof of behavioral equivalence.
For a support-triage workflow, replay a sanitized fixed case set through both systems, compare routing and escalation outcomes, then shadow a bounded slice of live traffic without allowing duplicate customer actions. Increase traffic only after the recorded thresholds pass. If escalation recall falls below the agreed floor, route all traffic back to the source automation and preserve the failed comparison for prompt and condition redesign.
| 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 |
This completes the Flagship tier. Review Standard and Pro skills for comprehensive Lindy mastery.
