lindy-migration-deep-dive

Advanced migration strategies for moving to Lindy AI from other platforms. Use when migrating from Zapier, Make, n8n, custom automations, or consolidating fragmented agent systems. Trigger with phrases like "lindy migration", "migrate to lindy", "zapier to lindy", "switch to lindy", "consolidate automations".

仓库: jeremylongshore/claude-code-plugins-plus-skills
最近来源活动: 2026年7月17日 22:18
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name	lindy-migration-deep-dive
description	Advanced migration strategies for moving to Lindy AI from other platforms. Use when migrating from Zapier, Make, n8n, custom automations, or consolidating fragmented agent systems. Trigger with phrases like "lindy migration", "migrate to lindy", "zapier to lindy", "switch to lindy", "consolidate automations".
allowed-tools	Read, Write, Edit, Bash(curl:*)
version	1.15.0
license	MIT
author	Jeremy Longshore <jeremy@intentsolutions.io>
tags	["saas","lindy","migration"]
compatibility	Designed for Claude Code, also compatible with Codex and OpenClaw

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

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)

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

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)

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

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"

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