Jeremylongshore Claude Code Plugins Plus Skills Lindy Reference Architecture

Lindy Reference Architecture

Overview

Production-ready architecture patterns for integrating Lindy AI agents into applications. Covers webhook integration, multi-agent societies, event-driven pipelines, and high-availability patterns.

Prerequisites

• Understanding of Lindy agent model (triggers, actions, skills)
• Familiarity with webhook-based architectures
• Production requirements defined (throughput, latency, reliability)

Architecture 1: Simple Webhook Integration

Single agent triggered by your application, results sent via callback.

┌─────────────┐       POST (webhook)       ┌──────────────┐
│  Your App   │ ─────────────────────────→  │ Lindy Agent  │
│             │                             │              │
│  /callback  │ ←─────────────────────────  │ HTTP Request │
│             │       POST (callback)       │   Action     │
└─────────────┘                             └──────────────┘

Implementation:

• Your app sends webhook with callbackUrl field
• Lindy agent processes and responds via Send POST Request to Callback
• Your app receives results asynchronously

Best for: Simple automations (email triage, lead scoring, content generation)

Architecture 2: Event-Driven Pipeline

Multiple event sources feed agents through a central webhook router.

┌──────────┐
│ Stripe   │──webhook──┐
└──────────┘           │
                       ▼
┌──────────┐     ┌───────────┐     ┌──────────────┐
│ Shopify  │──→  │  Router   │──→  │ Lindy Agents │
└──────────┘     │  Service  │     │              │
                 └───────────┘     │ • Order Bot  │
┌──────────┐           ▲          │ • Support Bot│
│ Your App │──webhook──┘          │ • Analytics  │
└──────────┘                      └──────────────┘

Implementation:

// Event router — maps events to specific Lindy agents
const agentWebhooks: Record<string, string> = {
  'order.created': process.env.LINDY_ORDER_AGENT_WEBHOOK!,
  'customer.support_request': process.env.LINDY_SUPPORT_AGENT_WEBHOOK!,
  'analytics.daily_report': process.env.LINDY_ANALYTICS_AGENT_WEBHOOK!,
};

app.post('/events', async (req, res) => {
  const { event, data } = req.body;
  const webhookUrl = agentWebhooks[event];

  if (!webhookUrl) {
    return res.status(400).json({ error: `Unknown event: ${event}` });
  }

  await fetch(webhookUrl, {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.LINDY_WEBHOOK_SECRET}`,
      'Content-Type': 'application/json',
    },
    body: JSON.stringify({ event, data, callbackUrl: `${BASE_URL}/callback` }),
  });

  res.json({ routed: true, agent: event });
});

Best for: Multiple event sources, different agents per event type

Architecture 3: Multi-Agent Society (Delegation)

Specialized agents collaborate through Lindy's built-in delegation system.

┌─────────────────┐
│ Orchestrator    │
│ Lindy           │
│ (receives       │
│  initial task)  │
└───┬────────┬────┘
    │        │
    ▼        ▼
┌────────┐ ┌────────┐
│Research│ │Analysis│
│ Lindy  │ │ Lindy  │
└───┬────┘ └───┬────┘
    │          │
    ▼          ▼
┌─────────────────┐
│ Writer Lindy    │
│ (synthesizes    │
│  final output)  │
└─────────────────┘

Setup in Lindy:

1. Create specialized agents with Agent Message Received triggers
2. Orchestrator uses Agent Send Message action to delegate
3. Each agent completes its specialty and sends results forward
4. Writer agent synthesizes and delivers final output

Key decisions:

Best for: Complex tasks requiring multiple specialties (research + analysis + writing)

Architecture 4: Scheduled Pipeline

Agents run on schedules, each feeding data to the next.

                    Schedule: Daily 6 AM
                         │
                         ▼
                  ┌──────────────┐
                  │ Data Fetch   │ Pulls from APIs/databases
                  │ Lindy        │
                  └──────┬───────┘
                         │ Agent Send Message
                         ▼
                  ┌──────────────┐
                  │ Analysis     │ Processes & summarizes
                  │ Lindy        │
                  └──────┬───────┘
                         │ Agent Send Message
                         ▼
                  ┌──────────────┐
                  │ Report       │ Formats & delivers
                  │ Lindy        │
                  │  → Slack     │
                  │  → Email     │
                  └──────────────┘

Best for: Daily reports, weekly digests, scheduled data processing

Architecture 5: Chat + Knowledge Base

Agent deployed as customer-facing chatbot with RAG-powered responses.

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  Website     │     │ Lindy Agent  │     │ Knowledge    │
│  (Embed      │◀──▶ │              │◀──▶ │ Base         │
│   Widget)    │     │ Chat Trigger │     │ PDFs, Docs,  │
└──────────────┘     │ + KB Search  │     │ Websites     │
                     │ + Condition  │     └──────────────┘
                     │ + Escalate   │
                     └──────────────┘
                            │
                            ▼ (if escalation needed)
                     ┌──────────────┐
                     │ Slack DM to  │
                     │ human agent  │
                     └──────────────┘

Deploy the embed widget:

<!-- Paste near end of <body> tag -->
<script src="https://embed.lindy.ai/widget.js"
  data-lindy-id="YOUR_AGENT_ID"></script>

KB configuration:

• Sources: Product docs, FAQ PDFs, knowledge articles
• Fuzziness: 100 (semantic search)
• Max Results: 5 (balance relevance vs context size)
• Auto-resync: every 24 hours

Best for: Customer support, FAQ bots, internal knowledge assistants

Architecture Decision Matrix

Error Handling

Resources

• Lindy Introduction
• Delegation 101
• Building a Chatbot
• Lindy Embed

Next Steps

Proceed to Flagship tier skills for enterprise features: multi-env, observability, incident response, data handling, RBAC, and migration.

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