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lindy-reference-architecture Reference architectures for Lindy AI integrations.
Use when designing systems, planning architecture,
or implementing production patterns.
Trigger with phrases like "lindy architecture", "lindy design",
"lindy system design", "lindy patterns".
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill lindy-reference-architecture ai automation claude-code devops mcp ai-agents
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 │
└──────────┘ └──────────────┘
// 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) │
└─────────────────┘
Create specialized agents with Agent Message Received triggers
Orchestrator uses Agent Send Message action to delegate
Each agent completes its specialty and sends results forward
Writer agent synthesizes and delivers final output
Decision Option A Option B Context passing Full context (accurate, expensive) Selective context (cheap, focused) Error handling Agent retries Orchestrator retry logic Parallelism Sequential delegation Parallel delegation with merge
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 │
└──────────────┘
<!-- Paste near end of <body> tag -->
<script src="https://embed.lindy.ai/widget.js"
data-lindy-id="YOUR_AGENT_ID"></script>
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 Pattern Throughput Latency Complexity Cost Simple webhook Low-Med 2-15s Low Low Event-driven pipeline High 5-30s Medium Medium Multi-agent society Low-Med 30-120s High High Scheduled pipeline Batch N/A Medium Predictable Chat + KB Interactive 2-10s Low-Med Per-message
Error Handling Pattern Failure Mode Recovery Simple webhook Agent fails Retry webhook with backoff Event-driven Router crash Queue events, replay on recovery Multi-agent Delegation fails Orchestrator retries or skips Scheduled Missed schedule Next run catches up Chat + KB KB empty Fallback to generic response + escalate
Resources
Next Steps Proceed to Flagship tier skills for enterprise features: multi-env, observability,
incident response, data handling, RBAC, and migration.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).