Lindy AI SDK best practices and common patterns. Use when learning SDK patterns, optimizing API usage, or implementing advanced agent features. Trigger with phrases like "lindy SDK patterns", "lindy best practices", "lindy API patterns", "lindy code patterns".
npxskills add jeremylongshore/claude-code-plugins-plus-skills--skill lindy-sdk-patternsLoading…
Lindy is primarily a no-code platform. External integration happens through three channels: Webhook triggers (inbound), HTTP Request actions (outbound), and Run Code actions (inline Python/JS execution via E2B sandbox). This skill covers patterns for each.
lindy-install-auth setupYour application fires webhooks to wake Lindy agents:
// lindy-client.ts — Reusable Lindy webhook trigger client
class LindyClient {
private webhookUrl: string;
private secret: string;
constructor(webhookUrl: string, secret: string) {
this.webhookUrl = webhookUrl;
this.secret = secret;
}
async trigger(payload: Record<string, unknown>): Promise<{ status: number }> {
const response = await fetch(this.webhookUrl, {
method: 'POST',
headers: {
'Authorization': `Bearer ${this.secret}`,
'Content-Type': 'application/json',
},
body: JSON.stringify(payload),
});
if (!response.ok) {
throw new Error(`Lindy webhook failed: ${response.status} ${response.statusText}`);
}
return { status: response.status };
}
async triggerWithCallback(
payload: Record<string, unknown>,
callbackUrl: string
): Promise<{ status: number }> {
return this.trigger({ ...payload, callbackUrl });
}
}
// Usage
const lindy = new LindyClient(
'https://public.lindy.ai/api/v1/webhooks/YOUR_ID',
process.env.LINDY_WEBHOOK_SECRET!
);
await lindy.trigger({ event: 'lead.created', name: 'Jane Doe', email: '[email protected]' });
Configure a Lindy agent to call your API as an action step:
In Lindy Dashboard — Add HTTP Request action:
Method: POST
URL: https://api.yourapp.com/process
Headers: Authorization: Bearer {{your_api_key}}, Content-Type: application/json
Body (AI Prompt mode):
Send the processed data as JSON with fields matching the API schema.
Include: name from {{trigger.data.name}}, analysis from previous step.
Your API endpoint receives the call:
// Your API receiving Lindy agent calls
app.post('/process', async (req, res) => {
const { name, analysis } = req.body;
const result = await processData(name, analysis);
res.json({ result, processedAt: new Date().toISOString() });
});
Execute Python or JavaScript directly in Lindy workflows. Code runs in isolated Firecracker microVMs with ~150ms startup time.
Python example (data transformation in a workflow):
# Run Code action — Python
# Input variables: raw_data (string from previous step)
import json
data = json.loads(raw_data) # Input vars are always strings
# Process
cleaned = [
{"name": item["name"].strip(), "score": float(item["score"])}
for item in data["items"]
if float(item["score"]) > 0.5
]
# Sort by score descending
cleaned.sort(key=lambda x: x["score"], reverse=True)
# Return value accessible as {{run_code.result}} in next step
return json.dumps({"filtered_count": len(cleaned), "items": cleaned})
JavaScript example (API call + processing):
// Run Code action — JavaScript
// Input variables: query (string), api_key (string)
const response = await fetch(`https://api.example.com/search?q=${query}`, {
headers: { 'Authorization': `Bearer ${api_key}` }
});
const data = await response.json();
const summary = data.results.map(r => `${r.title}: ${r.snippet}`).join('\n');
return JSON.stringify({ count: data.results.length, summary });
Run Code outputs (available to subsequent steps):
| Output | Contents |
|---|---|
{{run_code.result}} | Value from return statement |
{{run_code.text}} | stdout from print() / console.log() |
{{run_code.stderr}} | Error output for debugging |
Available Python libraries: pandas, numpy, scipy, scikit-learn, matplotlib, requests, aiohttp, beautifulsoup4, nltk, spacy, openpyxl, python-docx
Key constraint: All input variables arrive as strings. Cast explicitly:
count = int(count_str), data = json.loads(json_str)
Send a callbackUrl in your webhook payload. Lindy can respond back using
the Send POST Request to Callback action:
// Your app triggers Lindy with a callback URL
await lindy.trigger({
event: 'analyze.request',
data: { text: 'Analyze this quarterly report...' },
callbackUrl: 'https://api.yourapp.com/lindy-callback'
});
// Your callback handler receives Lindy's response
app.post('/lindy-callback', (req, res) => {
const { analysis, sentiment, summary } = req.body;
saveAnalysis(analysis);
res.sendStatus(200);
});
async function triggerWithRetry(
client: LindyClient,
payload: Record<string, unknown>,
maxRetries = 3
): Promise<void> {
for (let attempt = 0; attempt <= maxRetries; attempt++) {
try {
await client.trigger(payload);
return;
} catch (error: any) {
if (attempt === maxRetries) throw error;
const delay = Math.pow(2, attempt) * 1000; // 1s, 2s, 4s
console.warn(`Retry ${attempt + 1}/${maxRetries} in ${delay}ms`);
await new Promise(r => setTimeout(r, delay));
}
}
}
| Pattern | Failure Mode | Solution |
|---|---|---|
| Webhook trigger | 401 Unauthorized | Verify Bearer token matches dashboard secret |
| HTTP Request action | Target API unreachable | Check URL, verify HTTPS, test with curl |
| Run Code | Timeout | Avoid infinite loops; keep execution under 30s |
| Run Code | Import error | Use only pre-installed libraries (see list above) |
| Callback | Callback URL unreachable | Ensure HTTPS endpoint is publicly accessible |
Proceed to lindy-core-workflow-a for full agent creation workflows.
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).