lindy-observability

Lindy Observability

Overview

Monitor Lindy AI agent execution health, task completion rates, step-level failures, trigger frequency, and credit consumption. Lindy provides built-in task history in the dashboard. External observability requires webhook callbacks, the Task Completed trigger, and application-side metrics collection.

Prerequisites

• Lindy workspace with active agents
• For external monitoring: webhook receiver + metrics stack (Prometheus/Grafana, Datadog)
• For alerts: Slack or email integration configured

Key Observability Signals

Instructions

Step 1: Dashboard Monitoring (Built-In)

Lindy's Tasks tab provides per-agent monitoring:

1. Open agent > Tasks tab
2. Filter by status: Completed, Failed, In Progress
3. For failed tasks: click to see which step failed and why
4. Track patterns: same step failing? same time of day? same trigger type?

Step 2: Task Completed Trigger (Agent-to-Agent Monitoring)

Use Lindy's built-in Task Completed trigger to build an observability agent:

Monitoring Agent:
  Trigger: Task Completed (from Production Support Agent)
  Condition: "Go down this path if the task failed"
    → Action: Slack Send Channel Message to #ops-alerts
      Message: "Support Agent task failed: {{task.error}}"
  Condition: "Go down this path if task duration > 30 seconds"
    → Action: Slack Send Channel Message to #ops-alerts
      Message: "Support Agent slow: {{task.duration}}s"

Step 3: Webhook-Based Metrics Collection

Configure agents to call your metrics endpoint on task completion:

// metrics-collector.ts — Receive agent metrics via HTTP Request action
import express from 'express';
import { Counter, Histogram, Gauge } from 'prom-client';

const app = express();
app.use(express.json());

// Prometheus metrics
const taskCounter = new Counter({
  name: 'lindy_tasks_total',
  help: 'Total Lindy agent tasks',
  labelNames: ['agent', 'status'],
});

const taskDuration = new Histogram({
  name: 'lindy_task_duration_seconds',
  help: 'Lindy task execution duration',
  labelNames: ['agent'],
  buckets: [1, 2, 5, 10, 30, 60, 120],
});

const creditGauge = new Gauge({
  name: 'lindy_credits_consumed',
  help: 'Credits consumed per task',
  labelNames: ['agent'],
});

// Receive metrics from Lindy HTTP Request action
app.post('/lindy/metrics', (req, res) => {
  const auth = req.headers.authorization;
  if (auth !== `Bearer ${process.env.LINDY_WEBHOOK_SECRET}`) {
    return res.status(401).json({ error: 'Unauthorized' });
  }

  const { agent, status, duration, credits } = req.body;

  taskCounter.inc({ agent, status });
  taskDuration.observe({ agent }, duration);
  creditGauge.set({ agent }, credits);

  res.json({ recorded: true });
});

// Prometheus scrape endpoint
app.get('/metrics', async (req, res) => {
  res.set('Content-Type', 'text/plain');
  res.send(await register.metrics());
});

Lindy agent configuration: Add an HTTP Request action as the last step in each monitored agent:

• URL: https://monitoring.yourapp.com/lindy/metrics

• Method: POST

• Body (Set Manually):

  {
    "agent": "support-bot",
    "status": "{{task.status}}",
    "duration": "{{task.duration}}",
    "credits": "{{task.credits}}"
  }
  

Step 4: Grafana Dashboard Panels

Key panels for a Lindy monitoring dashboard:

Step 5: Alert Rules

# Prometheus alert rules
groups:
  - name: lindy
    rules:
      - alert: LindyAgentHighFailureRate
        expr: rate(lindy_tasks_total{status="failed"}[30m]) > 0.1
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: "Lindy agent {{ $labels.agent }} failure rate > 10%"

      - alert: LindyAgentDown
        expr: absent(lindy_tasks_total{agent="support-bot"}[1h])
        for: 30m
        labels:
          severity: critical
        annotations:
          summary: "No tasks from support-bot in 1 hour"

      - alert: LindyCreditsBurnRate
        expr: rate(lindy_credits_consumed[1h]) * 720 > 5000
        for: 15m
        labels:
          severity: warning
        annotations:
          summary: "Credit burn rate will exhaust monthly budget"

Step 6: Evals (Built-In Quality Monitoring)

Use Lindy Evals to catch quality regressions:

1. Click the test tube icon below any agent step

2. Define scoring criteria (LLM-as-judge):

   Score 1 (pass) if the response is professional, accurate, and under 200 words.
   Score 0 (fail) if the response contains hallucinations or exceeds 200 words.
   

3. Run evals against historical task data

4. Track scores over time to detect quality drift

Note: Eval runs consume credits but do NOT execute real actions (safe simulation).

Observability Maturity Levels

Error Handling

Resources

• Lindy Evals
• Lindy Tasks
• Lindy Documentation

Next Steps

Proceed to lindy-incident-runbook for incident response procedures.