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

Signal	Source	Why It Matters
Task completion rate	Tasks tab / callback	Measures agent reliability
Task duration	Task detail view	Tracks performance over time
Step failure rate	Task detail (red steps)	Identifies broken actions
Credit consumption	Billing dashboard	Budget tracking
Trigger frequency	Task count over time	Detects trigger storms
Agent error rate	Failed tasks / total tasks	Overall health indicator

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:

Panel	Metric	Type
Task Success Rate	rate(lindy_tasks_total{status="completed"}[1h])	Percentage gauge
Task Failures	rate(lindy_tasks_total{status="failed"}[1h])	Counter
Duration p50/p95	histogram_quantile(0.95, lindy_task_duration_seconds)	Time series
Credit Burn Rate	rate(lindy_credits_consumed[1h])	Counter
Active Agents	Count of agents with tasks in last 24h	Stat panel
Trigger Frequency	Tasks per hour by agent	Bar chart

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

Level	What You Monitor	How
L0	Nothing	Manual dashboard checks
L1	Task failures	Task Completed trigger + Slack alerts
L2	Success rate + duration	HTTP Request action + Prometheus
L3	Credit burn + quality	Evals + Grafana dashboards
L4	Automated remediation	Monitoring agent auto-restarts failed agents

Error Handling

Issue	Cause	Solution
Metrics endpoint down	Monitoring server crashed	Alert on scrape failures
Task Completed not firing	Monitoring agent paused	Check monitoring agent is active
Credit burn alert false positive	Legitimate traffic spike	Tune alert threshold
Eval scores dropping	Prompt drift or model change	Review recent prompt/model changes

Resources

• Lindy Evals
• Lindy Tasks
• Lindy Documentation

Next Steps

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