Skip to main content
lindy-observability Monitor Lindy AI agent health, task success rates, and credit consumption.
Use when setting up monitoring, building dashboards, configuring alerts,
or tracking agent performance over time.
Trigger with phrases like "lindy monitoring", "lindy observability",
"lindy metrics", "lindy logging", "lindy dashboard".
跳到安装 Skills Marketplace 发现并探索由社区构建的 Agent Skills
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill lindy-observability命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
下载 Zip 下载中... jeremylongshore
jeremylongshore/claude-code-plugins-plus-skills
打开 GitHub 仓库 name lindy-observability description Monitor Lindy AI agent health, task success rates, and credit consumption.
Use when setting up monitoring, building dashboards, configuring alerts,
or tracking agent performance over time.
Trigger with phrases like "lindy monitoring", "lindy observability",
"lindy metrics", "lindy logging", "lindy dashboard".
allowed-tools Read, Write, Edit, Bash(curl:*) version 1.15.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","lindy","monitoring","observability","dashboard"] compatibility Designed for Claude Code, also compatible with Codex and OpenClaw
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:
Open agent > Tasks tab
Filter by status: Completed , Failed , In Progress
For failed tasks: click to see which step failed and why
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:
import express from 'express' ;
import { Counter , Histogram , Gauge } from 'prom-client' ;
const app = express ();
app.use (express.json ());
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' ],
});
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 });
});
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
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:
Click the test tube icon below any agent step
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.
Run evals against historical task data
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
Next Steps Proceed to lindy-incident-runbook for incident response procedures.
