Lindy AI Observability

Overview

Monitor Lindy AI agent execution health, automation success rates, and response latency. Key observability signals for Lindy include agent run duration, step-level success/failure within multi-step automations, trigger frequency (how often agents are invoked), and per-agent cost tracking based on Lindy's per-agent pricing model where each active agent incurs a fixed monthly cost.

Prerequisites

• Lindy Team or Enterprise workspace
• API access with a valid LINDY_API_KEY
• External monitoring stack (Prometheus/Grafana, Datadog, or similar)

Instructions

Step 1: Poll Agent Run Status via API

Step 2: Emit Metrics from Run Data

Step 3: Set Up Webhook-Based Real-Time Monitoring

Configure Lindy webhooks to push events on agent run completion:

Step 4: Alert on Agent Failures

Step 5: Build a Dashboard

Key panels: agent run success/failure rate (stacked bar), run duration p50/p95 by agent, step failure heatmap (which steps fail most), trigger frequency (runs/hour), and active agent count vs billing (since Lindy charges per active agent).

For detailed implementation code and configurations, load the reference guide: Read(${CLAUDE_SKILL_DIR}/references/implementation-guide.md)

Error Handling

Examples

Basic usage: Apply lindy observability to a standard project setup with default configuration options.

Advanced scenario: Customize lindy observability for production environments with multiple constraints and team-specific requirements.

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale

See monitoring implementation details for output format specifications.

Resources

• Official monitoring documentation
• Community best practices and patterns
• Related skills in this plugin pack