lindy-observability

name	lindy-observability
description	Implement observability for Lindy AI integrations. Use when setting up monitoring, logging, tracing, or building dashboards for Lindy operations. Trigger with phrases like "lindy monitoring", "lindy observability", "lindy metrics", "lindy logging", "lindy tracing".
allowed-tools	Read, Write, Edit
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>

Lindy Observability

Overview

Implement comprehensive observability for Lindy AI integrations.

Prerequisites

• Production Lindy integration
• Observability stack (Datadog, New Relic, Prometheus, etc.)
• Log aggregation system

Instructions

Step 1: Structured Logging

// lib/logger.ts
import pino from 'pino';

export const logger = pino({
  level: process.env.LOG_LEVEL || 'info',
  formatters: {
    level: (label) => ({ level: label }),
  },
  base: {
    service: 'lindy-integration',
    environment: process.env.NODE_ENV,
  },
});

// Lindy-specific logger
export function lindyLogger(operation: string) {
  return logger.child({ component: 'lindy', operation });
}

Step 2: Instrumented Client

// lib/instrumented-lindy.ts
import { Lindy } from '@lindy-ai/sdk';
import { lindyLogger } from './logger';
import { metrics } from './metrics';
import { tracer } from './tracer';

export class InstrumentedLindy {
  private lindy: Lindy;

  constructor() {
    this.lindy = new Lindy({ apiKey: process.env.LINDY_API_KEY });
  }

  async runAgent(agentId: string, input: string) {
    const log = lindyLogger('runAgent');
    const span = tracer.startSpan('lindy.agent.run');

    const startTime = Date.now();

    try {
      span.setAttributes({
        'lindy.agent_id': agentId,
        'lindy.input_length': input.length,
      });

      log.info({ agentId, : input. }, );

       result =  ...(agentId, { input });

       duration = .() - startTime;

      
      metrics.(, duration, { agentId });
      metrics.(, , { agentId });

      
      log.({
        agentId,
        duration,
        : result..,
      }, );

      span.({
        : duration,
        : result..,
        : ,
      });

       result;
    }  (: ) {
       duration = .() - startTime;

      
      metrics.(, , {
        agentId,
        : error.,
      });

      
      log.({
        agentId,
        duration,
        : error.,
        : error.,
      }, );

      span.({
        : ,
        : error.,
      });
      span.(error);

       error;
    }  {
      span.();
    }
  }
}

Step 3: Metrics Collection

// lib/metrics.ts
import { Counter, Histogram, Registry } from 'prom-client';

const registry = new Registry();

export const metrics = {
  agentDuration: new Histogram({
    name: 'lindy_agent_duration_ms',
    help: 'Duration of Lindy agent runs in milliseconds',
    labelNames: ['agent_id', 'status'],
    buckets: [100, 500, 1000, 2000, 5000, 10000, 30000],
    registers: [registry],
  }),

  agentRuns: new Counter({
    name: 'lindy_agent_runs_total',
    help: 'Total number of Lindy agent runs',
    labelNames: ['agent_id', 'status'],
    registers: [registry],
  }),

  apiCalls: new Counter({
    name: 'lindy_api_calls_total',
    help: 'Total Lindy API calls',
    labelNames: ['endpoint', 'status'],
    : [registry],
  }),

  
  () {
     metric = registry.(name)  ;
    metric?.(labels, value);
  },

  () {
     metric = registry.(name)  ;
    metric?.(labels, value);
  },
};


  (): <> {
   registry.();
}

Step 4: Distributed Tracing

// lib/tracer.ts
import { trace, SpanStatusCode } from '@opentelemetry/api';
import { NodeTracerProvider } from '@opentelemetry/sdk-trace-node';
import { SimpleSpanProcessor } from '@opentelemetry/sdk-trace-base';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';

const provider = new NodeTracerProvider();

provider.addSpanProcessor(
  new SimpleSpanProcessor(
    new OTLPTraceExporter({
      url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT,
    })
  )
);

provider.register();

export const tracer = trace.getTracer('lindy-integration');

Step 5: Dashboard Configuration

# grafana/dashboards/lindy.json
{
  "title": "Lindy AI Monitoring",
  "panels": [
    {
      "title": "Agent Runs per Minute",
      "type": "graph",
      "targets": [
        {
          "expr": "rate(lindy_agent_runs_total[1m])",
          "legendFormat": "{{agent_id}}"
        }
      ]
    },
    {
      "title": "P95 Latency",
      "type": "stat",
      "targets": [
        {
          "expr": "histogram_quantile(0.95, rate(lindy_agent_duration_ms_bucket[5m]))"
        }
      ]
    },
    {
      "title": "Error Rate",
      "type": "graph",
      "targets": [
        {
          "expr": "rate(lindy_agent_runs_total{status='error'}[5m]) / rate(lindy_agent_runs_total[5m])"
        }
      ]
    }
  ]
}

Output

• Structured logging
• Prometheus metrics
• Distributed tracing
• Grafana dashboards
• Alerting rules

Error Handling

Issue	Cause	Solution
Missing traces	OTEL not configured	Set OTEL endpoint
Metrics not visible	Wrong labels	Check label names
Logs not searchable	Missing context	Add structured fields

Examples

Alert Configuration

# alerts/lindy.yml
groups:
  - name: lindy
    rules:
      - alert: LindyHighErrorRate
        expr: rate(lindy_agent_runs_total{status="error"}[5m]) > 0.1
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "High Lindy error rate"

      - alert: LindyHighLatency
        expr: histogram_quantile(0.95, rate(lindy_agent_duration_ms_bucket[5m])) > 10000
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Lindy P95 latency above 10s"

Resources

• OpenTelemetry
• Prometheus
• Grafana

Next Steps

Proceed to lindy-incident-runbook for incident response.