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deepgram-observability Set up comprehensive observability for Deepgram integrations.
Use when implementing monitoring, setting up dashboards,
or configuring alerting for Deepgram integration health.
Trigger: "deepgram monitoring", "deepgram metrics", "deepgram observability",
"monitor deepgram", "deepgram alerts", "deepgram dashboard".
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2 arquivos Mais deste repositório langchain-deploy-integration Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
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langchain-langgraph-human-in-loop Build LangGraph 1.0 human-in-the-loop approval flows with interrupt_before /
interrupt_after and Command(resume=...) — JSON-serializable state, clean
resume semantics, and UI wiring for approval decisions. Use when adding an
approval gate before an expensive tool call, wiring a Slack/web UI for agent
approvals, or debugging a graph that crashes on interrupt.
Trigger with "langgraph human in loop", "langgraph interrupt_before",
"langgraph approval flow", "Command resume", "langgraph HITL".
name deepgram-observability description Set up comprehensive observability for Deepgram integrations.
Use when implementing monitoring, setting up dashboards,
or configuring alerting for Deepgram integration health.
Trigger: "deepgram monitoring", "deepgram metrics", "deepgram observability",
"monitor deepgram", "deepgram alerts", "deepgram dashboard".
allowed-tools Read, Write, Edit, Bash(curl:*) version 1.13.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","deepgram","monitoring","observability","prometheus"] compatibility Designed for Claude Code
Deepgram Observability
Overview
Full observability stack for Deepgram: Prometheus metrics (request counts, latency histograms, audio processed, cost tracking), OpenTelemetry distributed tracing, structured JSON logging with Pino, Grafana dashboard JSON, and AlertManager rules.
Four Pillars
Pillar Tool What It Tracks Metrics Prometheus Request rate, latency, error rate, audio minutes, estimated cost Traces OpenTelemetry End-to-end request flow, Deepgram API span timing Logs Pino (JSON) Request details, errors, audit trail Alerts AlertManager Error rate >5%, P95 latency >10s, rate limit hits
Instructions
Step 1: Prometheus Metrics Definition
import { Counter , Histogram , Gauge , Registry , collectDefaultMetrics } from 'prom-client' ;
const registry = new Registry ();
collectDefaultMetrics ({ register : registry });
const requestsTotal = new Counter ({
name : 'deepgram_requests_total' ,
help : 'Total Deepgram API requests' ,
labelNames : ['method' , 'model' , 'status' ] as const ,
registers : [registry],
});
const latencyHistogram = new Histogram ({
name : 'deepgram_request_duration_seconds'
help
'Deepgram API request duration'
labelNames
'method'
'model'
as
const
buckets
0.1
0.5
1
2
5
10
30
60
registers
const
new
Counter
name
'deepgram_audio_processed_seconds_total'
help
'Total audio seconds processed'
labelNames
'model'
as
const
registers
const
new
Counter
name
'deepgram_estimated_cost_dollars_total'
help
'Estimated cost in USD'
labelNames
'model'
'method'
as
const
registers
const
new
Gauge
name
'deepgram_active_websocket_connections'
help
'Currently active WebSocket connections'
registers
const
new
Counter
name
'deepgram_rate_limit_hits_total'
help
'Number of 429 rate limit responses'
registers
export
Step 2: Instrumented Deepgram Client import { createClient, DeepgramClient } from '@deepgram/sdk' ;
class InstrumentedDeepgram {
private client : DeepgramClient ;
private costPerMinute : Record <string , number > = {
'nova-3' : 0.0043 , 'nova-2' : 0.0043 , 'base' : 0.0048 , 'whisper-large' : 0.0048 ,
};
constructor (apiKey : string this .client = createClient (apiKey);
}
async transcribeUrl (url : string , options : Record <string , any > = {}const model = options.model ?? 'nova-3' ;
const timer = latencyHistogram.startTimer ({ method : 'prerecorded' , model });
try {
const { result, error } = await this .client .listen .prerecorded .transcribeUrl (
{ url }, { model, smart_format : true , ...options }
);
const status = error ? 'error' : 'success' ;
timer ();
requestsTotal.inc ({ method : 'prerecorded' , model, status });
if (error) {
if ((error as any ).status === 429 ) rateLimitHits.inc ();
throw error;
}
const duration = result.metadata .duration ;
audioProcessedSeconds.inc ({ model }, duration);
estimatedCostDollars.inc (
{ model, method : 'prerecorded' },
(duration / 60 ) * (this .costPerMinute [model] ?? 0.0043 )
);
return result;
} catch (err) {
timer ();
requestsTotal.inc ({ method : 'prerecorded' , model, status : 'error' });
throw err;
}
}
connectLive (options : Record <string , any >const model = options.model ?? 'nova-3' ;
activeConnections.inc ();
const connection = this .client .listen .live (options);
const originalFinish = connection.finish .bind (connection);
connection.finish = () => {
activeConnections.dec ();
return originalFinish ();
};
return connection;
}
}
Step 3: OpenTelemetry Tracing import { NodeSDK } from '@opentelemetry/sdk-node' ;
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http' ;
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node' ;
import { Resource } from '@opentelemetry/resources' ;
import { SEMRESATTRS_SERVICE_NAME } from '@opentelemetry/semantic-conventions' ;
import { trace } from '@opentelemetry/api' ;
const sdk = new NodeSDK ({
resource : new Resource ({
[SEMRESATTRS_SERVICE_NAME ]: 'deepgram-service' ,
'deployment.environment' : process.env .NODE_ENV ?? 'development' ,
}),
traceExporter : new OTLPTraceExporter ({
url : process.env .OTEL_EXPORTER_OTLP_ENDPOINT ?? 'http://localhost:4318/v1/traces' ,
}),
instrumentations : [
getNodeAutoInstrumentations ({
'@opentelemetry/instrumentation-http' : {
ignoreIncomingPaths : ['/health' , '/metrics' ],
},
}),
],
});
sdk.start ();
const tracer = trace.getTracer ('deepgram' );
async function tracedTranscribe (url : string , model : string return tracer.startActiveSpan ('deepgram.transcribe' , async (span) => {
span.setAttribute ('deepgram.model' , model);
span.setAttribute ('deepgram.audio_url' , url.substring (0 , 100 ));
try {
const instrumented = new InstrumentedDeepgram (process.env .DEEPGRAM_API_KEY !);
const result = await instrumented.transcribeUrl (url, { model });
span.setAttribute ('deepgram.duration_seconds' , result.metadata .duration );
span.setAttribute ('deepgram.request_id' , result.metadata .request_id );
span.setAttribute ('deepgram.confidence' ,
result.results .channels [0 ].alternatives [0 ].confidence );
return result;
} catch (err : any ) {
span.recordException (err);
span.setStatus ({ code : 2 , message : err.message });
throw err;
} finally {
span.end ();
}
});
}
Step 4: Structured Logging with Pino import pino from 'pino' ;
const logger = pino ({
level : process.env .LOG_LEVEL ?? 'info' ,
formatters : {
level : (label ) => ({ level : label }),
},
timestamp : pino.stdTimeFunctions .isoTime ,
base : {
service : 'deepgram-integration' ,
env : process.env .NODE_ENV ,
},
});
const transcriptionLog = logger.child ({ component : 'transcription' });
const metricsLog = logger.child ({ component : 'metrics' });
transcriptionLog.info ({
action : 'transcribe' ,
model : 'nova-3' ,
audioUrl : url.substring (0 , 100 ),
requestId : result.metadata .request_id ,
duration : result.metadata .duration ,
confidence : result.results .channels [0 ].alternatives [0 ].confidence ,
}, 'Transcription completed' );
transcriptionLog.error ({
action : 'transcribe' ,
model : 'nova-3' ,
error : err.message ,
statusCode : err.status ,
}, 'Transcription failed' );
Step 5: Grafana Dashboard Panels {
"title" : "Deepgram Observability" ,
"panels" : [
{
"title" : "Request Rate" ,
"type" : "timeseries" ,
"targets" : [ { "expr" : "rate(deepgram_requests_total[5m])" } ]
} ,
{
"title" : "P95 Latency" ,
"type" : "gauge" ,
"targets" : [ { "expr" : "histogram_quantile(0.95, rate(deepgram_request_duration_seconds_bucket[5m]))" } ]
} ,
{
"title" : "Error Rate %" ,
"type" : "stat" ,
"targets" : [ { "expr" : "rate(deepgram_requests_total{status='error'}[5m]) / rate(deepgram_requests_total[5m]) * 100" } ]
} ,
{
"title" : "Audio Processed (min/hr)" ,
"type" : "timeseries" ,
"targets" : [ { "expr" : "rate(deepgram_audio_processed_seconds_total[1h]) / 60" } ]
} ,
{
"title" : "Estimated Daily Cost" ,
"type" : "stat" ,
"targets" : [ { "expr" : "increase(deepgram_estimated_cost_dollars_total[24h])" } ]
} ,
{
"title" : "Active WebSocket Connections" ,
"type" : "gauge" ,
"targets" : [ { "expr" : "deepgram_active_websocket_connections" } ]
}
]
}
Step 6: AlertManager Rules groups:
- name: deepgram-alerts
rules:
- alert: DeepgramHighErrorRate
expr: >
rate(deepgram_requests_total{status="error"}[5m])
/ rate(deepgram_requests_total[5m]) > 0.05
for: 5m
labels: { severity: critical }
annotations:
summary: "Deepgram error rate > 5% for 5 minutes"
- alert: DeepgramHighLatency
expr: >
histogram_quantile(0.95,
rate(deepgram_request_duration_seconds_bucket[5m])
) > 10
for: 5m
labels: { severity: warning }
annotations:
summary: "Deepgram P95 latency > 10 seconds"
- alert: DeepgramRateLimited
expr: rate(deepgram_rate_limit_hits_total[1h]) > 10
for: 10m
labels: { severity: warning }
annotations:
summary: "Deepgram rate limit hits > 10/hour"
- alert: DeepgramCostSpike
expr: >
increase(deepgram_estimated_cost_dollars_total[24h])
> 2 * increase(deepgram_estimated_cost_dollars_total[24h] offset 1d)
for: 30m
labels: { severity: warning }
annotations:
summary: "Deepgram daily cost > 2x yesterday"
- alert: DeepgramZeroRequests
expr: rate(deepgram_requests_total[15m]) == 0
for: 15m
labels: { severity: warning }
annotations:
summary: "No Deepgram requests for 15 minutes"
Metrics Endpoint import express from 'express' ;
const app = express ();
app.get ('/metrics' , async (req, res) => {
res.set ('Content-Type' , registry.contentType );
res.send (await registry.metrics ());
});
Output
Prometheus metrics (6 metrics covering requests, latency, usage, cost)
Instrumented Deepgram client with auto-tracking
OpenTelemetry distributed tracing with custom spans
Structured JSON logging (Pino)
Grafana dashboard panel definitions
AlertManager rules (5 alerts)
Error Handling Issue Cause Solution Metrics not appearing Registry not exported Check /metrics endpoint High cardinality Too many label values Limit labels to known set Alert storms Thresholds too sensitive Add for: duration, tune values Missing traces OTEL exporter not configured Set OTEL_EXPORTER_OTLP_ENDPOINT
Resources 