From langfuse-pack
Configures Langfuse for dev, staging, and prod environments with isolated API keys, custom sampling/debug settings, OTel integration, and secret/CI/CD management.
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/langfuse-pack:langfuse-multi-env-setupThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Configure Langfuse across dev/staging/production with isolated API keys, environment-specific SDK settings, secret management, and CI/CD integration to prevent cross-environment data leakage.
Configure Langfuse across dev/staging/production with isolated API keys, environment-specific SDK settings, secret management, and CI/CD integration to prevent cross-environment data leakage.
| Environment | API Key Source | Langfuse Project | Settings |
|---|---|---|---|
| Development | .env.local | Dev project | Debug on, flush immediately, 100% sampling |
| Staging | CI/CD secrets | Staging project | Prod-like settings, 50% sampling |
| Production | Secret manager | Prod project | Optimized batching, 10% sampling |
// src/config/langfuse.ts
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";
import { LangfuseClient } from "@langfuse/client";
type Env = "development" | "staging" | "production";
interface LangfuseEnvConfig {
exportIntervalMillis: number;
maxExportBatchSize: number;
debug: boolean;
sampleRate: number;
}
const ENV_CONFIGS: Record<Env, LangfuseEnvConfig> = {
development: {
exportIntervalMillis: 1000,
maxExportBatchSize: 1,
debug: true,
sampleRate: 1.0,
},
staging: {
exportIntervalMillis: 5000,
maxExportBatchSize: 25,
debug: false,
sampleRate: 0.5,
},
production: {
exportIntervalMillis: 10000,
maxExportBatchSize: 50,
debug: false,
sampleRate: 0.1,
},
};
function detectEnvironment(): Env {
const env = process.env.NODE_ENV || "development";
if (env === "production") return "production";
if (env === "staging" || process.env.VERCEL_ENV === "preview") return "staging";
return "development";
}
export function initLangfuse() {
const env = detectEnvironment();
const config = ENV_CONFIGS[env];
// Validate credentials
const required = ["LANGFUSE_PUBLIC_KEY", "LANGFUSE_SECRET_KEY"];
for (const key of required) {
if (!process.env[key]) {
throw new Error(`${key} not set for environment: ${env}`);
}
}
// Initialize OTel with env-specific settings
const processor = new LangfuseSpanProcessor({
exportIntervalMillis: config.exportIntervalMillis,
maxExportBatchSize: config.maxExportBatchSize,
});
const sdk = new NodeSDK({ spanProcessors: [processor] });
sdk.start();
// Client for prompts, datasets, scores
const client = new LangfuseClient();
console.log(`Langfuse initialized [${env}] (sample: ${config.sampleRate * 100}%)`);
return { sdk, client, env, config };
}
# .env.local (development -- git-ignored)
LANGFUSE_PUBLIC_KEY=pk-lf-dev-...
LANGFUSE_SECRET_KEY=sk-lf-dev-...
LANGFUSE_BASE_URL=https://cloud.langfuse.com
NODE_ENV=development
# .env.staging (used by CI/CD)
LANGFUSE_BASE_URL=https://cloud.langfuse.com
NODE_ENV=staging
# Keys injected via CI secrets
# .env.production (used by CI/CD)
LANGFUSE_BASE_URL=https://cloud.langfuse.com
NODE_ENV=production
# Keys injected via secret manager
# .gitignore
.env
.env.local
.env.*.local
set -euo pipefail
# GitHub Actions: Add per-environment secrets
# Settings > Environments > staging > Secrets
# Settings > Environments > production > Secrets
# AWS Secrets Manager
aws secretsmanager create-secret \
--name "langfuse/production/public-key" \
--secret-string "pk-lf-prod-..."
aws secretsmanager create-secret \
--name "langfuse/production/secret-key" \
--secret-string "sk-lf-prod-..."
# GCP Secret Manager
echo -n "pk-lf-prod-..." | gcloud secrets create langfuse-public-key-prod --data-file=-
echo -n "sk-lf-prod-..." | gcloud secrets create langfuse-secret-key-prod --data-file=-
# .github/workflows/deploy.yml
name: Deploy
on:
push:
branches: [main, staging]
jobs:
deploy-staging:
if: github.ref == 'refs/heads/staging'
runs-on: ubuntu-latest
environment: staging
env:
LANGFUSE_PUBLIC_KEY: ${{ secrets.LANGFUSE_PUBLIC_KEY }}
LANGFUSE_SECRET_KEY: ${{ secrets.LANGFUSE_SECRET_KEY }}
LANGFUSE_BASE_URL: https://cloud.langfuse.com
NODE_ENV: staging
steps:
- uses: actions/checkout@v4
- run: npm ci && npm run build && npm run deploy:staging
deploy-production:
if: github.ref == 'refs/heads/main'
runs-on: ubuntu-latest
environment: production
env:
LANGFUSE_PUBLIC_KEY: ${{ secrets.LANGFUSE_PUBLIC_KEY }}
LANGFUSE_SECRET_KEY: ${{ secrets.LANGFUSE_SECRET_KEY }}
LANGFUSE_BASE_URL: https://cloud.langfuse.com
NODE_ENV: production
steps:
- uses: actions/checkout@v4
- run: npm ci && npm run build && npm run deploy:production
import { z } from "zod";
const langfuseConfigSchema = z.object({
LANGFUSE_PUBLIC_KEY: z.string().startsWith("pk-lf-", "Must start with pk-lf-"),
LANGFUSE_SECRET_KEY: z.string().startsWith("sk-lf-", "Must start with sk-lf-"),
LANGFUSE_BASE_URL: z.string().url().optional(),
NODE_ENV: z.enum(["development", "staging", "production"]).default("development"),
});
// Validate at startup -- fail fast on misconfiguration
const config = langfuseConfigSchema.parse(process.env);
console.log(`Langfuse config validated for ${config.NODE_ENV}`);
| Risk | Mitigation |
|---|---|
| Dev traces in prod project | Separate API keys per environment |
| Prod keys in dev env | Validate key prefix at startup |
| Leaked keys in git | .env in .gitignore, secret scanning in CI |
| Wrong env detected | Explicit NODE_ENV in deployment config |
| Config drift | Zod schema validation at startup |
| Issue | Cause | Solution |
|---|---|---|
| Wrong environment | Missing NODE_ENV | Set explicitly in deployment config |
| Secret not found | Wrong secret path | Verify secret manager paths match |
| Cross-env data leak | Shared API key | Use separate keys per environment |
| Startup crash | Missing config | Add Zod validation with clear error messages |
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin langfuse-packProvides Langfuse production readiness checklist with SDK configs, error handling, graceful shutdown, and verification steps for Node.js apps.
Provides Langfuse expertise for LLM observability: tracing, prompt management, evaluations, datasets. Integrates with LangChain, LlamaIndex, OpenAI for production monitoring and debugging.
Queries Langfuse API resources (traces, prompts, datasets, scores, sessions) via CLI and fetches current documentation for instrumentation, prompt migration, and error analysis.