Langfuse Observability

Optional. This is one observability backend within the ai-infra platform plugin. OMA's core (ontology + harness DSL) needs no observability backend — the default profile mode is none. Use this skill only if you have opted into Langfuse (observability.mode: langfuse-self-hosted); opentelemetry-only or any OTLP-compatible store are equally valid alternatives.

When to Use

• Agent / LLM 호출에 대한 end-to-end trace, token cost, quality 점수를 한곳에서 보고 싶을 때
• LangSmith SaaS 대신 self-hosted 관측성이 필요한 규제 환경
• 프롬프트 버저닝, 평가(Ragas), 데이터셋 기반 회귀 테스트가 필요할 때
• vLLM·Bifrost·LangChain·LangGraph 모두를 하나의 trace tree 로 연결해야 할 때

When NOT to Use

• Cloud SaaS 로 충분 → Langfuse Cloud 사용
• 메트릭만 필요 (trace 불필요) → Prometheus + Grafana 단독
• OTel 표준과 호환되지 않는 legacy agent → OTel 마이그레이션 선행 필요

Preconditions

• EKS 1.32+, IRSA 구성, S3 버킷 생성
• PostgreSQL, ClickHouse, Redis 를 in-cluster 또는 managed 로 배포할지 결정 (RDS, Aurora, ElastiCache 가능)
• OTel Collector 를 observability namespace 에 배포 예정

Procedure

Step 1. S3 버킷 + IRSA 생성

AmazonS3FullAccess 같은 account-wide s3:* managed policy 는 절대 attach 하지 않습니다. Langfuse 버킷 한 개로 scope 한 최소권한 customer-managed policy 만 사용합니다.

export BUCKET=my-langfuse-blobs
export REGION=ap-northeast-2
export ACCOUNT_ID=$(aws sts get-caller-identity --query Account --output text)

aws s3api create-bucket \
  --bucket "$BUCKET" \
  --region "$REGION" \
  --create-bucket-configuration LocationConstraint="$REGION"

# Langfuse 버킷 하나로 scope 된 policy 작성 (bucket ARN + object ARN).
cat > /tmp/langfuse-s3-policy.json <<EOF
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Sid": "LangfuseBucketListAndMeta",
      "Effect": "Allow",
      "Action": [
        "s3:ListBucket",
        "s3:GetBucketLocation"
      ],
      "Resource": "arn:aws:s3:::${BUCKET}"
    },
    {
      "Sid": "LangfuseBucketObjectRW",
      "Effect": "Allow",
      "Action": [
        "s3:GetObject",
        "s3:PutObject",
        "s3:DeleteObject",
        "s3:AbortMultipartUpload"
      ],
      "Resource": "arn:aws:s3:::${BUCKET}/*"
    }
  ]
}
EOF

aws iam create-policy \
  --policy-name LangfuseBlobStoreRW \
  --policy-document file:///tmp/langfuse-s3-policy.json

eksctl create iamserviceaccount \
  --cluster agentic-prod \
  --namespace langfuse \
  --name langfuse \
  --attach-policy-arn "arn:aws:iam::${ACCOUNT_ID}:policy/LangfuseBlobStoreRW" \
  --approve

금지: arn:aws:iam::aws:policy/AmazonS3FullAccess 는 s3:* 를 account-wide 로 허용하므로 다른 버킷의 blob 도 열람·삭제 가능합니다. IRSA 가 탈취되면 데이터 전체가 노출되는 벡터이므로 사용하지 않습니다.

Step 2. Langfuse v3 Helm 배포

helm repo add langfuse https://langfuse.github.io/langfuse-k8s
helm upgrade --install langfuse langfuse/langfuse \
  --namespace langfuse --create-namespace \
  --version 1.3.0 \
  -f langfuse-values.yaml

langfuse-values.yaml:

langfuse:
  image:
    tag: "3.162.0"
  web:
    replicas: 2
  worker:
    replicas: 2
postgresql:
  deploy: true
  auth:
    database: langfuse
clickhouse:
  deploy: true
  shards: 1
  replicas: 2
redis:
  deploy: true
  architecture: replication
s3:
  bucket: my-langfuse-blobs
  region: ap-northeast-2
  useIamRole: true

Step 3. OTel Collector 파이프라인

receivers:
  otlp:
    protocols:
      grpc: { endpoint: 0.0.0.0:4317 }
      http: { endpoint: 0.0.0.0:4318 }
processors:
  batch: {}
  attributes:
    actions:
      - key: service.namespace
        value: agentic
        action: insert
exporters:
  otlphttp/langfuse:
    endpoint: http://langfuse-web.langfuse.svc:3000/api/public/otel
    headers:
      Authorization: Basic ${env:LANGFUSE_API_BASIC}
service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch, attributes]
      exporters: [otlphttp/langfuse]

Step 4. vLLM / Bifrost 연결

kubectl -n inference set env deployment/vllm-llama3 \
  OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector.observability.svc:4317 \
  OTEL_SERVICE_NAME=vllm-llama3

kubectl -n inference set env deployment/bifrost \
  OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector.observability.svc:4317 \
  OTEL_SERVICE_NAME=bifrost

Step 5. 검증

kubectl -n langfuse get pods
kubectl -n langfuse port-forward svc/langfuse-web 3000:3000
# 브라우저에서 http://localhost:3000 → Organization/Project 생성
# Settings → API Keys → OTel Basic 토큰 발급 → Collector 에 주입

• 샘플 요청 후 Langfuse Traces 탭에서 span tree 확인

Step 6. 알림·대시보드

• Prometheus alert: langfuse_worker_queue_depth > 10000
• Langfuse Dashboards: token/day, error rate, p95 latency, cost by model
• Ragas 평가 결과를 Langfuse Scores 로 업로드

Good Examples

• 프로덕션: PostgreSQL RDS Multi-AZ + ClickHouse 2-replica + Redis ElastiCache + S3
• PoC: in-cluster PG + ClickHouse single + Redis single (replicas=1)
• OTel attribute gen_ai.usage.input_tokens, gen_ai.usage.output_tokens 주입으로 정확 cost

Bad Examples (금지)

• ClickHouse 비활성 + 1M events/day 초과 → 대시보드 로딩 타임아웃
• OTel Collector 없이 SDK → Langfuse 직통 (retries·sampling 미지원)
• disable-model-invocation 모드에서 프롬프트 sanitize 누락 → PII 유출 위험
• Langfuse v2.x 구버전 → Prompt Experiments, ClickHouse 미지원

References

• Agent 모니터링 개요 (community resource)
• LLMOps Observability 도구 비교 (community resource)
• 모니터링 스택 구성 가이드 (community resource)
• Langfuse 공식 문서
• Langfuse Self-Host 가이드
• OpenTelemetry GenAI Semantic Conventions
• 플러그인 내부: ../../references/langfuse-self-hosted-setup.md