Agent skill

Langfuse Deploy Integration

by jeremylongshore in jeremylongshore/tons-of-skills-marketplace

Deploy Langfuse with your application across different platforms.

MITAuto-check passedDevOps & Cloud

Install Langfuse Deploy Integration

skills CLI
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill langfuse-deploy-integration -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install jeremylongshore/tons-of-skills-marketplace langfuse-deploy-integration --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/saas-packs/langfuse-pack/skills/langfuse-deploy-integration .claude/skills/langfuse-deploy-integration && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
langfuse-deploy-integration
GitHub stars
2.8k
Token cost
~1.9k tokens
SKILL.md length
292 words
Files
1
Skills in repo
3,342
Repo updated
First seen
Licence
MIT

At a glance

Deploy Langfuse with your application across different platforms.

  • Works in 5 steps: Vercel / Next.js Deployment → AWS Lambda / Serverless → Self-Hosted Langfuse Server (Docker) → …
  • Deploying Langfuse to Vercel
  • SKILL.md covers Overview, Prerequisites, Instructions and Platform-Specific Considerations, plus 4 more sections
  • Calls openssl, vercel and gcloud; reaches cloud.langfuse.com; needs DB_PASSWORD and NEXTAUTH_SECRET

What it does

Langfuse Deploy Integration is an agent skill from jeremylongshore/tons-of-skills-marketplace. Deploy Langfuse with your application across different platforms. Use when deploying Langfuse to Vercel, AWS, GCP, or Docker, or integrating Langfuse into your deployment pipeline. Trigger with phrases like "deploy langfuse", "langfuse Vercel", "langfuse AWS", "langfuse Docker", "langfuse production deploy".

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Designed for Claude Code

It sits in DevOps & Cloud, covering LLM observability. It works with Langfuse, Docker, Vercel and Amazon Web Services. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.

When your agent uses it

  • Deploying Langfuse to Vercel
  • Integrating Langfuse into your deployment pipeline
  • With phrases like deploy langfuse
  • Langfuse Vercel

Example prompts

  • “deploy langfuse”
  • “langfuse Vercel”
  • “langfuse AWS”
  • “/langfuse-deploy-integration”

Requirements

  • Docker
  • A credential in LANGFUSE_PUBLIC_KEY
  • A credential in LANGFUSE_SECRET_KEY
  • Compatibility (from SKILL.md): Designed for Claude Code
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(docker:*), Bash(vercel:*), Bash(gcloud:*)

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Vercel / Next.js Deployment
  2. AWS Lambda / Serverless
  3. Self-Hosted Langfuse Server (Docker)
  4. Google Cloud Run
  5. Health Check Endpoint

What it can do on your machine

Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(docker:*)
    • Bash(vercel:*)
    • Bash(gcloud:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • openssl
    • vercel
    • gcloud
    • docker
    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • cloud.langfuse.com

    Also links to:

    • langfuse.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • DB_PASSWORD
    • NEXTAUTH_SECRET
    • ENCRYPTION_KEY
    • LANGFUSE_PUBLIC_KEY
    • LANGFUSE_SECRET_KEY
    • POSTGRES_PASSWORD

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Designed for Claude Code

    From compatibility in the SKILL.md frontmatter.

Context cost

Langfuse Deploy Integration loads about 1.9k tokens when it runs. Until then it costs about 84 tokens; SKILL.md has 292 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~84
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 292 words, ~1,878 tokens.

Download SKILL.mdSave it as .claude/skills/langfuse-deploy-integration/SKILL.md (or your agent's skills folder).
name
langfuse-deploy-integration
description
Deploy Langfuse with your application across different platforms. Use when deploying Langfuse to Vercel, AWS, GCP, or Docker, or integrating Langfuse into your deployment pipeline. Trigger with phrases like "deploy langfuse", "langfuse Vercel", "langfuse AWS", "langfuse Docker", "langfuse production deploy".
allowed-tools
Read, Write, Edit, Bash(docker:*), Bash(vercel:*), Bash(gcloud:*)
compatibility
Designed for Claude Code
version
1.17.0
license
MIT
author
Jeremy Longshore <jeremy@intentsolutions.io>
tags
saas, langfuse, deployment, docker

Langfuse Deploy Integration

Overview

Deploy Langfuse LLM observability alongside your application. Covers integrating the SDK for serverless (Vercel/Lambda), Docker, Cloud Run, and self-hosting the Langfuse server itself.

Prerequisites

  • Langfuse API keys (cloud or self-hosted)
  • Application using Langfuse SDK
  • Target platform CLI installed

Instructions

Step 1: Vercel / Next.js Deployment
bash
set -euo pipefail
# Add secrets to Vercel
vercel env add LANGFUSE_PUBLIC_KEY production
vercel env add LANGFUSE_SECRET_KEY production
vercel env add LANGFUSE_BASE_URL production
typescript
// app/api/chat/route.ts (Next.js App Router)
import { NextRequest, NextResponse } from "next/server";
import { LangfuseClient } from "@langfuse/client";
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";
import OpenAI from "openai";

const langfuse = new LangfuseClient();
const openai = new OpenAI();

export async function POST(req: NextRequest) {
  const { messages } = await req.json();

  const response = await startActiveObservation(
    { name: "chat-api", asType: "generation" },
    async () => {
      updateActiveObservation({
        model: "gpt-4o",
        input: messages,
        metadata: { endpoint: "/api/chat" },
      });

      const result = await openai.chat.completions.create({
        model: "gpt-4o",
        messages,
      });

      updateActiveObservation({
        output: result.choices[0].message,
        usage: {
          promptTokens: result.usage?.prompt_tokens,
          completionTokens: result.usage?.completion_tokens,
        },
      });

      return result.choices[0].message;
    }
  );

  return NextResponse.json(response);
}

Serverless note: Langfuse SDK v4+ uses OTel which handles flushing asynchronously. For v3, always call await langfuse.flushAsync() before the response returns -- serverless functions may freeze after response.

Step 2: AWS Lambda / Serverless
typescript
// handler.ts
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";

// Initialize OUTSIDE handler for connection reuse
const sdk = new NodeSDK({
  spanProcessors: [
    new LangfuseSpanProcessor({
      exportIntervalMillis: 1000, // Flush fast in serverless
    }),
  ],
});
sdk.start();

export const handler = async (event: any) => {
  return await startActiveObservation("lambda-handler", async () => {
    updateActiveObservation({ input: event });

    const result = await processRequest(event);

    updateActiveObservation({ output: result });

    // Force flush before Lambda freezes
    await sdk.shutdown();

    return { statusCode: 200, body: JSON.stringify(result) };
  });
};
Step 3: Self-Hosted Langfuse Server (Docker)
yaml
# docker-compose.yml
services:
  langfuse:
    image: langfuse/langfuse:latest
    ports:
      - "3000:3000"
    environment:
      - DATABASE_URL=postgresql://langfuse:${DB_PASSWORD}@postgres:5432/langfuse
      - NEXTAUTH_SECRET=${NEXTAUTH_SECRET}
      - NEXTAUTH_URL=https://langfuse.your-domain.com
      - SALT=${SALT}
      - ENCRYPTION_KEY=${ENCRYPTION_KEY}
      - AUTH_DISABLE_SIGNUP=true
      - LANGFUSE_DEFAULT_PROJECT_ROLE=VIEWER
    depends_on:
      postgres:
        condition: service_healthy

  postgres:
    image: postgres:16-alpine
    environment:
      POSTGRES_USER: langfuse
      POSTGRES_PASSWORD: ${DB_PASSWORD}
      POSTGRES_DB: langfuse
    volumes:
      - pgdata:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U langfuse"]
      interval: 5s
      timeout: 5s
      retries: 5

volumes:
  pgdata:
bash
set -euo pipefail
# Generate secrets
export DB_PASSWORD=$(openssl rand -hex 16)
export NEXTAUTH_SECRET=$(openssl rand -hex 32)
export SALT=$(openssl rand -hex 16)
export ENCRYPTION_KEY=$(openssl rand -hex 32)

# Start
docker compose up -d

# Wait and verify
sleep 10
curl -s http://localhost:3000/api/public/health
Step 4: Google Cloud Run
bash
set -euo pipefail
# Build and push
gcloud builds submit --tag gcr.io/$PROJECT_ID/my-llm-app

# Deploy with Langfuse env vars from Secret Manager
gcloud run deploy my-llm-app \
  --image gcr.io/$PROJECT_ID/my-llm-app \
  --set-secrets="LANGFUSE_PUBLIC_KEY=langfuse-public-key:latest" \
  --set-secrets="LANGFUSE_SECRET_KEY=langfuse-secret-key:latest" \
  --set-env-vars="LANGFUSE_BASE_URL=https://cloud.langfuse.com"
Step 5: Health Check Endpoint
typescript
// app/api/health/route.ts
import { LangfuseClient } from "@langfuse/client";

const langfuse = new LangfuseClient();

export async function GET() {
  try {
    // Quick connectivity check
    await langfuse.prompt.get("__health__").catch(() => {});
    return Response.json({ status: "healthy", tracing: "enabled" });
  } catch {
    return Response.json(
      { status: "degraded", tracing: "disabled" },
      { status: 503 }
    );
  }
}

Platform-Specific Considerations

PlatformKey ConcernSolution
Vercel/EdgeFunction timeoutFlush before response; use v4+
AWS LambdaCold startsInitialize SDK outside handler
Cloud RunConcurrencySingleton client, shared OTel SDK
DockerSelf-hosted networkingEnsure app can reach Langfuse host
KubernetesPod lifecycleShutdown hook on SIGTERM

Error Handling

IssueCauseSolution
Traces missing in serverlessNot flushed before freezesdk.shutdown() before response
Auth error after deployWrong env for environmentVerify secrets match deployment
Self-hosted 502DB not readyAdd healthcheck + depends_on
High latency in prodSmall batch sizeIncrease flushAt / maxExportBatchSize

Output

Produce a deployment receipt with the target environment, deployed revision, secret reference names (never values), health endpoint result, and one verified Langfuse trace. If tracing is degraded, report the application status separately from the telemetry status so an observability outage is not misrepresented as an application outage.

Examples

Deploy a staging revision with secret-manager references, call the health endpoint, and submit one synthetic request; verify its trace appears in the staging Langfuse project. For self-hosting, wait for the database health check before accepting application traffic and retain the compose revision used for the deployment.

Resources

© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/saas-packs/langfuse-pack/skills/langfuse-deploy-integration of jeremylongshore/tons-of-skills-marketplace.

Open the folder on GitHubat commit cfae287

Compare with similar skills

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GCP To AWSaws/agent-toolkit-for-aws2.8k—~15kAutomated safety check: PassApache-2.0
Senior DevOps Toolkitmaslennikov-ig/claude-code-orchestrator-kit2606 repos~1.1kAutomated safety check: NotesCustom licence
Devopsnicepkg/auto-company1952 repos~814Automated safety check: PassMIT
FrugalyuanboP/frugal198—~2.1kAutomated safety check: PassMIT

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Questions about Langfuse Deploy Integration

What does Langfuse Deploy Integration do?

Deploy Langfuse with your application across different platforms. Langfuse Deploy Integration is an agent skill from jeremylongshore/tons-of-skills-marketplace. Deploy Langfuse with your application across different platforms.

When should I use Langfuse Deploy Integration?

Langfuse Deploy Integration fits situations like: deploying Langfuse to Vercel; integrating Langfuse into your deployment pipeline; with phrases like deploy langfuse; langfuse Vercel.

How do I install Langfuse Deploy Integration in Claude Code?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill langfuse-deploy-integration -a claude-code`. Or copy the skill folder (plugins/saas-packs/langfuse-pack/skills/langfuse-deploy-integration in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/langfuse-deploy-integration in your project. Claude Code loads it when a task matches its description.

How do I install Langfuse Deploy Integration in Codex?

Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill langfuse-deploy-integration -a codex`. Or copy the skill folder (plugins/saas-packs/langfuse-pack/skills/langfuse-deploy-integration in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/langfuse-deploy-integration in your project. Codex loads it when a task matches its description.

Can I use Langfuse Deploy Integration in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill langfuse-deploy-integration -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/langfuse-deploy-integration, .gemini/skills/langfuse-deploy-integration, .github/skills/langfuse-deploy-integration and .opencode/skills/langfuse-deploy-integration in your project.

What does Langfuse Deploy Integration need to run?

Going by SKILL.md and its folder, Langfuse Deploy Integration needs the command-line tools its instructions call (openssl, vercel, gcloud, docker and curl) and credentials named DB_PASSWORD, NEXTAUTH_SECRET, ENCRYPTION_KEY and LANGFUSE_PUBLIC_KEY. Our summary lists: Docker; A credential in LANGFUSE_PUBLIC_KEY; A credential in LANGFUSE_SECRET_KEY. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(docker:*), Bash(vercel:*), Bash(gcloud:*). Compatibility (from SKILL.md): Designed for Claude Code.

Does Langfuse Deploy Integration access the network?

SKILL.md names 2 domains. In commands or code: cloud.langfuse.com; the agent is likely to contact it when it follows the instructions. As links in the text: langfuse.com. This is read from the text; nothing was executed.

Is Langfuse Deploy Integration safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Langfuse Deploy Integration use?

Langfuse Deploy Integration is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Langfuse Deploy Integration use?

About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Langfuse Deploy Integration?

Skills that share tags, products or a category with Langfuse Deploy Integration: Ak Dev New Tracing Provider (yaalalabs/agent-kernel, 192 stars), GCP To AWS (aws/agent-toolkit-for-aws, 2.8k stars), Senior DevOps Toolkit (maslennikov-ig/claude-code-orchestrator-kit, 260 stars) and Devops (nicepkg/auto-company, 195 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Langfuse Deploy Integration?

jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.

Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.