Skip to main content Configure Langfuse CI/CD integration with GitHub Actions and automated testing.
Use when setting up automated testing, configuring CI pipelines,
or integrating Langfuse tests into your build process.
Trigger with phrases like "langfuse CI", "langfuse GitHub Actions",
"langfuse automated tests", "CI langfuse", "langfuse pipeline".
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill langfuse-ci-integration ai automation claude-code devops mcp ai-agents
Langfuse CI Integration
Overview
Integrate Langfuse into CI/CD pipelines: trace validation tests, prompt regression testing, experiment-driven quality gates, automated prompt deployment from version control, and score monitoring.
Prerequisites
Langfuse API keys stored as GitHub secrets (LANGFUSE_PUBLIC_KEY, LANGFUSE_SECRET_KEY)
Test framework (Vitest or Jest)
OpenAI API key for LLM tests
Instructions
Step 1: GitHub Actions Workflow for AI Quality Tests
# .github/workflows/langfuse-tests.yml
name: AI Quality Tests
on:
pull_request:
paths: ["src/ai/**", "src/prompts/**", "tests/ai/**"]
jobs:
ai-quality:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with: { node-version: "20", cache: "npm" }
- run: npm ci
- name: Run AI quality tests with tracing
env:
LANGFUSE_PUBLIC_KEY: ${{ secrets.LANGFUSE_PUBLIC_KEY }}
LANGFUSE_SECRET_KEY: ${{ secrets.LANGFUSE_SECRET_KEY }}
LANGFUSE_BASE_URL: ${{ vars.LANGFUSE_BASE_URL || 'https://cloud.langfuse.com' }}
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
run: npx vitest run tests/ai/ --reporter=verbose
- name: Langfuse connectivity check
env:
LANGFUSE_PUBLIC_KEY: ${{ secrets.LANGFUSE_PUBLIC_KEY }}
LANGFUSE_SECRET_KEY: ${{ secrets.LANGFUSE_SECRET_KEY }}
run: |
node -e "
const { LangfuseClient } = require('@langfuse/client');
const lf = new LangfuseClient();
lf.prompt.get('__ci-health__').catch(() => {});
console.log('Langfuse SDK initialized OK');
"
Step 2: Prompt Regression Tests // tests/ai/prompt-quality.test.ts
import { describe, it, expect, afterAll } from "vitest";
import { LangfuseClient } from "@langfuse/client";
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";
import OpenAI from "openai";
const langfuse = new LangfuseClient();
const openai = new OpenAI();
describe("Prompt Quality Regression", () => {
it("summarization prompt produces valid output", async () => {
const prompt = await langfuse.prompt.get("summarize-article", { type: "text" });
const compiled = prompt.compile({ maxLength: "100 words" });
const result = await startActiveObservation(
{ name: "ci-test-summarize", asType: "generation" },
async () => {
updateActiveObservation({ model: "gpt-4o-mini", input: compiled });
const response = await openai.chat.completions.create({
model: "gpt-4o-mini",
messages: [{ role: "user", content: compiled }],
temperature: 0,
});
const output = response.choices[0].message.content || "";
updateActiveObservation({
output,
usage: {
promptTokens: response.usage?.prompt_tokens,
completionTokens: response.usage?.completion_tokens,
},
});
return output;
}
);
expect(result.length).toBeGreaterThan(20);
expect(result.length).toBeLessThan(600);
});
it("classification prompt returns valid intent", async () => {
const prompt = await langfuse.prompt.get("classify-intent", { type: "text" });
const compiled = prompt.compile({ userMessage: "I want to cancel my subscription" });
const response = await openai.chat.completions.create({
model: "gpt-4o-mini",
messages: [{ role: "user", content: compiled }],
temperature: 0,
});
const intent = response.choices[0].message.content?.trim().toLowerCase() || "";
const validIntents = ["billing", "cancellation", "support", "feedback"];
expect(validIntents).toContain(intent);
});
});
Step 3: Experiment-Driven Quality Gates // tests/ai/experiment-gate.test.ts
import { describe, it, expect } from "vitest";
import { LangfuseClient } from "@langfuse/client";
import OpenAI from "openai";
const langfuse = new LangfuseClient();
const openai = new OpenAI();
describe("Quality Gate: Intent Classification", () => {
it("scores above 80% accuracy on test dataset", async () => {
async function classifyIntent(input: { query: string }) {
const response = await openai.chat.completions.create({
model: "gpt-4o-mini",
messages: [
{ role: "system", content: "Classify intent. Return one word." },
{ role: "user", content: input.query },
],
temperature: 0,
});
return response.choices[0].message.content?.trim() || "";
}
const result = await langfuse.runExperiment({
datasetName: "intent-classification-test",
runName: `ci-${process.env.GITHUB_SHA?.slice(0, 7) || "local"}`,
task: classifyIntent,
evaluators: [
({ output, expectedOutput }) => ({
name: "exact-match",
value: output.toLowerCase() === expectedOutput.intent.toLowerCase() ? 1 : 0,
dataType: "BOOLEAN" as const,
}),
],
});
// Calculate accuracy
const scores = result.runs.flatMap((r) => r.scores || []);
const accuracy = scores.filter((s) => s.value === 1).length / scores.length;
console.log(`Accuracy: ${(accuracy * 100).toFixed(1)}%`);
expect(accuracy).toBeGreaterThanOrEqual(0.8);
});
});
Step 4: Automated Prompt Deployment # .github/workflows/deploy-prompts.yml
name: Deploy Prompts to Langfuse
on:
push:
branches: [main]
paths: ["src/prompts/**"]
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: actions/setup-node@v4
with: { node-version: "20", cache: "npm" }
- run: npm ci
- name: Deploy prompts
env:
LANGFUSE_PUBLIC_KEY: ${{ secrets.LANGFUSE_PUBLIC_KEY }}
LANGFUSE_SECRET_KEY: ${{ secrets.LANGFUSE_SECRET_KEY }}
run: node scripts/deploy-prompts.mjs
// scripts/deploy-prompts.mjs
import { LangfuseClient } from "@langfuse/client";
import { readdirSync, readFileSync } from "fs";
import { join } from "path";
const langfuse = new LangfuseClient();
const promptDir = join(process.cwd(), "src/prompts");
for (const file of readdirSync(promptDir).filter((f) => f.endsWith(".json"))) {
const config = JSON.parse(readFileSync(join(promptDir, file), "utf-8"));
await langfuse.api.prompts.create({
name: config.name,
prompt: config.template,
type: config.type || "text",
config: config.config || {},
labels: ["production", `deploy-${new Date().toISOString().split("T")[0]}`],
});
console.log(`Deployed: ${config.name}`);
}
Step 5: Score Regression Monitoring // scripts/check-quality-regression.ts
import { LangfuseClient } from "@langfuse/client";
const langfuse = new LangfuseClient();
async function checkRegression() {
const scores = await langfuse.api.scores.list({
name: "quality",
limit: 100,
});
const values = scores.data.map((s) => s.value).filter((v): v is number => v !== null);
const avg = values.reduce((a, b) => a + b, 0) / values.length;
console.log(`Average quality score: ${avg.toFixed(3)} (n=${values.length})`);
if (avg < 0.7) {
console.error("QUALITY REGRESSION: Score below 0.7 threshold");
process.exit(1);
}
}
checkRegression();
CI Best Practices Practice Why Use temperature: 0 in CI tests Deterministic outputs, fewer false failures Separate CI API keys Isolate test traces from production Run experiments on dataset changes Catch regressions before deploy Assert on ranges, not exact strings LLM output varies even at temp 0 Flush/shutdown in afterAll Ensure all traces reach Langfuse
Error Handling Issue Cause Solution Traces not in dashboard No flush in CI Add sdk.shutdown() or afterAll flush Flaky quality tests Non-deterministic LLM Use temperature: 0, assert on ranges Prompt not found Not yet deployed Deploy prompts before running tests Missing secrets in CI Not configured Add to GitHub Settings > Secrets > Actions
Resources Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
