Skip to main content
langfuse-ci-integration 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".
跳到安装 Skills Marketplace 发现并探索由社区构建的 Agent Skills
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill langfuse-ci-integration命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
下载 Zip 下载中... jeremylongshore
jeremylongshore/claude-code-plugins-plus-skills
打开 GitHub 仓库 name langfuse-ci-integration description 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".
allowed-tools Read, Write, Edit, Bash(gh:*) version 1.12.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","langfuse","testing","ci-cd"] compatibility Designed for Claude Code, also compatible with Codex and OpenClaw
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
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
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
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 ,
}),
],
});
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
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: ${{ secrets.LANGFUSE_SECRET_KEY }}
run: node scripts/deploy-prompts.mjs
import { LangfuseClient } from "@langfuse/client" ;
import { readdirSync, readFileSync } from "fs" ;
import { join } from "path" ;
const langfuse = new LangfuseClient ();
const promptDir = join (process.cwd (), "src/prompts" );
for (const file of readdirSync (promptDir).filter ((f ) => f.endsWith (".json" ))) {
const config = JSON .parse (readFileSync (join (promptDir, file), "utf-8" ));
await langfuse.api .prompts .create ({
name : config.name ,
prompt : config.template ,
type : config.type || "text" ,
config : config.config || {},
labels : ["production" , `deploy-${new Date ().toISOString().split("T" )[0 ]} ` ],
});
console .log (`Deployed: ${config.name} ` );
}
Step 5: Score Regression Monitoring
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 