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langfuse-core-workflow-a Execute Langfuse primary workflow: Tracing LLM calls and spans.
Use when implementing LLM tracing, building traced AI features,
or adding observability to existing LLM applications.
Trigger with phrases like "langfuse tracing", "trace LLM calls",
"add langfuse to openai", "langfuse spans", "track llm requests".
インストールへ移動 Skills Marketplace コミュニティが作成したAIスキルを発見・探索
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill langfuse-core-workflow-aコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
Zipをダウンロード ダウンロード中... このリポジトリの他の Skills Implement user sign-up and sign-in flows with Clerk.
Use when building authentication UI, customizing sign-in experience,
or implementing OAuth social login.
Trigger with phrases like "clerk sign-in", "clerk sign-up",
"clerk login flow", "clerk OAuth", "clerk social login".
Implement session management and middleware with Clerk.
Use when managing user sessions, configuring route protection,
or implementing token refresh and custom JWT templates.
Trigger with phrases like "clerk session", "clerk middleware",
"clerk route protection", "clerk token", "clerk JWT".
Configure enterprise SSO, role-based access control, and organization management.
Use when implementing SSO integration, configuring role-based permissions,
or setting up organization-level controls.
Trigger with phrases like "clerk SSO", "clerk RBAC",
"clerk enterprise", "clerk roles", "clerk permissions", "clerk organizations".
jeremylongshore
jeremylongshore/claude-code-plugins-plus-skills
GitHub リポジトリを開く name langfuse-core-workflow-a description Execute Langfuse primary workflow: Tracing LLM calls and spans.
Use when implementing LLM tracing, building traced AI features,
or adding observability to existing LLM applications.
Trigger with phrases like "langfuse tracing", "trace LLM calls",
"add langfuse to openai", "langfuse spans", "track llm requests".
allowed-tools Read, Write, Edit, Bash(npm:*), Grep version 1.12.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","langfuse","observability","llm","workflow"] compatibility Designed for Claude Code, also compatible with Codex and OpenClaw
Langfuse Core Workflow A: Tracing LLM Calls
Overview
End-to-end tracing of LLM calls, chains, and agents. Covers the OpenAI drop-in wrapper, manual tracing with startActiveObservation, RAG pipeline instrumentation, streaming response tracking, and LangChain integration.
Prerequisites
Completed langfuse-install-auth setup
OpenAI SDK installed (npm install openai)
For v4+: @langfuse/openai, @langfuse/tracing, @langfuse/otel, @opentelemetry/sdk-node
Instructions
Step 1: OpenAI Drop-In Wrapper (Zero-Code Tracing)
import OpenAI from "openai" ;
import { observeOpenAI } from "@langfuse/openai" ;
const openai = observeOpenAI (new OpenAI ());
const response = await openai.chat .completions .create ({
model : "gpt-4o" ,
messages : [
{ role : "system" , content : "You are a helpful assistant." },
{ role : "user" , content : "What is Langfuse?" },
],
});
const res = await observeOpenAI (new OpenAI (), {
generationName
"product-description"
generationMetadata
feature
"onboarding"
sessionId
"session-abc"
userId
"user-123"
tags
"production"
"onboarding"
chat
completions
create
model
"gpt-4o-mini"
messages
role
"user"
content
"Describe this product"
Step 2: Manual Tracing -- RAG Pipeline (v4+ SDK) import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing" ;
async function ragPipeline (query : string return await startActiveObservation ("rag-pipeline" , async () => {
updateActiveObservation ({ input : { query }, metadata : { pipeline : "rag-v2" } });
const embedding = await startActiveObservation ("embed-query" , async () => {
updateActiveObservation ({ input : { text : query } });
const vector = await embedText (query);
updateActiveObservation ({
output : { dimensions : vector.length },
metadata : { model : "text-embedding-3-small" },
});
return vector;
});
const documents = await startActiveObservation ("vector-search" , async () => {
updateActiveObservation ({ input : { dimensions : embedding.length } });
const docs = await searchVectorDB (embedding);
updateActiveObservation ({
output : { documentCount : docs.length , topScore : docs[0 ]?.score },
});
return docs;
});
const answer = await startActiveObservation (
{ name : "generate-answer" , asType : "generation" },
async () => {
updateActiveObservation ({
model : "gpt-4o" ,
input : { query, context : documents.map ((d ) => d.content ) },
});
const result = await generateAnswer (query, documents);
updateActiveObservation ({
output : result.content ,
usage : {
promptTokens : result.usage .prompt_tokens ,
completionTokens : result.usage .completion_tokens ,
},
});
return result.content ;
}
);
updateActiveObservation ({ output : { answer } });
return answer;
});
}
Step 3: Manual Tracing -- RAG Pipeline (v3 Legacy) import { Langfuse } from "langfuse" ;
const langfuse = new Langfuse ();
async function ragPipeline (query : string const trace = langfuse.trace ({
name : "rag-pipeline" ,
input : { query },
metadata : { pipeline : "rag-v1" },
});
const embedSpan = trace.span ({ name : "embed-query" , input : { text : query } });
const embedding = await embedText (query);
embedSpan.end ({ output : { dimensions : embedding.length } });
const searchSpan = trace.span ({ name : "vector-search" });
const documents = await searchVectorDB (embedding);
searchSpan.end ({ output : { count : documents.length , topScore : documents[0 ]?.score } });
const generation = trace.generation ({
name : "generate-answer" ,
model : "gpt-4o" ,
modelParameters : { temperature : 0.7 , maxTokens : 500 },
input : { query, context : documents.map ((d ) => d.content ) },
});
const answer = await generateAnswer (query, documents);
generation.end ({
output : answer.content ,
usage : {
promptTokens : answer.usage .prompt_tokens ,
completionTokens : answer.usage .completion_tokens ,
totalTokens : answer.usage .total_tokens ,
},
});
trace.update ({ output : { answer : answer.content } });
await langfuse.flushAsync ();
return answer.content ;
}
Step 4: Streaming Response Tracking import OpenAI from "openai" ;
import { observeOpenAI } from "@langfuse/openai" ;
const openai = observeOpenAI (new OpenAI ());
const stream = await openai.chat .completions .create ({
model : "gpt-4o" ,
messages : [{ role : "user" , content : "Tell me a story" }],
stream : true ,
stream_options : { include_usage : true },
});
let fullContent = "" ;
for await (const chunk of stream) {
const content = chunk.choices [0 ]?.delta ?.content || "" ;
fullContent += content;
process.stdout .write (content);
}
Step 5: Anthropic Claude Tracing (Manual) import Anthropic from "@anthropic-ai/sdk" ;
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing" ;
const anthropic = new Anthropic ();
async function callClaude (prompt : string return await startActiveObservation (
{ name : "claude-call" , asType : "generation" },
async () => {
updateActiveObservation ({
model : "claude-sonnet-4-20250514" ,
input : [{ role : "user" , content : prompt }],
});
const response = await anthropic.messages .create ({
model : "claude-sonnet-4-20250514" ,
max_tokens : 1024 ,
messages : [{ role : "user" , content : prompt }],
});
updateActiveObservation ({
output : response.content [0 ].text ,
usage : {
promptTokens : response.usage .input_tokens ,
completionTokens : response.usage .output_tokens ,
},
});
return response.content [0 ].text ;
}
);
}
Step 6: LangChain Integration (Python) from langfuse.callback import CallbackHandler
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
langfuse_handler = CallbackHandler()
llm = ChatOpenAI(model="gpt-4o" )
prompt = ChatPromptTemplate.from_messages([
("system" , "You are a helpful assistant." ),
("human" , "{input}" ),
])
chain = prompt | llm
result = chain.invoke(
{"input" : "What is Langfuse?" },
config={"callbacks" : [langfuse_handler]},
)
Error Handling Issue Cause Solution Missing generations OpenAI wrapper not applied Use observeOpenAI() from @langfuse/openai Orphaned spans Missing end or callback finish Use startActiveObservation (auto-ends) or .end() in finally No token usage on stream Stream usage not requested Add stream_options: { include_usage: true } Flat trace (no nesting) Missing OTel context Ensure NodeSDK is started with LangfuseSpanProcessor
Resources
Next Steps For evaluation and scoring workflows, see langfuse-core-workflow-b.
