Skip to main content Inicio Creadores jeremylongshore tons-of-skills-marketplace langfuse-sdk-patterns
langfuse-sdk-patterns Langfuse SDK best practices, patterns, and idiomatic usage.
Use when learning Langfuse SDK patterns, implementing proper tracing,
or following best practices for LLM observability.
Trigger with phrases like "langfuse patterns", "langfuse best practices",
"langfuse SDK guide", "how to use langfuse", "langfuse idioms".
Ir a la instalación Skills Marketplace Descubre y explora habilidades de IA creadas por la comunidad.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Mostrar detalles del prompt Un comando directo omite el prompt de revisión. Revisa el origen antes de ejecutarlo.
npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill langfuse-sdk-patternsEl comando permanece en una sola línea. Desplázate horizontalmente para revisarlo antes de copiarlo.
¿Prefieres una copia local? Descarga los archivos que SkillsMP tiene disponibles ahora.
Descargar Zip Descargando... Explorador de archivos
2 archivos Más de este repositorio langchain-deploy-integration Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".
langchain-langgraph-agents Build a correct LangGraph 1.0 ReAct agent with create_react_agent — typed tools, error propagation, recursion caps, and stop conditions that actually stop. Use when writing a first tool-calling agent, migrating from AgentExecutor or initialize_agent, or diagnosing an agent that loops on vague prompts. Trigger with "langgraph agent", "create_react_agent", "langgraph tool calling", "AgentExecutor migration", or "agent loop cost".
langchain-langgraph-human-in-loop Build LangGraph 1.0 human-in-the-loop approval flows with interrupt_before /
interrupt_after and Command(resume=...) — JSON-serializable state, clean
resume semantics, and UI wiring for approval decisions. Use when adding an
approval gate before an expensive tool call, wiring a Slack/web UI for agent
approvals, or debugging a graph that crashes on interrupt.
Trigger with "langgraph human in loop", "langgraph interrupt_before",
"langgraph approval flow", "Command resume", "langgraph HITL".
name langfuse-sdk-patterns description Langfuse SDK best practices, patterns, and idiomatic usage.
Use when learning Langfuse SDK patterns, implementing proper tracing,
or following best practices for LLM observability.
Trigger with phrases like "langfuse patterns", "langfuse best practices",
"langfuse SDK guide", "how to use langfuse", "langfuse idioms".
allowed-tools Read, Write, Edit version 1.12.0 license MIT author Jeremy Longshore <jeremy@intentsolutions.io> tags ["saas","langfuse","observability","llm","tracing"] compatibility Designed for Claude Code
Langfuse SDK Patterns
Overview
Production-quality patterns for the Langfuse SDK: singleton clients, the observe wrapper, startActiveObservation for nested traces, session tracking, graceful shutdown, and error-safe tracing.
Prerequisites
Completed langfuse-install-auth setup
Understanding of async/await patterns
For v4+: @langfuse/tracing, @langfuse/otel, @opentelemetry/sdk-node
Instructions
Pattern 1: Singleton Client with Graceful Shutdown
import { LangfuseClient } from "@langfuse/client" ;
import { LangfuseSpanProcessor } from "@langfuse/otel" ;
import { NodeSDK } from "@opentelemetry/sdk-node" ;
let client : LangfuseClient | null = null ;
export function getLangfuseClient (LangfuseClient {
if (!client) {
client = new LangfuseClient ();
}
return client;
}
let sdk : NodeSDK | null = null ;
export function initTracing (
NodeSDK
if
new
NodeSDK
spanProcessors
new
LangfuseSpanProcessor
start
const
shutdown
async
await
shutdown
exit
0
on
"SIGTERM"
on
"SIGINT"
return
import { Langfuse } from "langfuse" ;
let instance : Langfuse | null = null ;
export function getLangfuse (Langfuse {
if (!instance) {
instance = new Langfuse ({
flushAt : 15 ,
flushInterval : 10000 ,
});
process.on ("beforeExit" , () => instance?.shutdownAsync ());
}
return instance;
}
Pattern 2: observe Wrapper for Existing Functions The observe wrapper is the most ergonomic way to add tracing. It wraps any function and auto-creates a span.
import { observe, updateActiveObservation } from "@langfuse/tracing" ;
const fetchUserProfile = observe (async (userId : string ) => {
updateActiveObservation ({ input : { userId } });
const profile = await db.users .findById (userId);
updateActiveObservation ({ output : { found : !!profile } });
return profile;
});
const summarize = observe (
{ name : "summarize-text" , asType : "generation" },
async (text : string ) => {
updateActiveObservation ({ model : "gpt-4o-mini" , input : text });
const result = await openai.chat .completions .create ({
model : "gpt-4o-mini" ,
messages : [{ role : "user" , content : `Summarize: ${text} ` }],
});
const output = result.choices [0 ].message .content ;
updateActiveObservation ({
output,
usage : {
promptTokens : result.usage ?.prompt_tokens ,
completionTokens : result.usage ?.completion_tokens ,
},
});
return output;
}
);
const pipeline = observe (async (userId : string ) => {
const profile = await fetchUserProfile (userId);
const summary = await summarize (profile.bio );
return { profile, summary };
});
Pattern 3: startActiveObservation for Inline Control Use when you need fine-grained control over observation lifecycle within a function:
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing" ;
async function processOrder (orderId : string return await startActiveObservation ("process-order" , async () => {
updateActiveObservation ({ input : { orderId } });
const validated = await startActiveObservation ("validate" , async () => {
const result = await validateOrder (orderId);
updateActiveObservation ({ output : { valid : result.valid } });
return result;
});
if (!validated.valid ) {
updateActiveObservation ({ output : { error : "validation failed" } });
return { success : false };
}
const description = await startActiveObservation (
{ name : "generate-confirmation" , asType : "generation" },
async () => {
updateActiveObservation ({ model : "gpt-4o-mini" });
const result = await generateConfirmation (orderId);
updateActiveObservation ({ output : result });
return result;
}
);
updateActiveObservation ({ output : { success : true } });
return { success : true , description };
});
}
Pattern 4: Session and User Tracking Link traces across conversation turns for user-level analytics:
await startActiveObservation ("chat-turn" , async () => {
updateActiveObservation ({
metadata : {
sessionId : "session-abc-123" ,
userId : "user-456" ,
},
});
await handleUserMessage (message);
});
const trace = langfuse.trace ({
name : "chat-turn" ,
sessionId : "session-abc-123" ,
userId : "user-456" ,
input : { message },
});
Pattern 5: Error-Safe Tracing Never let tracing failures break your application:
import { observe, updateActiveObservation } from "@langfuse/tracing" ;
const safeObserve = <T extends (...args : any []) => Promise <any >>(
name : string ,
fn : T
): T =>return (async (...args : Parameters <T>) => {
try {
return await observe ({ name }, async () => {
updateActiveObservation ({ input : args });
const result = await fn (...args);
updateActiveObservation ({ output : result });
return result;
})();
} catch (tracingError) {
console .warn (`Tracing error in ${name} :` , tracingError);
return fn (...args);
}
}) as T;
};
const processRequest = safeObserve ("process-request" , async (input : string ) => {
return await callLLM (input);
});
Pattern 6: Legacy v3 -- Always End Spans
const span = trace.span ({ name : "risky-operation" , input : data });
try {
const result = await riskyOperation (data);
span.end ({ output : result });
return result;
} catch (error) {
span.end ({ level : "ERROR" , statusMessage : String (error) });
throw error;
}
Anti-Patterns to Avoid Anti-Pattern Problem Correct Pattern new Langfuse() per requestMemory leaks, duplicate traces Singleton client Awaiting flush in hot path Adds latency to every request Background flush, shutdown handler Logging full request bodies Trace payloads too large Truncate/summarize inputs Missing .end() on spans (v3) Spans show "in progress" forever Use try/finally or observe wrapper Hardcoding API keys Security risk Environment variables only
Resources
Next Steps For OpenAI/LangChain tracing examples, see langfuse-core-workflow-a.
