Langfuse Sdk PatternsSAFE
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Overview
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4f83675ca38aOBSERVED · 2026-10-09Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| claude-code | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- 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: 1.17.0 license: MIT author: Jeremy Longshore <[email protected]> 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 ```typescript // src/lib/langfuse.ts -- single file, import everywhere import { LangfuseClient } from "@langfuse/client"; import { LangfuseSpanProcessor } from "@langfuse/otel"; import { NodeSDK } from "@opentelemetry/sdk-node"; // Singleton client for prompts, datasets, scores let client: LangfuseClient | null = null; export function getLangfuseClient(): LangfuseClient { if (!client) { client = new LangfuseClient(); } return client; } // One-time OTel setup (call at app entry point) let sdk: NodeSDK | null = null; export function initTracing(): NodeSDK { if (!sdk) { sdk = new NodeSDK({ spanProcessors: [new LangfuseSpanProcessor()], }); sdk.start(); // Graceful shutdown on process exit const shutdown = async () => { await sdk?.shutdown(); process.exit(0); }; process.on("SIGTERM", shutdown); process.on("SIGINT", shutdown); } return sdk; } ``` **Legacy v3 singleton:** ```typescript 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. ```typescript import { observe, updateActiveObservation } from "@langfuse/tracing"; // Wrap existing functions -- no internal changes needed const fetchUserProfile = observe(async (userId: string) => { updateActiveObservation({ input: { userId } }); const profile = await db.users.findById(userId); updateActiveObservation({ output: { found: !!profile } }); return profile; }); // Mark LLM calls as generations 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; } ); // When called inside another observed function, spans auto-nest 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: ```typescript import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing"; async function processOrder(orderId: string) { return await startActiveObservation("process-order", async () => { updateActiveObservation({ input: { orderId } }); // Nested spans are automatic 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 }; } // Generation span for LLM call 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: ```typescript // v4+: Set session/user via observation metadata await startActiveObservation("chat-turn", async () => { updateActiveObservation({ metadata: { sessionId: "session-abc-123", userId: "user-456", }, }); // All nested observations inherit this context await handleUserMessage(message); }); // v3: Set directly on trace const trace = langfuse.trace({ name: "chat-turn", sessionId: "session-abc-123", // Groups traces into a session userId: "user-456", // Links to user analytics input: { message }, }); ``` ### Pattern 5: Error-Safe Tracing Never let tracing failures break your application: ```typescript import { observe, updateActiveObservation } from "@langfuse/tracing"; const safeObserve = <T ex
Trust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | PASS |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (0)
No findings outside the package's declared scope.
Gates applied: no_behavioural_pass.
4f83675ca38afull audit observations/trust-audit/skill/jeremylongshore__langfuse-sdk-patterns.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-09 | 4f83675ca38a | SAFE | B | 89 | first audit |
Questions
What does the Langfuse Sdk Patterns skill do?
Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.
Is Langfuse Sdk Patterns safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Langfuse Sdk Patterns access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does Langfuse Sdk Patterns work with?
Its documentation mentions claude-code. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (4f83675ca38a), read on 2026-10-09. The repository is watched, and a new audit runs when it changes — this is the first audit.