LA

langfuse-rate-limits

Optimize Langfuse trace ingestion with batching, retry patterns, and request limiting.

Install

mkdir -p .claude/skills/langfuse-rate-limits && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/8358" && unzip -o skill.zip -d .claude/skills/langfuse-rate-limits && rm skill.zip

Installs to .claude/skills/langfuse-rate-limits

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Implement Langfuse rate limiting, batching, and backoff patterns.
65 charsno explicit “when” trigger
Advanced

Key capabilities

  • Tune Langfuse SDK batching settings
  • Implement exponential backoff with jitter for API retries
  • Limit concurrent Langfuse API calls using a queue
  • Configure sampling for high-volume trace workloads
  • Handle Langfuse API rate limit errors

How it works

The skill optimizes Langfuse SDK batching, implements exponential backoff for custom API calls, and uses a queue to limit concurrent requests. For ultra-high volumes, it applies configurable sampling to manage trace ingestion.

Inputs & outputs

You give it
High-volume Langfuse API calls or trace events
You get back
Successful Langfuse API requests and trace ingestion without rate limits

When to use langfuse-rate-limits

  • Configure batching for trace ingestion
  • Implement exponential backoff for API errors
  • Manage high-volume trace workloads
  • Optimize OTel span processors

About this skill

Langfuse Rate Limits

Overview

Handle Langfuse API rate limits with optimized SDK batching, exponential backoff with jitter, concurrent request limiting, and configurable sampling for ultra-high-volume workloads.

Prerequisites

  • Langfuse SDK installed and configured
  • High-volume trace workload (1,000+ events/minute)

Instructions

Step 1: Optimize SDK Batching Configuration

The Langfuse SDK batches events internally before sending. Tuning batch settings is the first defense against rate limits.

// v3 Legacy: Direct configuration
import { Langfuse } from "langfuse";

const langfuse = new Langfuse({
  flushAt: 50,           // Events per batch (default: 15, max ~200)
  flushInterval: 10000,  // Milliseconds between flushes (default: 10000)
  requestTimeout: 30000, // Timeout per batch request
});

// v4+: Configure via OTel span processor
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";

const processor = new LangfuseSpanProcessor({
  exportIntervalMillis: 10000, // Flush interval
  maxExportBatchSize: 50,      // Events per batch
});

const sdk = new NodeSDK({ spanProcessors: [processor] });
sdk.start();

Step 2: Implement Retry with Exponential Backoff

For custom API calls (scores, datasets, prompts) that hit rate limits:

async function withRetry<T>(
  fn: () => Promise<T>,
  options: { maxRetries?: number; baseDelayMs?: number; maxDelayMs?: number } = {}
): Promise<T> {
  const { maxRetries = 5, baseDelayMs = 1000, maxDelayMs = 30000 } = options;

  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    try {
      return await fn();
    } catch (error: any) {
      const status = error?.status || error?.response?.status;

      // Only retry on rate limits (429) and server errors (5xx)
      if (attempt === maxRetries || (status && status < 429)) {
        throw error;
      }

      // Honor Retry-After header if present
      const retryAfter = error?.response?.headers?.["retry-after"];
      let delay: number;

      if (retryAfter) {
        delay = parseInt(retryAfter, 10) * 1000;
      } else {
        // Exponential backoff with jitter
        delay = Math.min(baseDelayMs * Math.pow(2, attempt), maxDelayMs);
        delay += Math.random() * 500; // Jitter
      }

      console.warn(`Rate limited. Retry ${attempt + 1}/${maxRetries} in ${Math.round(delay)}ms`);
      await new Promise((r) => setTimeout(r, delay));
    }
  }
  throw new Error("Unreachable");
}

// Usage with Langfuse client operations
const langfuse = new LangfuseClient();

await withRetry(() =>
  langfuse.score.create({
    traceId: "trace-123",
    name: "quality",
    value: 0.95,
    dataType: "NUMERIC",
  })
);

Step 3: Queue-Based Concurrency Limiting

Use p-queue to cap concurrent Langfuse API calls:

import PQueue from "p-queue";
import { LangfuseClient } from "@langfuse/client";

const langfuse = new LangfuseClient();

// Max 10 concurrent API calls, 50 per second
const queue = new PQueue({
  concurrency: 10,
  interval: 1000,
  intervalCap: 50,
});

// Queue score submissions
async function queueScore(params: {
  traceId: string;
  name: string;
  value: number;
}) {
  return queue.add(() =>
    langfuse.score.create({
      ...params,
      dataType: "NUMERIC",
    })
  );
}

// Queue dataset item creation
async function queueDatasetItem(datasetName: string, item: any) {
  return queue.add(() =>
    langfuse.api.datasetItems.create({
      datasetName,
      input: item.input,
      expectedOutput: item.expectedOutput,
    })
  );
}

// Monitor queue health
setInterval(() => {
  console.log(`Queue: ${queue.pending} pending, ${queue.size} queued`);
}, 10000);

Step 4: Configurable Sampling for Ultra-High Volume

When tracing volume exceeds rate limits, sample traces instead of dropping them:

import { observe, updateActiveObservation, startActiveObservation } from "@langfuse/tracing";

class TraceSampler {
  private rate: number;
  private windowCounts: number[] = [];
  private windowMs = 60000; // 1 minute window
  private maxPerWindow: number;

  constructor(sampleRate: number, maxPerMinute: number) {
    this.rate = sampleRate;
    this.maxPerWindow = maxPerMinute;
  }

  shouldSample(tags?: string[]): boolean {
    // Always sample errors
    if (tags?.includes("error") || tags?.includes("critical")) {
      return true;
    }

    // Check window limit
    const now = Date.now();
    this.windowCounts = this.windowCounts.filter((t) => t > now - this.windowMs);
    if (this.windowCounts.length >= this.maxPerWindow) {
      return false;
    }

    // Probabilistic sampling
    if (Math.random() > this.rate) {
      return false;
    }

    this.windowCounts.push(now);
    return true;
  }
}

// 10% sampling, max 1000 traces/minute
const sampler = new TraceSampler(0.1, 1000);

async function sampledOperation(name: string, fn: () => Promise<any>) {
  if (!sampler.shouldSample()) {
    return fn(); // Run without tracing
  }

  return startActiveObservation(name, async () => {
    updateActiveObservation({ metadata: { sampled: true } });
    return fn();
  });
}

Rate Limit Reference

TierTraces/minBatch SizeStrategy
Hobby~50015Default settings
Pro~5,00050Increase flushAt
Team~10,000100+ Queue-based limiting
EnterpriseCustomCustom+ Sampling

Error Handling

ErrorResponseAction
429 Too Many RequestsRetry-After: NBackoff for N seconds
503 Service UnavailableServer overloadedBackoff 30s+
Flush timeoutLarge batchReduce flushAt, increase requestTimeout
Memory growthQueue backupAdd maxSize to PQueue

Resources

Prerequisites

Langfuse SDK installed and configuredHigh-volume trace workload (1,000+ events/minute)

Limitations

  • Memory growth can occur if the queue backs up
  • Flush timeouts can happen with large batches

How it compares

This skill provides specific strategies for managing Langfuse API rate limits and high-volume data, offering more resilience than default SDK configurations.

Compared to similar skills

langfuse-rate-limits side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
langfuse-rate-limits (this skill)027dNo flagsAdvanced
worker-benchmarks32moReviewAdvanced
sentry-rate-limits127dCautionIntermediate
optimizing-performance12moReviewIntermediate

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