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.zipInstalls 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.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
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
| Tier | Traces/min | Batch Size | Strategy |
|---|---|---|---|
| Hobby | ~500 | 15 | Default settings |
| Pro | ~5,000 | 50 | Increase flushAt |
| Team | ~10,000 | 100 | + Queue-based limiting |
| Enterprise | Custom | Custom | + Sampling |
Error Handling
| Error | Response | Action |
|---|---|---|
429 Too Many Requests | Retry-After: N | Backoff for N seconds |
503 Service Unavailable | Server overloaded | Backoff 30s+ |
| Flush timeout | Large batch | Reduce flushAt, increase requestTimeout |
| Memory growth | Queue backup | Add maxSize to PQueue |
Resources
Prerequisites
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.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| langfuse-rate-limits (this skill) | 0 | 27d | No flags | Advanced |
| worker-benchmarks | 3 | 2mo | Review | Advanced |
| sentry-rate-limits | 1 | 27d | Caution | Intermediate |
| optimizing-performance | 1 | 2mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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