perplexity-rate-limits
Implement Perplexity rate limiting, backoff, and request queuing. Use when handling 429 errors, implementing retry logic, or optimizing API request throughput for Perplexity Sonar. Trigger with phrases like "perplexity rate limit", "perplexity throttling", "perplexity 429", "perplexity retry", "perplexity backoff".
What this skill does
# Perplexity Rate Limits
## Overview
Handle Perplexity Sonar API rate limits. Perplexity uses a leaky bucket algorithm: burst capacity is available, with tokens refilling continuously at your assigned rate. Rate limits are based on requests per minute (RPM).
## Rate Limit Tiers
| Tier | RPM | Notes |
|------|-----|-------|
| Free / Starter | 50 | Default for new API keys |
| Search API | ~3 req/sec | Per-endpoint limit |
| Higher tiers | Contact sales | Custom limits available |
Rate limits apply per API key, not per model. Using `sonar-pro` counts against the same RPM as `sonar`.
## Prerequisites
- `PERPLEXITY_API_KEY` set
- Understanding of HTTP 429 responses
## Instructions
### Step 1: Exponential Backoff with Jitter
```typescript
async function withExponentialBackoff<T>(
operation: () => Promise<T>,
config = { maxRetries: 5, baseDelayMs: 1000, maxDelayMs: 30000, jitterMs: 500 }
): Promise<T> {
for (let attempt = 0; attempt <= config.maxRetries; attempt++) {
try {
return await operation();
} catch (error: any) {
if (attempt === config.maxRetries) throw error;
const status = error.status || error.response?.status;
// Only retry on 429 (rate limit) and 5xx (server errors)
if (status && status !== 429 && status < 500) throw error;
const exponentialDelay = config.baseDelayMs * Math.pow(2, attempt);
const jitter = Math.random() * config.jitterMs;
const delay = Math.min(exponentialDelay + jitter, config.maxDelayMs);
console.warn(`[Perplexity] ${status || "error"} — retry ${attempt + 1}/${config.maxRetries} in ${delay.toFixed(0)}ms`);
await new Promise((r) => setTimeout(r, delay));
}
}
throw new Error("Unreachable");
}
// Usage
const result = await withExponentialBackoff(() =>
perplexity.chat.completions.create({
model: "sonar",
messages: [{ role: "user", content: "test query" }],
})
);
```
### Step 2: Queue-Based Rate Limiting
```typescript
import PQueue from "p-queue";
// 50 RPM = ~0.83 req/sec. Set intervalCap=1, interval=1200ms for safety.
const perplexityQueue = new PQueue({
concurrency: 3,
interval: 1200,
intervalCap: 1,
});
async function queuedSearch(query: string, model = "sonar") {
return perplexityQueue.add(() =>
withExponentialBackoff(() =>
perplexity.chat.completions.create({
model,
messages: [{ role: "user", content: query }],
})
)
);
}
// Batch queries are automatically rate-limited
const queries = ["query 1", "query 2", "query 3", "query 4", "query 5"];
const results = await Promise.all(queries.map((q) => queuedSearch(q)));
```
### Step 3: Token Bucket Implementation (No Dependencies)
```typescript
class TokenBucket {
private tokens: number;
private lastRefill: number;
constructor(
private maxTokens: number = 50,
private refillRate: number = 50 / 60 // 50 per minute = 0.83/sec
) {
this.tokens = maxTokens;
this.lastRefill = Date.now();
}
async acquire(): Promise<void> {
this.refill();
if (this.tokens >= 1) {
this.tokens -= 1;
return;
}
// Wait until a token is available
const waitMs = (1 / this.refillRate) * 1000;
await new Promise((r) => setTimeout(r, waitMs));
this.refill();
this.tokens -= 1;
}
private refill() {
const now = Date.now();
const elapsed = (now - this.lastRefill) / 1000;
this.tokens = Math.min(this.maxTokens, this.tokens + elapsed * this.refillRate);
this.lastRefill = now;
}
get available(): number {
this.refill();
return Math.floor(this.tokens);
}
}
const bucket = new TokenBucket(50, 50 / 60);
async function rateLimitedSearch(query: string) {
await bucket.acquire();
return perplexity.chat.completions.create({
model: "sonar",
messages: [{ role: "user", content: query }],
});
}
```
### Step 4: Python Rate Limiting
```python
import time, asyncio
from collections import deque
class RateLimiter:
def __init__(self, rpm: int = 50):
self.rpm = rpm
self.window = deque()
def wait_if_needed(self):
now = time.time()
# Remove timestamps older than 60 seconds
while self.window and self.window[0] < now - 60:
self.window.popleft()
if len(self.window) >= self.rpm:
sleep_time = 60 - (now - self.window[0])
time.sleep(max(0, sleep_time))
self.window.append(time.time())
limiter = RateLimiter(rpm=50)
def rate_limited_search(client, query: str, model: str = "sonar"):
limiter.wait_if_needed()
return client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": query}],
)
```
## Error Handling
| Signal | Meaning | Action |
|--------|---------|--------|
| HTTP 429 | RPM exceeded | Backoff and retry |
| `Retry-After` header | Seconds until reset | Honor this value exactly |
| Repeated 429s | Sustained overload | Reduce concurrency or add queue |
| 429 on burst | Bucket empty | Space requests 1.2s apart |
## Output
- Automatic retry with exponential backoff and jitter
- Queue-based rate limiting for batch operations
- Token bucket for fine-grained control
- Python rate limiter for synchronous code
## Resources
- [Perplexity Rate Limits](https://docs.perplexity.ai/guides/rate-limits)
- [p-queue Documentation](https://github.com/sindresorhus/p-queue)
## Next Steps
For security configuration, see `perplexity-security-basics`.
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.