---

name: linear-rate-limits description: | Handle Linear API rate limiting and quotas effectively. Use when dealing with rate limit errors, implementing throttling, or optimizing API usage patterns. Trigger with phrases like "linear rate limit", "linear throttling", "linear API quota", "linear 429 error", "linear request limits". allowed-tools: Read, Write, Edit, Grep version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io

Linear Rate Limits

Overview

Understand and handle Linear API rate limits for reliable integrations.

Prerequisites

• Linear SDK configured
• Understanding of HTTP headers
• Familiarity with async patterns

Linear Rate Limit Structure

Current Limits

| Tier | Requests/min | Complexity/min | Notes | |------|-------------|----------------|-------| | Standard | 1,500 | 250,000 | Most integrations | | Enterprise | Higher | Higher | Contact Linear |

Headers Returned

X-RateLimit-Limit: 1500
X-RateLimit-Remaining: 1499
X-RateLimit-Reset: 1640000000
X-Complexity-Limit: 250000
X-Complexity-Cost: 50
X-Complexity-Remaining: 249950

Instructions

Step 1: Basic Rate Limit Handler

// lib/rate-limiter.ts
interface RateLimitState {
  remaining: number;
  reset: Date;
  complexityRemaining: number;
}

class LinearRateLimiter {
  private state: RateLimitState = {
    remaining: 1500,
    reset: new Date(),
    complexityRemaining: 250000,
  };

  updateFromHeaders(headers: Headers): void {
    const remaining = headers.get("x-ratelimit-remaining");
    const reset = headers.get("x-ratelimit-reset");
    const complexityRemaining = headers.get("x-complexity-remaining");

    if (remaining) this.state.remaining = parseInt(remaining);
    if (reset) this.state.reset = new Date(parseInt(reset) * 1000);
    if (complexityRemaining) {
      this.state.complexityRemaining = parseInt(complexityRemaining);
    }
  }

  async waitIfNeeded(): Promise<void> {
    // If very low on requests, wait until reset
    if (this.state.remaining < 10) {
      const waitMs = this.state.reset.getTime() - Date.now();
      if (waitMs > 0) {
        console.log(`Rate limit low, waiting ${waitMs}ms...`);
        await new Promise(r => setTimeout(r, waitMs));
      }
    }
  }

  getState(): RateLimitState {
    return { ...this.state };
  }
}

export const rateLimiter = new LinearRateLimiter();

Step 2: Exponential Backoff

// lib/backoff.ts
interface BackoffOptions {
  maxRetries?: number;
  baseDelayMs?: number;
  maxDelayMs?: number;
  jitter?: boolean;
}

export async function withBackoff<T>(
  fn: () => Promise<T>,
  options: BackoffOptions = {}
): Promise<T> {
  const {
    maxRetries = 5,
    baseDelayMs = 1000,
    maxDelayMs = 30000,
    jitter = true,
  } = options;

  let lastError: Error | undefined;

  for (let attempt = 0; attempt < maxRetries; attempt++) {
    try {
      return await fn();
    } catch (error: any) {
      lastError = error;

      // Only retry on rate limit errors
      const isRateLimited =
        error?.extensions?.code === "RATE_LIMITED" ||
        error?.response?.status === 429;

      if (!isRateLimited || attempt === maxRetries - 1) {
        throw error;
      }

      // Calculate delay with exponential backoff
      let delay = Math.min(baseDelayMs * Math.pow(2, attempt), maxDelayMs);

      // Add jitter to prevent thundering herd
      if (jitter) {
        delay += Math.random() * delay * 0.1;
      }

      // Check Retry-After header if available
      const retryAfter = error?.response?.headers?.get?.("retry-after");
      if (retryAfter) {
        delay = Math.max(delay, parseInt(retryAfter) * 1000);
      }

      console.log(
        `Rate limited, attempt ${attempt + 1}/${maxRetries}, ` +
        `retrying in ${Math.round(delay)}ms...`
      );

      await new Promise(r => setTimeout(r, delay));
    }
  }

  throw lastError;
}

Step 3: Request Queue

// lib/queue.ts
type QueuedRequest<T> = {
  fn: () => Promise<T>;
  resolve: (value: T) => void;
  reject: (error: Error) => void;
};

class RequestQueue {
  private queue: QueuedRequest<any>[] = [];
  private processing = false;
  private requestsPerSecond = 20; // Conservative rate

  async add<T>(fn: () => Promise<T>): Promise<T> {
    return new Promise((resolve, reject) => {
      this.queue.push({ fn, resolve, reject });
      this.process();
    });
  }

  private async process(): Promise<void> {
    if (this.processing) return;
    this.processing = true;

    while (this.queue.length > 0) {
      const request = this.queue.shift()!;

      try {
        const result = await request.fn();
        request.resolve(result);
      } catch (error) {
        request.reject(error as Error);
      }

      // Throttle requests
      await new Promise(r =>
        setTimeout(r, 1000 / this.requestsPerSecond)
      );
    }

    this.processing = false;
  }

  get pending(): number {
    return this.queue.length;
  }
}

export const requestQueue = new RequestQueue();

Step 4: Batch Operations

// lib/batch.ts
import { LinearClient } from "@linear/sdk";

interface BatchConfig {
  batchSize: number;
  delayBetweenBatches: number;
}

export async function batchProcess<T, R>(
  items: T[],
  processor: (item: T) => Promise<R>,
  config: BatchConfig = { batchSize: 10, delayBetweenBatches: 1000 }
): Promise<R[]> {
  const results: R[] = [];
  const batches: T[][] = [];

  // Split into batches
  for (let i = 0; i < items.length; i += config.batchSize) {
    batches.push(items.slice(i, i + config.batchSize));
  }

  for (let i = 0; i < batches.length; i++) {
    const batch = batches[i];
    console.log(`Processing batch ${i + 1}/${batches.length}...`);

    // Process batch in parallel
    const batchResults = await Promise.all(batch.map(processor));
    results.push(...batchResults);

    // Delay between batches (except last)
    if (i < batches.length - 1) {
      await new Promise(r => setTimeout(r, config.delayBetweenBatches));
    }
  }

  return results;
}

// Usage example
async function updateManyIssues(
  client: LinearClient,
  updates: { id: string; priority: number }[]
) {
  return batchProcess(
    updates,
    ({ id, priority }) => client.updateIssue(id, { priority }),
    { batchSize: 10, delayBetweenBatches: 2000 }
  );
}

Step 5: Query Optimization

// Reduce complexity by limiting fields
const optimizedQuery = `
  query Issues($filter: IssueFilter) {
    issues(filter: $filter, first: 50) {
      nodes {
        id
        identifier
        title
        # Avoid nested connections in loops
      }
    }
  }
`;

// Use SDK efficiently
async function getIssuesOptimized(client: LinearClient, teamKey: string) {
  // Good: Single query with filter
  return client.issues({
    filter: { team: { key: { eq: teamKey } } },
    first: 50,
  });

  // Bad: N+1 queries
  // const teams = await client.teams();
  // for (const team of teams.nodes) {
  //   const issues = await team.issues(); // N queries!
  // }
}

Output

• Rate limit monitoring
• Automatic retry with backoff
• Request queuing and throttling
• Batch processing utilities
• Optimized query patterns

Error Handling

| Error | Cause | Solution | |-------|-------|----------| | 429 Too Many Requests | Rate limit exceeded | Use backoff and queue | | Complexity exceeded | Query too expensive | Simplify query structure | | Timeout | Long-running query | Paginate or split queries |

Resources

• Linear Rate Limiting
• GraphQL Complexity
• Best Practices

Next Steps

Learn security best practices with linear-security-basics.