Linear rate limits

Linear Rate Limits

Overview

Linear uses the leaky bucket algorithm with two rate limiting dimensions. Understanding both is critical for reliable integrations:

Budget	Limit	Refill Rate
Requests	5,000/hour per API key	~83/min constant refill
Complexity	250,000 points/hour	~4,167/min constant refill
Max single query	10,000 points	Hard reject if exceeded

Complexity scoring: Each property = 0.1 pt, each object = 1 pt, connections multiply children by first arg (default 50), then round up.

Prerequisites

• @linear/sdk installed
• Understanding of HTTP response headers
• Familiarity with async/await patterns

Instructions

Step 1: Read Rate Limit Headers

Linear returns rate limit info on every response.

const response = await fetch("https://api.linear.app/graphql", {
  method: "POST",
  headers: {
    Authorization: process.env.LINEAR_API_KEY!,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({ query: "{ viewer { id } }" }),
});

// Key headers
const headers = {
  requestsRemaining: response.headers.get("x-ratelimit-requests-remaining"),
  requestsLimit: response.headers.get("x-ratelimit-requests-limit"),
  requestsReset: response.headers.get("x-ratelimit-requests-reset"),
  complexityRemaining: response.headers.get("x-ratelimit-complexity-remaining"),
  complexityLimit: response.headers.get("x-ratelimit-complexity-limit"),
  queryComplexity: response.headers.get("x-complexity"),
};

console.log(`Requests: ${headers.requestsRemaining}/${headers.requestsLimit}`);
console.log(`Complexity: ${headers.complexityRemaining}/${headers.complexityLimit}`);
console.log(`This query cost: ${headers.queryComplexity} points`);

Step 2: Exponential Backoff with Jitter

import { LinearClient } from "@linear/sdk";

class RateLimitedClient {
  private client: LinearClient;

  constructor(apiKey: string) {
    this.client = new LinearClient({ apiKey });
  }

  async withRetry<T>(fn: () => Promise<T>, maxRetries = 5): Promise<T> {
    for (let attempt = 0; attempt < maxRetries; attempt++) {
      try {
        return await fn();
      } catch (error: any) {
        const isRateLimited = error.status === 429 ||
          error.message?.includes("rate") ||
          error.type === "ratelimited";

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

        // Exponential backoff: 1s, 2s, 4s, 8s, 16s + jitter
        const delay = 1000 * Math.pow(2, attempt) + Math.random() * 500;
        console.warn(`Rate limited (attempt ${attempt + 1}/${maxRetries}), waiting ${Math.round(delay)}ms`);
        await new Promise(r => setTimeout(r, delay));
      }
    }
    throw new Error("Unreachable");
  }

  get sdk() { return this.client; }
}

Step 3: Request Queue with Token Bucket

Prevent bursts by spacing requests evenly.

class RequestQueue {
  private queue: Array<{ fn: () => Promise<any>; resolve: Function; reject: Function }> = [];
  private processing = false;
  private intervalMs: number;

  constructor(requestsPerSecond = 10) {
    this.intervalMs = 1000 / requestsPerSecond;
  }

  async enqueue<T>(fn: () => Promise<T>): Promise<T> {
    return new Promise((resolve, reject) => {
      this.queue.push({ fn, resolve, reject });
      if (!this.processing) this.processQueue();
    });
  }

  private async processQueue() {
    this.processing = true;
    while (this.queue.length > 0) {
      const { fn, resolve, reject } = this.queue.shift()!;
      try {
        resolve(await fn());
      } catch (error) {
        reject(error);
      }
      if (this.queue.length > 0) {
        await new Promise(r => setTimeout(r, this.intervalMs));
      }
    }
    this.processing = false;
  }
}

// Usage: 8 requests/second max
const queue = new RequestQueue(8);
const client = new LinearClient({ apiKey: process.env.LINEAR_API_KEY! });

const teamResults = await Promise.all(
  teamIds.map(id => queue.enqueue(() => client.team(id)))
);

Step 4: Reduce Query Complexity

// HIGH COMPLEXITY (~12,500 pts):
// 250 issues * (1 issue + 50 labels * 0.1 per field) = expensive
// const heavy = await client.issues({ first: 250 });

// LOW COMPLEXITY (~55 pts):
// 50 issues * (5 fields * 0.1 + 1 object) = cheap
const light = await client.issues({
  first: 50,
  filter: { team: { id: { eq: teamId } } },
});

// Use rawRequest for minimal field selection
const minimal = await client.client.rawRequest(`
  query { issues(first: 50) { nodes { id identifier title priority } } }
`);

// Sort by updatedAt to get fresh data first, avoid paginating everything
const fresh = await client.issues({
  first: 50,
  orderBy: "updatedAt",
  filter: { updatedAt: { gte: lastSyncTime } },
});

Step 5: Batch Mutations

Combine multiple mutations into one GraphQL request.

// Instead of 100 separate issueUpdate calls (~100 requests):
async function batchUpdatePriority(client: LinearClient, issueIds: string[], priority: number) {
  const chunkSize = 20; // Keep each batch under complexity limit
  for (let i = 0; i < issueIds.length; i += chunkSize) {
    const chunk = issueIds.slice(i, i + chunkSize);
    const mutations = chunk.map((id, j) =>
      `u${j}: issueUpdate(id: "${id}", input: { priority: ${priority} }) { success }`
    ).join("\n");

    await queue.enqueue(() =>
      client.client.rawRequest(`mutation BatchUpdate { ${mutations} }`)
    );
  }
}

// Batch archive
async function batchArchive(client: LinearClient, issueIds: string[]) {
  for (let i = 0; i < issueIds.length; i += 20) {
    const chunk = issueIds.slice(i, i + 20);
    const mutations = chunk.map((id, j) =>
      `a${j}: issueArchive(id: "${id}") { success }`
    ).join("\n");

    await client.client.rawRequest(`mutation { ${mutations} }`);
  }
}

Step 6: Rate Limit Monitor

class RateLimitMonitor {
  private remaining = { requests: 5000, complexity: 250000 };

  update(headers: Headers) {
    const reqRemaining = headers.get("x-ratelimit-requests-remaining");
    const cxRemaining = headers.get("x-ratelimit-complexity-remaining");
    if (reqRemaining) this.remaining.requests = parseInt(reqRemaining);
    if (cxRemaining) this.remaining.complexity = parseInt(cxRemaining);
  }

  isLow(): boolean {
    return this.remaining.requests < 100 || this.remaining.complexity < 5000;
  }

  getStatus() {
    return {
      requests: this.remaining.requests,
      complexity: this.remaining.complexity,
      healthy: !this.isLow(),
    };
  }
}

Error Handling

Error	Cause	Solution
HTTP 429	Request or complexity budget exceeded	Parse headers, back off exponentially
Query complexity too high	Single query > 10,000 pts	Reduce first to 50, remove nested relations
Burst of 429s on startup	Init fetches too much data	Stagger startup queries, cache static data
Timeout on SDK call	Server under load	Add 30s timeout, retry once

Examples

Rate Limit Status Check

curl -s -I -X POST https://api.linear.app/graphql \
  -H "Authorization: $LINEAR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query": "{ viewer { id } }"}' 2>&1 | grep -i ratelimit

Safe Bulk Import

const rlClient = new RateLimitedClient(process.env.LINEAR_API_KEY!);
const items = [/* issues to import */];

for (let i = 0; i < items.length; i++) {
  await rlClient.withRetry(() =>
    rlClient.sdk.createIssue({ teamId: "team-uuid", title: items[i].title })
  );
  if ((i + 1) % 50 === 0) console.log(`Imported ${i + 1}/${items.length}`);
}

Resources

• Linear Rate Limiting
• Query Complexity
• Best Practices