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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesGitHub API capacity depends on the credential, endpoint family and traffic pattern. Unauthenticated REST calls are limited to 60 requests per hour per originating IP. An ordinary authenticated user gets 5,000 requests per hour, while certain GitHub Enterprise Cloud contexts can receive 15,000. These hourly figures are only the primary limit: GitHub can also return 403 or 429 responses for secondary-limit violations before the hourly bucket is empty.
The reliable way to operate a client is to inspect the x-ratelimit-* headers on every response, use x-ratelimit-reset as a UTC Unix-epoch deadline, pace and serialize work, and honor retry-after when GitHub sends it.
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What a GitHub API rate limit actually measures
GitHub applies several controls rather than one universal counter. The primary limit is the published request budget for a credential and resource family over a time window. REST API requests normally draw from an hourly bucket. Other resources, such as Git LFS, have their own minute-based bucket, and GraphQL uses points instead of REST request counts.
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Secondary limits protect service capacity and abuse controls. They consider concurrency, per-endpoint activity, server CPU, content creation and authentication traffic. A client can therefore have primary requests remaining and still be throttled. Treat the status code and response headers from the request you just made as the immediate truth.
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Primary quotas by credential and resource
The following values are GitHub’s documented current policies. They are ceilings for the stated context, not a guarantee that every endpoint or operation will be available at that rate.
| Credential or API family | Primary allowance | Important qualification |
|---|---|---|
| Unauthenticated REST | 60 requests per hour | Applied per originating IP address. |
| Authenticated user (ordinary REST) | 5,000 requests per hour | Applies to authenticated user requests under the standard policy. |
| Qualifying GitHub Enterprise Cloud GitHub App or OAuth context | 15,000 requests per hour | Enterprise Cloud qualification is required; it is not a general increase for every account. |
| GitHub App installation token | At least 5,000 requests per hour; up to 12,500 for non-Enterprise installations | The scalable ceiling depends on repositories and organization users. Enterprise Cloud contexts can have different limits. |
GITHUB_TOKEN |
1,000 requests per hour per repository | For GitHub Enterprise Cloud resources, the documented value is 15,000 per hour per repository. |
| Git LFS API, unauthenticated | 300 requests per minute | This is a separate LFS bucket, not the ordinary REST hourly bucket. |
| Git LFS API, authenticated | 3,000 requests per minute | Authentication changes the LFS minute-based allowance. |
| GraphQL API | 5,000 points per hour per user | GraphQL has point-based accounting and a separate primary budget from REST. |
Authentication therefore raises the ordinary REST ceiling from 60 requests per hour to 5,000 for a user, but it does not erase secondary controls or merge all API families into one counter. Keep separate accounting for REST, GraphQL, LFS and any resource-specific bucket your application uses.
How authentication changes the limit
Unauthenticated REST requests
Requests without credentials are identified by the originating IP and receive 60 requests per hour. Multiple users behind one NAT, proxy or office gateway can consume that allowance together, so an apparently small application may hit the limit sooner than expected.
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Personal, OAuth and user-authorized requests
An ordinary authenticated user receives 5,000 REST requests per hour. GitHub also documents 15,000 requests per hour for qualifying GitHub Enterprise Cloud GitHub App or OAuth contexts. The higher figure is conditional on the Enterprise Cloud context; do not assume it for a personal token or an unrelated organization.
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GitHub App installation tokens
Installation tokens have a minimum of 5,000 requests per hour. For non-Enterprise installations, GitHub can scale the limit according to the number of repositories and organization users, up to 12,500 requests per hour. Your client should still read the live headers instead of hard-coding the maximum.
Actions workflow tokens
GITHUB_TOKEN is scoped to a repository. Its documented primary allowance is 1,000 requests per hour per repository, or 15,000 per hour per repository for GitHub Enterprise Cloud resources. A workflow that runs in many repositories has separate repository buckets, while a single busy repository can exhaust its own bucket.
Read the live limit and reset time from response headers
Inspect these headers on every response, especially when you are diagnosing a throttle:
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchx-ratelimit-limit: the primary ceiling for the relevant resource and window.x-ratelimit-remaining: units left after the request.x-ratelimit-used: units consumed in the current window.x-ratelimit-reset: the reset deadline as UTC Unix epoch seconds.x-ratelimit-resource: the resource family associated with the bucket, such as core, search or graphql.
The headers are authoritative for the request just made. If their values differ from a snapshot returned by GET /rate_limit, use the response headers for scheduling that request’s follow-up work. A limit can differ by credential, resource family and the current window, so there is no universal daily or clock-time reset.
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Convert x-ratelimit-reset to a human time
The value is epoch seconds, not milliseconds. On a Unix-like system, convert the value you received in the header with:
date -u -d @RESET_EPOCHReplace RESET_EPOCH with the numeric header value. In application code, multiply by 1,000 only when your runtime expects milliseconds. Always calculate the wait from the current UTC clock and add a small safety margin for clock skew and network transit.
What GET /rate_limit tells you—and what it does not
GET /rate_limit returns categorized snapshots, including families such as core, search, code_search, graphql, integration_manifest, dependency_snapshots and actions_runner_registration. Calling this endpoint does not consume the primary REST quota.
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It is an overview, not a replacement for per-response headers. The endpoint can itself count toward secondary limits, and a snapshot can become stale immediately after it is returned. Use it for dashboards or an occasional inventory; use x-ratelimit-* from the operation you just performed to decide whether that operation may be retried.
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Why GitHub returns 403 or 429 before the hourly quota is empty
A 403 or 429 can indicate a secondary limit rather than primary exhaustion. GitHub documents several secondary triggers:
- More than 100 concurrent requests shared across REST and GraphQL.
- More than 900 REST secondary points per minute to a single endpoint.
- More than 2,000 GraphQL points per minute.
- Excessive server CPU time: 90 seconds per 60-second interval overall, including a 60-second GraphQL component.
- Excessive content creation, generally above 80 operations per minute or 500 per hour.
- More than 2,000 OAuth access-token requests per hour.
Some endpoint costs and other triggers are undisclosed, and thresholds can change. Most REST GET, HEAD and OPTIONS requests cost one secondary point. Most POST, PATCH, PUT and DELETE requests cost five, although individual endpoints can differ. A burst of expensive mutations can therefore trigger protection while the primary hourly counter still shows substantial remaining capacity.
A safe retry and pacing procedure
Build throttling into the client rather than treating a 403 or 429 as an invitation to retry immediately.
- Check the primary headers. If
x-ratelimit-remainingis zero, stop sending requests to that resource and wait until the UTC epoch inx-ratelimit-reset. - Honor
retry-after. When a secondary-limit response includes this header, wait the specified number of seconds before trying again. - Use a conservative default. If the response is a secondary-limit failure and neither
retry-afternor a usable reset signal is present, wait at least one minute. - Increase delays exponentially. If failures continue, multiply the delay after each attempt, add jitter so workers do not wake simultaneously, and stop after a defined retry budget.
- Prevent a retry storm. Continuing to send requests while limited can lead to an integration ban. Queue work and fail it visibly when the retry budget is exhausted.
- Control concurrency. Prefer serial requests for sensitive or mutation-heavy jobs. During large sequences of mutative requests, pause at least one second between requests.
send request
read status and x-ratelimit-* headers
if remaining == 0: sleep until reset epoch
else if status is secondary-limit and retry-after exists: sleep retry-after
else if status is secondary-limit: sleep at least 60 seconds
on repeated failures: exponential backoff with jitter, then stopKeep the retry state per resource and credential. A search bucket can be constrained while the core bucket remains healthy, and a GraphQL budget should not be mistaken for available REST capacity.
REST and GraphQL use different primary budgets
GraphQL has a separate point-based primary limit of 5,000 points per hour per user. Query cost is calculated differently from REST request counting, and GraphQL has its own secondary-limit behavior. REST and GraphQL therefore require separate primary-budget tracking.
Some secondary protections are shared: the 100-concurrent-request ceiling covers REST and GraphQL together, and overall CPU controls can involve both. Moving traffic from REST to GraphQL may reduce request count but does not guarantee more capacity; measure query points and watch the headers returned by each API.
Choose an implementation strategy by risk, not just quota
| Approach | Primary budget to track | Main secondary exposure | Reset or retry signals |
|---|---|---|---|
| Unauthenticated REST | 60/hour per originating IP | Shared-IP bursts and endpoint point limits | x-ratelimit-reset; possible retry-after |
| Authenticated user token | 5,000/hour in ordinary contexts | Concurrency, CPU and mutation pacing | Per-response x-ratelimit-* and optional retry-after |
| GitHub App installation token | 5,000 minimum; scalable up to 12,500 for qualifying non-Enterprise installations | Installation-wide bursts and endpoint costs | Live headers for that installation and resource |
GITHUB_TOKEN |
1,000/hour per repository, or 15,000 for Enterprise Cloud resources | Busy workflow concurrency and repository-local bursts | Headers on each repository’s requests |
| GraphQL | 5,000 points/hour per user | Query points, 2,000-point-per-minute behavior and shared concurrency | GraphQL response headers plus secondary retry guidance |
| Git LFS API | 300/minute unauthenticated or 3,000/minute authenticated | Minute-rate bursts | Headers for the LFS resource |
For any option, log credential type, resource family, endpoint, status, remaining budget, reset epoch, retry-after value, latency and retry count. This turns an intermittent throttle into an observable scheduling problem.
Troubleshoot a 403 or 429 systematically
First determine which bucket is empty
Capture the complete response headers and check x-ratelimit-remaining, x-ratelimit-resource and x-ratelimit-reset. If remaining is zero, wait for that resource’s reset. Do not infer the state of search, GraphQL or LFS from the core bucket.
Then look for secondary signals
Check for retry-after, inspect the response body for GitHub’s throttling explanation, and review concurrency, per-endpoint rates, CPU-heavy queries and mutation volume. A nonzero primary remainder alongside a 403 or 429 strongly suggests secondary protection, although the exact trigger may not be disclosed.
Quick Recap
Reduce pressure before retrying
- Collapse duplicate reads and cache stable data.
- Lower worker concurrency, including across REST and GraphQL.
- Serialize writes and insert at least one second between large mutation sequences.
- Replace tight polling with event-driven or longer-interval checks.
- Apply exponential backoff with a finite retry budget.
Operational checklist
- Authenticate when the workload is legitimate and the credential scope is appropriate.
- Track REST, GraphQL, LFS and resource-specific buckets independently.
- Record every response’s
x-ratelimit-*headers. - Use the live reset epoch instead of a hard-coded clock time.
- Treat
GET /rate_limitas an overview, not an authorization to burst. - Keep total concurrency below the documented 100-request secondary threshold.
- Throttle expensive mutations and content creation.
- Honor
retry-after; otherwise wait at least one minute for a secondary-limit response. - Stop retrying after a defined budget to avoid an integration ban.
- Recheck GitHub’s documentation when policies, Enterprise Cloud status or API defaults change.
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