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Short answer: an unauthenticated GitHub REST request gets 60 requests per hour per originating IP. An ordinary authenticated user gets 5,000 requests per hour. Qualifying GitHub Enterprise Cloud GitHub App or OAuth contexts can receive 15,000 requests per hour. GitHub App installation tokens start at 5,000 requests per hour and can scale to 12,500 for eligible non-Enterprise installations, while GITHUB_TOKEN is limited per repository.
Those hourly figures are only the primary limit. GitHub also applies secondary controls for concurrency, request bursts, endpoint points, CPU time and content creation. A client can therefore receive HTTP 403 or 429 while its hourly counter still shows requests remaining. The response headers on the request you just made—especially x-ratelimit-remaining and x-ratelimit-reset—are the source of truth for what to do next.
GitHub API request quotas at a glance
GitHub assigns limits by authentication method and resource family. The ordinary REST quota is an hourly bucket, but several specialized resources use different budgets. The following values are the current limits documented by GitHub.
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| Request context | Primary limit | How the limit is scoped |
|---|---|---|
| Unauthenticated REST | 60 requests per hour | Per originating IP address |
| Authenticated user (ordinary REST) | 5,000 requests per hour | Per authenticated user |
| Qualifying GitHub Enterprise Cloud GitHub App or OAuth context | 15,000 requests per hour | Per qualifying authenticated context |
| GitHub App installation token | At least 5,000 requests per hour; up to 12,500 for eligible non-Enterprise installations | Installation-based, with scaling tied to repositories and organization users |
GITHUB_TOKEN |
1,000 requests per hour per repository; 15,000 per hour per repository for GitHub Enterprise Cloud resources | Repository-scoped token |
| Git LFS API, unauthenticated | 300 requests per minute | LFS API bucket |
| Git LFS API, authenticated | 3,000 requests per minute | LFS API bucket |
These are primary limits, meaning the main allowance GitHub counts for the relevant resource. They do not guarantee that a burst of requests will be accepted: secondary protections can intervene first.
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What authentication changes
Unauthenticated REST calls
Without credentials, GitHub allows 60 REST requests per hour from an originating IP address. This is easy to exhaust when several users, containers or services share one public address, because they draw from the same bucket. IP-based accounting also means that moving a process to another address changes the bucket but is not a dependable capacity strategy.
Authenticated user requests
Authentication raises the ordinary REST allowance to 5,000 requests per hour for the authenticated user. A personal access token, OAuth access token or another user-authenticated credential identifies the caller, but the exact resource and endpoint still matter. Search, code search, GraphQL and other categories can expose separate counters.
GitHub Enterprise Cloud and app contexts
Qualifying GitHub Enterprise Cloud GitHub App or OAuth contexts can receive 15,000 requests per hour. “Qualifying” matters: the higher value is not a blanket increase for every account or token associated with an Enterprise organization.
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An installation token has a minimum primary allowance of 5,000 requests per hour. For non-Enterprise installations, GitHub can scale that allowance according to the installation’s repositories and organization users, up to 12,500 requests per hour. Design a client to read the live limit rather than assuming it has reached the maximum.
GITHUB_TOKEN in Actions
The automatically provided GITHUB_TOKEN is limited to 1,000 requests per hour per repository. For GitHub Enterprise Cloud resources, the documented value is 15,000 requests per hour per repository. This repository scope is different from a user-wide personal token, so several repositories do not necessarily share one hourly counter.
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How to find your remaining quota and reset time
Inspect the rate-limit headers on every response that your client receives. The important fields are:
| Header | Meaning |
|---|---|
x-ratelimit-limit |
The primary limit assigned to the request’s resource and authentication context. |
x-ratelimit-remaining |
How many primary requests remain in the current window after the request. |
x-ratelimit-used |
How many requests have been consumed in the current window. |
x-ratelimit-reset |
The reset instant as a UTC Unix epoch-seconds timestamp. |
x-ratelimit-resource |
The resource family whose bucket was charged, such as core, search or graphql. |
The reset value is not a clock time such as “the top of the hour.” It is an epoch timestamp for the specific caller, resource and rate-limit window. Convert it to a local display time only for logging or user messages; use the numeric value for scheduling.
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wait_seconds = max(0, int(reset_epoch) - current_unix_time())GitHub states that these headers are authoritative for the request just made. If a header and a separate overview response disagree, follow the header from the live API response.
What GET /rate_limit tells you—and what it does not
GET /rate_limit returns a categorized snapshot. Depending on the account and enabled features, the response can include buckets 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. It can still count toward secondary protections, so polling it on every request is not harmless. A practical design is to record the headers from normal API responses and call GET /rate_limit when an operator needs a broader inventory of categories or when diagnosing an unexpected bucket.
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Do not use the overview as a replacement for response headers. The overview is a snapshot that can become stale between the time it is fetched and the time another request is processed. When the two disagree, the headers on the request being handled win.
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GitHub’s secondary limits are separate from the primary hourly allowance. They protect service capacity and abuse-sensitive operations. A response can therefore show a positive x-ratelimit-remaining value and still be rejected.
| Secondary trigger documented by GitHub | Threshold or behavior |
|---|---|
| Concurrent requests | More than 100 concurrent requests shared across REST and GraphQL |
| REST endpoint points | More than 900 REST points per minute to a single endpoint |
| GraphQL points | More than 2,000 GraphQL points per minute |
| Overall CPU time | More than 90 seconds of CPU time per 60 seconds, including up to 60 seconds attributed to GraphQL |
| Content creation | Generally more than 80 content-creating actions per minute or 500 per hour |
| OAuth token traffic | More than 2,000 OAuth access-token requests per hour |
Some endpoint costs and other triggers are undisclosed, and GitHub can change thresholds. Most REST GET, HEAD and OPTIONS requests cost one secondary point. Most POST, PATCH, PUT and DELETE requests cost five points, although individual endpoints can differ.
HTTP 403 is commonly used for a secondary-limit response, while HTTP 429 can also indicate that a rate or abuse control was triggered. Read the response body and headers instead of treating either status as proof that the primary hourly bucket is exhausted.
A safe retry and pacing procedure
Implement rate handling as a queueing policy, not as an unconditional retry loop. The following order prevents a client from making a secondary incident worse.
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- Record the response metadata. Save the status code, response body,
x-ratelimit-*headers and anyretry-afterheader for diagnostics. - Handle an empty primary bucket first. If
x-ratelimit-remainingis zero, wait until the UTC epoch inx-ratelimit-reset. Recalculate the delay at the moment you schedule the next attempt. - Honor
retry-after. On a secondary-limit response, wait the number of seconds specified byretry-afterbefore sending another request. - Use a minimum pause when no signal is supplied. If neither a usable reset time nor
retry-afteris present, wait at least one minute before retrying. - Back off exponentially if the block persists. Increase the delay after each continuing failure and cap the number of retries with a defined budget. Do not keep sending requests while limited; GitHub warns that continued traffic can lead to an integration ban.
- Reduce concurrency. Serialize work where possible. For large sequences of mutative requests, pause at least one second between requests rather than issuing a burst.
- Make retries idempotent where possible. Before retrying a write, determine whether the earlier request may have succeeded. Use an application-level operation identifier or a follow-up read when your workflow cannot safely repeat a mutation.
A queue should share a concurrency limit across REST and GraphQL because the documented 100-request ceiling covers both. Separate primary counters can still be maintained for each resource family.
REST and GraphQL use different primary budgets
GraphQL has a separate point-based primary limit: GitHub documents 5,000 points per hour per user. Each query receives a calculated cost rather than simply consuming one REST request. GraphQL also has its own point accounting for secondary controls, including the documented 2,000-point-per-minute threshold.
Track REST and GraphQL primary usage separately. A full REST core bucket does not imply that the GraphQL budget is full, and a healthy GraphQL budget does not authorize an unlimited REST burst. Shared secondary protections, such as concurrent-request limits and CPU controls, still apply across both protocols.
Choosing an authentication and request strategy
Choose the credential based on who owns the data and how the workload is isolated. Compare the options along five axes: authentication type, resource family, primary budget, secondary exposure and the retry signals available in response headers.
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| Approach | Best fit | Primary-budget consideration | Operational caution |
|---|---|---|---|
| Unauthenticated REST | Small public lookups or prototypes | 60 per hour per originating IP | Shared IP traffic can exhaust the bucket unexpectedly. |
| Personal or user OAuth token | Work performed on behalf of one user | Usually 5,000 REST requests per hour per authenticated user; qualifying Enterprise Cloud OAuth contexts can be 15,000 | Protect the credential and avoid using one user token for unrelated tenants. |
| GitHub App user token | Actions requiring a user’s permissions through an app | Qualifying Enterprise Cloud contexts can receive 15,000 per hour | Still subject to secondary controls and endpoint-specific costs. |
| GitHub App installation token | Service-to-organization or service-to-repository automation | At least 5,000 per hour; eligible non-Enterprise installations can scale to 12,500 | Read the live limit because installation size affects scaling. |
GITHUB_TOKEN |
Workflow actions inside a repository | 1,000 per hour per repository, or 15,000 for GitHub Enterprise Cloud resources | Do not assume a repository token shares a user-wide bucket. |
Regardless of credential, centralize request scheduling so every worker sees the same counters. A dozen workers that each believe they have a full bucket can collectively trigger concurrency or burst controls.
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Troubleshooting common symptoms
“I received 403, but x-ratelimit-remaining is positive.”
Treat this as a likely secondary-limit or endpoint-policy response, not as proof that authentication failed. Check for retry-after, inspect the response body, stop concurrent work, and apply the waiting procedure. Review recent write volume, per-endpoint request points and total concurrency.
“I received 429.”
Handle 429 as a rate-control signal. Honor retry-after when present. If it is absent, wait at least one minute, then increase delays exponentially if failures continue. Keep the retry budget finite.
“The reset time is not at the next hour.”
That is expected. x-ratelimit-reset is a UTC epoch for the current caller, resource and window, not a universal clock boundary. Convert the value to a timestamp for display, but schedule from the numeric epoch.
“GET /rate_limit says one thing and my response header says another.”
Use the headers from the request just made. The overview endpoint can be stale or can describe a different resource category, while the response headers identify the bucket actually charged.
“A token that worked yesterday now has a different limit.”
Check the authentication context and x-ratelimit-limit on the current response. Installation scaling, Enterprise Cloud qualification, repository scope and resource family can all change which bucket applies. Avoid hard-coding a single quota.
Quick Recap
Production checklist
- Authenticate requests whenever the workload is more than a small public lookup.
- Log
x-ratelimit-limit,x-ratelimit-remaining,x-ratelimit-used,x-ratelimit-resetandx-ratelimit-resource. - Maintain separate primary counters for REST resources and GraphQL.
- Share one queue and concurrency budget across REST and GraphQL workers.
- Serialize large mutation batches and pause at least one second between mutative requests.
- Honor
retry-after; otherwise wait for reset or at least one minute when the signal is missing. - Use exponential backoff and a finite retry budget.
- Alert on rising 403/429 rates, falling remaining capacity and repeated secondary-limit responses.
- Recheck GitHub’s current documentation before deploying because quotas, Enterprise Cloud exceptions and secondary thresholds can change.
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