Send Authorization: Bearer YOUR_API_KEY. Get an API key →
Parameters
Send these fields in a flat JSON body. Add _run to choose execution options.
querystringRequiredSearch query. Minimum length: 1. Maximum length: 2000.
limitintegerOptionalMaximum number of items to return. Default: 25. Minimum: 1. Maximum: 100.
afterstringOptionalCursor returned by the previous response.
beforestringOptionalCursor returned by the previous response.
sortstringOptionalSort order. Global search supports relevance, new, top, and comment_count; subreddit search also supports hot and comments. Allowed: "relevance", "new", "top", "comment_count", "hot", "comments".
subredditstringOptionalSubreddit name, r/name, or subreddit URL.
timeframestringOptionalTimeframe for results where supported. Allowed: "hour", "day", "week", "month", "year", "all".
Response
Every response describes a run. Read result once run_status is succeeded.
run_id- The run to track. Keep it when contacting support.
run_status- Queued, running, waiting for sign-in, succeeded, or failed. Check this even after an HTTP 200.
status_url- Poll this URL while the run is pending. Keep the same run instead of starting another request.
result- The endpoint’s returned data. The example is illustrative; actual fields may be empty or unavailable.
error- On failure, inspect its code, message, retryable and action fields.
View the complete output schema
{
"type": "object",
"$schema": "https://json-schema.org/draft/2020-12/schema",
"required": [
"results",
"count"
],
"properties": {
"after": {
"type": "string"
},
"count": {
"type": "integer"
},
"before": {
"type": "string"
},
"results": {
"type": "array",
"items": {
"type": "object",
"required": [
"id",
"title",
"author",
"subreddit",
"score",
"upvoteRatio",
"numComments",
"created",
"url",
"isVideo"
],
"properties": {
"id": {
"type": "string"
},
"url": {
"type": "string"
},
"score": {
"type": "number"
},
"title": {
"type": "string"
},
"author": {
"type": "string"
},
"created": {
"type": "number"
},
"isVideo": {
"type": "boolean"
},
"selftext": {
"type": "string"
},
"subreddit": {
"type": "string"
},
"thumbnail": {
"type": "string"
},
"numComments": {
"type": "number"
},
"upvoteRatio": {
"type": "number"
}
},
"additionalProperties": false
}
}
},
"additionalProperties": false
}View a polled run response
{
"run_id": "00000000-0000-4000-8000-000000000001",
"task_key": "reddit-search",
"task_name": "Reddit Search",
"run_status": "succeeded",
"status_url": "/v1/runs/00000000-0000-4000-8000-000000000001",
"result": {
"count": 2,
"results": [
{
"id": "1k9x2ab",
"title": "What are the best resources for learning machine learning in 2026?",
"author": "ml_beginner_42",
"subreddit": "MachineLearning",
"score": 187,
"upvoteRatio": 0.96,
"numComments": 54,
"created": 1773328800,
"url": "https://www.reddit.com/r/MachineLearning/comments/1k9x2ab/what_are_the_best_resources_for_learning_machine/",
"isVideo": false,
"selftext": "I have a solid Python background and want to move into practical ML projects. Looking for courses, books, and hands-on resources.",
"thumbnail": "https://styles.redditmedia.com/t5_2r3gv/styles/communityIcon_example.png"
},
{
"id": "1k93yz7",
"title": "Show HN: I built a small model to classify research paper abstracts by topic",
"author": "latent_vector",
"subreddit": "programming",
"score": 92,
"upvoteRatio": 0.91,
"numComments": 18,
"created": 1773242400,
"url": "https://www.reddit.com/r/programming/comments/1k93yz7/show_hn_i_built_a_small_model_to_classify/",
"isVideo": false,
"selftext": "It uses a lightweight embedding model and a simple classifier. I wrote up the approach and linked the repo in the comments.",
"thumbnail": "https://b.thumbs.redditmedia.com/example-thumbnail.png"
}
]
},
"error": null
}Errors & retries
400Fix the fields listed inerrorsbefore retrying.428Connect the requested website account using the returned sign-in link.429Wait for the duration inRetry-After.5xxRetry transient failures with backoff. Keep the request ID.
A failed run has its own error inside the response. Retry it only when error.retryable is true.
More examples
Async request with polling
RUN=$(curl -s -X POST "https://api-ingress-worker-staging.browsable-staging.workers.dev/v1/reddit/search" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query":"machine learning"}')
RUN_ID=$(echo "$RUN" | jq -er '.run_id // empty') || { echo "$RUN" >&2; exit 1; }
POLL_DELAY=3
while true; do
RESULT=$(curl -s -H "Authorization: Bearer YOUR_API_KEY" "https://api-ingress-worker-staging.browsable-staging.workers.dev/v1/runs/$RUN_ID")
STATUS=$(echo "$RESULT" | jq -r '.run_status')
if [[ "$STATUS" =~ ^(succeeded|failed|errored|cancelled|canceled|expired)$ ]]; then
echo "$RESULT"
break
fi
sleep "$(awk "BEGIN { print $POLL_DELAY * (0.9 + ($RANDOM % 21) / 100) }")"
POLL_DELAY=$((POLL_DELAY < 30 ? POLL_DELAY * 2 : 30))
[ "$POLL_DELAY" -gt 30 ] && POLL_DELAY=30
doneWait-for-results request
curl -X POST "https://api-ingress-worker-staging.browsable-staging.workers.dev/v1/reddit/search" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"query":"machine learning","_run":{"async":false}}'Canonical & compatibility routes
You can also send the same JSON body to /v1/tasks/reddit-search/runs.