GET /v1/sectors

Sector breakdown of all discovered entities across your tracked accounts.

Cost: 4 credits per call

curl "https://frontrun.vc/v1/sectors" \
  -H "X-API-Key: your_api_key"

Parameters

No parameters.

Response

Example, truncated to the top of each list:

{
  "total_entities": 12480,
  "classified_entities": 6310,
  "sectors": [
    { "name": "AI", "count": 1042, "percentage": 17 },
    { "name": "DeFi", "count": 604, "percentage": 10 },
    { "name": "Infrastructure", "count": 528, "percentage": 8 },
    { "name": "Trading", "count": 461, "percentage": 7 },
    { "name": "payments_banking", "count": 388, "percentage": 6 },
    { "name": "Gaming", "count": 351, "percentage": 6 },
    { "name": "Social & Creator", "count": 297, "percentage": 5 },
    { "name": "Payments", "count": 214, "percentage": 3 },
    { "name": "Prediction Markets", "count": 142, "percentage": 2 },
    { "name": "RWA", "count": 118, "percentage": 2 },
    { "name": "Wallets & Identity", "count": 96, "percentage": 2 },
    { "name": "Security & Auditing", "count": 74, "percentage": 1 }
  ],
  "entity_types": [
    { "name": "person", "count": 4102 },
    { "name": "company", "count": 1348 },
    { "name": "project", "count": 271 },
    { "name": "unknown", "count": 208 },
    { "name": "media", "count": 141 },
    { "name": "vc_firm", "count": 96 },
    { "name": "community", "count": 62 },
    { "name": "institution", "count": 44 },
    { "name": "newsletter", "count": 38 }
  ],
  "cost_credits": 4,
  "balance_credits": 9992
}

Sector names are data, not a fixed enum

There is no closed list of sectors. Sector names come from the classifier, so the vocabulary is data-driven and it grows. That means two things:

Call /v1/sectors to discover the exact strings for your own coverage. The sectors in the example above are real values from the classification data, but yours depend on who you track. Do not hardcode a taxonomy from this page.
The same idea can appear under more than one spelling. RWA and rwa, or Social & Creator and social_creator, are separate rows. Two naming conventions coexist in the data.

Sector values are matched case-insensitively as substrings by /v1/search, /v1/follows/enriched, and /v1/feed. So ?sector=pay matches both Payments and payments_banking, and ?sector=rwa matches RWA and rwa. Use a short stem when you want to catch every variant, and the full string when you want one specific bucket.

Noise labels (unknown, general_crypto, other, n/a, none, and empty) are filtered out of sectors before the response is built. They are not filtered out of entity_types, which is why unknown can appear there.

Field	Type	Description
`total_entities`	integer	Total unique entities discovered across all tracked accounts.
`classified_entities`	integer	Entities that have a classification record. Typically well under `total_entities`: anything not yet classified is not counted here.
`sectors[].count`	integer	Entities carrying this exact sector string.
`sectors[].percentage`	integer	Percentage of classified entities in this sector, rounded to a whole number. Percentages do not sum to 100 because noise sectors are filtered out.
`entity_types[].name`	string	Classified entity type, for example `person`, `company`, `project`, `media`, `vc_firm`. Also data-driven. Pass one as `entity_type` to `/v1/search` or `/v1/follows/enriched`.

GET /v1/feed GET /v1/discover