API Reference

Complete reference for every Slapshot API endpoint. All paths are relative to the base URL https://autopilot.slapshot.ai/api, and every request must include your x-api-key header.

Method	Endpoint	Description
POST	`/jobs`	Create a job
GET	`/jobs`	List jobs
GET	`/jobs/{job_id}`	Retrieve a job
DELETE	`/jobs/{job_id}`	Cancel a job
GET	`/inferences`	List inferences
GET	`/inferences/{inference_id}`	Retrieve an inference
DELETE	`/inferences/{inference_id}`	Cancel an inference

Jobs

A job is a batch of assets submitted together. Each asset lists the services to run on it, and every asset–service pair becomes an inference.

The job object

job_idstring (UUID)

Unique identifier for the job.

created_atstring (ISO 8601)

When the job was created.

created_bystring

The user who created the job.

metadataobject

The job-level metadata you supplied when creating the job.

percent_completenumber

Overall progress of the job, from 0 to 100.

totalinteger

Total number of inferences in the job.

total_pending · total_running · total_completed · total_failed · total_cancelledinteger

Number of inferences in each status.

pending_inferences · running_inferences · completed_inferences · failed_inferences · canceled_inferencesarray

The inferences in each status. Each entry includes inference_id, asset_id, asset_name, inference_type, frame_count, metadata and percent_complete.

Create a job

POST/jobs

Submits one or more assets for processing. Each asset must request at least one service. The response contains the new job_id and an inference_id for every asset–service pair, which you can use to track progress.

Body parameters

assetsarrayRequired

The assets to process. Each item is an asset object (below).

metadataobject

Optional key–value pairs to attach to the job, such as a project ID or submitter. Returned with the job and usable as a filter.

Asset object

source_pathstringRequired

Path to the source video, for example autopilot_reel/102825/Chocolate.mov.

servicesarrayRequired

The services to run on this asset. At least one of roto, tracking or depth_map is required.

services[].typestringRequired

Service to run: roto, tracking or depth_map.

services[].output_pathstring

Where outputs for this service are written.

services[].metadataobjecttracking only

Camera and lens hints for tracking. See tracking options. If omitted, values are calculated during inference.

services[].export_typestringdepth_map only

Output format for depth maps: jpg or mov. Defaults to jpg.

metadataobject

Optional key–value pairs for this asset, such as project and shot names.

Tracking options (services[].metadata)

working_fpsfloat

Working frames per second. Defaults to the asset’s frame rate.

lensfloat

Lens focal length in millimetres. Calculated during inference if omitted.

sensor_widthfloatwith sensor_height

Sensor width in millimetres. Required if sensor_height is provided.

sensor_heightfloatwith sensor_width

Sensor height in millimetres. Required if sensor_width is provided.

estimated_closest_pointfloat

Estimated closest depth value, in metres.

estimated_farthest_pointfloat

Estimated farthest depth value, in metres.

calculate_distortionstring

Solve for lens distortion. "True" or "False" (as a string, not a boolean). Defaults to "False".

fix_focal_lengthstring

Lock the focal length to lens. "True" or "False". Defaults to "False".

fix_sensor_sizestring

Lock the sensor size. "True" or "False". Defaults to "True".

Request

curl -X POST "https://autopilot.slapshot.ai/api/jobs" \
  -H "x-api-key: $SLAPSHOT_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "assets": [
      {
        "source_path": "autopilot_reel/102825/Chocolate.mov",
        "services": [
          { "type": "roto", "output_path": "output/110625" },
          {
            "type": "tracking",
            "output_path": "output/110625",
            "metadata": {
              "working_fps": 24.0,
              "lens": 30.0,
              "sensor_width": 24.0,
              "sensor_height": 24.0,
              "calculate_distortion": "True"
            }
          },
          { "type": "depth_map", "output_path": "output/110625", "export_type": "jpg" }
        ],
        "metadata": { "project": "Project Chocolate", "shot": "Chocolate_ch01" }
      },
      {
        "source_path": "autopilot_reel/102825/Cheering.mov",
        "services": [
          { "type": "roto", "output_path": "output/110625" }
        ],
        "metadata": { "project": "Project Chocolate", "shot": "Cheering_ch02" }
      }
    ],
    "metadata": { "project_id": 123, "project": "Project Chocolate", "submitter": "John Doe" }
  }'

import os
import requests

API_KEY = os.environ["SLAPSHOT_API_KEY"]
BASE_URL = "https://autopilot.slapshot.ai/api"
HEADERS = {"x-api-key": API_KEY}

payload = {
    "assets": [
        {
            "source_path": "autopilot_reel/102825/Chocolate.mov",
            "services": [
                {"type": "roto", "output_path": "output/110625"},
                {
                    "type": "tracking",
                    "output_path": "output/110625",
                    "metadata": {
                        "working_fps": 24.0,
                        "lens": 30.0,
                        "sensor_width": 24.0,
                        "sensor_height": 24.0,
                        "calculate_distortion": "True",
                    },
                },
                {"type": "depth_map", "output_path": "output/110625", "export_type": "jpg"},
            ],
            "metadata": {"project": "Project Chocolate", "shot": "Chocolate_ch01"},
        },
        {
            "source_path": "autopilot_reel/102825/Cheering.mov",
            "services": [{"type": "roto", "output_path": "output/110625"}],
            "metadata": {"project": "Project Chocolate", "shot": "Cheering_ch02"},
        },
    ],
    "metadata": {"project_id": 123, "project": "Project Chocolate", "submitter": "John Doe"},
}

resp = requests.post(f"{BASE_URL}/jobs", json=payload, headers=HEADERS)
resp.raise_for_status()
job = resp.json()
print("Job ID:", job["job_id"])
for asset in job["data"]:
    for inf in asset["inferences"]:
        print(asset["metadata"]["shot"], inf["inference_type"], inf["inference_id"])

Response

{
  "job_id": "d5ab291d-6b72-4a71-acf6-0f856c182cb1",
  "data": [
    {
      "asset_id": "890b2590-a381-4f8b-87c5-e3e2305eab7b",
      "inferences": [
        {
          "inference_id": "fd5f5f28-f16f-4f82-8aa8-fb53533b2c85",
          "inference_type": "roto",
          "status": "pending",
          "output_path": "output/110625"
        }
      ],
      "metadata": { "project": "Project Chocolate", "shot": "Chocolate_ch01" }
    },
    {
      "asset_id": "6c1fb1ab-f33d-407d-99ad-6bf5a3097ef5",
      "inferences": [
        {
          "inference_id": "e823aa39-17ed-47d7-a175-dae3081ee218",
          "inference_type": "roto",
          "status": "pending",
          "output_path": "output/110625"
        }
      ],
      "metadata": { "project": "Project Chocolate", "shot": "Cheering_ch02" }
    }
  ],
  "metadata": {
    "project_id": 123,
    "project": "Project Chocolate",
    "submitter": "John Doe"
  }
}

List jobs

GET/jobs

Returns a paginated list of your jobs, newest first. Filter by service, status or the metadata you supplied when creating the job.

Query parameters

servicestring

Only return results for this inference type: roto, tracking or depth_map.

statusstring

Only return results with this status: pending, running, completed, failed or cancelled.

metadataJSON string

Only return results whose metadata contains these key–value pairs, for example {"project_id": 123}. URL-encode the value.

pageinteger

Page number to return. Defaults to the first page.

Request

curl -G "https://autopilot.slapshot.ai/api/jobs" \
  -H "x-api-key: $SLAPSHOT_API_KEY" \
  --data-urlencode "service=roto" \
  --data-urlencode "status=pending" \
  --data-urlencode 'metadata={"project_id": 123}' \
  --data-urlencode "page=1"

import os
import requests

API_KEY = os.environ["SLAPSHOT_API_KEY"]
BASE_URL = "https://autopilot.slapshot.ai/api"
HEADERS = {"x-api-key": API_KEY}

import json

params = {
    "service": "roto",
    "status": "pending",
    "metadata": json.dumps({"project_id": 123}),
    "page": 1,
}

resp = requests.get(f"{BASE_URL}/jobs", headers=HEADERS, params=params)
resp.raise_for_status()
data = resp.json()
print(f"{data['count']} matching jobs")
for job in data["results"]:
    print(job["job_id"], f"{job['percent_complete']}%")

Response

{
  "count": 1000,
  "next": "https://autopilot.slapshot.ai/api/jobs?page=2",
  "previous": null,
  "results": [
    {
      "job_id": "98696f34-39fa-4bf5-b526-6368a32c0eb4",
      "created_at": "2026-01-30T06:18:20.574023Z",
      "created_by": "John Doe",
      "metadata": { "project": "Project Chocolate", "submitter": "John Doe", "project_id": 123 },
      "percent_complete": 0,
      "total": 2,
      "total_pending": 2,
      "total_running": 0,
      "total_completed": 0,
      "total_failed": 0,
      "total_cancelled": 0,
      "pending_inferences": [ ... ],
      "running_inferences": [],
      "completed_inferences": [],
      "failed_inferences": [],
      "canceled_inferences": []
    }
  ]
}

Retrieve a job

GET/jobs/{job_id}

Returns a single job, including overall progress and the status of every inference in it.

Path parameters

job_idstringRequired

The ID of the job to retrieve.

Request

curl "https://autopilot.slapshot.ai/api/jobs/98696f34-39fa-4bf5-b526-6368a32c0eb4" \
  -H "x-api-key: $SLAPSHOT_API_KEY"

import os
import requests

API_KEY = os.environ["SLAPSHOT_API_KEY"]
BASE_URL = "https://autopilot.slapshot.ai/api"
HEADERS = {"x-api-key": API_KEY}

job_id = "98696f34-39fa-4bf5-b526-6368a32c0eb4"

resp = requests.get(f"{BASE_URL}/jobs/{job_id}", headers=HEADERS)
resp.raise_for_status()
job = resp.json()
print(f"{job['percent_complete']}% complete")

Response

{
  "job_id": "98696f34-39fa-4bf5-b526-6368a32c0eb4",
  "created_at": "2026-01-30T06:18:20.574023Z",
  "created_by": "John Doe",
  "metadata": {
    "project": "Project Chocolate",
    "submitter": "John Doe",
    "project_id": 123
  },
  "percent_complete": 0,
  "total": 2,
  "total_pending": 2,
  "total_running": 0,
  "total_completed": 0,
  "total_failed": 0,
  "total_cancelled": 0,
  "failed_inferences": [],
  "running_inferences": [],
  "completed_inferences": [],
  "pending_inferences": [
    {
      "inference_id": "76f88fd3-6093-403d-ad2f-ff272c243cb2",
      "asset_id": "e201af6a-509a-44c8-a5b9-ceae27d06312",
      "asset_name": "Chocolate",
      "inference_type": "roto",
      "frame_count": 0,
      "metadata": {
        "shot": "Chocolate_ch01",
        "project": "Project Chocolate"
      },
      "percent_complete": 0
    },
    {
      "inference_id": "64850812-7244-40e4-80f2-44ef5ca8aa1d",
      "asset_id": "0e3a2fcc-19bc-4498-a9d6-b1ce0500b73b",
      "asset_name": "Cheering",
      "inference_type": "roto",
      "frame_count": 0,
      "metadata": {
        "shot": "Cheering_ch02",
        "project": "Project Chocolate"
      },
      "percent_complete": 0
    }
  ],
  "canceled_inferences": []
}

Cancel a job

DELETE/jobs/{job_id}

Cancels every inference in the job that hasn’t finished yet. Completed inferences and their outputs are not affected.

Path parameters

job_idstringRequired

The ID of the job to cancel.

Request

curl -X DELETE "https://autopilot.slapshot.ai/api/jobs/98696f34-39fa-4bf5-b526-6368a32c0eb4" \
  -H "x-api-key: $SLAPSHOT_API_KEY"

import os
import requests

API_KEY = os.environ["SLAPSHOT_API_KEY"]
BASE_URL = "https://autopilot.slapshot.ai/api"
HEADERS = {"x-api-key": API_KEY}

job_id = "98696f34-39fa-4bf5-b526-6368a32c0eb4"

resp = requests.delete(f"{BASE_URL}/jobs/{job_id}", headers=HEADERS)
resp.raise_for_status()
print(resp.json()["message"])

Response

{
  "message": "Job has been cancelled successfully.",
  "data": null
}

Inferences

An inference is one service (roto, tracking or depth_map) running on one asset. Use these endpoints to follow individual shots across all of your jobs.

The inference object

inference_idstring (UUID)

Unique identifier for the inference.

job_idstring (UUID)

The job this inference belongs to.

created_atstring (ISO 8601)

When the inference was created.

created_bystring

The user who created the job.

asset_idstring (UUID)

The asset being processed.

asset_namestring

Name of the source asset.

inference_typestring

roto, tracking or depth_map.

frame_countinteger

Number of frames in the asset.

metadataobject

The asset metadata you supplied when creating the job.

percent_completenumber

Progress of this inference, from 0 to 100.

statusstring

One of the statuses.

List inferences

GET/inferences

Returns a paginated list of your inferences across all jobs. Filter by service, status or asset metadata.

Query parameters

servicestring

Only return results for this inference type: roto, tracking or depth_map.

statusstring

Only return results with this status: pending, running, completed, failed or cancelled.

metadataJSON string

Only return results whose metadata contains these key–value pairs, for example {"project_id": 123}. URL-encode the value.

pageinteger

Page number to return. Defaults to the first page.

Request

curl -G "https://autopilot.slapshot.ai/api/inferences" \
  -H "x-api-key: $SLAPSHOT_API_KEY" \
  --data-urlencode "service=roto" \
  --data-urlencode "status=running" \
  --data-urlencode 'metadata={"project_id": 123}'

import os
import requests

API_KEY = os.environ["SLAPSHOT_API_KEY"]
BASE_URL = "https://autopilot.slapshot.ai/api"
HEADERS = {"x-api-key": API_KEY}

import json

params = {
    "service": "roto",
    "status": "running",
    "metadata": json.dumps({"project_id": 123}),
}

resp = requests.get(f"{BASE_URL}/inferences", headers=HEADERS, params=params)
resp.raise_for_status()
for inf in resp.json()["results"]:
    print(inf["asset_name"], inf["inference_type"], inf["status"], f"{inf['percent_complete']}%")

Response

{
  "count": 1000,
  "next": "https://autopilot.slapshot.ai/api/inferences?page=2",
  "previous": null,
  "results": [
    {
      "inference_id": "64db5dbb-9d37-4b3e-9844-23f79269da3b",
      "job_id": "392a2586-865b-4595-baa4-fa95c9af8183",
      "created_by": "John Doe",
      "created_at": "2026-01-30T06:56:20.541742Z",
      "asset_id": "2dcae016-7cc4-4943-89f3-9ef54fb458a0",
      "asset_name": "Chocolate",
      "inference_type": "roto",
      "frame_count": 0,
      "metadata": { "project_id": "123" },
      "percent_complete": 0,
      "status": "pending"
    }
  ]
}

Retrieve an inference

GET/inferences/{inference_id}

Returns a single inference with its current status and progress.

Path parameters

inference_idstringRequired

The ID of the inference to retrieve.

Request

curl "https://autopilot.slapshot.ai/api/inferences/64db5dbb-9d37-4b3e-9844-23f79269da3b" \
  -H "x-api-key: $SLAPSHOT_API_KEY"

import os
import requests

API_KEY = os.environ["SLAPSHOT_API_KEY"]
BASE_URL = "https://autopilot.slapshot.ai/api"
HEADERS = {"x-api-key": API_KEY}

inference_id = "64db5dbb-9d37-4b3e-9844-23f79269da3b"

resp = requests.get(f"{BASE_URL}/inferences/{inference_id}", headers=HEADERS)
resp.raise_for_status()
inf = resp.json()
print(inf["status"], f"{inf['percent_complete']}%")

Response

{
  "inference_id": "64db5dbb-9d37-4b3e-9844-23f79269da3b",
  "job_id": "392a2586-865b-4595-baa4-fa95c9af8183",
  "created_by": "John Doe",
  "created_at": "2026-01-30T06:56:20.541742Z",
  "asset_id": "2dcae016-7cc4-4943-89f3-9ef54fb458a0",
  "asset_name": "Chocolate",
  "inference_type": "roto",
  "frame_count": 0,
  "metadata": {
    "project_id": "123"
  },
  "percent_complete": 0,
  "status": "pending"
}

Cancel an inference

DELETE/inferences/{inference_id}

Cancels a single inference. Other inferences in the same job keep running.

Path parameters

inference_idstringRequired

The ID of the inference to cancel.

Request

curl -X DELETE "https://autopilot.slapshot.ai/api/inferences/64db5dbb-9d37-4b3e-9844-23f79269da3b" \
  -H "x-api-key: $SLAPSHOT_API_KEY"

import os
import requests

API_KEY = os.environ["SLAPSHOT_API_KEY"]
BASE_URL = "https://autopilot.slapshot.ai/api"
HEADERS = {"x-api-key": API_KEY}

inference_id = "64db5dbb-9d37-4b3e-9844-23f79269da3b"

resp = requests.delete(f"{BASE_URL}/inferences/{inference_id}", headers=HEADERS)
resp.raise_for_status()
print(resp.json()["message"])

Response

{
  "message": "Inference has been cancelled successfully.",
  "data": null
}

Getting Started

Camera Tracking

Rotoscoping

Troubleshooting

Pricing

API

Jobs

The job object

Create a job

List jobs

Retrieve a job

Cancel a job

Inferences

The inference object

List inferences

Retrieve an inference

Cancel an inference

What are your Feelings

How can we help you?

Jobs

The job object

Create a job

List jobs

Retrieve a job

Cancel a job

Inferences

The inference object

List inferences

Retrieve an inference

Cancel an inference

What are your Feelings

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