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Protocol-level facts for Roboflow REST and Inference APIs — URL patterns, auth, parameters, error codes, and SDK quick-start. For deployment strategy and Workflow execution patterns, see roboflow-inference.
デフォルトでは、最初にソースを確認する Prompt が選択されています。直接コマンドに切り替えるか、ローカルコピーをダウンロードすることもできます。
インストールを決める前に、SKILL.md と SkillsMP に表示されている付属ファイルをお読みください。
SOC 職業分類に基づく
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
直接コマンドでは確認用 Prompt が省略されます。実行前にソースを確認してください。
npx skills add https://github.com/roboflow/computer-vision-skills --skill roboflow-api-referenceコマンドは1行のまま表示されます。コピー前に横へスクロールして全体を確認してください。
ローカルで確認しますか？SkillsMP が現在取得できるファイルをダウンロードできます。
SKILL.md を表示中
Use when training Roboflow models, improving accuracy, or setting up a production feedback loop — covers architecture selection, model IDs, checkpoints, evaluation metrics, the iterative improvement playbook, and Active Learning through the Project Model Workflow block.
Use when running a Roboflow Workflow over many images or videos with batch processing — Asset Library jobs (the platform stages workspace images for you) or staged jobs (you stage external local/cloud files with the inference-cli), plus monitoring jobs, downloading results, and routing imports to datasources (bucket mirror) instead.
Use when connecting cloud storage (AWS S3 / S3-compatible or Google Cloud Storage) to Roboflow to mirror images into a workspace — creating storage credentials, defining datasources (bucket-mirror configs), selecting objects with glob rules, validating access, and running/scheduling mirror jobs.
| name | roboflow-api-reference |
| description | Protocol-level facts for Roboflow REST and Inference APIs — URL patterns, auth, parameters, error codes, and SDK quick-start. For deployment strategy and Workflow execution patterns, see roboflow-inference. |
For agents — source-of-truth: This skill is authored in
roboflow/computer-vision-skillsand shipped with the Roboflow plugin. If your client has loaded the plugin (you'll seeroboflow:<name>skills in your available skills list), use those local skills — they're read fresh from disk every session. The same content served as MCP resources atroboflow://skills/<name>/...is a fallback for clients without the plugin and may lag this repo. Don't callReadMcpResourceToolforroboflow://skills/...URIs when a localroboflow:<name>skill is available.
Tip: If you're connected to the Roboflow MCP server, prefer its tools (
projects_*,versions_*,models_*,workflows_*,images_*, …) over raw REST calls — they handle auth, pagination, and typed responses for you. The REST patterns below stay relevant if you're not using MCP.
| Host | Base URL | Purpose |
|---|---|---|
| Platform API | https://api.roboflow.com | CRUD for projects, images, versions, training, upload |
| Serverless Inference | https://serverless.roboflow.com | Model inference + Workflow execution |
| Dedicated Deployment | https://<name>.roboflow.cloud | Private GPU inference (same API as serverless) |
| Self-hosted Inference | http://localhost:9001 | Local inference server via inference package |
Use the inference-sdk Python package as the preferred client for all inference hosts. It handles auth, retries, and response parsing.
| Method | Where | Format |
|---|---|---|
| Query parameter | All hosts | ?api_key=YOUR_KEY |
| Request body | Platform API + Workflow inference | "api_key": "YOUR_KEY" in JSON body |
| Header | MCP server (mcp.roboflow.com) | x-api-key: YOUR_KEY (handled automatically by MCP) |
API keys are workspace-scoped. Get yours from Workspace Settings > API Keys in the Roboflow dashboard (app.roboflow.com/{workspace}/settings/api). Personal API keys are at /settings/account → API Keys tab.
| SDK | Install | Primary Use |
|---|---|---|
Python (inference-sdk) | pip install inference-sdk | Inference via InferenceHTTPClient |
Python (roboflow) | pip install roboflow | Upload, training, project management |
JavaScript (roboflow.js) | Browser script tag | Real-time on-device web inference |
| iOS (Swift) | CocoaPods/SPM | On-device mobile inference |
from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
api_url="https://serverless.roboflow.com", # or dedicated URL, or localhost
api_key="YOUR_KEY"
)
result = CLIENT.infer("image.jpg", model_id="your-project/1")
import roboflow
rf = roboflow.Roboflow(api_key="YOUR_KEY")
project = rf.workspace("my-workspace").project("my-project")
# Upload
project.upload(image_path="image.jpg", split="train")
# Inference
model = project.version(1).model
result = model.predict("image.jpg", confidence=40).json()
| Task | Host to Use |
|---|---|
| Run model inference | serverless.roboflow.com |
| Run Workflows | serverless.roboflow.com |
| Upload images | api.roboflow.com |
| Manage projects/versions | api.roboflow.com |
| Start training | api.roboflow.com |
| High-throughput / SLA inference | Dedicated deployment URL |
| Air-gapped / on-prem inference | Self-hosted localhost:9001 |
| Real-time video / webcam / RTSP | WebRTC via inference_sdk.webrtc against serverless or local — see roboflow://skills/roboflow-inference/workflows ("Video Stream" section). Not a plain HTTP call. |
roboflow://skills/roboflow-api-reference/inference — inference URL patterns, request/response formatsroboflow://skills/roboflow-api-reference/rest-api — platform REST API endpoints (CRUD, upload, training)roboflow://skills/roboflow-api-reference/api-key-management — creating and managing API keys via REST, MCP tools, and Python CLI. When creating a key, scope it to least privilege if the workspace has Advanced API Keys; if not, tell the user scoped keys are an Enterprise feature.