Roboflow + ChatGPT
Find datasets, label images, train models and build Workflows in Roboflow by asking ChatGPT, with the Roboflow plugin connected to your workspace.
How to connect ChatGPT to Roboflow
5 stepsInstall the Roboflow plugin
Open Plugins in the ChatGPT sidebar, search for Roboflow, and select Install plugin.
Connect your Roboflow account
Select Connect when prompted and sign in to Roboflow, or create a free account.
Start a chat with @Roboflow
Open a new chat and type @Roboflow, or choose Roboflow from the + menu in the message box.
Check your workspace
Ask "Show me the projects in my workspace" and confirm ChatGPT returns your workspace and projects.
Describe what you want to build
Ask ChatGPT to find a dataset, label your images, train a model, or build a Workflow.
What teams build with ChatGPT
Find a dataset to start from
Compare Roboflow Universe datasets by classes, image count and license, then fork one into your workspace.
Label a batch of images
Upload a ZIP, preview an Auto Label model on a few images, then label the rest.
Train and evaluate a model
Generate a dataset version, train RF-DETR, and get mAP, precision and recall explained.
Questions
No, the plugin itself has no extra cost beyond your Roboflow plan. Training and Auto Label use Roboflow credits, and ChatGPT shows the estimated cost before a job starts.
It can search and fork Universe datasets, create projects, upload images, run Auto Label, manage splits, generate dataset versions, train models, summarize evaluation results, and build and save Workflows.
It builds, validates and saves Workflows, and gives you instructions for running them on Roboflow's hosted API.
Why are companies choosing Roboflow?
The end-to-end platform for vision AI: create datasets, train models and deploy to production.
Flexible deployment modes
Move from a scalable cloud API to edge hardware like NVIDIA Jetson without managing complex dependencies.
Integrated workflow builder
Combine multiple AI models, custom business logic and third-party integrations into one production-ready pipeline in a low-code editor.
Model monitoring and alerts
Dashboards track confidence, inference time and request volume for your deployed models, and email alerts tell your team when confidence suddenly drops.
