FiftyOne Agent#
The FiftyOne Agent is an AI-powered assistant built into the FiftyOne Enterprise App. It lets you work with your datasets using natural language. You can import data, run model inference, find duplicates, evaluate predictions, and more, all from a conversational interface.
Setup#
Contact your Customer Success representative to enable the FiftyOne Agent for your deployment.
Open any dataset in the FiftyOne Enterprise App. You will see a new Agent button in the upper-right corner of the App.
Configuring model providers#
The first time you open the Agent, you will be prompted to configure a model provider. The Agent supports over 100 providers, including Anthropic, OpenAI, Google, and more.
To add a provider, fill in the following fields:
Name: a label for this provider configuration
Provider: select from the list of supported providers
Endpoint (optional): use this to route requests to a custom URL, such as an internal enterprise gateway or a self-hosted model server
API key: your provider’s API key
Models: select one or more models to make available
Custom model names (optional): enter model identifiers that are not in the standard picker, such as non-standard IDs used by an enterprise gateway. Prefix with the provider slug (e.g.
openai/my-model-id) to ensure correct routing when the model name alone is ambiguousExtra headers (optional): static key-value HTTP headers sent with every request (e.g.
User-Agent, project tokens required by your gateway)Default: mark this provider as the default
You can click Test connection to verify your credentials before saving.
Note
Need help configuring a provider? Contact your Customer Success representative, or see Secrets for how to store API keys securely in your deployment.
Custom endpoints and enterprise gateways#
If your organization routes LLM traffic through an internal gateway or proxy, you can point the Agent at it using the Endpoint and Extra headers fields on any provider configuration.
Provider, match the API format, not the model brand
The Provider field controls the request format the Agent uses, not which model it calls. Set it to match what your gateway expects:
If your gateway exposes an OpenAI-compatible API (
/chat/completions), selectopenai, even if the underlying model is Claude or GeminiIf your gateway exposes the Anthropic Messages API (
/v1/messages) natively, selectanthropic
Endpoint, base URL only
Enter only the base URL of your gateway — do not include the API path. The Agent appends the correct path automatically based on the provider you selected. For example:
âś“ https://gateway.internal/api/openai/v1
âś— https://gateway.internal/api/openai/v1/chat/completions
Model names, always prefix with the provider slug
Use the model identifier your gateway provides, prefixed with the provider slug. The prefix prevents the model ID from being misrouted to a cloud provider instead of your gateway, and is stripped before the name is sent:
openai/your-model-id
anthropic/your-model-id
This is especially important when your gateway returns model IDs that start
with a vendor name (e.g. anthropic.claude-sonnet). Without the prefix,
those IDs may be misrouted to a cloud provider instead of your gateway.
Use Test connection to verify the full configuration works before saving.
Use Extra headers for any additional authentication or routing headers your
gateway requires, such as project tokens or custom User-Agent values.
Per-user attribution
When a custom endpoint is configured, the Agent automatically adds an
X-FiftyOne-User-Email header to every request containing the email address
of the currently logged-in user. Gateways can use this header to attribute
requests to individual users rather than a shared system account, which is
useful for enforcing per-user quotas or audit logging.
Note
Admins are responsible for ensuring that the configured endpoint’s data handling and retention align with their organization’s privacy policy.
Using the agent#
Once a provider is configured, you can start a conversation with the Agent. Type any task in plain language and the Agent will execute it against your dataset.
Some examples of what you can ask:
“Find and remove duplicate images from this dataset”
“Run object detection and show me low-confidence predictions”
“Export this dataset to COCO format”
To start a new conversation, click the + button.
To return to a previous conversation, click History.
Skills#
The Agent ships with a set of built-in skills that cover the most common computer vision workflows. Skills are structured instructions that tell the agent exactly how to perform a task, step by step.
Note
You can also build and add your own custom skills to extend the agent’s capabilities. See Developing skills for full instructions.