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The Openlayer workspace is organized around projects. Each project represents an AI initiative within your organization and provides a structured space to evaluate, observe, and govern your AI efforts. This guide walks you through creating a project using both the UI and programmatically.
Prerequisite: you need an Openlayer account to follow this guide.
Create new project

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Create a project in the UI

The UI is the quickest way to set up a new project.
  1. Log into your Openlayer account
  2. Navigate to the workspace home page.
  3. Click the “Create” button on the top right.
  4. Follow the onboarding prompts to set the project name, type, and mode, and review suggested tests for your use case.
After the project is created, you are ready to start using it to evaluate your AI system.

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Create a project using the REST API or CLI

You can also create projects using our REST API or CLI tool. This is particularly useful for automation workflows or CI/CD integration. To authenticate, you need to create an API key.
  • REST API
  • CLI
You can create a project by making a POST request to the /projects endpoint. Refer to the Create project API reference page for details.
curl --request POST \
  --url https://api.openlayer.com/v1/projects \
  --header 'Authorization: Bearer <YOUR_API_KEY>' \
  --header 'Content-Type: application/json' \
  --data '{
    "name": "My Project",
    "description": "My project description.",
    "taskType": "llm-base"
  }'

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Next steps

Once you have created a project, you can immediately start evaluating your AI system. Common paths are:

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FAQ

Yes, Openlayer supports role-based access control (RBAC) at the project level.You can create access groups with different roles, such as “Admin,” “Member,” “Viewer,” and others. Refer to the Access groups page for details.
A project on Openlayer has different modes, such as “Development” and “Monitoring.” Each mode has a particular purpose in the AI lifecycle:
  • Development mode is used pre-production and helps you iterate on your AI system, test each version, and track improvements across them.
  • Monitoring mode is used in production and helps you observe and test an AI system that is serving live requests.
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