Choose an interface
Use the console for inspection, HTTP for integration, and the tool boundary for agent memory.
All interfaces describe the same objects: projects, subjects, jobs, and adapter versions. Choose the interface that fits the caller; keep job IDs and subject IDs stable when moving between them.
Console
The console is the place to create API keys, inspect requests, review adapters, use the playground, and manage project spending. It authenticates with your session, so you do not paste a project key into the playground.
Use it to understand a response or investigate a specific job. A request sent through the API appears in the same project history as a playground request. The console does not create a second set of adapters or a separate billing balance.
HTTP API
The public API is the portable integration surface. It accepts JSON over HTTPS and returns asynchronous jobs for inference and internalization. You can use any server-side HTTP client capable of setting bearer and idempotency headers.
Choose HTTP when you need a language not covered by the workspace SDK, want to generate a client from OpenAPI, or prefer to own transport behavior. Read API conventions before implementing retries or pagination.
/v1/chat/completions provides OpenAI-compatible text, function calls, and buffered SSE. Existing frameworks can connect with a base URL, key, and model ID. Native /v1/inferences and /v1/internalizations return immediate durable job receipts. See Integrations for setup and compatibility limits.
TypeScript client
The repository includes a private workspace package, @internalize/sdk. It provides typed submission, job reads, explicit activation, and a polling helper. It does not automatically retry writes. The package is not currently published to a public registry, so an external application should use HTTP or code shared through an authorized repository checkout.
See TypeScript for the exact constructor and method signatures. Do not add /v1 to the client's baseUrl; it accepts the platform origin and supplies endpoint paths.
Python
There is no public Python SDK at present. The Python guide uses the standard library to demonstrate submission, error handling, and polling without inventing an installable package. The private inference worker is an implementation detail, not a client SDK or a customer endpoint.
Agent tool
An agent can request internalize through a tool handler in your application. The handler authorizes the subject, submits the learning job, waits or schedules a later read, and reports the actual validation and activation result. The model does not hold your API key.
There is no hosted MCP server in the current release. Framework-specific tool registration stays in your application. Start with the agent integration guide for a tool schema and a reliable execution loop.