Class: DatabricksAdapter

Adapter that talks directly to Databricks Model Serving /invocations endpoint.

No dependency on the Vercel AI SDK or LangChain. Uses raw fetch() to POST OpenAI-compatible payloads and parses the SSE stream itself. Calls authenticate() per-request so tokens are always fresh.

Handles both structured tool_calls responses and text-based tool call fallback parsing for models that output tool calls as text.

Examples

import { createApp, createAgent, agents, createWorkspaceClient } from "@databricks/appkit";
import { DatabricksAdapter } from "@databricks/appkit/beta";

const adapter = DatabricksAdapter.fromServingEndpoint({
  workspaceClient: createWorkspaceClient(),
  endpointName: "my-endpoint",
});

await createApp({
  plugins: [
    agents({
      agents: {
        assistant: createAgent({
          instructions: "You are a helpful assistant.",
          model: adapter,
        }),
      },
    }),
  ],
});

const adapter = new DatabricksAdapter({
  endpointUrl: "https://host/serving-endpoints/my-endpoint/invocations",
  authenticate: async () => ({ Authorization: `Bearer ${token}` }),
});

Implements

• AgentAdapter

Constructors

Constructor

new DatabricksAdapter(options: DatabricksAdapterOptions): DatabricksAdapter;

Parameters

Returns

DatabricksAdapter

Methods

run()

run(input: AgentInput, context: AgentRunContext): AsyncGenerator<AgentEvent, void, unknown>;

Parameters

Returns

AsyncGenerator<AgentEvent, void, unknown>

Implementation of

AgentAdapter.run

fromModelServing()

static fromModelServing(endpointName?: string, options?: ModelServingOptions): Promise<DatabricksAdapter>;

Creates a DatabricksAdapter from a Model Serving endpoint name. Auto-creates a WorkspaceClient internally. Reads the endpoint name from the argument or the DATABRICKS_SERVING_ENDPOINT_NAME env var.

Parameters

Returns

Promise<DatabricksAdapter>

Example

// Reads endpoint from DATABRICKS_SERVING_ENDPOINT_NAME env var
const adapter = await DatabricksAdapter.fromModelServing();

// Explicit endpoint
const adapter = await DatabricksAdapter.fromModelServing("my-endpoint");

// With options
const adapter = await DatabricksAdapter.fromModelServing("my-endpoint", {
  maxSteps: 5,
  maxTokens: 2048,
});

fromServingEndpoint()

static fromServingEndpoint(options: ServingEndpointOptions): Promise<DatabricksAdapter>;

Creates a DatabricksAdapter for a Databricks Model Serving endpoint.

Routes through the shared connectors/serving/stream helper, which delegates to the SDK's apiClient.request({ raw: true }). That gives the adapter centralised URL encoding + authentication with the rest of the serving surface — no bespoke fetch() + authenticate() plumbing.

Parameters

Returns

Promise<DatabricksAdapter>

fromSupervisorApi()

static fromSupervisorApi(options: SupervisorApiAdapterOptions): Promise<AgentAdapter>;

Discoverability shim for the Supervisor API adapter. Returns an AgentAdapter (a SupervisorApiAdapter at runtime), NOT a DatabricksAdapter — the two are separate classes (different wire formats, different lifecycle). The return type is the AgentAdapter interface so callers aren't bound to the concrete class. Surfaced here so application developers see a single DatabricksAdapter.from* autocomplete root.

Dynamic-imports ./supervisor-api to avoid forming a load-time cycle: both files share connectors/serving/client.ts.

Parameters

Returns

Promise<AgentAdapter>

Example

import { DatabricksAdapter } from "@databricks/appkit/beta";

const model = await DatabricksAdapter.fromSupervisorApi({
  model: "databricks-claude-sonnet-4-5",
});