@databricks/ai-sdk-provider

Databricks provider for the Vercel AI SDK.

Features

• 🚀 Support for all Databricks endpoint types:
  • Responses (agent/v1/responses) - Foundation model and agent responses API (docs)
  • Chat Completions (llm/v1/chat) - Foundation model chat completions API (docs)
  • Chat Agent (agent/v2/chat) - Legacy Databricks chat agent API (docs)
• 🔄 Stream and non-stream (generate) support for all endpoint types
• 🛠️ Tool calling and agent support
• 🔐 Flexible authentication (bring your own tokens/headers)
• 🎯 Full TypeScript support

Installation

npm install @databricks/ai-sdk-provider

Peer Dependencies

This package requires the following peer dependencies:

npm install @ai-sdk/provider @ai-sdk/provider-utils

To use the provider with AI SDK functions like generateText or streamText, also install:

npm install ai

Quick Start

import { createDatabricksProvider } from '@databricks/ai-sdk-provider'
import { generateText } from 'ai'

// Create provider with your workspace URL and authentication
const provider = createDatabricksProvider({
  baseURL: 'https://your-workspace.databricks.com/serving-endpoints',
  headers: {
    Authorization: `Bearer ${token}`,
  },
})

// Use the Responses endpoint
const model = provider.responses('your-agent-endpoint')

const result = await generateText({
  model,
  prompt: 'Hello, how are you?',
})

console.log(result.text)

Authentication

The provider requires you to pass authentication headers:

const provider = createDatabricksProvider({
  baseURL: 'https://your-workspace.databricks.com/serving-endpoints',
  headers: {
    Authorization: `Bearer ${token}`,
  },
})

API Reference

Main Export

createDatabricksProvider(settings)

Creates a Databricks provider instance.

Parameters:

• settings.baseURL (string, required): Base URL for the Databricks API calls
• settings.headers (object, optional): Custom headers to include in requests
• settings.provider (string, optional): Provider name (defaults to "databricks")
• settings.fetch (function, optional): Custom fetch implementation
• settings.formatUrl (function, optional): Optional function to format the URL
• settings.useRemoteToolCalling (boolean, optional): Enable remote tool calling mode (defaults to false). See Remote Tool Calling below.

Returns: DatabricksProvider with three model creation methods:

• responses(modelId: string): Create a Responses model
• chatCompletions(modelId: string): Create a Chat Completions model
• chatAgent(modelId: string): Create a Chat Agent model

Remote Tool Calling

The useRemoteToolCalling option controls how tool calls from Databricks agents are handled. When enabled, tool calls are marked as dynamic: true and providerExecuted: true, which tells the AI SDK that:

1. Dynamic: The tools are not pre-registered - the agent decides which tools to call at runtime
2. Provider-executed: The tools are executed remotely by Databricks, not by your application

When to use useRemoteToolCalling: true

Enable this option when your Databricks agent handles tool execution internally:

• Databricks Agents with built-in tools: Agents that use tools like Python execution, SQL queries, or other Databricks-managed tools
• Agents on Apps: When deploying agents that manage their own tool execution
• MCP (Model Context Protocol) integrations: When tools are executed via MCP servers managed by Databricks

const provider = createDatabricksProvider({
  baseURL: 'https://your-workspace.databricks.com/serving-endpoints',
  headers: { Authorization: `Bearer ${token}` },
  useRemoteToolCalling: true, // Enable for Databricks-managed tool execution
})

When NOT to use useRemoteToolCalling

Keep this option disabled (the default) when:

• You define and execute tools locally: Your application registers tools with the AI SDK and handles their execution
• Standard chat completions: You're using the Chat Completions endpoint without agent features
• Hybrid scenarios: You want to intercept tool calls and handle some locally

// Default behavior - you handle tool execution
const provider = createDatabricksProvider({
  baseURL: 'https://your-workspace.databricks.com/serving-endpoints',
  headers: { Authorization: `Bearer ${token}` },
  // useRemoteToolCalling defaults to false
})

const result = await generateText({
  model: provider.chatCompletions('my-model'),
  prompt: 'What is the weather?',
  tools: {
    getWeather: {
      description: 'Get weather for a location',
      parameters: z.object({ location: z.string() }),
      execute: async ({ location }) => {
        // Your local tool execution
        return fetchWeather(location)
      },
    },
  },
})

Example: Remote tool calling with Databricks agents

import { streamText } from 'ai'
import { createDatabricksProvider } from '@databricks/ai-sdk-provider'

const provider = createDatabricksProvider({
  baseURL: 'https://your-workspace.databricks.com/serving-endpoints',
  headers: { Authorization: `Bearer ${token}` },
  useRemoteToolCalling: true,
})

const model = provider.responses('my-agent-endpoint')

const result = streamText({
  model,
  messages: convertToModelMessages(uiMessages),
  // No need to pre-register tools - they're handled by Databricks
})

// Tool calls will have the actual tool name from Databricks
for await (const part of result.fullStream) {
  if (part.type === 'tool-call') {
    console.log(`Agent called: ${part.toolName}`)
    // Tool is executed remotely - result will come from Databricks
  }
}

Examples

Responses Endpoint

const model = provider.responses('my-responses-agent')

const result = await generateText({
  model,
  prompt: 'Analyze this data...',
})

console.log(result.text)

Links

• Vercel AI SDK Documentation
• Databricks Documentation
• GitHub Repository

Contributing

This package is part of the databricks-ai-bridge monorepo.