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| 1 | +# @databricks/appkit-agent |
| 2 | + |
| 3 | +Agent plugin for [Databricks AppKit](https://github.com/databricks/appkit). Provides two things: |
| 4 | + |
| 5 | +1. **`AgentInterface`** — a contract for writing custom agent implementations that speak the OpenAI Responses API format (streaming + non-streaming). |
| 6 | +2. **`StandardAgent`** — a ready-to-use LangGraph-based ReAct agent that implements `AgentInterface`, with streaming Responses API support, function tools, and Databricks-hosted tool integration (Genie, Vector Search, MCP servers). |
| 7 | + |
| 8 | +## Installation |
| 9 | + |
| 10 | +```bash |
| 11 | +npm install @databricks/appkit-agent |
| 12 | +``` |
| 13 | + |
| 14 | +The LangChain peer dependencies are required when using the built-in ReAct agent (not needed if you provide a custom `agentInstance`): |
| 15 | + |
| 16 | +```bash |
| 17 | +npm install @databricks/langchainjs @langchain/core @langchain/langgraph |
| 18 | +``` |
| 19 | + |
| 20 | +If you use hosted MCP tools (Genie, Vector Search, custom/external MCP servers): |
| 21 | + |
| 22 | +```bash |
| 23 | +npm install @langchain/mcp-adapters |
| 24 | +``` |
| 25 | + |
| 26 | +## Quick Start |
| 27 | + |
| 28 | +```typescript |
| 29 | +import { createApp, server } from "@databricks/appkit"; |
| 30 | +import { agent } from "@databricks/appkit-agent"; |
| 31 | + |
| 32 | +const app = await createApp({ |
| 33 | + plugins: [ |
| 34 | + server(), |
| 35 | + agent({ |
| 36 | + model: "databricks-claude-sonnet-4-5", |
| 37 | + systemPrompt: "You are a helpful assistant.", |
| 38 | + }), |
| 39 | + ], |
| 40 | +}); |
| 41 | + |
| 42 | +app.server.start(); |
| 43 | +``` |
| 44 | + |
| 45 | +The plugin registers `POST /api/agent` which accepts the [OpenAI Responses API](https://platform.openai.com/docs/api-reference/responses) request format with SSE streaming. |
| 46 | + |
| 47 | +## Environment Variables |
| 48 | + |
| 49 | +| Variable | Description | |
| 50 | +| ------------------ | ------------------------------------------------------------------ | |
| 51 | +| `DATABRICKS_MODEL` | Default model serving endpoint name. Overridden by `config.model`. | |
| 52 | + |
| 53 | +## Configuration |
| 54 | + |
| 55 | +```typescript |
| 56 | +agent({ |
| 57 | + // Model serving endpoint (or set DATABRICKS_MODEL env var) |
| 58 | + model: "databricks-claude-sonnet-4-5", |
| 59 | + |
| 60 | + // System prompt injected at the start of every conversation |
| 61 | + systemPrompt: "You are a helpful assistant.", |
| 62 | + |
| 63 | + // Tools available to the agent (see Tools section below) |
| 64 | + tools: [myTool, genieTool], |
| 65 | + |
| 66 | + // Or bring your own AgentInterface implementation (skips LangGraph setup) |
| 67 | + agentInstance: myCustomAgent, |
| 68 | +}); |
| 69 | +``` |
| 70 | + |
| 71 | +## Tools |
| 72 | + |
| 73 | +The agent supports two kinds of tools: **function tools** (local code) and **hosted tools** (Databricks-managed services). |
| 74 | + |
| 75 | +### Function Tools |
| 76 | + |
| 77 | +Define tools as plain objects following the OpenResponses FunctionTool schema: |
| 78 | + |
| 79 | +```typescript |
| 80 | +import type { FunctionTool } from "@databricks/appkit-agent"; |
| 81 | + |
| 82 | +const weatherTool: FunctionTool = { |
| 83 | + type: "function", |
| 84 | + name: "get_weather", |
| 85 | + description: "Get the current weather for a location", |
| 86 | + parameters: { |
| 87 | + type: "object", |
| 88 | + properties: { |
| 89 | + location: { |
| 90 | + type: "string", |
| 91 | + description: "City name, e.g. 'San Francisco'", |
| 92 | + }, |
| 93 | + }, |
| 94 | + required: ["location"], |
| 95 | + }, |
| 96 | + execute: async ({ location }) => { |
| 97 | + // Call your weather API here |
| 98 | + return `Weather in ${location}: sunny, 72°F`; |
| 99 | + }, |
| 100 | +}; |
| 101 | + |
| 102 | +agent({ model: "databricks-claude-sonnet-4-5", tools: [weatherTool] }); |
| 103 | +``` |
| 104 | + |
| 105 | +### Hosted Tools |
| 106 | + |
| 107 | +Connect to Databricks-managed services without writing tool handlers: |
| 108 | + |
| 109 | +```typescript |
| 110 | +// Genie Space — natural-language queries over your data |
| 111 | +const genie = { |
| 112 | + type: "genie-space" as const, |
| 113 | + genie_space: { id: "01efg..." }, |
| 114 | +}; |
| 115 | + |
| 116 | +// Vector Search Index — semantic search over indexed documents |
| 117 | +const vectorSearch = { |
| 118 | + type: "vector_search_index" as const, |
| 119 | + vector_search_index: { name: "catalog.schema.my_index" }, |
| 120 | +}; |
| 121 | + |
| 122 | +// Custom MCP Server — a Databricks App running an MCP server |
| 123 | +const customMcp = { |
| 124 | + type: "custom_mcp_server" as const, |
| 125 | + custom_mcp_server: { app_name: "my-app", app_url: "my-app-url" }, |
| 126 | +}; |
| 127 | + |
| 128 | +// External MCP Server — a Unity Catalog connection to an external MCP endpoint |
| 129 | +const externalMcp = { |
| 130 | + type: "external_mcp_server" as const, |
| 131 | + external_mcp_server: { connection_name: "my-connection" }, |
| 132 | +}; |
| 133 | + |
| 134 | +agent({ |
| 135 | + model: "databricks-claude-sonnet-4-5", |
| 136 | + tools: [genie, vectorSearch, customMcp, externalMcp], |
| 137 | +}); |
| 138 | +``` |
| 139 | + |
| 140 | +### Adding Tools After Creation |
| 141 | + |
| 142 | +```typescript |
| 143 | +const app = await createApp({ |
| 144 | + plugins: [ |
| 145 | + server(), |
| 146 | + agent({ model: "databricks-claude-sonnet-4-5", tools: [weatherTool] }), |
| 147 | + ], |
| 148 | +}); |
| 149 | + |
| 150 | +// Add more tools after the app is running |
| 151 | +await app.agent.addTools([timeTool]); |
| 152 | +``` |
| 153 | + |
| 154 | +## Programmatic API |
| 155 | + |
| 156 | +After `createApp`, the plugin exposes methods on `app.agent`: |
| 157 | + |
| 158 | +```typescript |
| 159 | +// Non-streaming invoke — returns the assistant's text reply |
| 160 | +const reply = await app.agent.invoke([ |
| 161 | + { role: "user", content: "What's the weather in SF?" }, |
| 162 | +]); |
| 163 | + |
| 164 | +// Streaming — yields Responses API SSE events |
| 165 | +for await (const event of app.agent.stream([ |
| 166 | + { role: "user", content: "Tell me a story" }, |
| 167 | +])) { |
| 168 | + if (event.type === "response.output_text.delta") { |
| 169 | + process.stdout.write(event.delta); |
| 170 | + } |
| 171 | +} |
| 172 | + |
| 173 | +// Add tools dynamically |
| 174 | +await app.agent.addTools([myNewTool]); |
| 175 | +``` |
| 176 | + |
| 177 | +## Databricks Apps Deployment |
| 178 | + |
| 179 | +Databricks product UIs (AI Playground, Agent Evaluation, the built-in chat UI) interact with agents via the `/invocations` endpoint by convention. Since the AppKit agent plugin mounts at `/api/agent` by default, add a redirect so these UIs can chat with your agent: |
| 180 | + |
| 181 | +```typescript |
| 182 | +app.server.extend((expressApp) => { |
| 183 | + expressApp.post("/invocations", (req, res) => { |
| 184 | + req.url = "/api/agent"; |
| 185 | + expressApp(req, res); |
| 186 | + }); |
| 187 | +}); |
| 188 | + |
| 189 | +app.server.start(); |
| 190 | +``` |
| 191 | + |
| 192 | +## Custom Agent Implementation |
| 193 | + |
| 194 | +To bring your own agent logic, implement the `AgentInterface` and pass it as `agentInstance`: |
| 195 | + |
| 196 | +```typescript |
| 197 | +import type { |
| 198 | + AgentInterface, |
| 199 | + InvokeParams, |
| 200 | + ResponseOutputItem, |
| 201 | + ResponseStreamEvent, |
| 202 | +} from "@databricks/appkit-agent"; |
| 203 | + |
| 204 | +class MyAgent implements AgentInterface { |
| 205 | + async invoke(params: InvokeParams): Promise<ResponseOutputItem[]> { |
| 206 | + // Your invoke logic — return Responses API output items |
| 207 | + } |
| 208 | + |
| 209 | + async *stream(params: InvokeParams): AsyncGenerator<ResponseStreamEvent> { |
| 210 | + // Your streaming logic — yield Responses API SSE events |
| 211 | + } |
| 212 | +} |
| 213 | + |
| 214 | +agent({ agentInstance: new MyAgent() }); |
| 215 | +``` |
| 216 | + |
| 217 | +The `StandardAgent` class (exported from this package) is the built-in implementation that wraps a LangGraph `createReactAgent` and translates its stream events into Responses API format. When you pass `model` instead of `agentInstance`, the plugin uses `StandardAgent` under the hood. |
| 218 | + |
| 219 | +## API Reference |
| 220 | + |
| 221 | +### Exports |
| 222 | + |
| 223 | +| Export | Kind | Description | |
| 224 | +| --------------------- | -------------- | -------------------------------------------------------- | |
| 225 | +| `agent` | Plugin factory | Main entry point — call with config, pass to `createApp` | |
| 226 | +| `StandardAgent` | Class | LangGraph-backed `AgentInterface` implementation | |
| 227 | +| `createInvokeHandler` | Function | Express handler factory for the `/api/agent` endpoint | |
| 228 | +| `isFunctionTool` | Function | Type guard for `FunctionTool` | |
| 229 | +| `isHostedTool` | Function | Type guard for `HostedTool` | |
| 230 | + |
| 231 | +### Types |
| 232 | + |
| 233 | +| Type | Description | |
| 234 | +| --------------------- | ------------------------------------------------------------- | |
| 235 | +| `IAgentConfig` | Plugin configuration options | |
| 236 | +| `AgentInterface` | Contract for custom agent implementations | |
| 237 | +| `AgentTool` | Union of `FunctionTool \| HostedTool` | |
| 238 | +| `FunctionTool` | Local tool with JSON Schema parameters and `execute` handler | |
| 239 | +| `HostedTool` | Union of Genie, Vector Search, Custom MCP, External MCP tools | |
| 240 | +| `InvokeParams` | Input to `invoke()` / `stream()` | |
| 241 | +| `ResponseOutputItem` | Output item (message, function call, or function call output) | |
| 242 | +| `ResponseStreamEvent` | SSE event types for streaming responses | |
| 243 | + |
| 244 | +## License |
| 245 | + |
| 246 | +See [LICENSE](./LICENSE). |
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