@tanstack/ai-client

Framework-agnostic headless client for managing chat state and streaming.

Installation#

npm install @tanstack/ai-client

ChatClient#

The main client class for managing chat state.

import {
  ChatClient,
  clientTools,
  fetchServerSentEvents,
} from "@tanstack/ai-client";
import { myClientTool } from "./tools";

const client = new ChatClient({
  connection: fetchServerSentEvents("/api/chat"),
  initialMessages: [],
  tools: clientTools(myClientTool),
  onMessagesChange: (messages) => {
    console.log("Messages updated:", messages);
  },
});

Constructor Options#

• connection - Connection adapter for streaming

• initialMessages? - Initial messages array

• id? - Unique identifier for this chat instance

• threadId? - Thread ID for AG-UI run correlation. Persists across sends; auto-generated if omitted

• forwardedProps? - Arbitrary client-controlled JSON forwarded to the server in the AG-UI RunAgentInput.forwardedProps field

• body? - Deprecated. Use forwardedProps instead. Still works — values are merged into forwardedProps on the wire and mirrored under the legacy data field for backward compatibility

• context? - Typed client-local runtime context passed to client tool implementations. This value is not serialized to the server

• tools? - Registered .client() tool implementations. The client automatically executes matching tools when the model calls them

• onResponse? - Callback when response is received

• onChunk? - Callback when stream chunk is received

• onFinish? - Callback when response finishes

• onError? - Callback when error occurs

• onMessagesChange? - Callback when messages change

• onLoadingChange? - Callback when loading state changes

• onErrorChange? - Callback when error state changes

• streamProcessor? - Stream processing configuration

Methods#

sendMessage(content: string)#

Sends a user message and gets a response.

await client.sendMessage("Hello!");

append(message: ModelMessage | UIMessage)#

Appends a message to the conversation.

await client.append({
  role: "user",
  content: "Additional context",
});

reload()#

Reloads the last assistant message.

await client.reload();

stop()#

Stops the current response generation.

client.stop();

clear()#

Clears all messages.

client.clear();

setMessagesManually(messages: UIMessage[])#

Manually sets the messages array.

client.setMessagesManually([...newMessages]);

addToolResult(result)#

Adds the result of a client-side tool execution.

await client.addToolResult({
  toolCallId: "call_123",
  tool: "toolName",
  output: { result: "..." },
  state: "output-available",
});

addToolApprovalResponse(response)#

Responds to a tool approval request.

await client.addToolApprovalResponse({
  id: "approval_123",
  approved: true,
});

Properties#

• messages: UIMessage[] - Current messages

• isLoading: boolean - Whether a response is being generated

• error: Error | undefined - Current error, if any

Connection Adapters#

For a complete transport walkthrough, see Connection Adapters. For React Native and Expo, see Quick Start: React Native.

fetchServerSentEvents(url, options?)#

Creates an SSE connection adapter.

import { fetchServerSentEvents } from "@tanstack/ai-client";

const adapter = fetchServerSentEvents("/api/chat", {
  headers: {
    Authorization: "Bearer token",
  },
});

fetchHttpStream(url, options?)#

Creates a newline-delimited JSON HTTP stream connection adapter. Pair it with toHttpResponse() on the server.

import { fetchHttpStream } from "@tanstack/ai-client";

const adapter = fetchHttpStream("/api/chat");

fetchHttpStream() requires a runtime with streaming fetch, Response.body.getReader(), and TextDecoder. If the runtime cannot expose an incremental response body, it throws UnsupportedResponseStreamError; use the XHR adapters in React Native or Expo.

xhrHttpStream(url, options?)#

Creates an XMLHttpRequest-backed newline-delimited JSON stream adapter. This is the recommended default for React Native and Expo chat screens. Pair it with toHttpResponse() on the server.

import { xhrHttpStream } from "@tanstack/ai-client";

const adapter = xhrHttpStream("http://192.168.1.10:8787/chat/http", {
  headers: { Authorization: "Bearer token" },
  withCredentials: true,
});

xhrServerSentEvents(url, options?)#

Creates an XMLHttpRequest-backed SSE adapter for runtimes where XHR progress events are more reliable than streaming fetch. Pair it with toServerSentEventsResponse() on the server.

import { xhrServerSentEvents } from "@tanstack/ai-client";

const adapter = xhrServerSentEvents("http://192.168.1.10:8787/chat/sse");

Adapter options#

Fetch adapters accept:

• headers?: Record<string, string> | Headers

• credentials?: RequestCredentials

• signal?: AbortSignal

• body?: Record<string, any>

• fetchClient?: typeof globalThis.fetch

  XHR adapters accept:

• headers?: Record<string, string> | Headers

• withCredentials?: boolean

• signal?: AbortSignal

• body?: Record<string, any>

• xhrFactory?: () => XMLHttpRequest

  body is merged into the AG-UI forwardedProps payload. Values from forwardedProps on the client and per-message sendMessage(..., data) calls override static adapter body values.

Stream errors#

• UnsupportedResponseStreamError - thrown by fetch-based adapters when Response.body, Response.body.getReader(), or TextDecoder is missing.

• StreamTruncatedError - thrown when an SSE or NDJSON stream ends with unterminated trailing data, usually because the server, proxy, or network cut the connection mid-line.

stream(connectFn)#

Creates a custom connection adapter.

import { stream } from "@tanstack/ai-client";

const adapter = stream(async (messages, data, signal) => {
  // `data` here carries the merged forwardedProps. The fetch-based
  // adapters serialize it as the AG-UI `RunAgentInput.forwardedProps`
  // field on the wire (with a backward-compat `data` mirror).
  const response = await fetch("/api/chat", {
    method: "POST",
    body: JSON.stringify({ messages, forwardedProps: data }),
    signal,
  });
  return processStream(response);
});

Helper Functions#

clientTools(...tools)#

Creates a typed array of client tools with proper type inference. This eliminates the need for as const when defining tool arrays and enables proper discriminated union type narrowing.

import { clientTools } from "@tanstack/ai-client";
import { myTool1, myTool2 } from "./tools";

// Create client implementations
const tool1Client = myTool1.client((input) => {
  // Implementation
  return { result: "..." };
});

const tool2Client = myTool2.client((input) => {
  // Implementation
  return { result: "..." };
});

// Create typed tools array (no 'as const' needed!)
const tools = clientTools(tool1Client, tool2Client);

// Now when you use these tools in chat options:
const chatOptions = createChatClientOptions({
  connection: fetchServerSentEvents("/api/chat"),
  tools, // Fully typed with literal tool names
});

// In your component:
messages.forEach((message) => {
  message.parts.forEach((part) => {
    if (part.type === "tool-call" && part.name === "myTool1") {
      // ✅ TypeScript knows part.name is literally "myTool1"
      // ✅ part.input is typed from myTool1's input schema
      // ✅ part.output is typed from myTool1's output schema
    }
  });
});

createChatClientOptions(options)#

Helper function to create typed chat client options with proper type inference.

import { createChatClientOptions, clientTools } from "@tanstack/ai-client";

const tools = clientTools(tool1, tool2);

const chatOptions = createChatClientOptions({
  connection: fetchServerSentEvents("/api/chat"),
  tools,
});

// Use InferChatMessages to extract message types
type ChatMessages = InferChatMessages<typeof chatOptions>;

createChatClientOptions also preserves typed client runtime context:

type ClientContext = {
  activeProjectId: string;
};

const tool = projectTool.client<ClientContext>((input, ctx) => {
  return runProjectAction(ctx.context.activeProjectId, input);
});

const chatOptions = createChatClientOptions({
  connection: fetchServerSentEvents("/api/chat"),
  tools: clientTools(tool),
  context: {
    activeProjectId: "project_123",
  },
});

Client runtime context is local to the client instance. Use forwardedProps for explicit client-to-server handoff of serializable values, then validate and map those values into server chat({ context }).

Types#

UIMessage#

interface UIMessage {
  id: string;
  role: "user" | "assistant";
  parts: MessagePart[];
  createdAt?: Date;
}

MessagePart#

type MessagePart = TextPart | ThinkingPart | ToolCallPart | ToolResultPart;

TextPart#

interface TextPart {
  type: "text";
  content: string;
}

ThinkingPart#

interface ThinkingPart {
  type: "thinking";
  content: string;
}

Thinking parts represent the model's internal reasoning process. They are typically displayed in a collapsible format and automatically collapse when the response text appears. Thinking parts are UI-only and are not sent back to the model in subsequent requests.

Note: Thinking parts are only available when using models that support reasoning/thinking (e.g., Anthropic Claude with thinking enabled, OpenAI GPT-5 with reasoning enabled).

ToolCallPart#

interface ToolCallPart {
  type: "tool-call";
  id: string;
  name: string;
  arguments: string; // JSON string (may be incomplete during streaming)
  input?: any; // Parsed tool input (typed from tool's inputSchema)
  state: ToolCallState;
  approval?: ApprovalRequest;
  output?: any; // Tool execution output (typed from tool's outputSchema)
}

When using typed tools with clientTools() and createChatClientOptions(), the input and output fields are automatically typed based on your tool's Zod schemas, and name becomes a discriminated union enabling type narrowing.

ToolResultPart#

interface ToolResultPart {
  type: "tool-result";
  toolCallId: string;
  content: string;
  state: ToolResultState;
  error?: string;
}

ToolCallState#

type ToolCallState =
  | "awaiting-input"
  | "input-streaming"
  | "input-complete"
  | "approval-requested"
  | "approval-responded"
  | "complete";

ToolResultState#

type ToolResultState =
  | "streaming"
  | "complete"
  | "error";

Stream Processing#

Configure stream processing with chunk strategies:

import { ImmediateStrategy, fetchServerSentEvents } from "@tanstack/ai-client";

const client = new ChatClient({
  connection: fetchServerSentEvents("/api/chat"),
  streamProcessor: {
    chunkStrategy: new ImmediateStrategy(), // Emit every chunk
  },
});

Next Steps#

• Getting Started - Learn the basics
• Connection Adapters - Learn about adapters
• @tanstack/ai-react API - React hooks wrapper