AG-UI Protocol#

This skill builds on ai-core. Read it first for critical rules.

Setup — Server Endpoint Producing AG-UI Events via SSE#

import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'

export async function POST(request: Request) {
  const { messages, threadId } = await request.json()
  const stream = chat({
    adapter: openaiText('gpt-6.1-sol'),
    messages,
    threadId,
  })
  return toServerSentEventsResponse(stream)
}

chat() returns an AsyncIterable<StreamChunk>. Each StreamChunk is a typed AG-UI event (discriminated union on type). The toServerSentEventsResponse() helper encodes that iterable into an SSE-formatted Response with correct headers.

Setup — Receiving AG-UI RunAgentInput on the Server#

import {
  chat,
  chatParamsFromRequestBody,
  mergeAgentTools,
  toServerSentEventsResponse,
} from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { serverTools } from './tools'

export async function POST(req: Request) {
  let params
  try {
    params = await chatParamsFromRequestBody(await req.json())
  } catch (error) {
    return new Response(
      error instanceof Error ? error.message : 'Bad request',
      { status: 400 },
    )
  }

  const stream = chat({
    adapter: openaiText('gpt-6.1-sol'),
    messages: params.messages,
    threadId: params.threadId,
    tools: mergeAgentTools(serverTools, params.tools),
  })

  return toServerSentEventsResponse(stream)
}

chatParamsFromRequestBody validates the body against RunAgentInputSchema from @ag-ui/core. mergeAgentTools merges the server's tool registry with client-declared tools (server wins on collision; client-only tools become no-execute stubs that flow through the runtime's ClientToolRequest path).

params.messages is a mixed array of TanStack UIMessage anchors (with parts) and AG-UI fan-out duplicates ({role:'tool',...}, {role:'reasoning',...}). The existing convertMessagesToModelMessages (called inside chat()) handles dedup automatically.

Wire shape (POST body): AG-UI RunAgentInput — {threadId, runId, parentRunId?, state, messages, tools, context, forwardedProps}. The messages array carries TanStack UIMessage anchors with their canonical parts plus AG-UI mirror fields (content, toolCalls) inline; tool results and thinking parts are additionally emitted as fan-out {role:'tool',...} and {role:'reasoning',...} entries.

forwardedProps security: Don't spread it directly into chat() — clients could override adapter, model, tools, etc. Always allowlist specific fields.

Core Patterns#

1. SSE Format — toServerSentEventsStream / toServerSentEventsResponse#

Wire format: Each event is data: <JSON>\n\n. The last event is RUN_FINISHED (or RUN_ERROR). There is no [DONE] line.

import {
  chat,
  toServerSentEventsStream,
  toServerSentEventsResponse,
} from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'

const messages = [{ role: 'user' as const, content: 'Hello' }]

// Option A: Get a ReadableStream (manual Response construction)
const abortController = new AbortController()
const stream = chat({
  adapter: openaiText('gpt-6.1-sol'),
  messages,
  threadId: 'thread-1',
  abortController,
})
const sseStream = toServerSentEventsStream(stream, abortController)

const response = new Response(sseStream, {
  headers: {
    'Content-Type': 'text/event-stream',
    'Cache-Control': 'no-cache',
    Connection: 'keep-alive',
  },
})

// Option B: Use the helper (sets headers automatically)
const response2 = toServerSentEventsResponse(stream, { abortController })
// Default headers: Content-Type: text/event-stream, Cache-Control: no-cache, Connection: keep-alive

Default response headers set by toServerSentEventsResponse():

Custom headers merge on top (user headers override defaults):

toServerSentEventsResponse(stream, {
  headers: {
    'X-Accel-Buffering': 'no', // Disable nginx buffering
    'Cache-Control': 'no-store', // Override default
  },
  abortController,
})

Error handling: If the stream throws, a RUN_ERROR event is emitted automatically before the stream closes. If the abortController is already aborted, the error event is suppressed and the stream closes silently.

2. HTTP Stream (NDJSON) — toHttpStream / toHttpResponse#

Wire format: Each event is <JSON>\n (newline-delimited JSON, no SSE prefix, no [DONE] marker).

import { chat, toHttpStream, toHttpResponse } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'

const messages = [{ role: 'user' as const, content: 'Hello' }]

// Option A: Get a ReadableStream
const abortController = new AbortController()
const stream = chat({
  adapter: openaiText('gpt-6.1-sol'),
  messages,
  threadId: 'thread-1',
  abortController,
})
const ndjsonStream = toHttpStream(stream, abortController)

const response = new Response(ndjsonStream, {
  headers: {
    'Content-Type': 'application/x-ndjson',
  },
})

// Option B: Use the helper (does NOT set headers automatically)
const response2 = toHttpResponse(stream, { abortController })
// Note: toHttpResponse does NOT set Content-Type automatically.
// You should pass headers explicitly:
const response3 = toHttpResponse(stream, {
  headers: { 'Content-Type': 'application/x-ndjson' },
  abortController,
})

Client-side pairing: SSE endpoints are consumed by fetchServerSentEvents(). HTTP stream endpoints are consumed by fetchHttpStream(). Both are connection adapters from @tanstack/ai-react (or the framework-specific package).

3. AG-UI Event Types Reference#

All events extend BaseAGUIEvent which carries type, timestamp, optional model, and optional rawEvent.

Typical event sequence for a text-only response:

RUN_STARTED -> TEXT_MESSAGE_START -> TEXT_MESSAGE_CONTENT (repeated) -> TEXT_MESSAGE_END -> RUN_FINISHED

Typical event sequence with tool calls:

RUN_STARTED -> TEXT_MESSAGE_START -> TEXT_MESSAGE_CONTENT* -> TEXT_MESSAGE_END
            -> TOOL_CALL_START -> TOOL_CALL_ARGS* -> TOOL_CALL_END
            -> RUN_FINISHED (finishReason: 'tool_calls')

Type aliases: StreamChunk is an alias for AGUIEvent (the discriminated union of all event interfaces). For event type names, use the EventType enum, or `${EventType}` for the string union.

4. Typed CUSTOM Events — ChatStream and KnownCustomEvent#

The CUSTOM row above describes the raw StreamChunk union, where the single generic CustomEvent member types value as any -- once merged into a union, that any poisons every other member too, so narrowing on name still leaves value: any. chat() doesn't return raw StreamChunk; by default (no outputSchema, stream not explicitly false) it returns ChatStream, which swaps that generic member for KnownCustomEvent -- a discriminated union of every CUSTOM event TanStack AI itself emits, each with a literal name and a concrete value. Narrow with a plain if -- no helper, no cast:

import { chat } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'

const messages = [{ role: 'user' as const, content: 'Hello' }]

const stream = chat({
  adapter: openaiText('gpt-6.1-sol'),
  messages,
  threadId: 'thread-1',
})

for await (const chunk of stream) {
  if (chunk.type === 'CUSTOM' && chunk.name === 'sandbox.file.diff') {
    console.log(chunk.value.path, chunk.value.diff) // typed, no helper, no cast
  } else if (
    chunk.type === 'CUSTOM' &&
    chunk.name === 'structured-output.complete'
  ) {
    console.log(chunk.value.object) // typed, no helper, no cast
  }
}

Caveat -- .endsWith() (or any non-literal check) does not narrow. SessionIdEvent['name'] is the template-literal type `${string}.session-id`. TypeScript's control-flow narrowing only understands exact comparisons (===) and in/type-predicate checks against a discriminant -- a runtime chunk.name.endsWith('.session-id') check doesn't inform the type system, so chunk.value stays the union of every KnownCustomEvent's value, not { sessionId: string }. Compare against the exact literal you expect, or write a user-defined type predicate ((c): c is SessionIdEvent => c.name.endsWith('.session-id')) and call that in the if instead.

User-emitted emitCustomEvent names are typed out of ChatStream. Tools that call context.emitCustomEvent('my-app:progress', ...) still stream a CUSTOM chunk at runtime, but 'my-app:progress' isn't one of KnownCustomEvent's literal names, so it's intentionally absent from ChatStream's type -- including a generic fallback member would reintroduce the value: any poison for every other event on the stream. To read your own event with a type, annotate the stream as the wider StreamChunk instead of ChatStream for that branch; its generic CUSTOM member already types value as any, so no cast is needed there either.

Source: docs/protocol/custom-events.md

Common Mistakes#

MEDIUM: Proxy buffering breaks SSE streaming#

Reverse proxies (nginx, Cloudflare, AWS ALB) buffer SSE responses by default, causing events to arrive in batches instead of streaming token-by-token.

Fix: Set proxy-bypass headers on the response.

toServerSentEventsResponse(stream, {
  headers: {
    'X-Accel-Buffering': 'no', // nginx
    'X-Content-Type-Options': 'nosniff', // Some CDNs
  },
  abortController,
})

For Cloudflare Workers, SSE streams automatically. For Cloudflare proxied origins, ensure "Response Buffering" is disabled in the dashboard.

Source: docs/protocol/sse-protocol.md

MEDIUM: Assuming all AG-UI events arrive in every response#

Not all event types appear in every stream:

• REASONING_* events only appear with thinking-enabled models (e.g., o3, claude-sonnet-4-5 with extended thinking). Standard models skip these entirely. TanStack AI adapters do not send STEP_STARTED / STEP_FINISHED for thinking.
• TOOL_CALL_START / TOOL_CALL_ARGS / TOOL_CALL_END only appear when the model invokes tools. A text-only response has none.
• STATE_SNAPSHOT / STATE_DELTA only appear when server code explicitly emits them for stateful agent workflows.
• MESSAGES_SNAPSHOT only appears when the server explicitly sends a full transcript snapshot.
• CUSTOM events are application-defined and never emitted by default.

Code that expects a fixed sequence (e.g., always waiting for REASONING_END before processing text) will hang or break on models that don't emit those events.

Source: docs/protocol/chunk-definitions.md

Tension#

RESOLVED: TanStack AI is fully AG-UI compliant on both axes (server→client events AND client→server RunAgentInput). The wire format carries TanStack UIMessage anchors with their parts intact alongside AG-UI fan-out messages, so strict AG-UI servers see role-based messages while TanStack-aware servers read parts directly without transformation. See docs/migration/ag-ui-compliance.md for details.

Cross-References#

• See also: ai-core/custom-backend-integration/SKILL.md -- Custom backends must implement SSE or HTTP stream format to work with TanStack AI client connection adapters.
• See also: ai-core/middleware/SKILL.md -- sandbox.file.diff's { path, diff } value (one of KnownCustomEvent's members) is populated from the same lazy before()/after()/diff() accessors documented there for onFile* middleware hooks.
• Full CUSTOM event taxonomy: docs/protocol/custom-events.md.