@tanstack/ai/skills/ai-core/custom-backend-integration/SKILL.md

Type-safe TypeScript AI SDK for streaming chat, tool calling, agents, structured outputs, and multimodal generation.

---
name: ai-core/custom-backend-integration
description: >
  Connect useChat to a non-TanStack-AI backend through custom connection
  adapters. ConnectConnectionAdapter (single async iterable) vs
  SubscribeConnectionAdapter (separate subscribe/send). Customize
  fetchServerSentEvents() and fetchHttpStream() with auth headers,
  custom URLs, and request options. Import from framework package,
  not @tanstack/ai-client.
type: composition
library: tanstack-ai
library_version: '0.42.0'
sources:
  - 'TanStack/ai:docs/chat/connection-adapters.md'
---

# Custom Backend Integration

This skill builds on ai-core and ai-core/chat-experience. Read them first.

## Setup

Connect `useChat` to a custom SSE backend with auth headers:

```tsx
import { useChat, fetchServerSentEvents } from '@tanstack/ai-react'
import { token } from './auth'

function Chat() {
  const { messages, sendMessage, isLoading } = useChat({
    connection: fetchServerSentEvents('https://my-api.com/chat', {
      headers: {
        Authorization: `Bearer ${token}`,
      },
    }),
  })

  return (
    <div>
      {messages.map((msg) => (
        <div key={msg.id}>
          <strong>{msg.role}:</strong>
          {msg.parts.map((part, i) => {
            if (part.type === 'text') {
              return <p key={i}>{part.content}</p>
            }
            return null
          })}
        </div>
      ))}
      <button onClick={() => sendMessage('Hello')}>Send</button>
    </div>
  )
}
```

Both `fetchServerSentEvents` and `fetchHttpStream` accept a static URL string
or a function returning a string (evaluated per request), and a static options
object or a sync/async function returning options (also evaluated per request).
This allows dynamic auth tokens and URLs without re-creating the adapter.

## Core Patterns

### 1. Custom SSE Backend with fetchServerSentEvents

Use when your backend speaks SSE (`text/event-stream`) with `data: {json}\n\n`
framing. This is the recommended default.

**Static options:**

```typescript
import { useChat, fetchServerSentEvents } from '@tanstack/ai-react'
import { token, tenantId } from './auth'

const { messages, sendMessage } = useChat({
  connection: fetchServerSentEvents('https://my-api.com/chat', {
    headers: {
      Authorization: `Bearer ${token}`,
      'X-Tenant-Id': tenantId,
    },
    credentials: 'include',
  }),
})
```

**Dynamic URL and options (evaluated per request):**

```typescript
import { useChat, fetchServerSentEvents } from '@tanstack/ai-react'
import { sessionId, getAccessToken } from './auth'

const { messages, sendMessage } = useChat({
  connection: fetchServerSentEvents(
    () => `https://my-api.com/chat?session=${sessionId}`,
    async () => ({
      headers: {
        Authorization: `Bearer ${await getAccessToken()}`,
      },
      body: {
        provider: 'openai',
        model: 'gpt-5.5',
      },
    }),
  ),
})
```

The `body` field in options is merged into the POST request body alongside
`messages` and `data`, so the server receives `{ messages, data, provider, model }`.

**Custom fetch client (for proxies, interceptors, retries):**

```typescript
import { useChat, fetchServerSentEvents } from '@tanstack/ai-react'

// Same signature as globalThis.fetch — wrap it however you need.
const myCustomFetch: typeof fetch = (input, init) =>
  fetch(input, { ...init, credentials: 'include' })

const { messages, sendMessage } = useChat({
  connection: fetchServerSentEvents('/api/chat', {
    fetchClient: myCustomFetch,
  }),
})
```

### 2. Custom NDJSON Backend with fetchHttpStream

Use when your backend sends newline-delimited JSON (`application/x-ndjson`)
instead of SSE. Each line is one JSON-encoded `StreamChunk` followed by `\n`.

```typescript
import { useChat, fetchHttpStream } from '@tanstack/ai-react'
import { token } from './auth'

const { messages, sendMessage } = useChat({
  connection: fetchHttpStream('https://my-api.com/chat', {
    headers: {
      Authorization: `Bearer ${token}`,
    },
  }),
})
```

`fetchHttpStream` accepts the same URL and options signatures as
`fetchServerSentEvents` (static or dynamic, sync or async). The only difference
is the parsing: no `data:` prefix stripping, no `[DONE]` sentinel -- just one
JSON object per line.

**Dynamic options work identically:**

```typescript
import { useChat, fetchHttpStream } from '@tanstack/ai-react'
import { region, refreshToken } from './auth'

const { messages, sendMessage } = useChat({
  connection: fetchHttpStream(
    () => `/api/chat?region=${region}`,
    async () => ({
      headers: { Authorization: `Bearer ${await refreshToken()}` },
    }),
  ),
})
```

### 3. Fully Custom Connection Adapter

For protocols that don't fit SSE or NDJSON (WebSockets, gRPC-web, custom binary,
server functions), implement the `ConnectionAdapter` interface directly.

There are two mutually exclusive modes:

**ConnectConnectionAdapter (pull-based / async iterable):**

Use when the client initiates a request and consumes the response as a stream.
This is the simpler model and covers most HTTP-based protocols.

```typescript
import { useChat } from '@tanstack/ai-react'
import type { ConnectConnectionAdapter } from '@tanstack/ai-react'
import type { StreamChunk } from '@tanstack/ai'

const websocketAdapter: ConnectConnectionAdapter = {
  async *connect(messages, data, abortSignal) {
    const ws = new WebSocket('wss://my-api.com/chat')

    // Wait for connection
    await new Promise<void>((resolve, reject) => {
      ws.onopen = () => resolve()
      ws.onerror = (e) => reject(e)
    })

    // Send messages
    ws.send(JSON.stringify({ messages, ...data }))

    // Create an async queue to bridge WebSocket events to an async iterable
    const queue: Array<StreamChunk> = []
    let resolve: (() => void) | null = null
    let done = false

    ws.onmessage = (event) => {
      const chunk: StreamChunk = JSON.parse(event.data)
      queue.push(chunk)
      resolve?.()
    }

    ws.onclose = () => {
      done = true
      resolve?.()
    }

    ws.onerror = () => {
      done = true
      resolve?.()
    }

    abortSignal?.addEventListener('abort', () => {
      ws.close()
    })

    // Yield chunks as they arrive
    while (!done || queue.length > 0) {
      if (queue.length > 0) {
        yield queue.shift()!
      } else {
        await new Promise<void>((r) => {
          resolve = r
        })
      }
    }
  },
}

function Chat() {
  const { messages, sendMessage } = useChat({
    connection: websocketAdapter,
  })

  // ... render messages
}
```

**SubscribeConnectionAdapter (push-based / separate subscribe + send):**

Use for push-based protocols where the server can send data at any time
(persistent WebSocket connections, MQTT, server push). The `subscribe` method
returns an `AsyncIterable<StreamChunk>` that stays open, and `send` dispatches
messages through it.

```typescript
import { useChat } from '@tanstack/ai-react'
import type { SubscribeConnectionAdapter } from '@tanstack/ai-react'
import type { StreamChunk } from '@tanstack/ai'

// One socket for the lifetime of the client; every run's chunks arrive on it.
const ws = new WebSocket('wss://my-api.com/chat')
const ready = new Promise<void>((resolve) => {
  ws.addEventListener('open', () => resolve(), { once: true })
})

const pushAdapter: SubscribeConnectionAdapter = {
  async *subscribe(abortSignal) {
    // Long-lived async iterable: yields chunks whenever the server pushes
    // them, until the socket closes or the signal aborts
    const queue: Array<StreamChunk> = []
    let wake: (() => void) | null = null
    let closed = false

    ws.addEventListener('message', (event) => {
      const chunk: StreamChunk = JSON.parse(event.data)
      queue.push(chunk)
      wake?.()
    })
    ws.addEventListener('close', () => {
      closed = true
      wake?.()
    })
    abortSignal?.addEventListener('abort', () => ws.close())

    while (!closed || queue.length > 0) {
      const next = queue.shift()
      if (next !== undefined) {
        yield next
        continue
      }
      await new Promise<void>((r) => {
        wake = r
      })
    }
  },

  async send(messages, data) {
    // Dispatch messages; chunks arrive through subscribe()
    await ready
    ws.send(JSON.stringify({ messages, ...data }))
  },
}

function Chat() {
  const { messages, sendMessage } = useChat({
    connection: pushAdapter,
  })

  // ... render messages
}
```

The `stream()` helper function (re-exported from `@tanstack/ai-react`) provides
a shorthand for creating a `ConnectConnectionAdapter` from an async generator:

```typescript
import { useChat, stream } from '@tanstack/ai-react'
import type { StreamChunk } from '@tanstack/ai'

const directAdapter = stream(async function* (messages, data) {
  const response = await fetch('https://my-api.com/chat', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({ messages, ...data }),
  })

  const reader = response.body!.getReader()
  const decoder = new TextDecoder()
  let buffer = ''

  while (true) {
    const { done, value } = await reader.read()
    if (done) break

    buffer += decoder.decode(value, { stream: true })
    const lines = buffer.split('\n')
    buffer = lines.pop() || ''

    for (const line of lines) {
      if (line.trim()) {
        const chunk: StreamChunk = JSON.parse(line)
        yield chunk
      }
    }
  }
})

const { messages, sendMessage } = useChat({
  connection: directAdapter,
})
```

## Common Mistakes

### a. HIGH: Providing both connect and subscribe+send in connection adapter

The `ConnectionAdapter` interface has two mutually exclusive modes. Providing
both throws at runtime.

```typescript
import type {
  ConnectConnectionAdapter,
  ConnectionAdapter,
  SubscribeConnectionAdapter,
} from '@tanstack/ai-react'
import { channel } from './channel'

// WRONG -- type-checks (ConnectionAdapter is a union) but throws at runtime:
// "Connection adapter must provide either connect or both subscribe and
// send, not both modes"
const adapter: ConnectionAdapter = {
  async *connect(messages) {
    /* ... */
  },
  subscribe(signal) {
    return channel.chunks(signal)
  },
  async send(messages) {
    await channel.send(messages)
  },
}

// CORRECT -- pick one mode
// Option A: ConnectConnectionAdapter (pull-based)
const pullAdapter: ConnectConnectionAdapter = {
  async *connect(messages, data, abortSignal) {
    // ... yield StreamChunks
  },
}

// Option B: SubscribeConnectionAdapter (push-based)
const pushAdapter: SubscribeConnectionAdapter = {
  subscribe(abortSignal) {
    return channel.chunks(abortSignal)
  },
  async send(messages, data, abortSignal) {
    await channel.send({ messages, ...data }, abortSignal)
  },
}
```

Source: `ai-client/src/connection-adapters.ts` line 116

### b. MEDIUM: SSE browser connection limits

Browsers limit SSE connections to 6-8 per domain (the HTTP/1.1 connection
limit). Multiple chat sessions on the same page, or multiple tabs to the
same origin, can exhaust this limit. New connections queue indefinitely until
an existing one closes.

Mitigations:

- Use HTTP/2 (multiplexes streams over a single TCP connection; no per-domain limit)
- Use `fetchHttpStream` instead of `fetchServerSentEvents` (each request is a
  standard POST, not a long-lived EventSource)
- Close idle connections when not actively streaming
- Use a single persistent WebSocket via `SubscribeConnectionAdapter` instead of
  per-request SSE connections

Source: `docs/chat/connection-adapters.md`

### c. MEDIUM: HTTP stream without implementing reconnection

SSE has built-in browser auto-reconnection via the `EventSource` API. HTTP
stream (NDJSON via `fetchHttpStream`) does not -- if the connection drops
mid-stream, the partial response is silently lost with no automatic retry.

If your application needs resilience to transient network errors with HTTP
streaming, implement retry logic in your connection adapter:

```typescript
import { useChat } from '@tanstack/ai-react'
import type { ConnectConnectionAdapter } from '@tanstack/ai-react'
import type { StreamChunk } from '@tanstack/ai'

const resilientAdapter: ConnectConnectionAdapter = {
  async *connect(messages, data, abortSignal) {
    const maxRetries = 3
    let attempt = 0

    while (attempt < maxRetries) {
      try {
        const response = await fetch('https://my-api.com/chat', {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({ messages, ...data }),
          signal: abortSignal,
        })

        if (!response.ok) {
          throw new Error(`HTTP ${response.status}`)
        }

        const reader = response.body!.getReader()
        const decoder = new TextDecoder()
        let buffer = ''

        while (true) {
          const { done, value } = await reader.read()
          if (done) break

          buffer += decoder.decode(value, { stream: true })
          const lines = buffer.split('\n')
          buffer = lines.pop() || ''

          for (const line of lines) {
            if (line.trim()) {
              const chunk: StreamChunk = JSON.parse(line)
              yield chunk
            }
          }
        }

        return // Stream completed successfully
      } catch (err) {
        if (abortSignal?.aborted) throw err
        attempt++
        if (attempt >= maxRetries) throw err
        // Exponential backoff
        await new Promise((r) => setTimeout(r, 1000 * 2 ** attempt))
      }
    }
  },
}

const { messages, sendMessage } = useChat({
  connection: resilientAdapter,
})
```

Note: `fetchServerSentEvents` in TanStack AI uses `fetch()` under the hood (not
the browser `EventSource` API), so it also does not auto-reconnect. The SSE
auto-reconnection advantage only applies when using the native `EventSource` API
directly.

Source: `docs/protocol/http-stream-protocol.md`

## Cross-References

- See also: **ai-core/ag-ui-protocol/SKILL.md** -- Understanding the AG-UI protocol helps build compatible custom servers
- See also: **ai-core/chat-experience/SKILL.md** -- Full chat setup patterns including server-side `chat()` and `toServerSentEventsResponse()`
- See also: **ai-core/middleware/SKILL.md** -- Use middleware for analytics and lifecycle events on the server side