> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kitefishai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Streaming

> Stream chat responses token by token.

## Overview

Streaming returns tokens as they are generated rather than waiting for the full response. This gives users a faster perceived experience — important for long outputs like document summaries or policy analysis.

## Basic streaming

```python theme={null}
import kitefishai

client = kitefishai.Client(api_key="kf-...")

with client.chat.stream(
    model="kf-reasoning-10b",
    messages=[{"role": "user", "content": "Summarise the RBI Master Directions on KYC."}],
) as stream:
    for chunk in stream:
        print(chunk.delta, end="", flush=True)
print()  # newline after stream ends
```

## Get the full text after streaming

```python theme={null}
with client.chat.stream(
    model="kf-reasoning-10b",
    messages=[{"role": "user", "content": "List IRDAI compliance requirements."}],
) as stream:
    for chunk in stream:
        print(chunk.delta, end="", flush=True)

full_text = stream.get_final_text()
print(f"\nTotal characters: {len(full_text)}")
```

## StreamChunk fields

Each chunk yielded by the iterator has:

| Field           | Type            | Description                    |
| --------------- | --------------- | ------------------------------ |
| `id`            | `str`           | Request ID                     |
| `model`         | `str`           | Model that generated the chunk |
| `delta`         | `str`           | The text content of this chunk |
| `finish_reason` | `str` or `None` | `"stop"` on the final chunk    |

## With a system prompt

```python theme={null}
with client.chat.stream(
    model="kf-reasoning-10b",
    system="You are a BFSI compliance assistant. Be concise and cite regulations.",
    messages=[{"role": "user", "content": "What is Form 60?"}],
) as stream:
    for chunk in stream:
        print(chunk.delta, end="", flush=True)
```

## Collecting chunks manually

```python theme={null}
chunks = []

with client.chat.stream(model="kf-reasoning-10b", messages=[...]) as stream:
    for chunk in stream:
        chunks.append(chunk)

print(f"Received {len(chunks)} chunks")
print("".join(c.delta for c in chunks))
```