Chat & Streaming

Basic Chat

Send a message and get a response:

Python

import asyncio
import os

from liter_llm import create_client
from liter_llm._internal_bindings import ChatCompletionRequest


async def main() -> None:
    client = create_client(api_key=os.environ["OPENAI_API_KEY"])
    request = ChatCompletionRequest.from_json(
        '{"model":"openai/gpt-4o","messages":[{"role":"user","content":"Hello!"}]}'
    )
    response = await client.chat(request)
    print(response.choices[0].message.content)


asyncio.run(main())

TypeScript

import { createClient } from "@xberg-io/liter-llm";

const client = createClient(process.env.OPENAI_API_KEY!);
const response = await client.chat({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);

Rust

use liter_llm::{
    ChatCompletionRequest, ClientConfigBuilder, DefaultClient, LlmClient,
    Message, UserContent, UserMessage,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = ClientConfigBuilder::new(std::env::var("OPENAI_API_KEY")?)
        .build();
    let client = DefaultClient::new(config, Some("openai/gpt-4o"))?;

    let request = ChatCompletionRequest {
        model: "openai/gpt-4o".into(),
        messages: vec![Message::User(UserMessage {
            content: UserContent::Text("Hello!".into()),
            name: None,
        })],
        ..Default::default()
    };

    let response = client.chat(request).await?;
    if let Some(choice) = response.choices.first() {
        println!("{}", choice.message.content.as_deref().unwrap_or(""));
    }
    Ok(())
}

Go

package main

import (
  "encoding/json"
  "fmt"
  "os"

  llm "github.com/xberg-io/liter-llm/packages/go"
)

func main() {
  client, err := llm.CreateClient(os.Getenv("OPENAI_API_KEY"), nil, nil, nil, nil)
  if err != nil {
    panic(err)
  }

  var req llm.ChatCompletionRequest
  if err := json.Unmarshal([]byte(`{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "Hello!"}]
  }`), &req); err != nil {
    panic(err)
  }

  resp, err := client.Chat(req)
  if err != nil {
    panic(err)
  }
  if len(resp.Choices) > 0 && resp.Choices[0].Message.Content != nil {
    fmt.Println(*resp.Choices[0].Message.Content)
  }
}

Java

import io.xberg.literllm.*;
import java.util.List;

public class Main {
    public static void main(String[] args) throws Exception {
        try (var client = LiterLlm.createClient(System.getenv("OPENAI_API_KEY"))) {
            var request = ChatCompletionRequest.builder()
                .withModel("openai/gpt-4o")
                .withMessages(List.of(
                    new Message.User(new UserMessage(UserContent.of("Hello!"), null))
                ))
                .build();
            var response = client.chat(request);
            System.out.println(response.choices().getFirst().message().content());
        }
    }
}

C#

using LiterLlm;

using var client = LiterLlmLib.CreateClient(
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY")!,
    baseUrl: null, timeoutSecs: null, maxRetries: null, modelHint: null);

var response = await client.ChatAsync(new ChatCompletionRequest
{
    Model = "openai/gpt-4o",
    Messages = [new Message.User(new UserMessage { Content = UserContent.Of("Hello!") })]
});
Console.WriteLine(response.Choices[0].Message.Content);

Ruby

# frozen_string_literal: true

require 'liter_llm'

client = LiterLlm.create_client(ENV.fetch('OPENAI_API_KEY'))

result = client.chat_async(
  LiterLlm::ChatCompletionRequest.new(
    model: 'openai/gpt-4o-mini',
    messages: [{ 'role' => 'user', 'content' => 'Hello!' }]
  )
)

puts result.choices[0].message.content

PHP

<?php

declare(strict_types=1);

use Liter\Llm\LiterLlm;
use Liter\Llm\ChatCompletionRequest;

$client = LiterLlm::createClient(getenv('OPENAI_API_KEY') ?: '');

$request = ChatCompletionRequest::from_json(json_encode([
    'model' => 'openai/gpt-4o-mini',
    'messages' => [['role' => 'user', 'content' => 'Hello!']],
]));

$result = $client->chat($request);
echo $result->choices[0]->message->content . PHP_EOL;

Elixir

{:ok, client} = LiterLlm.create_client(System.get_env("OPENAI_API_KEY"))

request =
  Jason.encode!(%{
    model: "openai/gpt-4o-mini",
    messages: [%{role: "user", content: "Hello!"}]
  })

{:ok, result} = LiterLlm.defaultclient_chat_async(client, request)
IO.puts(Enum.at(result.choices, 0).message.content)

WASM

import init, { createClient, WasmChatCompletionRequest } from "@xberg-io/liter-llm-wasm";

await init();

const client = createClient(process.env.OPENAI_API_KEY!);

const request = WasmChatCompletionRequest.default();
request.model = "openai/gpt-4o";
request.messages = [{ role: "user", content: "Hello!" }];

const response = await client.chat(request);
console.log(response.choices[0].message.content);

Provider Routing

Liter-llm uses a provider/model prefix convention. The prefix determines which API endpoint, auth header, and parameter mappings to use:

openai/gpt-4o            -> OpenAI
anthropic/claude-sonnet-4-20250514  -> Anthropic
groq/llama3-70b          -> Groq
google/gemini-2.0-flash  -> Google AI
mistral/mistral-large    -> Mistral
bedrock/anthropic.claude-v2 -> AWS Bedrock

Switch providers by changing the model string – no other code changes needed.

Message Roles

Role	Purpose
system	Sets the assistant’s behavior. Sent once at the start.
user	User input – questions, instructions, data.
assistant	Previous assistant responses for multi-turn context.
tool	Results from tool calls.
developer	Developer-level instructions (some providers).

Multi-Turn Conversations

Append the assistant’s response and the next user message, then call chat again:

Python

import asyncio
import json
import os

from liter_llm import create_client
from liter_llm._internal_bindings import ChatCompletionRequest


async def main() -> None:
    client = create_client(api_key=os.environ["OPENAI_API_KEY"])
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"},
    ]

    first = await client.chat(
        ChatCompletionRequest.from_json(json.dumps({"model": "openai/gpt-4o", "messages": messages}))
    )
    reply = first.choices[0].message.content
    print(f"Assistant: {reply}")

    messages.append({"role": "assistant", "content": reply})
    messages.append({"role": "user", "content": "What about Germany?"})

    second = await client.chat(
        ChatCompletionRequest.from_json(json.dumps({"model": "openai/gpt-4o", "messages": messages}))
    )
    print(f"Assistant: {second.choices[0].message.content}")
    if second.usage:
        print(f"Tokens: {second.usage.prompt_tokens} in, {second.usage.completion_tokens} out")


asyncio.run(main())

TypeScript

import { createClient } from "@xberg-io/liter-llm";

const client = createClient(process.env.OPENAI_API_KEY!);
const messages: Array<{ role: string; content: string }> = [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "What is the capital of France?" },
];

let response = await client.chat({ model: "openai/gpt-4o", messages });
console.log(`Assistant: ${response.choices[0].message.content}`);

messages.push({ role: "assistant", content: response.choices[0].message.content! });
messages.push({ role: "user", content: "What about Germany?" });

response = await client.chat({ model: "openai/gpt-4o", messages });
console.log(`Assistant: ${response.choices[0].message.content}`);
console.log(`Tokens: ${response.usage?.promptTokens} in, ${response.usage?.completionTokens} out`);

Rust

use liter_llm::{
    ChatCompletionRequest, ClientConfigBuilder, DefaultClient, LlmClient,
    Message, UserContent, UserMessage, AssistantMessage, SystemMessage,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = ClientConfigBuilder::new(std::env::var("OPENAI_API_KEY")?)
        .build();
    let client = DefaultClient::new(config, Some("openai/gpt-4o"))?;

    let mut messages = vec![
        Message::System(SystemMessage {
            content: "You are a helpful assistant.".into(),
            name: None,
        }),
        Message::User(UserMessage {
            content: UserContent::Text("What is the capital of France?".into()),
            name: None,
        }),
    ];

    let request = ChatCompletionRequest {
        model: "openai/gpt-4o".into(),
        messages: messages.clone(),
        ..Default::default()
    };
    let response = client.chat(request).await?;
    let content = response.choices[0]
        .message
        .content
        .clone()
        .unwrap_or_default();
    println!("Assistant: {content}");

    // Continue the conversation
    messages.push(Message::Assistant(AssistantMessage {
        content: Some(content),
        ..Default::default()
    }));
    messages.push(Message::User(UserMessage {
        content: UserContent::Text("What about Germany?".into()),
        name: None,
    }));

    let request = ChatCompletionRequest {
        model: "openai/gpt-4o".into(),
        messages,
        ..Default::default()
    };
    let response = client.chat(request).await?;
    if let Some(choice) = response.choices.first() {
        println!("Assistant: {}", choice.message.content.as_deref().unwrap_or(""));
    }

    // Token usage
    if let Some(usage) = &response.usage {
        println!("Tokens: {} in, {} out", usage.prompt_tokens, usage.completion_tokens);
    }
    Ok(())
}

Go

package main

import (
  "encoding/json"
  "fmt"
  "os"

  llm "github.com/xberg-io/liter-llm/packages/go"
)

func main() {
  client, err := llm.CreateClient(os.Getenv("OPENAI_API_KEY"), nil, nil, nil, nil)
  if err != nil {
    panic(err)
  }

  body := map[string]any{
    "model": "openai/gpt-4o-mini",
    "messages": []map[string]string{
      {"role": "system", "content": "You are a helpful assistant."},
      {"role": "user", "content": "What is the capital of France?"},
    },
  }

  var req llm.ChatCompletionRequest
  raw, _ := json.Marshal(body)
  _ = json.Unmarshal(raw, &req)
  resp, err := client.Chat(req)
  if err != nil {
    panic(err)
  }
  answer := ""
  if len(resp.Choices) > 0 && resp.Choices[0].Message.Content != nil {
    answer = *resp.Choices[0].Message.Content
  }
  fmt.Printf("Assistant: %s\n", answer)

  body["messages"] = append(body["messages"].([]map[string]string),
    map[string]string{"role": "assistant", "content": answer},
    map[string]string{"role": "user", "content": "What about Germany?"},
  )
  raw, _ = json.Marshal(body)
  _ = json.Unmarshal(raw, &req)
  resp, err = client.Chat(req)
  if err != nil {
    panic(err)
  }
  if len(resp.Choices) > 0 && resp.Choices[0].Message.Content != nil {
    fmt.Printf("Assistant: %s\n", *resp.Choices[0].Message.Content)
  }
  if resp.Usage != nil {
    fmt.Printf("Tokens: %d in, %d out\n", resp.Usage.PromptTokens, resp.Usage.CompletionTokens)
  }
}

Java

import io.xberg.literllm.*;
import java.util.ArrayList;
import java.util.List;

public class Main {
    public static void main(String[] args) throws Exception {
        try (var client = LiterLlm.createClient(System.getenv("OPENAI_API_KEY"))) {
            var messages = new ArrayList<Message>(List.of(
                new Message.System(new SystemMessage("You are a helpful assistant.", null)),
                new Message.User(new UserMessage(UserContent.of("What is the capital of France?"), null))
            ));

            var response = client.chat(ChatCompletionRequest.builder()
                .withModel("openai/gpt-4o").withMessages(messages).build());
            var content = response.choices().getFirst().message().content();
            System.out.println("Assistant: " + content);

            messages.add(new Message.Assistant(new AssistantMessage(content, null, null, null, null)));
            messages.add(new Message.User(new UserMessage(UserContent.of("What about Germany?"), null)));

            response = client.chat(ChatCompletionRequest.builder()
                .withModel("openai/gpt-4o").withMessages(messages).build());
            System.out.println("Assistant: " + response.choices().getFirst().message().content());

            var usage = response.usage();
            if (usage != null) {
                System.out.printf("Tokens: %d in, %d out%n",
                    usage.promptTokens(), usage.completionTokens());
            }
        }
    }
}

C#

using LiterLlm;

using var client = LiterLlmLib.CreateClient(
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY")!,
    baseUrl: null, timeoutSecs: null, maxRetries: null, modelHint: null);

var messages = new List<Message>
{
    new Message.System(new SystemMessage { Content = "You are a helpful assistant." }),
    new Message.User(new UserMessage { Content = UserContent.Of("What is the capital of France?") }),
};

var response = await client.ChatAsync(new ChatCompletionRequest { Model = "openai/gpt-4o", Messages = messages });
var content = response.Choices[0].Message.Content;
Console.WriteLine($"Assistant: {content}");

messages.Add(new Message.Assistant(new AssistantMessage { Content = content }));
messages.Add(new Message.User(new UserMessage { Content = UserContent.Of("What about Germany?") }));

response = await client.ChatAsync(new ChatCompletionRequest { Model = "openai/gpt-4o", Messages = messages });
Console.WriteLine($"Assistant: {response.Choices[0].Message.Content}");

if (response.Usage is not null)
{
    Console.WriteLine($"Tokens: {response.Usage.PromptTokens} in, {response.Usage.CompletionTokens} out");
}

Ruby

# frozen_string_literal: true

require 'liter_llm'

client = LiterLlm.create_client(ENV.fetch('OPENAI_API_KEY'))

messages = [
  { 'role' => 'system', 'content' => 'You are a helpful assistant.' },
  { 'role' => 'user', 'content' => 'What is the capital of France?' }
]

result = client.chat_async(
  LiterLlm::ChatCompletionRequest.new(model: 'openai/gpt-4o-mini', messages: messages)
)
answer = result.choices[0].message.content
puts "Assistant: #{answer}"

messages << { 'role' => 'assistant', 'content' => answer }
messages << { 'role' => 'user', 'content' => 'What about Germany?' }

result = client.chat_async(
  LiterLlm::ChatCompletionRequest.new(model: 'openai/gpt-4o-mini', messages: messages)
)
puts "Assistant: #{result.choices[0].message.content}"

usage = result.usage
puts "Tokens: #{usage.prompt_tokens} in, #{usage.completion_tokens} out" if usage

PHP

<?php

declare(strict_types=1);

use Liter\Llm\LiterLlm;
use Liter\Llm\ChatCompletionRequest;

$client = LiterLlm::createClient(getenv('OPENAI_API_KEY') ?: '');

$messages = [
    ['role' => 'system', 'content' => 'You are a helpful assistant.'],
    ['role' => 'user', 'content' => 'What is the capital of France?'],
];

$result = $client->chat(ChatCompletionRequest::from_json(json_encode([
    'model' => 'openai/gpt-4o-mini',
    'messages' => $messages,
])));
$answer = $result->choices[0]->message->content;
echo "Assistant: {$answer}" . PHP_EOL;

$messages[] = ['role' => 'assistant', 'content' => $answer];
$messages[] = ['role' => 'user', 'content' => 'What about Germany?'];

$result = $client->chat(ChatCompletionRequest::from_json(json_encode([
    'model' => 'openai/gpt-4o-mini',
    'messages' => $messages,
])));
echo "Assistant: {$result->choices[0]->message->content}" . PHP_EOL;

if ($result->usage !== null) {
    echo "Tokens: {$result->usage->promptTokens} in, {$result->usage->completionTokens} out" . PHP_EOL;
}

Elixir

{:ok, client} = LiterLlm.create_client(System.get_env("OPENAI_API_KEY"))

messages = [
  %{role: "system", content: "You are a helpful assistant."},
  %{role: "user", content: "What is the capital of France?"}
]

{:ok, result} =
  LiterLlm.defaultclient_chat_async(
    client,
    Jason.encode!(%{model: "openai/gpt-4o-mini", messages: messages})
  )

answer = Enum.at(result.choices, 0).message.content
IO.puts("Assistant: #{answer}")

messages =
  messages ++
    [
      %{role: "assistant", content: answer},
      %{role: "user", content: "What about Germany?"}
    ]

{:ok, result} =
  LiterLlm.defaultclient_chat_async(
    client,
    Jason.encode!(%{model: "openai/gpt-4o-mini", messages: messages})
  )

IO.puts("Assistant: #{Enum.at(result.choices, 0).message.content}")

if result.usage do
  IO.puts("Tokens: #{result.usage.prompt_tokens} in, #{result.usage.completion_tokens} out")
end

WASM

import init, { createClient, WasmChatCompletionRequest } from "@xberg-io/liter-llm-wasm";

await init();

const client = createClient(process.env.OPENAI_API_KEY!);
const messages: Array<{ role: string; content: string }> = [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "What is the capital of France?" },
];

const first = WasmChatCompletionRequest.default();
first.model = "openai/gpt-4o";
first.messages = messages;
let response = await client.chat(first);
console.log(`Assistant: ${response.choices[0].message.content}`);

messages.push({ role: "assistant", content: response.choices[0].message.content! });
messages.push({ role: "user", content: "What about Germany?" });

const second = WasmChatCompletionRequest.default();
second.model = "openai/gpt-4o";
second.messages = messages;
response = await client.chat(second);
console.log(`Assistant: ${response.choices[0].message.content}`);
console.log(`Tokens: ${response.usage?.promptTokens} in, ${response.usage?.completionTokens} out`);

Streaming

Stream tokens as they arrive instead of waiting for the full response:

Python

import asyncio
import os

from liter_llm import create_client
from liter_llm._internal_bindings import ChatCompletionRequest


async def main() -> None:
    client = create_client(api_key=os.environ["OPENAI_API_KEY"])
    request = ChatCompletionRequest.from_json(
        '{"model":"openai/gpt-4o","messages":[{"role":"user","content":"Tell me a story"}],"stream":true}'
    )
    async for chunk in client.chat_stream(request):
        if chunk.choices and chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="", flush=True)
    print()


asyncio.run(main())

TypeScript

import { createClient } from "@xberg-io/liter-llm";

const client = createClient(process.env.OPENAI_API_KEY!);
const chunks = await client.chatStream({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Tell me a story" }],
});

for await (const chunk of chunks) {
  process.stdout.write(chunk.choices?.[0]?.delta?.content ?? "");
}
console.log();

Rust

use futures::StreamExt;
use liter_llm::{
    ChatCompletionRequest, ClientConfigBuilder, DefaultClient, LlmClient,
    Message, UserContent, UserMessage,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = ClientConfigBuilder::new(std::env::var("OPENAI_API_KEY")?)
        .build();
    let client = DefaultClient::new(config, Some("openai/gpt-4o"))?;

    let request = ChatCompletionRequest {
        model: "openai/gpt-4o".into(),
        messages: vec![Message::User(UserMessage {
            content: UserContent::Text("Tell me a story".into()),
            name: None,
        })],
        ..Default::default()
    };

    let mut stream = client.chat_stream(request).await?;
    while let Some(chunk) = stream.next().await {
        let chunk = chunk?;
        if let Some(choice) = chunk.choices.first() {
            if let Some(content) = &choice.delta.content {
                print!("{content}");
            }
        }
    }
    println!();
    Ok(())
}

Go

package main

import (
  "encoding/json"
  "fmt"
  "os"

  llm "github.com/xberg-io/liter-llm/packages/go"
)

func main() {
  client, err := llm.CreateClient(os.Getenv("OPENAI_API_KEY"), nil, nil, nil, nil)
  if err != nil {
    panic(err)
  }

  var req llm.ChatCompletionRequest
  if err := json.Unmarshal([]byte(`{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "Count from 1 to 5."}],
    "stream": true
  }`), &req); err != nil {
    panic(err)
  }

  stream, err := client.ChatStream(req)
  if err != nil {
    panic(err)
  }
  for chunk := range stream {
    if len(chunk.Choices) > 0 && chunk.Choices[0].Delta.Content != nil {
      fmt.Print(*chunk.Choices[0].Delta.Content)
    }
  }
  fmt.Println()
}

Java

import io.xberg.literllm.*;
import java.util.List;

public class Main {
    public static void main(String[] args) throws Exception {
        try (var client = LiterLlm.createClient(System.getenv("OPENAI_API_KEY"))) {
            var request = ChatCompletionRequest.builder()
                .withModel("openai/gpt-4o-mini")
                .withMessages(List.of(
                    new Message.User(new UserMessage(UserContent.of("Hello"), null))
                ))
                .build();
            var stream = client.chatStream(request);
            var iterator = stream.iterator();
            while (iterator.hasNext()) {
                var chunk = iterator.next();
                var delta = chunk.choices().getFirst().delta().content();
                if (delta != null) System.out.print(delta);
            }
            System.out.println();
        }
    }
}

C#

using LiterLlm;

using var client = LiterLlmLib.CreateClient(
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY")!,
    baseUrl: null, timeoutSecs: null, maxRetries: null, modelHint: null);

var request = new ChatCompletionRequest
{
    Model = "openai/gpt-4o-mini",
    Messages = [new Message.User(new UserMessage { Content = UserContent.Of("Hello") })]
};

await foreach (var chunk in client.ChatStreamAsync(request))
{
    var delta = chunk.Choices.Count > 0 ? chunk.Choices[0].Delta.Content : null;
    if (delta is not null) Console.Write(delta);
}
Console.WriteLine();

Ruby

# frozen_string_literal: true

require 'liter_llm'

client = LiterLlm.create_client(ENV.fetch('OPENAI_API_KEY'))

client.chat_stream(
  LiterLlm::ChatCompletionRequest.new(
    model: 'openai/gpt-4o-mini',
    messages: [{ 'role' => 'user', 'content' => 'Count from 1 to 5.' }],
    stream: true
  )
) do |chunk|
  delta = chunk.choices && chunk.choices[0] && chunk.choices[0].delta
  print delta.content if delta && delta.content
end
puts

PHP

<?php

declare(strict_types=1);

use Liter\Llm\LiterLlm;
use Liter\Llm\ChatCompletionRequest;

$client = LiterLlm::createClient(getenv('OPENAI_API_KEY') ?: '');

$request = ChatCompletionRequest::from_json(json_encode([
    'model' => 'openai/gpt-4o-mini',
    'messages' => [['role' => 'user', 'content' => 'Count from 1 to 5.']],
]));

foreach ($client->chatStream($request) as $chunkJson) {
    $chunk = json_decode($chunkJson, false, flags: JSON_THROW_ON_ERROR);
    echo $chunk->choices[0]->delta->content ?? '';
}
echo PHP_EOL;

Elixir

{:ok, client} = LiterLlm.create_client(System.get_env("OPENAI_API_KEY"))

request =
  Jason.encode!(%{
    model: "openai/gpt-4o-mini",
    messages: [%{role: "user", content: "Count from 1 to 5."}],
    stream: true
  })

{:ok, stream} = LiterLlm.defaultclient_chat_stream(client, request)

Enum.each(stream, fn chunk ->
  content = get_in(chunk, [:choices, Access.at(0), :delta, :content])
  if content, do: IO.write(content)
end)

IO.puts("")

WASM

import init, { createClient, WasmChatCompletionRequest } from "@xberg-io/liter-llm-wasm";

await init();

const client = createClient(process.env.OPENAI_API_KEY!);

const request = WasmChatCompletionRequest.default();
request.model = "openai/gpt-4o";
request.messages = [{ role: "user", content: "Tell me a story" }];
request.stream = true;

const stream = await client.chatStream(request);
while (true) {
  const chunk = await stream.next();
  if (chunk === null) {
    break;
  }
  process.stdout.write(chunk.choices?.[0]?.delta?.content ?? "");
}
console.log();

Each chunk contains choices[].delta.content with incremental text. The final chunk includes finish_reason: "stop".

Collecting the Full Response

Accumulate deltas to get both real-time output and the complete text:

Python

import asyncio
import os

from liter_llm import create_client
from liter_llm._internal_bindings import ChatCompletionRequest


async def main() -> None:
    client = create_client(api_key=os.environ["OPENAI_API_KEY"])
    request = ChatCompletionRequest.from_json(
        '{"model":"openai/gpt-4o","messages":[{"role":"user","content":"Explain quantum computing briefly"}],"stream":true}'
    )
    full_text = ""
    async for chunk in client.chat_stream(request):
        delta = chunk.choices[0].delta.content if chunk.choices else None
        if delta:
            full_text += delta
            print(delta, end="", flush=True)
    print()
    print(f"Full response length: {len(full_text)} characters")


asyncio.run(main())

TypeScript

import { createClient } from "@xberg-io/liter-llm";

const client = createClient(process.env.OPENAI_API_KEY!);
const chunks = await client.chatStream({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Explain quantum computing briefly" }],
});

let fullText = "";
for await (const chunk of chunks) {
  const delta = chunk.choices?.[0]?.delta?.content;
  if (delta) {
    fullText += delta;
    process.stdout.write(delta);
  }
}
console.log();
console.log(`Full response length: ${fullText.length} characters`);

Rust

use futures::StreamExt;
use liter_llm::{
    ChatCompletionRequest, ClientConfigBuilder, DefaultClient, LlmClient,
    Message, UserContent, UserMessage,
};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = ClientConfigBuilder::new(std::env::var("OPENAI_API_KEY")?)
        .build();
    let client = DefaultClient::new(config, Some("openai/gpt-4o"))?;

    let request = ChatCompletionRequest {
        model: "openai/gpt-4o".into(),
        messages: vec![Message::User(UserMessage {
            content: UserContent::Text("Explain quantum computing briefly".into()),
            name: None,
        })],
        ..Default::default()
    };

    let mut stream = client.chat_stream(request).await?;
    let mut full_text = String::new();
    while let Some(chunk) = stream.next().await {
        let chunk = chunk?;
        if let Some(choice) = chunk.choices.first() {
            if let Some(content) = &choice.delta.content {
                full_text.push_str(content);
                print!("{content}");
            }
        }
    }
    println!();
    println!("\nFull response length: {} characters", full_text.len());
    Ok(())
}

Go

package main

import (
  "encoding/json"
  "fmt"
  "os"
  "strings"

  llm "github.com/xberg-io/liter-llm/packages/go"
)

func main() {
  client, err := llm.CreateClient(os.Getenv("OPENAI_API_KEY"), nil, nil, nil, nil)
  if err != nil {
    panic(err)
  }

  var req llm.ChatCompletionRequest
  if err := json.Unmarshal([]byte(`{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "Explain quantum computing briefly"}],
    "stream": true
  }`), &req); err != nil {
    panic(err)
  }

  stream, err := client.ChatStream(req)
  if err != nil {
    panic(err)
  }

  var sb strings.Builder
  for chunk := range stream {
    if len(chunk.Choices) > 0 && chunk.Choices[0].Delta.Content != nil {
      delta := *chunk.Choices[0].Delta.Content
      sb.WriteString(delta)
      fmt.Print(delta)
    }
  }
  fmt.Println()
  fmt.Printf("Full response length: %d characters\n", sb.Len())
}

Java

import io.xberg.literllm.*;
import java.util.List;

public class Main {
    public static void main(String[] args) throws Exception {
        try (var client = LiterLlm.createClient(System.getenv("OPENAI_API_KEY"))) {
            var request = ChatCompletionRequest.builder()
                .withModel("openai/gpt-4o")
                .withMessages(List.of(
                    new Message.User(new UserMessage(UserContent.of("Explain quantum computing briefly"), null))
                ))
                .build();
            var sb = new StringBuilder();
            var stream = client.chatStream(request);
            var iterator = stream.iterator();
            while (iterator.hasNext()) {
                var chunk = iterator.next();
                var delta = chunk.choices().getFirst().delta().content();
                if (delta != null) {
                    sb.append(delta);
                    System.out.print(delta);
                }
            }
            System.out.println();
            System.out.printf("%nFull response length: %d characters%n", sb.length());
        }
    }
}

C#

using System.Text;
using LiterLlm;

using var client = LiterLlmLib.CreateClient(
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY")!,
    baseUrl: null, timeoutSecs: null, maxRetries: null, modelHint: null);

var request = new ChatCompletionRequest
{
    Model = "openai/gpt-4o",
    Messages = [new Message.User(new UserMessage { Content = UserContent.Of("Explain quantum computing briefly") })]
};

var sb = new StringBuilder();
await foreach (var chunk in client.ChatStreamAsync(request))
{
    var delta = chunk.Choices.Count > 0 ? chunk.Choices[0].Delta.Content : null;
    if (delta is not null)
    {
        sb.Append(delta);
        Console.Write(delta);
    }
}
Console.WriteLine();
Console.WriteLine($"\nFull response length: {sb.Length} characters");

Ruby

# frozen_string_literal: true

require 'liter_llm'

client = LiterLlm.create_client(ENV.fetch('OPENAI_API_KEY'))

full_text = +''
client.chat_stream(
  LiterLlm::ChatCompletionRequest.new(
    model: 'openai/gpt-4o-mini',
    messages: [{ 'role' => 'user', 'content' => 'Explain quantum computing briefly' }],
    stream: true
  )
) do |chunk|
  delta = chunk.choices && chunk.choices[0] && chunk.choices[0].delta
  if delta && delta.content
    full_text << delta.content
    print delta.content
  end
end
puts
puts "Full response length: #{full_text.length} characters"

PHP

<?php

declare(strict_types=1);

use Liter\Llm\LiterLlm;
use Liter\Llm\ChatCompletionRequest;

$client = LiterLlm::createClient(getenv('OPENAI_API_KEY') ?: '');

$request = ChatCompletionRequest::from_json(json_encode([
    'model' => 'openai/gpt-4o-mini',
    'messages' => [['role' => 'user', 'content' => 'Explain quantum computing briefly']],
]));

$fullText = '';
foreach ($client->chatStream($request) as $chunkJson) {
    $chunk = json_decode($chunkJson, false, flags: JSON_THROW_ON_ERROR);
    $delta = $chunk->choices[0]->delta->content ?? null;
    if ($delta !== null) {
        $fullText .= $delta;
        echo $delta;
    }
}
echo PHP_EOL;
echo 'Full response length: ' . strlen($fullText) . ' characters' . PHP_EOL;

Elixir

{:ok, client} = LiterLlm.create_client(System.get_env("OPENAI_API_KEY"))

request =
  Jason.encode!(%{
    model: "openai/gpt-4o-mini",
    messages: [%{role: "user", content: "Explain quantum computing briefly"}],
    stream: true
  })

{:ok, stream} = LiterLlm.defaultclient_chat_stream(client, request)

full_text =
  Enum.reduce(stream, "", fn chunk, acc ->
    delta = get_in(chunk, [:choices, Access.at(0), :delta, :content])
    if delta, do: (IO.write(delta); acc <> delta), else: acc
  end)

IO.puts("")
IO.puts("Full response length: #{String.length(full_text)} characters")

WASM

import init, { createClient, WasmChatCompletionRequest } from "@xberg-io/liter-llm-wasm";

await init();

const client = createClient(process.env.OPENAI_API_KEY!);

const request = WasmChatCompletionRequest.default();
request.model = "openai/gpt-4o";
request.messages = [{ role: "user", content: "Explain quantum computing briefly" }];
request.stream = true;

const stream = await client.chatStream(request);
let fullText = "";
while (true) {
  const chunk = await stream.next();
  if (chunk === null) {
    break;
  }
  const delta = chunk.choices?.[0]?.delta?.content;
  if (delta) {
    fullText += delta;
    process.stdout.write(delta);
  }
}
console.log();
console.log(`Full response length: ${fullText.length} characters`);

Tool Calling

Define tools as JSON schema functions. The model can request tool calls, which you execute and return results for:

Python

import asyncio
import json
import os

from liter_llm import create_client
from liter_llm._internal_bindings import ChatCompletionRequest

REQUEST = {
    "model": "openai/gpt-4o",
    "messages": [{"role": "user", "content": "What is the weather in Berlin?"}],
    "tools": [
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get the current weather for a location",
                "parameters": {
                    "type": "object",
                    "properties": {"location": {"type": "string"}},
                    "required": ["location"],
                },
            },
        }
    ],
    "tool_choice": "auto",
}


async def main() -> None:
    client = create_client(api_key=os.environ["OPENAI_API_KEY"])
    request = ChatCompletionRequest.from_json(json.dumps(REQUEST))
    response = await client.chat(request)
    for call in response.choices[0].message.tool_calls or []:
        print(f"Tool: {call.function.name}, Args: {call.function.arguments}")


asyncio.run(main())

TypeScript

import { createClient, ToolType } from "@xberg-io/liter-llm";

const client = createClient(process.env.OPENAI_API_KEY!);

const response = await client.chat({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "What is the weather in Berlin?" }],
  tools: [
    {
      toolType: ToolType.Function,
      function: {
        name: "get_weather",
        description: "Get the current weather for a location",
        parameters: {
          type: "object",
          properties: { location: { type: "string" } },
          required: ["location"],
        },
      },
    },
  ],
});

for (const call of response.choices[0]?.message?.toolCalls ?? []) {
  console.log(`Tool: ${call.function.name}, Args: ${call.function.arguments}`);
}

Rust

use liter_llm::{
    ChatCompletionRequest, ChatCompletionTool, ClientConfigBuilder, DefaultClient,
    FunctionDefinition, LlmClient, Message, ToolType, UserContent, UserMessage,
};
use serde_json::json;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let config = ClientConfigBuilder::new(std::env::var("OPENAI_API_KEY")?)
        .build();
    let client = DefaultClient::new(config, Some("openai/gpt-4o"))?;

    let tools = vec![ChatCompletionTool {
        tool_type: ToolType::Function,
        function: FunctionDefinition {
            name: "get_weather".into(),
            description: Some("Get the current weather for a location".into()),
            parameters: Some(json!({
                "type": "object",
                "properties": {
                    "location": { "type": "string", "description": "City name" }
                },
                "required": ["location"]
            })),
            strict: None,
        },
    }];

    let request = ChatCompletionRequest {
        model: "openai/gpt-4o".into(),
        messages: vec![Message::User(UserMessage {
            content: UserContent::Text("What is the weather in Berlin?".into()),
            name: None,
        })],
        tools: Some(tools),
        ..Default::default()
    };

    let response = client.chat(request).await?;
    if let Some(tool_calls) = &response.choices[0].message.tool_calls {
        for call in tool_calls {
            println!("Tool: {}, Args: {}", call.function.name, call.function.arguments);
        }
    }
    Ok(())
}

Go

package main

import (
  "encoding/json"
  "fmt"
  "os"

  llm "github.com/xberg-io/liter-llm/packages/go"
)

func main() {
  client, err := llm.CreateClient(os.Getenv("OPENAI_API_KEY"), nil, nil, nil, nil)
  if err != nil {
    panic(err)
  }

  body := `{
    "model": "openai/gpt-4o-mini",
    "messages": [{"role": "user", "content": "What is the weather in Berlin?"}],
    "tool_choice": "auto",
    "tools": [{
      "type": "function",
      "function": {
        "name": "get_weather",
        "description": "Get the current weather for a location",
        "parameters": {
          "type": "object",
          "properties": {"location": {"type": "string", "description": "City name"}},
          "required": ["location"]
        }
      }
    }]
  }`

  var req llm.ChatCompletionRequest
  if err := json.Unmarshal([]byte(body), &req); err != nil {
    panic(err)
  }

  resp, err := client.Chat(req)
  if err != nil {
    panic(err)
  }
  for _, call := range resp.Choices[0].Message.ToolCalls {
    fmt.Printf("Tool: %s, Args: %s\n", call.Function.Name, call.Function.Arguments)
  }
}

Java

import io.xberg.literllm.*;
import java.util.List;
import java.util.Map;
import java.util.Optional;

public class Main {
    public static void main(String[] args) throws Exception {
        try (var client = LiterLlm.createClient(System.getenv("OPENAI_API_KEY"))) {
            var tools = List.of(new ChatCompletionTool(
                ToolType.Function,
                new FunctionDefinition(
                    "get_weather",
                    "Get the current weather for a location",
                    Map.of(
                        "type", "object",
                        "properties", Map.of(
                            "location", Map.of("type", "string", "description", "City name")
                        ),
                        "required", List.of("location")
                    ),
                    null
                )
            ));

            var request = ChatCompletionRequest.builder()
                .withModel("openai/gpt-4o")
                .withMessages(List.of(
                    new Message.User(new UserMessage(UserContent.of("What is the weather in Berlin?"), null))
                ))
                .withTools(Optional.of(tools))
                .build();
            var response = client.chat(request);

            var toolCalls = response.choices().getFirst().message().toolCalls();
            if (toolCalls != null) {
                for (var call : toolCalls) {
                    System.out.printf("Tool: %s, Args: %s%n",
                        call.function().name(), call.function().arguments());
                }
            }
        }
    }
}

C#

using LiterLlm;

using var client = LiterLlmLib.CreateClient(
    apiKey: Environment.GetEnvironmentVariable("OPENAI_API_KEY")!,
    baseUrl: null, timeoutSecs: null, maxRetries: null, modelHint: null);

var tools = new List<ChatCompletionTool>
{
    new ChatCompletionTool
    {
        ToolType = ToolType.Function,
        Function = new FunctionDefinition
        {
            Name = "get_weather",
            Description = "Get the current weather for a location",
            Parameters = new
            {
                type = "object",
                properties = new
                {
                    location = new { type = "string", description = "City name" }
                },
                required = new[] { "location" }
            }
        }
    }
};

var response = await client.ChatAsync(new ChatCompletionRequest
{
    Model = "openai/gpt-4o",
    Messages = [new Message.User(new UserMessage { Content = UserContent.Of("What is the weather in Berlin?") })],
    Tools = tools
});

foreach (var call in response.Choices[0].Message.ToolCalls ?? [])
{
    Console.WriteLine($"Tool: {call.Function.Name}, Args: {call.Function.Arguments}");
}

Ruby

# frozen_string_literal: true

require 'liter_llm'

client = LiterLlm.create_client(ENV.fetch('OPENAI_API_KEY'))

tools = [
  {
    'type' => 'function',
    'function' => {
      'name' => 'get_weather',
      'description' => 'Get the current weather for a location',
      'parameters' => {
        'type' => 'object',
        'properties' => { 'location' => { 'type' => 'string', 'description' => 'City name' } },
        'required' => ['location']
      }
    }
  }
]

result = client.chat_async(
  LiterLlm::ChatCompletionRequest.new(
    model: 'openai/gpt-4o-mini',
    messages: [{ 'role' => 'user', 'content' => 'What is the weather in Berlin?' }],
    tools: tools,
    tool_choice: 'auto'
  )
)

(result.choices[0].message.tool_calls || []).each do |call|
  puts "Tool: #{call.function.name}, Args: #{call.function.arguments}"
end

PHP

<?php

declare(strict_types=1);

use Liter\Llm\LiterLlm;
use Liter\Llm\ChatCompletionRequest;

$client = LiterLlm::createClient(getenv('OPENAI_API_KEY') ?: '');

$tools = [[
    'type' => 'function',
    'function' => [
        'name' => 'get_weather',
        'description' => 'Get the current weather for a location',
        'parameters' => [
            'type' => 'object',
            'properties' => ['location' => ['type' => 'string', 'description' => 'City name']],
            'required' => ['location'],
        ],
    ],
]];

$request = ChatCompletionRequest::from_json(json_encode([
    'model' => 'openai/gpt-4o-mini',
    'messages' => [['role' => 'user', 'content' => 'What is the weather in Berlin?']],
    'tools' => $tools,
    'tool_choice' => 'auto',
]));

$result = $client->chat($request);
foreach ($result->choices[0]->message->toolCalls ?? [] as $call) {
    echo "Tool: {$call->function->name}, Args: {$call->function->arguments}" . PHP_EOL;
}

Elixir

{:ok, client} = LiterLlm.create_client(System.get_env("OPENAI_API_KEY"))

tools = [
  %{
    type: "function",
    function: %{
      name: "get_weather",
      description: "Get the current weather for a location",
      parameters: %{
        type: "object",
        properties: %{location: %{type: "string", description: "City name"}},
        required: ["location"]
      }
    }
  }
]

request =
  Jason.encode!(%{
    model: "openai/gpt-4o-mini",
    messages: [%{role: "user", content: "What is the weather in Berlin?"}],
    tools: tools,
    tool_choice: "auto"
  })

{:ok, result} = LiterLlm.defaultclient_chat_async(client, request)

for call <- Enum.at(result.choices, 0).message.tool_calls || [] do
  IO.puts("Tool: #{call.function.name}, Args: #{call.function.arguments}")
end

WASM

import init, {
  createClient,
  WasmChatCompletionRequest,
  WasmChatCompletionTool,
  WasmFunctionDefinition,
} from "@xberg-io/liter-llm-wasm";

await init();

const client = createClient(process.env.OPENAI_API_KEY!);

const tool = WasmChatCompletionTool.default();
tool.toolType = "function";
const fn = WasmFunctionDefinition.default();
fn.name = "get_weather";
fn.description = "Get the current weather for a location";
fn.parameters = {
  type: "object",
  properties: { location: { type: "string" } },
  required: ["location"],
};
tool.function = fn;

const request = WasmChatCompletionRequest.default();
request.model = "openai/gpt-4o";
request.messages = [{ role: "user", content: "What is the weather in Berlin?" }];
request.toolChoice = "auto";
request.tools = [tool];

const response = await client.chat(request);
for (const call of response.choices[0]?.message?.toolCalls ?? []) {
  console.log(`Tool: ${call.function.name}, Args: ${call.function.arguments}`);
}

Chat Parameters

All chat parameters work with both chat and chat_stream:

Parameter	Type	Description
model	string	Provider/model identifier (e.g. "openai/gpt-4o")
messages	array	Conversation messages
temperature	float	Sampling temperature (0.0-2.0)
max_tokens	int	Maximum tokens to generate
top_p	float	Nucleus sampling threshold
n	int	Number of completions to generate
stop	string/array	Stop sequences
tools	array	Tool/function definitions
tool_choice	string/object	Tool selection strategy
response_format	object	Force JSON output ({"type": "json_object"})
seed	int	Deterministic sampling seed
presence_penalty	float	Penalize new topics (-2.0 to 2.0)
frequency_penalty	float	Penalize repetition (-2.0 to 2.0)
reasoning_effort	string	Reasoning budget for o-series and extended-thinking models.
extra_body	object	Provider-specific fields passed through verbatim.

Reasoning Effort

OpenAI o-series models and Anthropic extended-thinking models accept a reasoning_effort parameter that controls how much compute the model spends on internal reasoning before producing the final response.

Python

response = client.chat({
    "model": "openai/o3-mini",
    "messages": [{"role": "user", "content": "Prove the Pythagorean theorem."}],
    "reasoning_effort": "high",
})

TypeScript

const response = await client.chat({
  model: "openai/o3-mini",
  messages: [{ role: "user", content: "Prove the Pythagorean theorem." }],
  reasoningEffort: "high",
});

Rust

use liter_llm::types::ReasoningEffort;

let req = ChatCompletionRequest {
    model: "openai/o3-mini".into(),
    messages: vec![/* ... */],
    reasoning_effort: Some(ReasoningEffort::High),
    ..Default::default()
};

Go

var req llm.ChatCompletionRequest
if err := json.Unmarshal([]byte(`{
    "model": "openai/o3-mini",
    "messages": [{"role": "user", "content": "Think step by step."}],
    "reasoning_effort": "high"
}`), &req); err != nil {
    return err
}

resp, err := client.Chat(req)

Accepted values for OpenAI o-series: "low", "medium", "high". Anthropic extended thinking uses a budget_tokens integer instead, which maps to reasoning_effort when the binding converts the field.

Structured Outputs (JSON Schema)

Pass a JSON Schema to response_format to constrain the model output to a specific structure. Use "type": "json_schema" instead of "type": "json_object" for schema-validated output.

Python

schema = {
    "type": "object",
    "properties": {
        "name": {"type": "string"},
        "age":  {"type": "integer"},
    },
    "required": ["name", "age"],
    "additionalProperties": False,
}

response = client.chat({
    "model": "openai/gpt-4o",
    "messages": [{"role": "user", "content": "Extract: Alice is 30 years old."}],
    "response_format": {
        "type": "json_schema",
        "json_schema": {
            "name": "person",
            "strict": True,
            "schema": schema,
        },
    },
})

TypeScript

const response = await client.chat({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Extract: Alice is 30 years old." }],
  responseFormat: {
    type: "json_schema",
    jsonSchema: {
      name: "person",
      strict: true,
      schema: {
        type: "object",
        properties: {
          name: { type: "string" },
          age:  { type: "integer" },
        },
        required: ["name", "age"],
        additionalProperties: false,
      },
    },
  },
});

Rust

use serde_json::json;

let req = ChatCompletionRequest {
    model: "openai/gpt-4o".into(),
    messages: vec![/* ... */],
    response_format: Some(json!({
        "type": "json_schema",
        "json_schema": {
            "name": "person",
            "strict": true,
            "schema": {
                "type": "object",
                "properties": {
                    "name": { "type": "string" },
                    "age":  { "type": "integer" }
                },
                "required": ["name", "age"],
                "additionalProperties": false
            }
        }
    })),
    ..Default::default()
};

Structured output availability depends on provider support. OpenAI gpt-4o and later support json_schema. Providers that do not support it fall back to json_object or return EndpointNotSupported.

Extra_body

Pass provider-specific parameters that liter-llm does not model natively via extra_body. Fields in extra_body are merged into the top-level request JSON before it is sent to the provider.

Python

response = client.chat({
    "model": "openai/gpt-4o",
    "messages": [{"role": "user", "content": "Hello"}],
    "extra_body": {
        "store": True,           # OpenAI conversation store
        "metadata": {"user": "alice"},
    },
})

TypeScript

const response = await client.chat({
  model: "openai/gpt-4o",
  messages: [{ role: "user", content: "Hello" }],
  extraBody: {
    store: true,
    metadata: { user: "alice" },
  },
});

Rust

use serde_json::json;

let req = ChatCompletionRequest {
    model: "openai/gpt-4o".into(),
    messages: vec![/* ... */],
    extra_body: Some(json!({ "store": true, "metadata": { "user": "alice" } })),
    ..Default::default()
};

extra_body fields take lower precedence than named fields. If a named field and an extra_body key conflict, the named field wins.

Audio Content Parts

Send audio inline in a user message using the input_audio content part type. The audio must be base64-encoded.

Python

import base64

with open("audio.wav", "rb") as f:
    audio_b64 = base64.b64encode(f.read()).decode()

response = client.chat({
    "model": "openai/gpt-4o-audio-preview",
    "messages": [{
        "role": "user",
        "content": [
            {
                "type": "input_audio",
                "input_audio": {
                    "data": audio_b64,
                    "format": "wav",
                },
            },
            {"type": "text", "text": "Transcribe and summarize this audio."},
        ],
    }],
})

TypeScript

import { readFileSync } from "fs";

const audioB64 = readFileSync("audio.wav").toString("base64");

const response = await client.chat({
  model: "openai/gpt-4o-audio-preview",
  messages: [{
    role: "user",
    content: [
      {
        type: "input_audio",
        inputAudio: { data: audioB64, format: "wav" },
      },
      { type: "text", text: "Transcribe and summarize this audio." },
    ],
  }],
});

Rust

use base64::{Engine, engine::general_purpose::STANDARD};
use liter_llm::types::{AudioContent, ContentPart};

let audio_bytes = std::fs::read("audio.wav")?;
let audio_b64 = STANDARD.encode(&audio_bytes);

let content = vec![
    ContentPart::InputAudio {
        input_audio: AudioContent {
            data: audio_b64,
            format: "wav".into(),
        },
    },
    ContentPart::Text { text: "Transcribe and summarize this audio.".into() },
];

Supported formats depend on the provider. OpenAI gpt-4o-audio-preview accepts wav, mp3, ogg, flac, m4a.

AWS EventStream Streaming

When routing to Bedrock providers, responses arrive in AWS EventStream framing rather than SSE. Liter-llm handles the framing transparently. chat_stream works the same way regardless of provider.

Rust

// EventStream framing is transparent to the caller.
let stream = client.chat_stream(ChatCompletionRequest {
    model: "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0".into(),
    messages: vec![/* ... */],
    ..Default::default()
}).await?;

// Consume exactly like any other stream.
pin_mut!(stream);
while let Some(chunk) = stream.next().await {
    let chunk = chunk?;
    if let Some(content) = chunk.choices[0].delta.content.as_deref() {
        print!("{content}");
    }
}

Python

# EventStream framing is transparent to the caller.
for chunk in client.chat_stream({
    "model": "bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0",
    "messages": [{"role": "user", "content": "Hello"}],
}):
    print(chunk["choices"][0]["delta"].get("content", ""), end="", flush=True)

Tower streaming buffer

When Bedrock streaming is routed through the Tower middleware stack (LlmService), the entire stream is buffered in memory before chunks are yielded. This is a Tower Service trait constraint. For unbuffered Bedrock streaming, call LlmClient::chat_stream() directly, bypassing the Tower stack. See Architecture for details.