llm_ollama #

Ollama backend implementation for LLM interactions in Dart.

Available on pub.dev.

Features #

• Streaming chat responses
• Tool/function calling
• Vision (image) support
• Embeddings
• Thinking mode support
• Model management (list, pull, show, version)

Streaming Reliability Guarantees #

• NDJSON chunk-boundary-safe parsing: stream frames are buffered across transport chunks and parsed only when newline-terminated
• Bounded malformed-line retries: parser tolerates up to 3 consecutive malformed non-empty lines
• Explicit failure on persistent corruption: parser throws LLMApiException after retry budget exhaustion (no silent infinite dropping)

Installation #

dependencies:
  llm_ollama: ^0.1.5

Prerequisites #

You need Ollama running locally or on a server. Install from ollama.com.

# Pull a model
ollama pull qwen3:0.6b

Usage #

Basic Chat #

import 'package:llm_ollama/llm_ollama.dart';

final repo = OllamaChatRepository(baseUrl: 'http://localhost:11434');

final stream = repo.streamChat('qwen3:0.6b', messages: [
  LLMMessage(role: LLMRole.user, content: 'Hello!'),
]);

await for (final chunk in stream) {
  print(chunk.message?.content ?? '');
}

With Thinking Mode #

final stream = repo.streamChat('qwen3:0.6b',
  messages: messages,
  think: true, // Enable thinking mode
);

await for (final chunk in stream) {
  if (chunk.message?.thinking != null) {
    print('Thinking: ${chunk.message!.thinking}');
  }
  print(chunk.message?.content ?? '');
}

Tool Calling #

final stream = repo.streamChat('qwen3:0.6b',
  messages: messages,
  tools: [MyTool()],
);

Vision #

import 'dart:convert';
import 'dart:io';

final imageBytes = await File('image.png').readAsBytes();
final base64Image = base64Encode(imageBytes);

final stream = repo.streamChat('llama3.2-vision:11b', messages: [
  LLMMessage(
    role: LLMRole.user,
    content: 'What is in this image?',
    images: [base64Image],
  ),
]);

Embeddings #

final embeddings = await repo.embed(
  model: 'nomic-embed-text',
  messages: ['Hello world', 'Goodbye world'],
);

Non-Streaming Response #

Get a complete response without streaming:

final response = await repo.chatResponse('qwen3:0.6b', messages: [
  LLMMessage(role: LLMRole.user, content: 'Hello!'),
]);

print(response.content);
print('Tokens: ${response.evalCount}');

Using StreamChatOptions #

Encapsulate all options in a single object:

import 'package:llm_core/llm_core.dart';

final options = StreamChatOptions(
  think: true,
  tools: [MyTool()],
  toolAttempts: 5,
  timeout: Duration(minutes: 5),
  retryConfig: RetryConfig(maxAttempts: 3),
);

final stream = repo.streamChat('qwen3:0.6b', messages: messages, options: options);

Python-Style Parity Options #

llm_ollama defaults to automatic tool execution for backward compatibility. If you want ollama-python style manual tool loops, disable auto-execution and consume toolCalls directly from streamed chunks.

final stream = repo.streamChat(
  'qwen3:0.6b',
  messages: messages,
  tools: [MyTool()],
  options: const StreamChatOptions(
    autoExecuteTools: false, // Manual tool loop
  ),
);

await for (final chunk in stream) {
  final toolCalls = chunk.message?.toolCalls ?? const [];
  if (toolCalls.isNotEmpty) {
    // Execute tools yourself, append tool messages, then call streamChat again.
  }
}

You can also pass Ollama chat fields supported by ollama-python:

final stream = repo.streamChat(
  'qwen3:0.6b',
  messages: messages,
  options: const StreamChatOptions(
    backendOptions: {
      'format': 'json',
      'options': {'temperature': 0},
      'keep_alive': '5m', // or keepAlive
    },
  ),
);

Model Management #

final ollamaRepo = OllamaRepository(baseUrl: 'http://localhost:11434');

// List models
final models = await ollamaRepo.models();

// Show model info
final info = await ollamaRepo.showModel('qwen3:0.6b');

// Pull a model
await for (final progress in ollamaRepo.pullModel('qwen3:0.6b')) {
  print('${progress.status}: ${progress.progress * 100}%');
}

// Get version
final version = await ollamaRepo.version();

Advanced Configuration #

Builder Pattern #

Use the builder for complex configurations:

import 'package:llm_core/llm_core.dart';

final repo = OllamaChatRepository.builder()
  .baseUrl('http://localhost:11434')
  .maxToolAttempts(10)
  .retryConfig(RetryConfig(
    maxAttempts: 5,
    initialDelay: Duration(seconds: 1),
    maxDelay: Duration(seconds: 30),
  ))
  .timeoutConfig(TimeoutConfig(
    connectionTimeout: Duration(seconds: 10),
    readTimeout: Duration(minutes: 3),
    totalTimeout: Duration(minutes: 10),
  ))
  .build();

Retry Configuration #

Configure automatic retries for failed requests:

import 'package:llm_core/llm_core.dart';

final repo = OllamaChatRepository(
  baseUrl: 'http://localhost:11434',
  retryConfig: RetryConfig(
    maxAttempts: 3,
    initialDelay: Duration(seconds: 1),
    maxDelay: Duration(seconds: 30),
    retryableStatusCodes: [429, 500, 502, 503, 504],
  ),
);

Timeout Configuration #

Configure timeouts for different scenarios:

import 'package:llm_core/llm_core.dart';

final repo = OllamaChatRepository(
  baseUrl: 'http://localhost:11434',
  timeoutConfig: TimeoutConfig(
    connectionTimeout: Duration(seconds: 10),
    readTimeout: Duration(minutes: 2),
    totalTimeout: Duration(minutes: 10),
    readTimeoutForLargePayloads: Duration(minutes: 5), // For large images
  ),
);