Nocodo LLM SDK

A general-purpose LLM SDK for Rust with support for multiple LLM providers.

Features

Type-safe: Leverages Rust's type system for compile-time guarantees
Async-first: Built with Tokio for high-performance async operations
Ergonomic: Builder pattern for easy request construction
Comprehensive error handling: Detailed error types with context
Tool/Function Calling: Type-safe tool calling with automatic JSON Schema generation
Claude support: Full Messages API implementation
Gemini support: Google Gemini 3 Pro and Flash with reasoning capabilities
Grok support: xAI and Zen (free) Grok integration with OpenAI-compatible API
Cerebras support: GPT OSS and Qwen models through an OpenAI-compatible API
GLM support: GLM models through zAI and Zen
Groq support: Groq Chat Completions API with tool use
OpenRouter support: Multi-model proxy with runtime discovery of free programming models
MixLayer support: OpenAI-compatible Chat Completions with reasoning, tools, structured output, and hosted web search
Xiaomi support: MiMo V2.6 and V2.5 chat models and MiMo V2.5 ASR through Xiaomi's OpenAI-compatible Chat Completions API
ElevenLabs support: Scribe v2 and Scribe v2 Medical batch speech-to-text transcription from files or source URLs
Ollama support: Local models via Ollama /api/chat
llama.cpp support: Local models via OpenAI-compatible API
Zen provider: Free access to select models during beta
OpenAI support: GPT-5 models (GPT-5, GPT-5 mini, GPT-5 nano, GPT-5.1, GPT-5.1 Codex) via Responses API
Voyage AI support: Text embeddings with multiple specialized models
Usage reporting: Provider-neutral input, output, and optional reasoning-token counts
Multi-provider: Same models available from different providers
Extensible: Designed for easy addition of other LLM providers

Architecture

Trait-Based Design

The SDK uses a trait-based architecture that provides a unified interface across all LLM providers while maintaining type safety and extensibility:

// Core trait that all providers implement
pub trait LlmClient: Send + Sync {
    type Response: LlmResponse;
    type MessageBuilder: MessageBuilder;
    
    fn message_builder(&self) -> Self::MessageBuilder;
    fn with_base_url(self, url: impl Into<String>) -> Self;
}

// Provider-specific implementations
impl LlmClient for ClaudeClient { /* ... */ }
impl LlmClient for GeminiClient { /* ... */ }
impl LlmClient for GrokClient { /* ... */ }
impl LlmClient for GlmClient { /* ... */ }
impl LlmClient for OpenAIClient { /* ... */ }
impl LlmClient for XiaomiClient { /* ... */ }

Benefits

Type Safety: Each provider has its own response types and message builders
Unified API: Common operations work across all providers via the trait
Extensibility: New providers can be added by implementing the trait
Zero-Cost Abstractions: No runtime overhead from the trait system
Provider-Specific Features: Access to unique capabilities of each provider

Multi-Provider Support

Access the same models through different providers for flexibility in cost, performance, and availability:

Model	Zen (Free)	xAI (Paid)	Cerebras (Paid)
Grok	`grok-code`	`grok-code-fast-1`	-
GLM 4.6	`big-pickle`	-	-

Installation

Add this to your Cargo.toml:

[dependencies]
llm-sdk = { path = "../llm-sdk" }  # For local development
# OR
llm-sdk = "0.1"

Quick Start

Claude (Anthropic)

use llm_sdk::claude::ClaudeClient;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create client with your Anthropic API key
    let client = ClaudeClient::new("your-anthropic-api-key")?;

    // Build and send a message
    let response = client
        .message_builder()
        .model("claude-sonnet-4-5-20250929")
        .max_tokens(1024)
        .user_message("Hello, Claude! How are you today?")
        .send()
        .await?;

    println!("Response: {}", response.content[0].text);
    Ok(())
}

Gemini (Google)

use llm_sdk::gemini::GeminiClient;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create client with your Google API key
    let client = GeminiClient::new("your-google-api-key")?;

    // Build and send a message
    let response = client
        .message_builder()
        .model("gemini-3-pro-preview")
        .system("You are a helpful assistant")
        .user_message("Explain quantum entanglement briefly")
        .thinking_level("high")  // Enable deep reasoning
        .temperature(1.0)        // Recommended: keep at 1.0
        .max_output_tokens(1024)
        .send()
        .await?;

    // Extract and print the response
    for candidate in &response.candidates {
        for part in &candidate.content.parts {
            if let Some(text) = &part.text {
                println!("Gemini: {}", text);
            }
        }
    }

    // Print token usage
    if let Some(usage) = response.usage_metadata {
        println!(
            "Usage: {} input, {} output, {} total tokens",
            usage.prompt_token_count,
            usage.candidates_token_count,
            usage.total_token_count
        );
    }

    Ok(())
}

Grok (xAI)

use llm_sdk::grok::GrokClient;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create client with your xAI API key
    let client = GrokClient::new("your-xai-api-key")?;

    // Build and send a message
    let response = client
        .message_builder()
        .model("grok-code-fast-1")
        .max_tokens(1024)
        .user_message("Write a Rust function to reverse a string.")
        .send()
        .await?;

    println!("Grok: {}", response.choices[0].message.content);
    println!(
        "Usage: {} input tokens, {} output tokens",
        response.usage.prompt_tokens, response.usage.completion_tokens
    );
    Ok(())
}

Cerebras

use llm_sdk::cerebras::{CerebrasClient, GPT_OSS_120B};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = CerebrasClient::new("your-cerebras-api-key")?;

    // Build and send a message
    let response = client
        .message_builder()
        .model(GPT_OSS_120B)
        .max_tokens(1024)
        .user_message("Explain quantum computing in simple terms.")
        .send()
        .await?;

    println!("Cerebras: {}", response.choices[0].message.get_text());
    println!(
        "Usage: {} input tokens, {} output tokens",
        response.usage.prompt_tokens, response.usage.completion_tokens
    );
    Ok(())
}

Xiaomi MiMo

use llm_sdk::models::xiaomi::MIMO_V2_5;
use llm_sdk::xiaomi::XiaomiClient;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = XiaomiClient::new("your-xiaomi-api-key")?;

    let response = client
        .message_builder()
        .model(MIMO_V2_5)
        .system_message("You are a helpful assistant.")
        .user_message("Explain Rust ownership briefly.")
        .max_completion_tokens(256)
        .send()
        .await?;

    println!(
        "MiMo: {}",
        response.choices[0].message.content.as_deref().unwrap_or_default()
    );
    Ok(())
}

XiaomiClient defaults to mimo-v2.5. Select MIMO_V2_6_PRO or MIMO_V2_6_FLASH with .model(...) to use the newer chat models. It also supports mimo-v2.5-pro, tool calling, tool results, sampling controls, stop sequences, JSON-object output, and strict JSON-schema output.

MiMo V2.5 ASR is available through a provider-specific speech recognition builder on the same Token Plan client:

use llm_sdk::models::xiaomi::MIMO_V2_5_ASR;
use llm_sdk::xiaomi::{XiaomiAsrLanguage, XiaomiClient};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = XiaomiClient::new("your-xiaomi-api-key")?;
    let audio_data_url = "data:audio/wav;base64,UklGRg==";

    let response = client
        .speech_recognition_builder()
        .model(MIMO_V2_5_ASR)
        .audio_data_url(audio_data_url)
        .language(XiaomiAsrLanguage::En)
        .send()
        .await?;

    println!(
        "Transcript: {}",
        response.choices[0].message.content.as_deref().unwrap_or_default()
    );
    Ok(())
}

The builder accepts wav and mp3 audio, either as a complete data URL with audio_data_url(...) or as raw Base64 plus XiaomiAudioFormat with audio_base64(...). Xiaomi documents a 10 MB limit for the Base64 audio input; the SDK sends the supplied input without enforcing that limit locally.

ElevenLabs Scribe v2

Use ElevenLabsClient for batch speech-to-text transcription. The provider and model constants are providers::ELEVENLABS, models::elevenlabs::SCRIBE_V2_ID, and models::elevenlabs::SCRIBE_V2_NAME. For clinical audio, set request.model_id to models::elevenlabs::SCRIBE_V2_MEDICAL_ID; its label is SCRIBE_V2_MEDICAL_NAME. Regional API hosts can be selected with with_base_url.

use llm_sdk::elevenlabs::{ElevenLabsClient, TranscriptionRequest, TranscriptionResult};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = ElevenLabsClient::new("your-elevenlabs-api-key")?;
    let audio = std::fs::read("meeting.mp3")?;
    let mut request = TranscriptionRequest::file(audio, "meeting.mp3");
    request.diarize = Some(true);
    request.language_code = Some("en".into());

    match client.transcribe(request).await? {
        TranscriptionResult::Transcript(transcript) => println!("{}", transcript.text),
        TranscriptionResult::Multichannel(result) => {
            for channel in result.transcripts {
                println!("{}", channel.text);
            }
        }
        TranscriptionResult::Accepted(result) => println!("Queued: {}", result.request_id),
    }
    Ok(())
}

For hosted media, use TranscriptionRequest::source_url(url). Set webhook = Some(true) to receive an asynchronous acceptance response.

OpenRouter

OpenRouter proxies many models through an OpenAI-compatible Chat Completions API. Free models are not hardcoded — discover the current free programming models at runtime, then pick one.

use llm_sdk::openrouter::OpenRouterClient;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create client with your OpenRouter API key
    let client = OpenRouterClient::new("your-openrouter-api-key")?;

    // Discover free programming models available right now
    let models = client.list_free_programming_models().await?;
    let model_id = &models[0].id;

    // Build and send a message
    let response = client
        .with_model(model_id)
        .message_builder()
        .model(model_id)
        .max_tokens(1024)
        .user_message("Write a Rust function to reverse a string.")
        .send()
        .await?;

    println!("OpenRouter: {}", response.choices[0].message.content);
    Ok(())
}

Ollama (Local)

use llm_sdk::ollama::OllamaClient;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Defaults to http://localhost:11434
    let client = OllamaClient::new()?;

    let response = client
        .message_builder()
        .model("llama3.1")
        .user_message("Hello from Ollama!")
        .send()
        .await?;

    println!("Ollama: {}", response.message.content);
    Ok(())
}

MixLayer

MixLayer exposes hosted models through an OpenAI-compatible Chat Completions endpoint. The client defaults to the free qwen/qwen3.5-4b-free model.

use llm_sdk::mixlayer::{MixlayerClient, QWEN_3_5_4B_FREE};

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = MixlayerClient::new("your-mixlayer-api-key")?;

    let response = client
        .message_builder()
        .model(QWEN_3_5_4B_FREE)
        .system_message("You are a helpful assistant.")
        .user_message("Explain ownership in Rust briefly.")
        .max_completion_tokens(256)
        .send()
        .await?;

    println!("{}", response.choices[0].message.content.as_deref().unwrap_or_default());
    Ok(())
}

The provider-specific builder also supports reasoning_effort, thinking, sampling penalties, strict JSON-schema output, tool calling, metadata/storage controls, and web_search_options.

llama.cpp (Local)

use llm_sdk::llama_cpp::LlamaCppClient;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Defaults to http://localhost:8080
    let client = LlamaCppClient::new()?;

    let response = client
        .message_builder()
        .model("gpt-3.5-turbo")
        .user_message("Hello from llama.cpp!")
        .send()
        .await?;

    println!("llama.cpp: {}", response.choices[0].message.content.clone().unwrap_or_default());
    Ok(())
}

Multi-Provider Support

llm-sdk supports accessing the same models via different providers, giving you flexibility in cost, performance, and availability.

Grok Models

Access Grok via different providers:

Zen (Free)

use llm_sdk::grok::zen::ZenGrokClient;

// No API key required for free model!
let client = ZenGrokClient::new()?;
let response = client
    .message_builder()
    .model("grok-code")
    .max_tokens(1024)
    .user_message("Hello, Grok!")
    .send()
    .await?;

println!("Response: {}", response.choices[0].message.content);

xAI (Paid)

use llm_sdk::grok::xai::XaiGrokClient;

let client = XaiGrokClient::new("your-xai-api-key")?;
let response = client
    .message_builder()
    .model("grok-code-fast-1")
    .max_tokens(1024)
    .user_message("Hello, Grok!")
    .send()
    .await?;

println!("Response: {}", response.choices[0].message.content);

Cerebras Models

use llm_sdk::cerebras::{CerebrasClient, QWEN_3_8_27B};

let client = CerebrasClient::new("your-cerebras-api-key")?;
let response = client
    .message_builder()
    .model(QWEN_3_8_27B)
    .max_tokens(1024)
    .user_message("Hello, Qwen!")
    .send()
    .await?;

println!("Response: {}", response.choices[0].message.get_text());

Provider Comparison

Model	Zen (OpenCode)	Native Provider
Grok	`grok-code` (free)	`grok-code-fast-1` (xAI, paid)
GLM 4.6	`big-pickle` (free, limited time)	zAI

OpenAI (GPT-5)

use llm_sdk::openai::OpenAIClient;

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create client with your OpenAI API key
    let client = OpenAIClient::new("your-openai-api-key")?;

    // Build and send a response request
    let response = client
        .response_builder()
        .model("gpt-5-mini")
        .input("Write a Python function to check if a number is prime.")
        .send()
        .await?;

    // Extract text from response
    let text_content: String = response
        .output
        .iter()
        .filter(|item| item.item_type == "message")
        .filter_map(|item| item.content.as_ref())
        .flatten()
        .filter(|block| block.content_type == "output_text")
        .map(|block| block.text.clone())
        .collect();

    println!("GPT: {}", text_content);
    println!(
        "Usage: {} input tokens, {} output tokens",
        response.usage.input_tokens.unwrap_or(0),
        response.usage.output_tokens.unwrap_or(0)
    );
    Ok(())
}

Gemini (Google)

Google's Gemini 3 models with reasoning capabilities and thinking level controls.

Gemini 3 Pro

The most intelligent model for complex reasoning tasks.

use llm_sdk::gemini::GeminiClient;
use llm_sdk::models::gemini::GEMINI_3_PRO;

let client = GeminiClient::new("your-google-api-key")?;

let response = client
    .message_builder()
    .model(GEMINI_3_PRO)
    .system("You are a helpful assistant")
    .user_message("Write a Rust function to calculate fibonacci numbers")
    .thinking_level("high")  // Enable deep reasoning (default)
    .temperature(1.0)        // Recommended: keep at 1.0
    .max_output_tokens(1024)
    .send()
    .await?;

// Extract response text
for candidate in &response.candidates {
    for part in &candidate.content.parts {
        if let Some(text) = &part.text {
            println!("{}", text);
        }
    }
}

Gemini 3 Flash

Pro-level intelligence at Flash speed for faster responses.

use llm_sdk::models::gemini::GEMINI_3_FLASH;

let response = client
    .message_builder()
    .model(GEMINI_3_FLASH)
    .thinking_level("low")  // Fast mode for quicker responses
    .user_message("What is a REST API?")
    .max_output_tokens(200)
    .send()
    .await?;

Thinking Levels

Gemini 3 supports dynamic thinking control to balance speed vs. reasoning depth:

Gemini 3 Pro:

low: Faster responses with less reasoning
high (default): Maximum reasoning capability

Gemini 3 Flash:

minimal: Fastest responses
low: Quick with basic reasoning
medium: Balanced speed and reasoning
high (default): Maximum reasoning

// Fast response mode
let response = client
    .message_builder()
    .model(GEMINI_3_FLASH)
    .thinking_level("minimal")
    .user_message("Quick question...")
    .send()
    .await?;

// Deep reasoning mode
let response = client
    .message_builder()
    .model(GEMINI_3_PRO)
    .thinking_level("high")
    .user_message("Complex reasoning task...")
    .send()
    .await?;

Multi-turn Conversations

let response = client
    .message_builder()
    .model(GEMINI_3_PRO)
    .system("You are a helpful coding assistant")
    .user_message("What's the best way to handle errors in Rust?")
    .model_message("The best way is to use Result<T, E> for recoverable errors.")
    .user_message("Can you show me an example?")
    .send()
    .await?;

Key Features

✅ 1M token context window - Process large documents
✅ 64k token output - Generate long-form content
✅ Tool/function calling - Integrate with external APIs
✅ Vision support - Analyze images (multimodal)
✅ Structured outputs - JSON response formatting
✅ Thinking level controls - Adjust reasoning depth
✅ Thought signature preservation - Maintains reasoning context for multi-step tasks

Model Specifications

Gemini 3 Pro (`gemini-3-pro-preview`)

Context: 1M input / 64k output tokens
Knowledge cutoff: January 2025
Thinking levels: low, high (default)
Best for: Complex reasoning, autonomous coding, agentic workflows
Pricing: $2/1M input tokens (<200k), $12/1M output tokens

Gemini 3 Flash (`gemini-3-flash-preview`)

Context: 1M input / 64k output tokens
Knowledge cutoff: January 2025
Thinking levels: minimal, low, medium, high (default)
Best for: Fast responses with Pro-level intelligence
Pricing: $0.50/1M input tokens, $3/1M output tokens

Important Notes

Temperature: Gemini 3 documentation strongly recommends keeping temperature at 1.0 (default). Lowering temperature may cause looping or degraded performance. Use thinking level instead to control response quality/speed.

Thought Signatures: For advanced tool calling with multi-step reasoning, Gemini 3 uses encrypted "thought signatures" to maintain reasoning context. The SDK automatically preserves these signatures across API calls.

Advanced Usage

Claude: Conversation with Multiple Messages

let response = client
    .message_builder()
    .model("claude-sonnet-4-5-20250929")
    .max_tokens(1024)
    .message("system", "You are a helpful assistant.")
    .message("user", "What's the capital of France?")
    .message("assistant", "The capital of France is Paris.")
    .message("user", "What's its population?")
    .send()
    .await?;

Claude: Custom Parameters

let response = client
    .message_builder()
    .model("claude-sonnet-4-5-20250929")
    .max_tokens(2048)
    .temperature(0.7)
    .top_p(0.9)
    .system("You are an expert programmer.")
    .user_message("Write a Rust function to calculate fibonacci numbers.")
    .send()
    .await?;

Grok: Multi-turn Conversation

let response = client
    .message_builder()
    .model("grok-code-fast-1")
    .max_tokens(1024)
    .system_message("You are an expert Rust developer.")
    .user_message("What's the best way to handle errors in Rust?")
    .assistant_message("The best way is to use Result<T, E> for recoverable errors and panic! for unrecoverable errors.")
    .user_message("Can you show me an example?")
    .send()
    .await?;

Error Handling

match client
    .message_builder()
    .model("claude-sonnet-4-5-20250929")
    .max_tokens(100)
    .user_message("Hello")
    .send()
    .await
{
    Ok(response) => println!("Success: {}", response.content[0].text),
    Err(llm_sdk::error::LlmError::Authentication { message }) => {
        eprintln!("Authentication failed: {}", message);
    }
    Err(llm_sdk::error::LlmError::RateLimit { message, retry_after }) => {
        eprintln!("Rate limited: {} - retry after {:?}", message, retry_after);
    }
    Err(e) => eprintln!("Other error: {:?}", e),
}

Tool Calling (Function Calling)

Enable LLMs to call external functions with type-safe parameter extraction.

Basic Example

use llm_sdk::{openai::OpenAIClient, tools::Tool};
use schemars::JsonSchema;
use serde::{Deserialize, Serialize};

// Define parameter schema
#[derive(Debug, Serialize, Deserialize, JsonSchema)]
struct WeatherParams {
    location: String,
    unit: String,
}

// Create tool
let tool = Tool::from_type::<WeatherParams>()
    .name("get_weather")
    .description("Get weather for a location")
    .build();

// Use tool
let response = client
    .message_builder()
    .user_message("What's the weather in NYC?")
    .tool(tool)
    .send()
    .await?;

// Handle tool calls
if let Some(calls) = response.tool_calls() {
    for call in calls {
        let params: WeatherParams = call.parse_arguments()?;
        // Execute your function...
    }
}

Advanced: Multi-Tool Agent

// Define multiple tools
let search_tool = Tool::from_type::<SearchParams>()
    .name("search")
    .description("Search the knowledge base")
    .build();

let calc_tool = Tool::from_type::<CalculateParams>()
    .name("calculate")
    .description("Evaluate mathematical expressions")
    .build();

// Use multiple tools with parallel execution
let response = client
    .message_builder()
    .user_message("Search for 'Rust' and calculate 123 * 456")
    .tools(vec![search_tool, calc_tool])
    .tool_choice(ToolChoice::Auto)
    .parallel_tool_calls(true)
    .send()
    .await?;

See examples/tool_calling_*.rs for complete examples.

Tool Choice Options

ToolChoice::Auto: Let the model decide whether to use tools
ToolChoice::Required: Force the model to use at least one tool
ToolChoice::None: Disable tool use
ToolChoice::Specific { name }: Force a specific tool by name

API Reference

ClaudeClient

new(api_key: impl Into<String>) -> Result<Self>: Create a new client
with_base_url(url: impl Into<String>) -> Self: Set custom API base URL
message_builder() -> ClaudeMessageBuilder: Start building a message request

GeminiClient

new(api_key: impl Into<String>) -> Result<Self>: Create a new client
with_base_url(url: impl Into<String>) -> Self: Set custom API base URL
message_builder() -> GeminiMessageBuilder: Start building a message request

GrokClient

new(api_key: impl Into<String>) -> Result<Self>: Create a new client
with_base_url(url: impl Into<String>) -> Self: Set custom API base URL
message_builder() -> GrokMessageBuilder: Start building a message request

GlmClient

new(api_key: impl Into<String>) -> Result<Self>: Create a new client
with_base_url(url: impl Into<String>) -> Self: Set custom API base URL
message_builder() -> GlmMessageBuilder: Start building a message request

OpenRouterClient

new(api_key: impl Into<String>) -> Result<Self>: Create a new client
with_model(model: impl Into<String>) -> Self: Set the model used by the LlmClient trait's complete()
with_base_url(url: impl Into<String>) -> Self: Set custom API base URL
with_provider_preferences(prefs: OpenRouterProviderPreferences) -> Self: Set provider routing used by complete()
with_response_format(format: OpenRouterResponseFormat) -> Self: Set the response format used by complete() (takes precedence over CompletionRequest::response_format, which cannot express a strict JSON schema)
list_free_programming_models() -> Result<Vec<OpenRouterModelInfo>>: Discover currently free programming models
message_builder() -> OpenRouterMessageBuilder: Start building a message request

OpenAIClient

new(api_key: impl Into<String>) -> Result<Self>: Create a new client
with_base_url(url: impl Into<String>) -> Self: Set custom API base URL
response_builder() -> OpenAIResponseBuilder: Start building a response request

OllamaClient

new() -> Result<Self>: Create a new client (no API key required)
with_base_url(url: impl Into<String>) -> Self: Set custom API base URL
message_builder() -> OllamaMessageBuilder: Start building a message request

LlamaCppClient

new() -> Result<Self>: Create a new client (no API key required)
with_api_key(api_key: impl Into<String>) -> Self: Set optional API key
with_base_url(url: impl Into<String>) -> Self: Set custom API base URL
message_builder() -> LlamaCppMessageBuilder: Start building a message request

XiaomiClient

new(api_key: impl Into<String>) -> Result<Self>: Create a client; defaults to mimo-v2.5
with_model(model: impl Into<String>) -> Self: Set the model used by the LlmClient trait's complete()
with_base_url(url: impl Into<String>) -> Self: Set a custom API base URL
message_builder() -> XiaomiMessageBuilder: Start building a chat completion
speech_recognition_builder() -> XiaomiSpeechRecognitionBuilder: Start building a MiMo ASR request
create_chat_completion(request: XiaomiChatCompletionRequest): Send a provider-native request
create_speech_recognition(request: XiaomiSpeechRecognitionRequest): Send a provider-native ASR request

MessageBuilder (Claude, Grok, GLM, OpenRouter & Xiaomi)

model(model: impl Into<String>) -> Self: Set the model
max_tokens(tokens: u32) -> Self: Set maximum tokens
message(role: impl Into<String>, content: impl Into<String>) -> Self: Add a message
user_message(content: impl Into<String>) -> Self: Add a user message
assistant_message(content: impl Into<String>) -> Self: Add an assistant message
system_message(content: impl Into<String>) -> Self: Add a system message
temperature(temp: f32) -> Self: Set temperature (0.0-2.0)
top_p(top_p: f32) -> Self: Set top-p sampling
stop_sequences(sequences: Vec<String>) -> Self: Set stop sequences
send() -> Result<Response>: Send the request

Note: Claude also supports system() for system prompts, while Grok and OpenRouter use system_message(). OpenRouter's builder also supports tool_choice(choice: ToolChoice) -> Self, response_format(format: OpenRouterResponseFormat) -> Self, json_schema_response_format(name, schema: serde_json::Value) -> Self, and tool_result(result: ToolResult) -> Self for feeding tool outputs back into the conversation. Xiaomi prefers max_completion_tokens() over its legacy max_tokens() field and supports json_schema_response_format(schema), tool_choice(), and tool_result().

OpenRouter response formats

OpenRouterResponseFormat has three constructors:

Constructor	Serialized wire shape
`text()`	`{"type":"text"}`
`json_object()`	`{"type":"json_object"}`
`json_schema(name, schema)`	`{"type":"json_schema","json_schema":{"name":"<name>","strict":true,"schema":{...}}}`

json_schema is always strict — OpenRouter only guarantees schema conformance for strict schemas.

use llm_sdk::openrouter::OpenRouterResponseFormat;

let schema = serde_json::json!({
    "type": "object",
    "properties": {"answer": {"type": "string"}},
    "required": ["answer"],
    "additionalProperties": false
});

let response = client
    .message_builder()
    .model(model_id)
    .user_message("Answer as JSON.")
    .response_format(OpenRouterResponseFormat::json_schema("answer_schema", schema))
    .send()
    .await?;

MessageBuilder (Gemini)

model(model: impl Into<String>) -> Self: Set the model
user_message(content: impl Into<String>) -> Self: Add a user message
model_message(content: impl Into<String>) -> Self: Add a model (assistant) message
content(content: GeminiContent) -> Self: Add a complete content object
system(text: impl Into<String>) -> Self: Set system instruction
thinking_level(level: impl Into<String>) -> Self: Set thinking level (minimal/low/medium/high)
temperature(temp: f32) -> Self: Set temperature (recommended: 1.0)
max_output_tokens(tokens: u32) -> Self: Set maximum output tokens
top_p(top_p: f32) -> Self: Set top-p sampling
top_k(top_k: u32) -> Self: Set top-k sampling
tool(tool: GeminiTool) -> Self: Add a tool/function declaration
tool_config(config: GeminiToolConfig) -> Self: Configure tool calling behavior
send() -> Result<GeminiGenerateContentResponse>: Send the request

Error Types

Authentication: Invalid API key
RateLimit: Rate limit exceeded (includes retry_after information)
InvalidRequest: Malformed request
Api / OpenRouterApi: Provider API errors
Network: Network or connection failures
Parse: JSON parsing errors
Internal: Unexpected internal errors
InvalidToolSchema, ToolArgumentParse, ToolExecutionFailed: Tool-related failures
NotSupported: Unsupported provider capability

Testing

Unit Tests

cargo test --lib

Integration Tests

Integration tests require valid API keys:

# Claude integration tests
ANTHROPIC_API_KEY=your-key-here cargo test --test claude_integration -- --ignored

# Gemini integration tests
GEMINI_API_KEY=your-key-here cargo test --test gemini_integration -- --ignored

# Grok integration tests
XAI_API_KEY=your-key-here cargo test --test grok_integration -- --ignored

# GLM integration tests
CEREBRAS_API_KEY=your-key-here cargo test --test glm_integration -- --ignored

# OpenRouter integration tests
OPENROUTER_API_KEY=your-key-here cargo test --test openrouter_integration -- --ignored

# Xiaomi MiMo integration tests
XIAOMI_API_KEY=your-key-here cargo test --test xiaomi_integration -- --ignored

# Xiaomi MiMo ASR live test (raw Base64 for a WAV file; no data-URL prefix)
XIAOMI_API_KEY=your-key-here XIAOMI_ASR_AUDIO_BASE64=... \
  cargo test --test xiaomi_integration test_xiaomi_mimo_v2_5_asr -- --ignored

Examples

See the examples/ directory for complete working examples:

simple_completion.rs: Basic Claude usage
gemini_simple.rs: Gemini 3 Pro example with reasoning
gemini_flash.rs: Gemini 3 Flash fast response example
grok_completion.rs: Basic Grok usage
glm_completion.rs: Basic GLM usage
voyage_embeddings.rs: Text embeddings with Voyage AI
tool_calling_weather.rs: Simple tool calling example
tool_calling_agent.rs: Multi-tool agent example

Development

This SDK is in active development. The v0.1 API provides provider-specific builders alongside the shared LlmClient completion interface.

Supported Providers

Claude (Anthropic): Full Messages API implementation
- Models: claude-sonnet-4-5, claude-opus-4-5, claude-haiku-4-5, etc.
Gemini (Google): Gemini API with reasoning capabilities
- Models: gemini-3-pro-preview, gemini-3-flash-preview
- Features: Thinking level controls, 1M context, tool calling
Grok (xAI): OpenAI-compatible API
- Models: grok-code-fast-1, grok-beta, grok-vision-beta
Cerebras: OpenAI-compatible API
- Models: gpt-oss-120b, qwen-3.8-27b
GLM: zAI and Zen APIs
Groq: OpenAI-compatible Chat Completions API with tool use
OpenRouter: Multi-model proxy, OpenAI-compatible Chat Completions API
- No fixed model list — call list_free_programming_models() to discover free models at runtime
MixLayer: OpenAI-compatible Chat Completions API
- Features: reasoning controls, tool calling, structured output, and hosted web search
Xiaomi MiMo: OpenAI-compatible Chat Completions API
- Models: mimo-v2.6-pro, mimo-v2.6-flash, mimo-v2.5, mimo-v2.5-pro, mimo-v2.5-asr
- Features: tool calling, structured output, and speech recognition for WAV/MP3 audio
Ollama (Local): /api/chat endpoint
- Models: local models installed in Ollama
llama.cpp (Local): OpenAI-compatible API
- Models: local models served by llama-server
OpenAI: Responses API
- Models: gpt-5, gpt-5-mini, gpt-5-nano, gpt-5.1, gpt-5.1-codex
Voyage AI: Text embeddings
- Models: voyage-4, voyage-4-large, voyage-4-lite, specialized domain models

Future Plans (v0.2+)

Streaming responses
Advanced features (vision, extended thinking)
Multi-language bindings (Python, Node.js)

License

Licensed under the same terms as the nocodo project.

Contributing

Contributions welcome! Please see the main nocodo repository for contribution guidelines.

Name		Name	Last commit message	Last commit date
bin		bin
docs		docs
examples		examples
external-docs		external-docs
src		src
tasks		tasks
tests		tests
.gitignore		.gitignore
AGENTS.md		AGENTS.md
Cargo.toml		Cargo.toml
DEVELOP.md		DEVELOP.md
README.md		README.md
TEST_RUNNER.md		TEST_RUNNER.md
rust-toolchain.toml		rust-toolchain.toml
test-config.example.toml		test-config.example.toml

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Nocodo LLM SDK

Features

Architecture

Trait-Based Design

Benefits

Multi-Provider Support

Installation

Quick Start

Claude (Anthropic)

Gemini (Google)

Grok (xAI)

Cerebras

Xiaomi MiMo

ElevenLabs Scribe v2

OpenRouter

Ollama (Local)

MixLayer

llama.cpp (Local)

Multi-Provider Support

Grok Models

Zen (Free)

xAI (Paid)

Cerebras Models

Provider Comparison

OpenAI (GPT-5)

Gemini (Google)

Gemini 3 Pro

Gemini 3 Flash

Thinking Levels

Multi-turn Conversations

Key Features

Model Specifications

Gemini 3 Pro (gemini-3-pro-preview)

Gemini 3 Flash (gemini-3-flash-preview)

Important Notes

Advanced Usage

Claude: Conversation with Multiple Messages

Claude: Custom Parameters

Grok: Multi-turn Conversation

Error Handling

Tool Calling (Function Calling)

Basic Example

Advanced: Multi-Tool Agent

Tool Choice Options

API Reference

ClaudeClient

GeminiClient

GrokClient

GlmClient

OpenRouterClient

OpenAIClient

OllamaClient

LlamaCppClient

XiaomiClient

MessageBuilder (Claude, Grok, GLM, OpenRouter & Xiaomi)

OpenRouter response formats

MessageBuilder (Gemini)

Error Types

Testing

Unit Tests

Integration Tests

Examples

Development

Supported Providers

Future Plans (v0.2+)

License

Contributing

About

Resources

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Releases

Packages

Contributors

Languages

Gemini 3 Pro (`gemini-3-pro-preview`)

Gemini 3 Flash (`gemini-3-flash-preview`)