🦀 Inference Gateway Rust SDK

A powerful and easy-to-use Rust SDK for the Inference Gateway

Connect to multiple LLM providers through a unified interface • Stream responses • Function calling • MCP tools support

Installation • Quick Start • Examples • Documentation

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• 🦀 Inference Gateway Rust SDK
  • Installation
  • Usage
    • Creating a Client
    • Listing Models
    • Listing Models from a specific provider
    • Listing MCP Tools
    • Generating Content
    • Streaming Content
    • Tool-Use
    • Health Check
  • Examples
  • Supported Providers
  • Documentation
  • Contributing
  • License

Installation

Run cargo add inference-gateway-sdk.

Usage

Creating a Client

Here is a full example of how to create a client and interact with the Inference Gateway API:

use inference_gateway_sdk::{
    CreateChatCompletionResponse, GatewayError, InferenceGatewayAPI,
    InferenceGatewayClient, ListModelsResponse, ListToolsResponse, Message,
    MessageContent, MessageRole, Provider,
};
use log::info;
use std::env;

#[tokio::main]
async fn main() -> Result<(), GatewayError> {
    if env::var("RUST_LOG").is_err() {
        env::set_var("RUST_LOG", "info");
    }
    env_logger::init();

    // Create a client
    let client = InferenceGatewayClient::new("http://localhost:8080/v1");

    // List all models and all providers
    let response: ListModelsResponse = client.list_models().await?;
    for model in response.data {
        info!("Model: {:?}", model.id);
    }

    // List models for a specific provider
    let response: ListModelsResponse = client.list_models_by_provider(Provider::Groq).await?;
    info!("Models for provider: {:?}", response.provider);
    for model in response.data {
        info!("Model: {:?}", model.id);
    }

    // Generate content - choose from available providers and models
    let response: CreateChatCompletionResponse = client
        .generate_content(
            Provider::Deepseek,
            "deepseek-v4-flash",
            vec![
                Message {
                    role: MessageRole::System,
                    content: MessageContent::String("You are a helpful assistant.".to_string()),
                    reasoning: None,
                    reasoning_content: None,
                    tool_call_id: None,
                    tool_calls: Vec::new(),
                },
                Message {
                    role: MessageRole::User,
                    content: MessageContent::String("Tell me a funny joke".to_string()),
                    reasoning: None,
                    reasoning_content: None,
                    tool_call_id: None,
                    tool_calls: Vec::new(),
                },
            ],
        )
        .await?;

    log::info!(
        "Generated content: {:?}",
        response.choices[0].message.content
    );

    Ok(())
}

Listing Models

To list all available models from all configured providers, use the list_models method:

use inference_gateway_sdk::{
    GatewayError
    InferenceGatewayAPI,
    InferenceGatewayClient,
    ListModelsResponse,
    Message,
};
use log::info;

#[tokio::main]
fn main() -> Result<(), GatewayError> {
    // ...Create a client

    // List models from all providers
    let response: ListModelsResponse = client.list_models().await?;
    for model in response.data {
        info!("Model: {:?}", model.id);
    }

    // ...
}

Listing Models from a specific provider

To list all available models from a specific provider, use the list_models_by_provider method:

use inference_gateway_sdk::{
    GatewayError
    InferenceGatewayAPI,
    InferenceGatewayClient,
    ListModelsResponse,
    Provider,
};
use log::info;

// ...Open main function

// List models for a specific provider
let response: ListModelsResponse = client.list_models_by_provider(Provider::Groq).await?;
info!("Models for provider: {:?}", response.provider);
for model in response.data {
    info!("Model: {:?}", model.id);
}

// Example with Google provider
let response: ListModelsResponse = client.list_models_by_provider(Provider::Google).await?;
info!("Google models: {:?}", response.provider);
for model in response.data {
    info!("Google Model: {:?}", model.id);
}

// ...Rest of the main function

Listing MCP Tools

To list all available MCP (Model Context Protocol) tools from all configured MCP servers, use the list_tools method:

use inference_gateway_sdk::{
    GatewayError,
    InferenceGatewayAPI,
    InferenceGatewayClient,
    ListToolsResponse,
};
use log::info;

#[tokio::main]
async fn main() -> Result<(), GatewayError> {
    // ...Create a client

    // List all MCP tools from all configured servers
    let response: ListToolsResponse = client.list_tools().await?;
    info!("Found {} MCP tools", response.data.len());

    for tool in response.data {
        info!("Tool: {} from server: {}", tool.name, tool.server);
        info!("Description: {}", tool.description);
        if !tool.input_schema.is_empty() {
            info!("Input schema: {:?}", tool.input_schema);
        }
    }

    Ok(())
}

Note: This functionality requires that MCP servers are configured and exposed in your Inference Gateway instance. If MCP is not exposed, you'll receive a 403 Forbidden error.

Generating Content

To generate content using a model, use the generate_content method:

use inference_gateway_sdk::{
    CreateChatCompletionResponse, GatewayError, InferenceGatewayAPI, InferenceGatewayClient,
    Message, MessageContent, MessageRole, Provider,
};

fn message(role: MessageRole, text: &str) -> Message {
    Message {
        role,
        content: MessageContent::String(text.to_string()),
        reasoning: None,
        reasoning_content: None,
        tool_call_id: None,
        tool_calls: Vec::new(),
    }
}

// Generate content - choose from available providers and models
let response: CreateChatCompletionResponse = client
    .generate_content(
        Provider::Deepseek,
        "deepseek-v4-flash",
        vec![
            message(MessageRole::System, "You are a helpful assistant."),
            message(MessageRole::User, "Tell me a funny joke"),
        ],
    )
    .await?;

log::info!(
    "Generated content: {:?}",
    response.choices[0].message.content
);

// Example with Google provider (Gemini models)
let response: CreateChatCompletionResponse = client
    .generate_content(
        Provider::Google,
        "gemini-3-pro",
        vec![
            message(MessageRole::System, "You are a helpful AI assistant."),
            message(MessageRole::User, "Explain quantum computing in simple terms"),
        ],
    )
    .await?;

log::info!(
    "Google generated content: {:?}",
    response.choices[0].message.content
);

Streaming Content

use futures_util::{pin_mut, StreamExt};
use inference_gateway_sdk::{
    CreateChatCompletionStreamResponse, FinishReason, GatewayError, InferenceGatewayAPI,
    InferenceGatewayClient, Message, MessageContent, MessageRole, Provider,
};
use log::info;
use std::env;

fn message(role: MessageRole, text: &str) -> Message {
    Message {
        role,
        content: MessageContent::String(text.to_string()),
        reasoning: None,
        reasoning_content: None,
        tool_call_id: None,
        tool_calls: Vec::new(),
    }
}

#[tokio::main]
async fn main() -> Result<(), GatewayError> {
    if env::var("RUST_LOG").is_err() {
        env::set_var("RUST_LOG", "info");
    }
    env_logger::init();

    let model = "deepseek-v4-flash";

    let client = InferenceGatewayClient::new("http://localhost:8080/v1");
    let stream = client.generate_content_stream(
        Provider::Deepseek,
        model,
        vec![
            message(MessageRole::System, "You are a helpful assistant."),
            message(MessageRole::User, "Write a poem"),
        ],
    );
    pin_mut!(stream);
    // Iterate over the stream of Server Sent Events
    while let Some(ssevent) = stream.next().await {
        let ssevent = ssevent?;
        if ssevent.data == "[DONE]" {
            break;
        }

        // Deserialize the event response
        let generate_response_stream: CreateChatCompletionStreamResponse =
            serde_json::from_str(&ssevent.data)?;

        let Some(choice) = generate_response_stream.choices.first() else {
            continue;
        };

        if let Some(usage) = generate_response_stream.usage.as_ref() {
            // Get the usage metrics from the response
            info!("Usage Metrics: {:?}", usage);
            // Probably send them over to a metrics service
            break;
        }

        // Print the token out as it's being sent from the server
        if let Some(content) = choice.delta.content.as_ref() {
            print!("{}", content);
        }

        if let Some(reason) = choice.finish_reason.as_ref() {
            if matches!(reason, FinishReason::Stop) {
                info!("Finished generating content");
                break;
            }
        }
    }

    Ok(())
}

Tool-Use

You can pass to the generate_content function also tools, which will be available for the LLM to use:

use inference_gateway_sdk::{
    ChatCompletionTool, ChatCompletionToolType, FunctionObject,
    FunctionParameters, GatewayError, InferenceGatewayAPI,
    InferenceGatewayClient, Message, MessageContent, MessageRole, Provider,
};
use log::{info, warn};
use serde::{Deserialize, Serialize};
use serde_json::json;
use std::env;

fn message(role: MessageRole, text: &str) -> Message {
    Message {
        role,
        content: MessageContent::String(text.to_string()),
        reasoning: None,
        reasoning_content: None,
        tool_call_id: None,
        tool_calls: Vec::new(),
    }
}

#[tokio::main]
async fn main() -> Result<(), GatewayError> {
    // Configure logging
    if env::var("RUST_LOG").is_err() {
        env::set_var("RUST_LOG", "info");
    }
    env_logger::init();

    // API endpoint - store as a variable so we can reuse it
    let api_endpoint = "http://localhost:8080/v1";

    // Initialize the API client
    let client = InferenceGatewayClient::new(api_endpoint);

    // Define the model and provider
    let provider = Provider::Deepseek;
    let model = "deepseek-v4-flash";

    // Define the weather tool
    let parameters = json!({
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city name"
            }
        },
        "required": ["location"]
    });
    let tools = vec![ChatCompletionTool {
        type_: ChatCompletionToolType::Function,
        function: FunctionObject {
            name: "get_current_weather".to_string(),
            description: Some("Get the weather for a location".to_string()),
            parameters: Some(FunctionParameters(
                parameters.as_object().unwrap().clone(),
            )),
            strict: false,
        },
    }];

    // Create initial conversation
    let initial_messages = vec![
        message(
            MessageRole::System,
            "You are a helpful assistant that can check the weather.",
        ),
        message(MessageRole::User, "What is the current weather in Berlin?"),
    ];

    // Make the initial API request
    info!("Sending initial request to model");
    let response = client
        .with_tools(Some(tools.clone()))
        .generate_content(provider, model, initial_messages)
        .await?;

    info!("Received response from model");

    let Some(choice) = response.choices.first() else {
        warn!("No choice returned");
        return Ok(());
    };

    // Check for tool calls in the response
    if !choice.message.tool_calls.is_empty() {
        // Echo the assistant turn (with the tool_calls) before the tool replies.
        let mut assistant_turn = choice.message.clone();
        // Tool-call assistant messages typically have empty content; keep what
        // the model sent so providers that include explanatory text round-trip.
        let mut follow_up_convo = vec![
            message(
                MessageRole::System,
                "You are a helpful assistant that can check the weather.",
            ),
            message(MessageRole::User, "What is the current weather in Berlin?"),
        ];
        // Take the assistant message verbatim so tool_calls are preserved.
        assistant_turn.role = MessageRole::Assistant;
        follow_up_convo.push(assistant_turn);

        // Process each tool call
        for tool_call in &choice.message.tool_calls {
            info!("Tool Call Requested: {}", tool_call.function.name);

            if tool_call.function.name == "get_current_weather" {
                // Parse arguments into a typed value
                let args: Weather = tool_call.function.parse_arguments()?;
                let weather_result = get_current_weather(args);

                // Add the tool response to the conversation
                follow_up_convo.push(Message {
                    role: MessageRole::Tool,
                    content: MessageContent::String(weather_result),
                    reasoning: None,
                    reasoning_content: None,
                    tool_call_id: Some(tool_call.id.clone()),
                    tool_calls: Vec::new(),
                });
            }
        }

        // Send the follow-up request with the tool results
        info!("Sending follow-up request with tool results");

        let follow_up_client = InferenceGatewayClient::new(api_endpoint);
        let follow_up_response = follow_up_client
            .with_tools(Some(tools))
            .generate_content(provider, model, follow_up_convo)
            .await?;

        if let Some(choice) = follow_up_response.choices.first() {
            info!("Final response: {:?}", choice.message.content);
        } else {
            warn!("No response in follow-up");
        }
    } else {
        info!("No tool calls in the response");
        info!("Model response: {:?}", choice.message.content);
    }

    Ok(())
}

#[derive(Debug, Deserialize, Serialize)]
struct Weather {
    location: String,
}

fn get_current_weather(args: Weather) -> String {
    info!(
        "Getting weather function was called for {}",
        args.location
    );

    // In a real application, we would call an actual weather API here.
    // For this example, we'll just return a mock response.
    format!(
        "The weather in {} is currently sunny with a temperature of 22°C",
        args.location
    )
}

Health Check

To check if the Inference Gateway is running, use the health_check method:

// ...rest of the imports
use log::info;

// ...main function
let is_healthy = client.health_check().await?;
info!("API is healthy: {}", is_healthy);

Examples

Runnable examples live in the examples directory - see its README for the full list and instructions.

Supported Providers

The Inference Gateway Rust SDK supports the following providers:

• Ollama (Provider::Ollama) - Local language model server
• Ollama Cloud (Provider::OllamaCloud) - Ollama Cloud-hosted models
• Groq (Provider::Groq) - High-speed inference provider
• OpenAI (Provider::Openai) - GPT models and other OpenAI services
• Cloudflare (Provider::Cloudflare) - Cloudflare Workers AI
• Cohere (Provider::Cohere) - Cohere language models
• Anthropic (Provider::Anthropic) - Claude models
• DeepSeek (Provider::Deepseek) - DeepSeek models
• Google (Provider::Google) - Google Gemini models via Generative AI API
• Mistral (Provider::Mistral) - Mistral AI models
• MiniMax (Provider::Minimax) - MiniMax models
• Moonshot (Provider::Moonshot) - Moonshot AI models

Each provider may support different models and capabilities. Use the list_models_by_provider() method to discover available models for each provider.

Example:

use inference_gateway_sdk::{Provider, InferenceGatewayClient, InferenceGatewayAPI};

let client = InferenceGatewayClient::new("http://localhost:8080/v1");

// List Google models
let google_models = client.list_models_by_provider(Provider::Google).await?;
for model in google_models.data {
    println!("Google model: {}", model.id);
}

Documentation

Full API documentation is published on docs.rs.

Contributing

Please refer to the CONTRIBUTING.md file for information about how to get involved. We welcome issues, questions, and pull requests.

License

This SDK is distributed under the Apache 2.0 License, see LICENSE for more information.