GitHub - clifton/rstructor: Pydantic + Instructor for Rust

rstructor: Structured LLM Outputs for Rust

Extract structured, validated data from LLMs using native Rust types. Define your schema as structs/enums, and rstructor handles JSON Schema generation, API communication, parsing, and validation.

The Rust equivalent of Instructor for Python.

Features

Type-safe schemas — Define models as Rust structs/enums with derive macros
Multi-provider — OpenAI, Anthropic, Grok (xAI), and Gemini with unified API
Auto-validation — Type checking plus custom business rules with automatic retry
Complex types — Nested objects, arrays, optionals, enums with associated data
Extended thinking — Native support for reasoning models (GPT-5.2, Claude 4.5, Gemini 3)

Installation

[dependencies]
rstructor = "0.2"
serde = { version = "1.0", features = ["derive"] }
tokio = { version = "1.0", features = ["rt-multi-thread", "macros"] }

Quick Start

use rstructor::{Instructor, LLMClient, OpenAIClient};
use serde::{Deserialize, Serialize};

#[derive(Instructor, Serialize, Deserialize, Debug)]
struct Movie {
    #[llm(description = "Title of the movie")]
    title: String,
    #[llm(description = "Director of the movie")]
    director: String,
    #[llm(description = "Year released", example = 2010)]
    year: u16,
}

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    let client = OpenAIClient::from_env()?
        .temperature(0.0);

    let movie: Movie = client.materialize("Tell me about Inception").await?;
    println!("{}: {} ({})", movie.title, movie.director, movie.year);
    Ok(())
}

Providers

use rstructor::{OpenAIClient, AnthropicClient, GrokClient, GeminiClient, LLMClient};

// OpenAI (reads OPENAI_API_KEY)
let client = OpenAIClient::from_env()?.model("gpt-5.2");

// Anthropic (reads ANTHROPIC_API_KEY)
let client = AnthropicClient::from_env()?.model("claude-opus-4-6");

// Grok/xAI (reads XAI_API_KEY)
let client = GrokClient::from_env()?.model("grok-4-1-fast-non-reasoning");

// Gemini (reads GEMINI_API_KEY)
let client = GeminiClient::from_env()?.model("gemini-3-flash-preview");

// Custom endpoint (local LLMs, proxies)
let client = OpenAIClient::new("key")?
    .base_url("http://localhost:1234/v1")
    .model("llama-3.1-70b");

Validation

Add custom validation with automatic retry on failure:

use rstructor::{Instructor, RStructorError, Result};

#[derive(Instructor, Serialize, Deserialize)]
#[llm(validate = "validate_movie")]
struct Movie {
    title: String,
    year: u16,
    rating: f32,
}

fn validate_movie(movie: &Movie) -> Result<()> {
    if movie.year < 1888 || movie.year > 2030 {
        return Err(RStructorError::ValidationError(
            format!("Invalid year: {}", movie.year)
        ));
    }
    if movie.rating < 0.0 || movie.rating > 10.0 {
        return Err(RStructorError::ValidationError(
            format!("Rating must be 0-10, got {}", movie.rating)
        ));
    }
    Ok(())
}

// Retries are enabled by default (3 attempts with error feedback)
// To increase retries:
let client = OpenAIClient::from_env()?.max_retries(5);

// To disable retries:
let client = OpenAIClient::from_env()?.no_retries();

Complex Types

Nested Structures

#[derive(Instructor, Serialize, Deserialize)]
struct Ingredient {
    name: String,
    amount: f32,
    unit: String,
}

#[derive(Instructor, Serialize, Deserialize)]
struct Recipe {
    name: String,
    ingredients: Vec<Ingredient>,
    prep_time_minutes: u16,
}

Enums with Data

#[derive(Instructor, Serialize, Deserialize)]
enum PaymentMethod {
    #[llm(description = "Credit card payment")]
    Card { number: String, expiry: String },
    #[llm(description = "PayPal account")]
    PayPal(String),
    #[llm(description = "Cash on delivery")]
    CashOnDelivery,
}

Serde Rename Support

rstructor respects #[serde(rename)] and #[serde(rename_all)] attributes:

#[derive(Instructor, Serialize, Deserialize)]
#[serde(rename_all = "camelCase")]
struct UserProfile {
    first_name: String,      // becomes "firstName" in schema
    last_name: String,       // becomes "lastName" in schema
    email_address: String,   // becomes "emailAddress" in schema
}

#[derive(Instructor, Serialize, Deserialize)]
struct CommitMessage {
    #[serde(rename = "type")]  // use "type" as JSON key
    commit_type: String,
    description: String,
}

#[derive(Instructor, Serialize, Deserialize)]
#[serde(rename_all = "lowercase")]
enum CommitType {
    Fix,       // becomes "fix"
    Feat,      // becomes "feat"
    Refactor,  // becomes "refactor"
}

Supported case conversions: lowercase, UPPERCASE, camelCase, PascalCase, snake_case, SCREAMING_SNAKE_CASE, kebab-case, SCREAMING-KEBAB-CASE.

Custom Types (Dates, UUIDs)

use chrono::{DateTime, Utc};
use rstructor::schema::CustomTypeSchema;

impl CustomTypeSchema for DateTime<Utc> {
    fn schema_type() -> &'static str { "string" }
    fn schema_format() -> Option<&'static str> { Some("date-time") }
}

#[derive(Instructor, Serialize, Deserialize)]
struct Event {
    name: String,
    start_time: DateTime<Utc>,
}

Multimodal (Image Input)

Analyze images with structured extraction using Gemini's inline data support:

use rstructor::{Instructor, LLMClient, GeminiClient, MediaFile};

#[derive(Instructor, Serialize, Deserialize, Debug)]
struct ImageAnalysis {
    subject: String,
    colors: Vec<String>,
    is_logo: bool,
    description: String,
}

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Download or load image bytes
    let image_bytes = reqwest::get("https://example.com/image.png")
        .await?.bytes().await?;

    // Create inline media from bytes (base64-encoded automatically)
    let media = MediaFile::from_bytes(&image_bytes, "image/png");

    let client = GeminiClient::from_env()?;
    let analysis: ImageAnalysis = client
        .materialize_with_media("Describe this image", &[media])
        .await?;
    println!("{:?}", analysis);
    Ok(())
}

MediaFile::new(uri, mime_type) is also available for Gemini Files API / GCS URIs.

Extended Thinking

Configure reasoning depth for supported models:

use rstructor::ThinkingLevel;

// GPT-5.2, Claude 4.5 (Sonnet/Opus), Gemini 3
let client = OpenAIClient::from_env()?
    .model("gpt-5.2")
    .thinking_level(ThinkingLevel::High);

// Levels: Off, Minimal, Low, Medium, High

Token Usage

let result = client.materialize_with_metadata::<Movie>("...").await?;
println!("Movie: {}", result.data.title);
if let Some(usage) = result.usage {
    println!("Tokens: {} in, {} out", usage.input_tokens, usage.output_tokens);
}

Error Handling

use rstructor::{ApiErrorKind, RStructorError};

match client.materialize::<Movie>("...").await {
    Ok(movie) => println!("{:?}", movie),
    Err(e) if e.is_retryable() => {
        println!("Transient error: {}", e);
        if let Some(delay) = e.retry_delay() {
            tokio::time::sleep(delay).await;
        }
    }
    Err(e) => match e.api_error_kind() {
        Some(ApiErrorKind::RateLimited { retry_after }) => { /* ... */ }
        Some(ApiErrorKind::AuthenticationFailed) => { /* ... */ }
        _ => eprintln!("Error: {}", e),
    }
}

Feature Flags

[dependencies]
rstructor = { version = "0.2", features = ["openai", "anthropic", "grok", "gemini"] }

openai, anthropic, grok, gemini — Provider backends
derive — Derive macro (default)
logging — Tracing integration

Examples

See examples/ for complete working examples:

export OPENAI_API_KEY=your_key
cargo run --example structured_movie_info
cargo run --example nested_objects_example
cargo run --example enum_with_data_example
cargo run --example serde_rename_example
cargo run --example gemini_multimodal_example

For Python Developers

If you're coming from Python and searching for:

"pydantic rust" or "rust pydantic" — rstructor provides similar schema validation and type safety
"instructor rust" or "rust instructor" — same structured LLM output extraction pattern
"structured output rust" or "llm structured output" — exactly what rstructor does
"type-safe llm rust" — ensures type safety from LLM responses to Rust structs

License

MIT — see LICENSE