Turn unstructured prose into strongly-typed, validated Rust data — define your schema as plain structs and enums, and rstructor handles JSON Schema generation, prompting, parsing, and validation with automatic retries.
- Type-safe schemas from Rust types — Derive
Instructoron structs and enums; rstructor generates the JSON Schema and validated parser for you, no hand-written prompts or DTOs - Multi-provider, one API — OpenAI, Anthropic, Grok (xAI), and Gemini behind a single
materialize()call with swappable clients - Validation with automatic re-ask — Built-in type checking plus custom business rules; validation failures are fed back to the model and retried until the data is correct
- Rich, nested data — Nested objects, arrays, optionals, maps, and enums with associated data, with validation that recurses through the whole tree
- The Instructor / Pydantic experience for Rust — The familiar structured-output workflow of Instructor + Pydantic, now with compile-time guarantees and reasoning-model support (GPT-5.5, Claude 4.6, Gemini 3.1)
[dependencies]
rstructor = "0.3"
serde = { version = "1.0", features = ["derive"] }
tokio = { version = "1.0", features = ["rt-multi-thread", "macros"] }Describe the shape you want as plain Rust types, then turn a sentence of prose into a fully-typed, validated value:
use rstructor::{Instructor, LLMClient, OpenAIClient};
use serde::{Deserialize, Serialize};
#[derive(Instructor, Serialize, Deserialize, Debug)]
enum Priority {
Low,
Medium,
High,
Urgent,
}
#[derive(Instructor, Serialize, Deserialize, Debug)]
#[llm(description = "A support ticket triaged from a free-form message")]
struct Ticket {
#[llm(description = "Short, imperative summary of what needs to be done")]
title: String,
#[llm(description = "How urgent this is, inferred from tone and deadlines")]
priority: Priority,
#[llm(description = "Email of the person on it, or null if unassigned")]
assignee: Option<String>,
#[llm(description = "Relevant topic tags", examples = ["billing", "auth", "outage"])]
tags: Vec<String>,
}
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let client = OpenAIClient::from_env()?.temperature(0.0);
let ticket: Ticket = client
.materialize(
"Hey, the login page is throwing 500s for half our users since the deploy. \
Sarah (sarah@acme.io) is on it but we need this fixed before the demo at 3pm!",
)
.await?;
println!("{ticket:#?}");
// Ticket {
// title: "Login page returning 500 errors after deploy",
// priority: Urgent,
// assignee: Some("sarah@acme.io"),
// tags: ["auth", "outage"],
// }
Ok(())
}Every field is inferred, not transcribed: the urgency is read from the tone and deadline, the email is plucked out of mid-sentence prose, and the tags are synthesized — all parsed into the exact types you declared.
materialize, generate, and (with the tools feature) tool run are also
available through a fluent builder that attaches context, images, and tools to a
single request. Bring RequestExt into scope and chain the pieces you need:
use rstructor::{Instructor, OpenAIClient, RequestExt};
let client = OpenAIClient::from_env()?;
// Add context that is prepended to the prompt, then materialize a struct.
let movie: Movie = client
.with_system("Assume USD; format dates as ISO-8601.")
.materialize("Describe Inception")
.await?;
// Or start from `.request()` and combine builders before a terminal.
let summary = client
.request()
.system("Be concise.")
.generate("Summarize the plot of Inception")
.await?;The terminals are materialize::<T>(prompt) (structured), generate(prompt)
(text), and — with the tools feature — run(prompt) (text, calling any
attached tools in a loop). Builders compose: with_system, with_media, and
with_tools can be chained in any order before the terminal.
use rstructor::{OpenAIClient, AnthropicClient, GrokClient, GeminiClient, LLMClient};
// OpenAI (reads OPENAI_API_KEY)
let client = OpenAIClient::from_env()?.model("gpt-5.5");
// Anthropic (reads ANTHROPIC_API_KEY)
let client = AnthropicClient::from_env()?.model("claude-sonnet-4-6");
// Grok/xAI (reads XAI_API_KEY)
let client = GrokClient::from_env()?.model("grok-4.3");
// Gemini (reads GEMINI_API_KEY)
let client = GeminiClient::from_env()?.model("gemini-3.5-flash");
// Custom endpoint (local LLMs, proxies)
let client = OpenAIClient::new("key")?
.base_url("http://localhost:1234/v1")
.model("llama-3.1-70b");LLMClient::materialize is generic, so the trait isn't object-safe (Box<dyn LLMClient> is impossible). Use AnyClient when the provider is decided at runtime (CLI flag, config, env) and you want to store it in a single type:
use rstructor::{AnyClient, Provider, LLMClient};
// Pick a provider dynamically, reading its key from the environment.
let provider = Provider::Anthropic; // e.g. parsed from a config file
let client = AnyClient::from_env_for(provider)?;
let movie: Movie = client.materialize("Describe Inception").await?;
// Or auto-detect from whichever API key is set:
let client = AnyClient::from_env()?;
// Or wrap a pre-configured client:
let client: AnyClient = OpenAIClient::from_env()?.model("gpt-5.5").into();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();#[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,
}#[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,
}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.
use chrono::{DateTime, NaiveDate, Utc};
use rstructor::Instructor;
use serde::{Deserialize, Serialize};
use uuid::Uuid;
#[derive(Instructor, Serialize, Deserialize)]
struct JobRun {
id: Uuid, // schema format: "uuid"
trade_date: NaiveDate, // schema format: "date"
started_at: DateTime<Utc>, // schema format: "date-time"
parent_id: Option<Uuid>, // optional UUID keeps format metadata
related_ids: Vec<Uuid>, // array items keep format metadata
}For your own domain-specific scalar types, implement CustomTypeSchema plus SchemaType:
use rstructor::schema::CustomTypeSchema;
use rstructor::{Schema, SchemaType};
use serde::{Deserialize, Serialize};
#[derive(Serialize, Deserialize)]
struct SecurityId(String);
impl CustomTypeSchema for SecurityId {
fn schema_type() -> &'static str { "string" }
fn schema_format() -> Option<&'static str> { Some("security-id") }
}
impl SchemaType for SecurityId {
fn schema() -> Schema { Schema::new(Self::json_schema()) }
fn schema_name() -> Option<String> { Some("SecurityId".to_string()) }
}Analyze images with structured extraction across all major providers by
attaching media to a request with with_media:
use rstructor::{Instructor, OpenAIClient, MediaFile, RequestExt};
#[derive(Instructor, Serialize, Deserialize, Debug)]
struct ImageAnalysis {
subject: String,
summary: String,
}
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
// Download or load image bytes (real-world fixture)
let image_bytes = reqwest::get("https://example.com/image.png")
.await?.bytes().await?;
// Inline media is base64-encoded automatically
let media = [MediaFile::from_bytes(&image_bytes, "image/png")];
// Works with OpenAI, Anthropic, Grok, and Gemini clients
let client = OpenAIClient::from_env()?;
let analysis: ImageAnalysis = client
.with_media(&media)
.materialize("Describe this image")
.await?;
println!("{:?}", analysis);
Ok(())
}MediaFile::new(uri, mime_type) is also available for URL/URI-based media input.
The lower-level LLMClient::materialize_with_media(prompt, &media) method does
the same thing in one call when you do not need the builder.
Provider examples:
cargo run --example openai_multimodal_example --features openaicargo run --example anthropic_multimodal_example --features anthropiccargo run --example grok_multimodal_example --features grokcargo run --example gemini_multimodal_example --features gemini
Configure reasoning depth for supported models:
use rstructor::ThinkingLevel;
// GPT-5.5, Claude 4.6 Sonnet, Gemini 3.1
let client = OpenAIClient::from_env()?
.model("gpt-5.5")
.thinking_level(ThinkingLevel::High);
// Levels: Off, Minimal, Low, Medium, Highlet 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);
}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),
}
}Enable the streaming feature to stream responses as they are generated.
rstructor = { version = "0.3", features = ["streaming"] }materialize_iter streams a list of structured objects, yielding each item as soon as it is fully generated and validated — the common case where you want a long list without buffering the whole response:
use futures_util::StreamExt;
use rstructor::{LLMClient, OpenAIClient, Instructor};
let client = OpenAIClient::from_env()?;
let mut stream = client.materialize_iter::<Invention>("List 10 important inventions.");
while let Some(item) = stream.next().await {
let invention = item?; // each item: fully parsed + validated
println!("{} ({})", invention.name, invention.year);
}generate_stream streams raw text deltas:
let mut stream = client.generate_stream("Write a haiku");
while let Some(chunk) = stream.next().await {
print!("{}", chunk?);
}There is also materialize_stream, which streams a single object as progressive StreamedObject::Partial(json) snapshots followed by a validated Complete(T).
All are available on every provider (OpenAI, Anthropic, Grok, Gemini). See examples/streaming_example.rs.
Enable the tools feature to let the model call your typed Rust functions and feed the results back, looping until it produces a final answer. Tool argument types derive Instructor, so their JSON Schema is generated automatically.
rstructor = { version = "0.3", features = ["tools"] }use rstructor::{OpenAIClient, Toolbox, FnTool, Instructor};
use serde::{Serialize, Deserialize};
use serde_json::json;
#[derive(Instructor, Serialize, Deserialize)]
struct WeatherArgs {
#[llm(description = "City name")]
city: String,
}
let toolbox = Toolbox::new().with(FnTool::new(
"get_weather",
"Get the current weather for a city",
|args: WeatherArgs| async move {
Ok(json!({ "city": args.city, "temp_f": 72 })) // call a real API here
},
));
let client = OpenAIClient::from_env()?;
let answer = client
.with_tools(&toolbox)
.system("Use tools when relevant.") // optional
.run("What's the weather in Paris?")
.await?;Works with all providers (OpenAI, Anthropic, Grok, Gemini). See examples/tool_calling_example.rs.
[dependencies]
rstructor = { version = "0.3", features = ["openai", "anthropic", "grok", "gemini"] }openai,anthropic,grok,gemini— Provider backends (each pulls in the shared HTTP/tokiostack)derive— Derive macro (default)logging— Tracing integrationstreaming— Streaming viagenerate_stream/materialize_iter/materialize_stream(opt-in)tools— Tool/function calling viaToolbox+client.with_tools(..).run(..)(opt-in)
All features are on by default. For a schema-only build — generate JSON Schema from your types with no networking, tokio, or reqwest — disable the providers:
[dependencies]
rstructor = { version = "0.3", default-features = false, features = ["derive"] }This keeps the derive macro, SchemaType, the Instructor trait, and the LLMClient trait (so you can implement your own backend) without the async/HTTP dependency tree.
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_exampleIf 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
MIT — see LICENSE