OpenAI Provider

Setup

export OPENAI_API_KEY="sk-..."

Quick Start

OpenAI is auto-detected from openai/..., gpt-..., o1-..., and o3-... model strings:

use appam::prelude::*;

#[tokio::main]
async fn main() -> Result<()> {
    let agent = Agent::quick(
        "openai/gpt-5.4",
        "You are a helpful assistant.",
        vec![],
    )?;

    agent
        .stream("Explain async/await in Rust.")
        .on_content(|text| print!("{}", text))
        .run()
        .await?;

    Ok(())
}

Explicit Builder Form

use appam::prelude::*;

let agent = AgentBuilder::new("openai-agent")
    .provider(LlmProvider::OpenAI)
    .model("gpt-5.4")
    .system_prompt("You are a helpful assistant.")
    .build()?;

Reasoning

The OpenAI config in this crate supports these reasoning effort levels:

None
Minimal
Low
Medium
High
XHigh

Use openai_reasoning(...) on the builder:

use appam::prelude::*;

let agent = AgentBuilder::new("reasoning-agent")
    .provider(LlmProvider::OpenAI)
    .model("gpt-5.4")
    .system_prompt("You are a reasoning assistant.")
    .openai_reasoning(ReasoningConfig::high_effort())
    .build()?;

Convenience constructors include auto(), high_effort(), xhigh_effort(), low_latency(), minimal(), and no_reasoning().

The current OpenAI config code only allows temperature, top_p, and top_logprobs for GPT-5.4 when reasoning is in None mode. Use ReasoningConfig::no_reasoning() if you need those sampling controls on GPT-5.4.

Model-Aware Helpers

The crate exposes model-aware helpers such as:

default_reasoning_effort_for_model
model_supports_xhigh_reasoning
resolve_reasoning_effort_for_model

These are useful if your application chooses effort dynamically from a model string.

Example Binary

cargo run --example coding-agent-openai-responses