use crate::auth::openai_oauth::extract_account_id_from_jwt;
use crate::auth::AuthService;
use crate::providers::traits::{ChatMessage, Provider};
use crate::providers::ProviderRuntimeOptions;
use async_trait::async_trait;
use reqwest::Client;
use serde::{Deserialize, Serialize};
use serde_json::Value;
use std::path::PathBuf;
const CODEX_RESPONSES_URL: &str = "https://chatgpt.com/backend-api/codex/responses";
const DEFAULT_CODEX_INSTRUCTIONS: &str =
"You are VelaClaw, a concise and helpful coding assistant.";
pub struct OpenAiCodexProvider {
auth: AuthService,
auth_profile_override: Option<String>,
client: Client,
}
#[derive(Debug, Serialize)]
struct ResponsesRequest {
model: String,
input: Vec<ResponsesInput>,
instructions: String,
store: bool,
stream: bool,
text: ResponsesTextOptions,
reasoning: ResponsesReasoningOptions,
include: Vec<String>,
tool_choice: String,
parallel_tool_calls: bool,
}
#[derive(Debug, Serialize)]
struct ResponsesInput {
role: String,
content: Vec<ResponsesInputContent>,
}
#[derive(Debug, Serialize)]
struct ResponsesInputContent {
#[serde(rename = "type")]
kind: String,
text: String,
}
#[derive(Debug, Serialize)]
struct ResponsesTextOptions {
verbosity: String,
}
#[derive(Debug, Serialize)]
struct ResponsesReasoningOptions {
effort: String,
summary: String,
}
#[derive(Debug, Deserialize)]
struct ResponsesResponse {
#[serde(default)]
output: Vec<ResponsesOutput>,
#[serde(default)]
output_text: Option<String>,
}
#[derive(Debug, Deserialize)]
struct ResponsesOutput {
#[serde(default)]
content: Vec<ResponsesContent>,
}
#[derive(Debug, Deserialize)]
struct ResponsesContent {
#[serde(rename = "type")]
kind: Option<String>,
text: Option<String>,
}
impl OpenAiCodexProvider {
pub fn new(options: &ProviderRuntimeOptions) -> Self {
let state_dir = options
.velaclaw_dir
.clone()
.unwrap_or_else(default_velaclaw_dir);
let auth = AuthService::new(&state_dir, options.secrets_encrypt);
Self {
auth,
auth_profile_override: options.auth_profile_override.clone(),
client: Client::builder()
.timeout(std::time::Duration::from_secs(120))
.connect_timeout(std::time::Duration::from_secs(10))
.build()
.unwrap_or_else(|_| Client::new()),
}
}
}
fn default_velaclaw_dir() -> PathBuf {
directories::UserDirs::new().map_or_else(
|| PathBuf::from(".velaclaw"),
|dirs| dirs.home_dir().join(".velaclaw"),
)
}
fn first_nonempty(text: Option<&str>) -> Option<String> {
text.and_then(|value| {
let trimmed = value.trim();
if trimmed.is_empty() {
None
} else {
Some(trimmed.to_string())
}
})
}
fn resolve_instructions(system_prompt: Option<&str>) -> String {
first_nonempty(system_prompt).unwrap_or_else(|| DEFAULT_CODEX_INSTRUCTIONS.to_string())
}
fn normalize_model_id(model: &str) -> &str {
model.rsplit('/').next().unwrap_or(model)
}
fn build_responses_input(messages: &[ChatMessage]) -> (String, Vec<ResponsesInput>) {
let mut system_parts: Vec<&str> = Vec::new();
let mut input: Vec<ResponsesInput> = Vec::new();
for msg in messages {
match msg.role.as_str() {
"system" => system_parts.push(&msg.content),
"user" => {
input.push(ResponsesInput {
role: "user".to_string(),
content: vec![ResponsesInputContent {
kind: "input_text".to_string(),
text: msg.content.clone(),
}],
});
}
"assistant" => {
input.push(ResponsesInput {
role: "assistant".to_string(),
content: vec![ResponsesInputContent {
kind: "output_text".to_string(),
text: msg.content.clone(),
}],
});
}
_ => {}
}
}
let instructions = if system_parts.is_empty() {
DEFAULT_CODEX_INSTRUCTIONS.to_string()
} else {
system_parts.join("\n\n")
};
(instructions, input)
}
fn clamp_reasoning_effort(model: &str, effort: &str) -> String {
let id = normalize_model_id(model);
if (id.starts_with("gpt-5.2") || id.starts_with("gpt-5.3")) && effort == "minimal" {
return "low".to_string();
}
if id == "gpt-5.1" && effort == "xhigh" {
return "high".to_string();
}
if id == "gpt-5.1-codex-mini" {
return if effort == "high" || effort == "xhigh" {
"high".to_string()
} else {
"medium".to_string()
};
}
effort.to_string()
}
fn resolve_reasoning_effort(model_id: &str) -> String {
let raw = std::env::var("VELACLAW_CODEX_REASONING_EFFORT")
.ok()
.and_then(|value| first_nonempty(Some(&value)))
.unwrap_or_else(|| "xhigh".to_string())
.to_ascii_lowercase();
clamp_reasoning_effort(model_id, &raw)
}
fn nonempty_preserve(text: Option<&str>) -> Option<String> {
text.and_then(|value| {
if value.is_empty() {
None
} else {
Some(value.to_string())
}
})
}
fn extract_responses_text(response: &ResponsesResponse) -> Option<String> {
if let Some(text) = first_nonempty(response.output_text.as_deref()) {
return Some(text);
}
for item in &response.output {
for content in &item.content {
if content.kind.as_deref() == Some("output_text") {
if let Some(text) = first_nonempty(content.text.as_deref()) {
return Some(text);
}
}
}
}
for item in &response.output {
for content in &item.content {
if let Some(text) = first_nonempty(content.text.as_deref()) {
return Some(text);
}
}
}
None
}
fn extract_stream_event_text(event: &Value, saw_delta: bool) -> Option<String> {
let event_type = event.get("type").and_then(Value::as_str);
match event_type {
Some("response.output_text.delta") => {
nonempty_preserve(event.get("delta").and_then(Value::as_str))
}
Some("response.output_text.done") if !saw_delta => {
nonempty_preserve(event.get("text").and_then(Value::as_str))
}
Some("response.completed" | "response.done") => event
.get("response")
.and_then(|value| serde_json::from_value::<ResponsesResponse>(value.clone()).ok())
.and_then(|response| extract_responses_text(&response)),
_ => None,
}
}
fn parse_sse_text(body: &str) -> anyhow::Result<Option<String>> {
let mut saw_delta = false;
let mut delta_accumulator = String::new();
let mut fallback_text = None;
let mut buffer = body.to_string();
let mut process_event = |event: Value| -> anyhow::Result<()> {
if let Some(message) = extract_stream_error_message(&event) {
return Err(anyhow::anyhow!("OpenAI Codex stream error: {message}"));
}
if let Some(text) = extract_stream_event_text(&event, saw_delta) {
let event_type = event.get("type").and_then(Value::as_str);
if event_type == Some("response.output_text.delta") {
saw_delta = true;
delta_accumulator.push_str(&text);
} else if fallback_text.is_none() {
fallback_text = Some(text);
}
}
Ok(())
};
let mut process_chunk = |chunk: &str| -> anyhow::Result<()> {
let data_lines: Vec<String> = chunk
.lines()
.filter_map(|line| line.strip_prefix("data:"))
.map(|line| line.trim().to_string())
.collect();
if data_lines.is_empty() {
return Ok(());
}
let joined = data_lines.join("\n");
let trimmed = joined.trim();
if trimmed.is_empty() || trimmed == "[DONE]" {
return Ok(());
}
if let Ok(event) = serde_json::from_str::<Value>(trimmed) {
return process_event(event);
}
for line in data_lines {
let line = line.trim();
if line.is_empty() || line == "[DONE]" {
continue;
}
if let Ok(event) = serde_json::from_str::<Value>(line) {
process_event(event)?;
}
}
Ok(())
};
while let Some(idx) = buffer.find("\n\n") {
let chunk = buffer[..idx].to_string();
buffer = buffer[idx + 2..].to_string();
process_chunk(&chunk)?;
}
if !buffer.trim().is_empty() {
process_chunk(&buffer)?;
}
if saw_delta {
return Ok(nonempty_preserve(Some(&delta_accumulator)));
}
Ok(fallback_text)
}
fn extract_stream_error_message(event: &Value) -> Option<String> {
let event_type = event.get("type").and_then(Value::as_str);
if event_type == Some("error") {
return first_nonempty(
event
.get("message")
.and_then(Value::as_str)
.or_else(|| event.get("code").and_then(Value::as_str))
.or_else(|| {
event
.get("error")
.and_then(|error| error.get("message"))
.and_then(Value::as_str)
}),
);
}
if event_type == Some("response.failed") {
return first_nonempty(
event
.get("response")
.and_then(|response| response.get("error"))
.and_then(|error| error.get("message"))
.and_then(Value::as_str),
);
}
None
}
async fn decode_responses_body(response: reqwest::Response) -> anyhow::Result<String> {
let body = response.text().await?;
if let Some(text) = parse_sse_text(&body)? {
return Ok(text);
}
let body_trimmed = body.trim_start();
let looks_like_sse = body_trimmed.starts_with("event:") || body_trimmed.starts_with("data:");
if looks_like_sse {
return Err(anyhow::anyhow!(
"No response from OpenAI Codex stream payload: {}",
super::sanitize_api_error(&body)
));
}
let parsed: ResponsesResponse = serde_json::from_str(&body).map_err(|err| {
anyhow::anyhow!(
"OpenAI Codex JSON parse failed: {err}. Payload: {}",
super::sanitize_api_error(&body)
)
})?;
extract_responses_text(&parsed).ok_or_else(|| anyhow::anyhow!("No response from OpenAI Codex"))
}
impl OpenAiCodexProvider {
async fn send_responses_request(
&self,
input: Vec<ResponsesInput>,
instructions: String,
model: &str,
) -> anyhow::Result<String> {
let profile = self
.auth
.get_profile("openai-codex", self.auth_profile_override.as_deref())
.await?;
let access_token = self
.auth
.get_valid_openai_access_token(self.auth_profile_override.as_deref())
.await?
.ok_or_else(|| {
anyhow::anyhow!(
"OpenAI Codex auth profile not found. Run `velaclaw auth login --provider openai-codex`."
)
})?;
let account_id = profile
.and_then(|profile| profile.account_id)
.or_else(|| extract_account_id_from_jwt(&access_token))
.ok_or_else(|| {
anyhow::anyhow!(
"OpenAI Codex account id not found in auth profile/token. Run `velaclaw auth login --provider openai-codex` again."
)
})?;
let normalized_model = normalize_model_id(model);
let request = ResponsesRequest {
model: normalized_model.to_string(),
input,
instructions,
store: false,
stream: true,
text: ResponsesTextOptions {
verbosity: "medium".to_string(),
},
reasoning: ResponsesReasoningOptions {
effort: resolve_reasoning_effort(normalized_model),
summary: "auto".to_string(),
},
include: vec!["reasoning.encrypted_content".to_string()],
tool_choice: "auto".to_string(),
parallel_tool_calls: true,
};
let response = self
.client
.post(CODEX_RESPONSES_URL)
.header("Authorization", format!("Bearer {access_token}"))
.header("chatgpt-account-id", account_id)
.header("OpenAI-Beta", "responses=experimental")
.header("originator", "pi")
.header("accept", "text/event-stream")
.header("Content-Type", "application/json")
.json(&request)
.send()
.await?;
if !response.status().is_success() {
return Err(super::api_error("OpenAI Codex", response).await);
}
decode_responses_body(response).await
}
}
#[async_trait]
impl Provider for OpenAiCodexProvider {
async fn chat_with_system(
&self,
system_prompt: Option<&str>,
message: &str,
model: &str,
_temperature: f64,
) -> anyhow::Result<String> {
let input = vec![ResponsesInput {
role: "user".to_string(),
content: vec![ResponsesInputContent {
kind: "input_text".to_string(),
text: message.to_string(),
}],
}];
self.send_responses_request(input, resolve_instructions(system_prompt), model)
.await
}
async fn chat_with_history(
&self,
messages: &[ChatMessage],
model: &str,
_temperature: f64,
) -> anyhow::Result<String> {
let (instructions, input) = build_responses_input(messages);
self.send_responses_request(input, instructions, model)
.await
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn extracts_output_text_first() {
let response = ResponsesResponse {
output: vec![],
output_text: Some("hello".into()),
};
assert_eq!(extract_responses_text(&response).as_deref(), Some("hello"));
}
#[test]
fn extracts_nested_output_text() {
let response = ResponsesResponse {
output: vec![ResponsesOutput {
content: vec![ResponsesContent {
kind: Some("output_text".into()),
text: Some("nested".into()),
}],
}],
output_text: None,
};
assert_eq!(extract_responses_text(&response).as_deref(), Some("nested"));
}
#[test]
fn default_state_dir_is_non_empty() {
let path = default_velaclaw_dir();
assert!(!path.as_os_str().is_empty());
}
#[test]
fn resolve_instructions_uses_default_when_missing() {
assert_eq!(
resolve_instructions(None),
DEFAULT_CODEX_INSTRUCTIONS.to_string()
);
}
#[test]
fn resolve_instructions_uses_default_when_blank() {
assert_eq!(
resolve_instructions(Some(" ")),
DEFAULT_CODEX_INSTRUCTIONS.to_string()
);
}
#[test]
fn resolve_instructions_uses_system_prompt_when_present() {
assert_eq!(
resolve_instructions(Some("Be strict")),
"Be strict".to_string()
);
}
#[test]
fn clamp_reasoning_effort_adjusts_known_models() {
assert_eq!(
clamp_reasoning_effort("gpt-5.3-codex", "minimal"),
"low".to_string()
);
assert_eq!(
clamp_reasoning_effort("gpt-5.1", "xhigh"),
"high".to_string()
);
assert_eq!(
clamp_reasoning_effort("gpt-5.1-codex-mini", "low"),
"medium".to_string()
);
assert_eq!(
clamp_reasoning_effort("gpt-5.1-codex-mini", "xhigh"),
"high".to_string()
);
assert_eq!(
clamp_reasoning_effort("gpt-5.3-codex", "xhigh"),
"xhigh".to_string()
);
}
#[test]
fn parse_sse_text_reads_output_text_delta() {
let payload = r#"data: {"type":"response.created","response":{"id":"resp_123"}}
data: {"type":"response.output_text.delta","delta":"Hello"}
data: {"type":"response.output_text.delta","delta":" world"}
data: {"type":"response.completed","response":{"output_text":"Hello world"}}
data: [DONE]
"#;
assert_eq!(
parse_sse_text(payload).unwrap().as_deref(),
Some("Hello world")
);
}
#[test]
fn parse_sse_text_falls_back_to_completed_response() {
let payload = r#"data: {"type":"response.completed","response":{"output_text":"Done"}}
data: [DONE]
"#;
assert_eq!(parse_sse_text(payload).unwrap().as_deref(), Some("Done"));
}
#[test]
fn build_responses_input_maps_content_types_by_role() {
let messages = vec![
ChatMessage {
tool_call_id: None,
role: "system".into(),
content: "You are helpful.".into(),
},
ChatMessage {
tool_call_id: None,
role: "user".into(),
content: "Hi".into(),
},
ChatMessage {
tool_call_id: None,
role: "assistant".into(),
content: "Hello!".into(),
},
ChatMessage {
tool_call_id: None,
role: "user".into(),
content: "Thanks".into(),
},
];
let (instructions, input) = build_responses_input(&messages);
assert_eq!(instructions, "You are helpful.");
assert_eq!(input.len(), 3);
let json: Vec<Value> = input
.iter()
.map(|item| serde_json::to_value(item).unwrap())
.collect();
assert_eq!(json[0]["role"], "user");
assert_eq!(json[0]["content"][0]["type"], "input_text");
assert_eq!(json[1]["role"], "assistant");
assert_eq!(json[1]["content"][0]["type"], "output_text");
assert_eq!(json[2]["role"], "user");
assert_eq!(json[2]["content"][0]["type"], "input_text");
}
#[test]
fn build_responses_input_uses_default_instructions_without_system() {
let messages = vec![ChatMessage {
tool_call_id: None,
role: "user".into(),
content: "Hello".into(),
}];
let (instructions, input) = build_responses_input(&messages);
assert_eq!(instructions, DEFAULT_CODEX_INSTRUCTIONS);
assert_eq!(input.len(), 1);
}
#[test]
fn build_responses_input_ignores_unknown_roles() {
let messages = vec![
ChatMessage {
tool_call_id: None,
role: "tool".into(),
content: "result".into(),
},
ChatMessage {
tool_call_id: None,
role: "user".into(),
content: "Go".into(),
},
];
let (instructions, input) = build_responses_input(&messages);
assert_eq!(instructions, DEFAULT_CODEX_INSTRUCTIONS);
assert_eq!(input.len(), 1);
let json = serde_json::to_value(&input[0]).unwrap();
assert_eq!(json["role"], "user");
}
}