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llmx/codex-rs/core/src/client.rs

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use std::io::BufRead;
use std::path::Path;
use std::time::Duration;
use bytes::Bytes;
use eventsource_stream::Eventsource;
use futures::prelude::*;
use reqwest::StatusCode;
use serde::Deserialize;
use serde::Serialize;
use serde_json::Value;
use tokio::sync::mpsc;
use tokio::time::timeout;
use tokio_util::io::ReaderStream;
use tracing::debug;
use tracing::trace;
use tracing::warn;
use crate::chat_completions::AggregateStreamExt;
feat: support the chat completions API in the Rust CLI (#862) This is a substantial PR to add support for the chat completions API, which in turn makes it possible to use non-OpenAI model providers (just like in the TypeScript CLI): * It moves a number of structs from `client.rs` to `client_common.rs` so they can be shared. * It introduces support for the chat completions API in `chat_completions.rs`. * It updates `ModelProviderInfo` so that `env_key` is `Option<String>` instead of `String` (for e.g., ollama) and adds a `wire_api` field * It updates `client.rs` to choose between `stream_responses()` and `stream_chat_completions()` based on the `wire_api` for the `ModelProviderInfo` * It updates the `exec` and TUI CLIs to no longer fail if the `OPENAI_API_KEY` environment variable is not set * It updates the TUI so that `EventMsg::Error` is displayed more prominently when it occurs, particularly now that it is important to alert users to the `CodexErr::EnvVar` variant. * `CodexErr::EnvVar` was updated to include an optional `instructions` field so we can preserve the behavior where we direct users to https://platform.openai.com if `OPENAI_API_KEY` is not set. * Cleaned up the "welcome message" in the TUI to ensure the model provider is displayed. * Updated the docs in `codex-rs/README.md`. To exercise the chat completions API from OpenAI models, I added the following to my `config.toml`: ```toml model = "gpt-4o" model_provider = "openai-chat-completions" [model_providers.openai-chat-completions] name = "OpenAI using Chat Completions" base_url = "https://api.openai.com/v1" env_key = "OPENAI_API_KEY" wire_api = "chat" ``` Though to test a non-OpenAI provider, I installed ollama with mistral locally on my Mac because ChatGPT said that would be a good match for my hardware: ```shell brew install ollama ollama serve ollama pull mistral ``` Then I added the following to my `~/.codex/config.toml`: ```toml model = "mistral" model_provider = "ollama" ``` Note this code could certainly use more test coverage, but I want to get this in so folks can start playing with it. For reference, I believe https://github.com/openai/codex/pull/247 was roughly the comparable PR on the TypeScript side.
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use crate::chat_completions::stream_chat_completions;
use crate::client_common::Prompt;
use crate::client_common::ResponseEvent;
use crate::client_common::ResponseStream;
use crate::client_common::ResponsesApiRequest;
use crate::client_common::create_reasoning_param_for_request;
use crate::config::Config;
use crate::config_types::ReasoningEffort as ReasoningEffortConfig;
use crate::config_types::ReasoningSummary as ReasoningSummaryConfig;
use crate::error::CodexErr;
use crate::error::Result;
use crate::flags::CODEX_RS_SSE_FIXTURE;
use crate::flags::OPENAI_REQUEST_MAX_RETRIES;
use crate::flags::OPENAI_STREAM_IDLE_TIMEOUT_MS;
use crate::model_provider_info::ModelProviderInfo;
feat: support the chat completions API in the Rust CLI (#862) This is a substantial PR to add support for the chat completions API, which in turn makes it possible to use non-OpenAI model providers (just like in the TypeScript CLI): * It moves a number of structs from `client.rs` to `client_common.rs` so they can be shared. * It introduces support for the chat completions API in `chat_completions.rs`. * It updates `ModelProviderInfo` so that `env_key` is `Option<String>` instead of `String` (for e.g., ollama) and adds a `wire_api` field * It updates `client.rs` to choose between `stream_responses()` and `stream_chat_completions()` based on the `wire_api` for the `ModelProviderInfo` * It updates the `exec` and TUI CLIs to no longer fail if the `OPENAI_API_KEY` environment variable is not set * It updates the TUI so that `EventMsg::Error` is displayed more prominently when it occurs, particularly now that it is important to alert users to the `CodexErr::EnvVar` variant. * `CodexErr::EnvVar` was updated to include an optional `instructions` field so we can preserve the behavior where we direct users to https://platform.openai.com if `OPENAI_API_KEY` is not set. * Cleaned up the "welcome message" in the TUI to ensure the model provider is displayed. * Updated the docs in `codex-rs/README.md`. To exercise the chat completions API from OpenAI models, I added the following to my `config.toml`: ```toml model = "gpt-4o" model_provider = "openai-chat-completions" [model_providers.openai-chat-completions] name = "OpenAI using Chat Completions" base_url = "https://api.openai.com/v1" env_key = "OPENAI_API_KEY" wire_api = "chat" ``` Though to test a non-OpenAI provider, I installed ollama with mistral locally on my Mac because ChatGPT said that would be a good match for my hardware: ```shell brew install ollama ollama serve ollama pull mistral ``` Then I added the following to my `~/.codex/config.toml`: ```toml model = "mistral" model_provider = "ollama" ``` Note this code could certainly use more test coverage, but I want to get this in so folks can start playing with it. For reference, I believe https://github.com/openai/codex/pull/247 was roughly the comparable PR on the TypeScript side.
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use crate::model_provider_info::WireApi;
use crate::models::ResponseItem;
use crate::openai_tools::create_tools_json_for_responses_api;
feat: show number of tokens remaining in UI (#1388) When using the OpenAI Responses API, we now record the `usage` field for a `"response.completed"` event, which includes metrics about the number of tokens consumed. We also introduce `openai_model_info.rs`, which includes current data about the most common OpenAI models available via the API (specifically `context_window` and `max_output_tokens`). If Codex does not recognize the model, you can set `model_context_window` and `model_max_output_tokens` explicitly in `config.toml`. When then introduce a new event type to `protocol.rs`, `TokenCount`, which includes the `TokenUsage` for the most recent turn. Finally, we update the TUI to record the running sum of tokens used so the percentage of available context window remaining can be reported via the placeholder text for the composer: ![Screenshot 2025-06-25 at 11 20 55 PM](https://github.com/user-attachments/assets/6fd6982f-7247-4f14-84b2-2e600cb1fd49) We could certainly get much fancier with this (such as reporting the estimated cost of the conversation), but for now, we are just trying to achieve feature parity with the TypeScript CLI. Though arguably this improves upon the TypeScript CLI, as the TypeScript CLI uses heuristics to estimate the number of tokens used rather than using the `usage` information directly: https://github.com/openai/codex/blob/296996d74e345b1b05d8c3451a06ace21c5ada96/codex-cli/src/utils/approximate-tokens-used.ts#L3-L16 Fixes https://github.com/openai/codex/issues/1242
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use crate::protocol::TokenUsage;
use crate::util::backoff;
use std::sync::Arc;
#[derive(Clone)]
pub struct ModelClient {
config: Arc<Config>,
model: String,
client: reqwest::Client,
provider: ModelProviderInfo,
effort: ReasoningEffortConfig,
summary: ReasoningSummaryConfig,
}
impl ModelClient {
pub fn new(
config: Arc<Config>,
provider: ModelProviderInfo,
effort: ReasoningEffortConfig,
summary: ReasoningSummaryConfig,
) -> Self {
let model = config.model.clone();
Self {
config,
model: model.to_string(),
client: reqwest::Client::new(),
provider,
effort,
summary,
}
}
feat: support the chat completions API in the Rust CLI (#862) This is a substantial PR to add support for the chat completions API, which in turn makes it possible to use non-OpenAI model providers (just like in the TypeScript CLI): * It moves a number of structs from `client.rs` to `client_common.rs` so they can be shared. * It introduces support for the chat completions API in `chat_completions.rs`. * It updates `ModelProviderInfo` so that `env_key` is `Option<String>` instead of `String` (for e.g., ollama) and adds a `wire_api` field * It updates `client.rs` to choose between `stream_responses()` and `stream_chat_completions()` based on the `wire_api` for the `ModelProviderInfo` * It updates the `exec` and TUI CLIs to no longer fail if the `OPENAI_API_KEY` environment variable is not set * It updates the TUI so that `EventMsg::Error` is displayed more prominently when it occurs, particularly now that it is important to alert users to the `CodexErr::EnvVar` variant. * `CodexErr::EnvVar` was updated to include an optional `instructions` field so we can preserve the behavior where we direct users to https://platform.openai.com if `OPENAI_API_KEY` is not set. * Cleaned up the "welcome message" in the TUI to ensure the model provider is displayed. * Updated the docs in `codex-rs/README.md`. To exercise the chat completions API from OpenAI models, I added the following to my `config.toml`: ```toml model = "gpt-4o" model_provider = "openai-chat-completions" [model_providers.openai-chat-completions] name = "OpenAI using Chat Completions" base_url = "https://api.openai.com/v1" env_key = "OPENAI_API_KEY" wire_api = "chat" ``` Though to test a non-OpenAI provider, I installed ollama with mistral locally on my Mac because ChatGPT said that would be a good match for my hardware: ```shell brew install ollama ollama serve ollama pull mistral ``` Then I added the following to my `~/.codex/config.toml`: ```toml model = "mistral" model_provider = "ollama" ``` Note this code could certainly use more test coverage, but I want to get this in so folks can start playing with it. For reference, I believe https://github.com/openai/codex/pull/247 was roughly the comparable PR on the TypeScript side.
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/// Dispatches to either the Responses or Chat implementation depending on
/// the provider config. Public callers always invoke `stream()` the
/// specialised helpers are private to avoid accidental misuse.
pub async fn stream(&self, prompt: &Prompt) -> Result<ResponseStream> {
match self.provider.wire_api {
WireApi::Responses => self.stream_responses(prompt).await,
WireApi::Chat => {
// Create the raw streaming connection first.
let response_stream =
stream_chat_completions(prompt, &self.model, &self.client, &self.provider)
.await?;
// Wrap it with the aggregation adapter so callers see *only*
// the final assistant message per turn (matching the
// behaviour of the Responses API).
let mut aggregated = response_stream.aggregate();
// Bridge the aggregated stream back into a standard
// `ResponseStream` by forwarding events through a channel.
let (tx, rx) = mpsc::channel::<Result<ResponseEvent>>(16);
tokio::spawn(async move {
use futures::StreamExt;
while let Some(ev) = aggregated.next().await {
// Exit early if receiver hung up.
if tx.send(ev).await.is_err() {
break;
}
}
});
Ok(ResponseStream { rx_event: rx })
feat: support the chat completions API in the Rust CLI (#862) This is a substantial PR to add support for the chat completions API, which in turn makes it possible to use non-OpenAI model providers (just like in the TypeScript CLI): * It moves a number of structs from `client.rs` to `client_common.rs` so they can be shared. * It introduces support for the chat completions API in `chat_completions.rs`. * It updates `ModelProviderInfo` so that `env_key` is `Option<String>` instead of `String` (for e.g., ollama) and adds a `wire_api` field * It updates `client.rs` to choose between `stream_responses()` and `stream_chat_completions()` based on the `wire_api` for the `ModelProviderInfo` * It updates the `exec` and TUI CLIs to no longer fail if the `OPENAI_API_KEY` environment variable is not set * It updates the TUI so that `EventMsg::Error` is displayed more prominently when it occurs, particularly now that it is important to alert users to the `CodexErr::EnvVar` variant. * `CodexErr::EnvVar` was updated to include an optional `instructions` field so we can preserve the behavior where we direct users to https://platform.openai.com if `OPENAI_API_KEY` is not set. * Cleaned up the "welcome message" in the TUI to ensure the model provider is displayed. * Updated the docs in `codex-rs/README.md`. To exercise the chat completions API from OpenAI models, I added the following to my `config.toml`: ```toml model = "gpt-4o" model_provider = "openai-chat-completions" [model_providers.openai-chat-completions] name = "OpenAI using Chat Completions" base_url = "https://api.openai.com/v1" env_key = "OPENAI_API_KEY" wire_api = "chat" ``` Though to test a non-OpenAI provider, I installed ollama with mistral locally on my Mac because ChatGPT said that would be a good match for my hardware: ```shell brew install ollama ollama serve ollama pull mistral ``` Then I added the following to my `~/.codex/config.toml`: ```toml model = "mistral" model_provider = "ollama" ``` Note this code could certainly use more test coverage, but I want to get this in so folks can start playing with it. For reference, I believe https://github.com/openai/codex/pull/247 was roughly the comparable PR on the TypeScript side.
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}
}
}
/// Implementation for the OpenAI *Responses* experimental API.
async fn stream_responses(&self, prompt: &Prompt) -> Result<ResponseStream> {
if let Some(path) = &*CODEX_RS_SSE_FIXTURE {
// short circuit for tests
warn!(path, "Streaming from fixture");
return stream_from_fixture(path).await;
}
let full_instructions = prompt.get_full_instructions(&self.model);
let tools_json = create_tools_json_for_responses_api(prompt, &self.model)?;
let reasoning = create_reasoning_param_for_request(&self.config, self.effort, self.summary);
let payload = ResponsesApiRequest {
model: &self.model,
fix: agent instructions were not being included when ~/.codex/instructions.md was empty (#908) I had seen issues where `codex-rs` would not always write files without me pressuring it to do so, and between that and the report of https://github.com/openai/codex/issues/900, I decided to look into this further. I found two serious issues with agent instructions: (1) We were only sending agent instructions on the first turn, but looking at the TypeScript code, we should be sending them on every turn. (2) There was a serious issue where the agent instructions were frequently lost: * The TypeScript CLI appears to keep writing `~/.codex/instructions.md`: https://github.com/openai/codex/blob/55142e3e6caddd1e613b71bcb89385ce5cc708bf/codex-cli/src/utils/config.ts#L586 * If `instructions.md` is present, the Rust CLI uses the contents of it INSTEAD OF the default prompt, even if `instructions.md` is empty: https://github.com/openai/codex/blob/55142e3e6caddd1e613b71bcb89385ce5cc708bf/codex-rs/core/src/config.rs#L202-L203 The combination of these two things means that I have been using `codex-rs` without these key instructions: https://github.com/openai/codex/blob/main/codex-rs/core/prompt.md Looking at the TypeScript code, it appears we should be concatenating these three items every time (if they exist): * `prompt.md` * `~/.codex/instructions.md` * nearest `AGENTS.md` This PR fixes things so that: * `Config.instructions` is `None` if `instructions.md` is empty * `Payload.instructions` is now `&'a str` instead of `Option<&'a String>` because we should always have _something_ to send * `Prompt` now has a `get_full_instructions()` helper that returns a `Cow<str>` that will always include the agent instructions first.
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instructions: &full_instructions,
input: &prompt.input,
feat: support mcp_servers in config.toml (#829) This adds initial support for MCP servers in the style of Claude Desktop and Cursor. Note this PR is the bare minimum to get things working end to end: all configured MCP servers are launched every time Codex is run, there is no recovery for MCP servers that crash, etc. (Also, I took some shortcuts to change some fields of `Session` to be `pub(crate)`, which also means there are circular deps between `codex.rs` and `mcp_tool_call.rs`, but I will clean that up in a subsequent PR.) `codex-rs/README.md` is updated as part of this PR to explain how to use this feature. There is a bit of plumbing to route the new settings from `Config` to the business logic in `codex.rs`. The most significant chunks for new code are in `mcp_connection_manager.rs` (which defines the `McpConnectionManager` struct) and `mcp_tool_call.rs`, which is responsible for tool calls. This PR also introduces new `McpToolCallBegin` and `McpToolCallEnd` event types to the protocol, but does not add any handlers for them. (See https://github.com/openai/codex/pull/836 for initial usage.) To test, I added the following to my `~/.codex/config.toml`: ```toml # Local build of https://github.com/hideya/mcp-server-weather-js [mcp_servers.weather] command = "/Users/mbolin/code/mcp-server-weather-js/dist/index.js" args = [] ``` And then I ran the following: ``` codex-rs$ cargo run --bin codex exec 'what is the weather in san francisco' [2025-05-06T22:40:05] Task started: 1 [2025-05-06T22:40:18] Agent message: Here’s the latest National Weather Service forecast for San Francisco (downtown, near 37.77° N, 122.42° W): This Afternoon (Tue): • Sunny, high near 69 °F • West-southwest wind around 12 mph Tonight: • Partly cloudy, low around 52 °F • SW wind 7–10 mph ... ``` Note that Codex itself is not able to make network calls, so it would not normally be able to get live weather information like this. However, the weather MCP is [currently] not run under the Codex sandbox, so it is able to hit `api.weather.gov` and fetch current weather information. --- [//]: # (BEGIN SAPLING FOOTER) Stack created with [Sapling](https://sapling-scm.com). Best reviewed with [ReviewStack](https://reviewstack.dev/openai/codex/pull/829). * #836 * __->__ #829
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tools: &tools_json,
tool_choice: "auto",
parallel_tool_calls: false,
reasoning,
previous_response_id: prompt.prev_id.clone(),
store: prompt.store,
stream: true,
};
trace!(
"POST to {}: {}",
self.provider.get_full_url(),
serde_json::to_string(&payload)?
);
let mut attempt = 0;
loop {
attempt += 1;
let req_builder = self
.provider
.create_request_builder(&self.client)?
.header("OpenAI-Beta", "responses=experimental")
.header(reqwest::header::ACCEPT, "text/event-stream")
.json(&payload);
let res = req_builder.send().await;
match res {
Ok(resp) if resp.status().is_success() => {
let (tx_event, rx_event) = mpsc::channel::<Result<ResponseEvent>>(16);
// spawn task to process SSE
let stream = resp.bytes_stream().map_err(CodexErr::Reqwest);
tokio::spawn(process_sse(stream, tx_event));
return Ok(ResponseStream { rx_event });
}
Ok(res) => {
let status = res.status();
// The OpenAI Responses endpoint returns structured JSON bodies even for 4xx/5xx
// errors. When we bubble early with only the HTTP status the caller sees an opaque
// "unexpected status 400 Bad Request" which makes debugging nearly impossible.
// Instead, read (and include) the response text so higher layers and users see the
// exact error message (e.g. "Unknown parameter: 'input[0].metadata'"). The body is
// small and this branch only runs on error paths so the extra allocation is
// negligible.
if !(status == StatusCode::TOO_MANY_REQUESTS || status.is_server_error()) {
// Surface the error body to callers. Use `unwrap_or_default` per Clippy.
let body = res.text().await.unwrap_or_default();
return Err(CodexErr::UnexpectedStatus(status, body));
}
if attempt > *OPENAI_REQUEST_MAX_RETRIES {
return Err(CodexErr::RetryLimit(status));
}
// Pull out RetryAfter header if present.
let retry_after_secs = res
.headers()
.get(reqwest::header::RETRY_AFTER)
.and_then(|v| v.to_str().ok())
.and_then(|s| s.parse::<u64>().ok());
let delay = retry_after_secs
.map(|s| Duration::from_millis(s * 1_000))
.unwrap_or_else(|| backoff(attempt));
tokio::time::sleep(delay).await;
}
Err(e) => {
if attempt > *OPENAI_REQUEST_MAX_RETRIES {
return Err(e.into());
}
let delay = backoff(attempt);
tokio::time::sleep(delay).await;
}
}
}
}
}
#[derive(Debug, Deserialize, Serialize)]
struct SseEvent {
#[serde(rename = "type")]
kind: String,
response: Option<Value>,
item: Option<Value>,
}
#[derive(Debug, Deserialize)]
struct ResponseCreated {}
#[derive(Debug, Deserialize)]
struct ResponseCompleted {
id: String,
feat: show number of tokens remaining in UI (#1388) When using the OpenAI Responses API, we now record the `usage` field for a `"response.completed"` event, which includes metrics about the number of tokens consumed. We also introduce `openai_model_info.rs`, which includes current data about the most common OpenAI models available via the API (specifically `context_window` and `max_output_tokens`). If Codex does not recognize the model, you can set `model_context_window` and `model_max_output_tokens` explicitly in `config.toml`. When then introduce a new event type to `protocol.rs`, `TokenCount`, which includes the `TokenUsage` for the most recent turn. Finally, we update the TUI to record the running sum of tokens used so the percentage of available context window remaining can be reported via the placeholder text for the composer: ![Screenshot 2025-06-25 at 11 20 55 PM](https://github.com/user-attachments/assets/6fd6982f-7247-4f14-84b2-2e600cb1fd49) We could certainly get much fancier with this (such as reporting the estimated cost of the conversation), but for now, we are just trying to achieve feature parity with the TypeScript CLI. Though arguably this improves upon the TypeScript CLI, as the TypeScript CLI uses heuristics to estimate the number of tokens used rather than using the `usage` information directly: https://github.com/openai/codex/blob/296996d74e345b1b05d8c3451a06ace21c5ada96/codex-cli/src/utils/approximate-tokens-used.ts#L3-L16 Fixes https://github.com/openai/codex/issues/1242
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usage: Option<ResponseCompletedUsage>,
}
#[derive(Debug, Deserialize)]
struct ResponseCompletedUsage {
input_tokens: u64,
input_tokens_details: Option<ResponseCompletedInputTokensDetails>,
output_tokens: u64,
output_tokens_details: Option<ResponseCompletedOutputTokensDetails>,
total_tokens: u64,
}
impl From<ResponseCompletedUsage> for TokenUsage {
fn from(val: ResponseCompletedUsage) -> Self {
TokenUsage {
input_tokens: val.input_tokens,
cached_input_tokens: val.input_tokens_details.map(|d| d.cached_tokens),
output_tokens: val.output_tokens,
reasoning_output_tokens: val.output_tokens_details.map(|d| d.reasoning_tokens),
total_tokens: val.total_tokens,
}
}
}
#[derive(Debug, Deserialize)]
struct ResponseCompletedInputTokensDetails {
cached_tokens: u64,
}
#[derive(Debug, Deserialize)]
struct ResponseCompletedOutputTokensDetails {
reasoning_tokens: u64,
}
async fn process_sse<S>(stream: S, tx_event: mpsc::Sender<Result<ResponseEvent>>)
where
S: Stream<Item = Result<Bytes>> + Unpin,
{
let mut stream = stream.eventsource();
// If the stream stays completely silent for an extended period treat it as disconnected.
let idle_timeout = *OPENAI_STREAM_IDLE_TIMEOUT_MS;
// The response id returned from the "complete" message.
feat: show number of tokens remaining in UI (#1388) When using the OpenAI Responses API, we now record the `usage` field for a `"response.completed"` event, which includes metrics about the number of tokens consumed. We also introduce `openai_model_info.rs`, which includes current data about the most common OpenAI models available via the API (specifically `context_window` and `max_output_tokens`). If Codex does not recognize the model, you can set `model_context_window` and `model_max_output_tokens` explicitly in `config.toml`. When then introduce a new event type to `protocol.rs`, `TokenCount`, which includes the `TokenUsage` for the most recent turn. Finally, we update the TUI to record the running sum of tokens used so the percentage of available context window remaining can be reported via the placeholder text for the composer: ![Screenshot 2025-06-25 at 11 20 55 PM](https://github.com/user-attachments/assets/6fd6982f-7247-4f14-84b2-2e600cb1fd49) We could certainly get much fancier with this (such as reporting the estimated cost of the conversation), but for now, we are just trying to achieve feature parity with the TypeScript CLI. Though arguably this improves upon the TypeScript CLI, as the TypeScript CLI uses heuristics to estimate the number of tokens used rather than using the `usage` information directly: https://github.com/openai/codex/blob/296996d74e345b1b05d8c3451a06ace21c5ada96/codex-cli/src/utils/approximate-tokens-used.ts#L3-L16 Fixes https://github.com/openai/codex/issues/1242
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let mut response_completed: Option<ResponseCompleted> = None;
loop {
let sse = match timeout(idle_timeout, stream.next()).await {
Ok(Some(Ok(sse))) => sse,
Ok(Some(Err(e))) => {
debug!("SSE Error: {e:#}");
let event = CodexErr::Stream(e.to_string());
let _ = tx_event.send(Err(event)).await;
return;
}
Ok(None) => {
feat: show number of tokens remaining in UI (#1388) When using the OpenAI Responses API, we now record the `usage` field for a `"response.completed"` event, which includes metrics about the number of tokens consumed. We also introduce `openai_model_info.rs`, which includes current data about the most common OpenAI models available via the API (specifically `context_window` and `max_output_tokens`). If Codex does not recognize the model, you can set `model_context_window` and `model_max_output_tokens` explicitly in `config.toml`. When then introduce a new event type to `protocol.rs`, `TokenCount`, which includes the `TokenUsage` for the most recent turn. Finally, we update the TUI to record the running sum of tokens used so the percentage of available context window remaining can be reported via the placeholder text for the composer: ![Screenshot 2025-06-25 at 11 20 55 PM](https://github.com/user-attachments/assets/6fd6982f-7247-4f14-84b2-2e600cb1fd49) We could certainly get much fancier with this (such as reporting the estimated cost of the conversation), but for now, we are just trying to achieve feature parity with the TypeScript CLI. Though arguably this improves upon the TypeScript CLI, as the TypeScript CLI uses heuristics to estimate the number of tokens used rather than using the `usage` information directly: https://github.com/openai/codex/blob/296996d74e345b1b05d8c3451a06ace21c5ada96/codex-cli/src/utils/approximate-tokens-used.ts#L3-L16 Fixes https://github.com/openai/codex/issues/1242
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match response_completed {
Some(ResponseCompleted {
id: response_id,
usage,
}) => {
let event = ResponseEvent::Completed {
response_id,
token_usage: usage.map(Into::into),
};
let _ = tx_event.send(Ok(event)).await;
}
None => {
let _ = tx_event
.send(Err(CodexErr::Stream(
"stream closed before response.completed".into(),
)))
.await;
}
}
return;
}
Err(_) => {
let _ = tx_event
.send(Err(CodexErr::Stream("idle timeout waiting for SSE".into())))
.await;
return;
}
};
let event: SseEvent = match serde_json::from_str(&sse.data) {
Ok(event) => event,
Err(e) => {
debug!("Failed to parse SSE event: {e}, data: {}", &sse.data);
continue;
}
};
trace!(?event, "SSE event");
match event.kind.as_str() {
// Individual output item finalised. Forward immediately so the
// rest of the agent can stream assistant text/functions *live*
// instead of waiting for the final `response.completed` envelope.
//
// IMPORTANT: We used to ignore these events and forward the
// duplicated `output` array embedded in the `response.completed`
// payload. That produced two concrete issues:
// 1. No realtime streaming the user only saw output after the
// entire turn had finished, which broke the “typing” UX and
// made longrunning turns look stalled.
// 2. Duplicate `function_call_output` items both the
// individual *and* the completed array were forwarded, which
// confused the backend and triggered 400
// "previous_response_not_found" errors because the duplicated
// IDs did not match the incremental turn chain.
//
// The fix is to forward the incremental events *as they come* and
// drop the duplicated list inside `response.completed`.
"response.output_item.done" => {
let Some(item_val) = event.item else { continue };
let Ok(item) = serde_json::from_value::<ResponseItem>(item_val) else {
debug!("failed to parse ResponseItem from output_item.done");
continue;
};
let event = ResponseEvent::OutputItemDone(item);
if tx_event.send(Ok(event)).await.is_err() {
return;
}
}
"response.created" => {
if event.response.is_some() {
let _ = tx_event.send(Ok(ResponseEvent::Created {})).await;
}
}
// Final response completed includes array of output items & id
"response.completed" => {
if let Some(resp_val) = event.response {
match serde_json::from_value::<ResponseCompleted>(resp_val) {
Ok(r) => {
feat: show number of tokens remaining in UI (#1388) When using the OpenAI Responses API, we now record the `usage` field for a `"response.completed"` event, which includes metrics about the number of tokens consumed. We also introduce `openai_model_info.rs`, which includes current data about the most common OpenAI models available via the API (specifically `context_window` and `max_output_tokens`). If Codex does not recognize the model, you can set `model_context_window` and `model_max_output_tokens` explicitly in `config.toml`. When then introduce a new event type to `protocol.rs`, `TokenCount`, which includes the `TokenUsage` for the most recent turn. Finally, we update the TUI to record the running sum of tokens used so the percentage of available context window remaining can be reported via the placeholder text for the composer: ![Screenshot 2025-06-25 at 11 20 55 PM](https://github.com/user-attachments/assets/6fd6982f-7247-4f14-84b2-2e600cb1fd49) We could certainly get much fancier with this (such as reporting the estimated cost of the conversation), but for now, we are just trying to achieve feature parity with the TypeScript CLI. Though arguably this improves upon the TypeScript CLI, as the TypeScript CLI uses heuristics to estimate the number of tokens used rather than using the `usage` information directly: https://github.com/openai/codex/blob/296996d74e345b1b05d8c3451a06ace21c5ada96/codex-cli/src/utils/approximate-tokens-used.ts#L3-L16 Fixes https://github.com/openai/codex/issues/1242
2025-06-25 23:31:11 -07:00
response_completed = Some(r);
}
Err(e) => {
debug!("failed to parse ResponseCompleted: {e}");
continue;
}
};
};
}
"response.content_part.done"
| "response.function_call_arguments.delta"
| "response.in_progress"
| "response.output_item.added"
| "response.output_text.delta"
| "response.output_text.done"
| "response.reasoning_summary_part.added"
| "response.reasoning_summary_text.delta"
| "response.reasoning_summary_text.done" => {
// Currently, we ignore these events, but we handle them
// separately to skip the logging message in the `other` case.
}
other => debug!(other, "sse event"),
}
}
}
/// used in tests to stream from a text SSE file
async fn stream_from_fixture(path: impl AsRef<Path>) -> Result<ResponseStream> {
let (tx_event, rx_event) = mpsc::channel::<Result<ResponseEvent>>(16);
let f = std::fs::File::open(path.as_ref())?;
let lines = std::io::BufReader::new(f).lines();
// insert \n\n after each line for proper SSE parsing
let mut content = String::new();
for line in lines {
content.push_str(&line?);
content.push_str("\n\n");
}
let rdr = std::io::Cursor::new(content);
let stream = ReaderStream::new(rdr).map_err(CodexErr::Io);
tokio::spawn(process_sse(stream, tx_event));
Ok(ResponseStream { rx_event })
}