Created this PR by:
- adding `redundant_clone` to `[workspace.lints.clippy]` in
`cargo-rs/Cargol.toml`
- running `cargo clippy --tests --fix`
- running `just fmt`
Though I had to clean up one instance of the following that resulted:
```rust
let codex = codex;
```
This PR does the following:
* Adds the ability to paste or type an API key.
* Removes the `preferred_auth_method` config option. The last login
method is always persisted in auth.json, so this isn't needed.
* If OPENAI_API_KEY env variable is defined, the value is used to
prepopulate the new UI. The env variable is otherwise ignored by the
CLI.
* Adds a new MCP server entry point "login_api_key" so we can implement
this same API key behavior for the VS Code extension.
<img width="473" height="140" alt="Screenshot 2025-09-04 at 3 51 04 PM"
src="https://github.com/user-attachments/assets/c11bbd5b-8a4d-4d71-90fd-34130460f9d9"
/>
<img width="726" height="254" alt="Screenshot 2025-09-04 at 3 51 32 PM"
src="https://github.com/user-attachments/assets/6cc76b34-309a-4387-acbc-15ee5c756db9"
/>
This PR changes get history op to get path. Then, forking will use a
path. This will help us have one unified codepath for resuming/forking
conversations. Will also help in having rollout history in order. It
also fixes a bug where you won't see the UI when resuming after forking.
This adds a simple endpoint that provides the email address encoded in
`$CODEX_HOME/auth.json`.
As noted, for now, we do not hit the server to verify this is the user's
true email address.
https://github.com/openai/codex/pull/3395 updated `mcp-types/src/lib.rs`
by hand, but that file is generated code that is produced by
`mcp-types/generate_mcp_types.py`. Unfortunately, we do not have
anything in CI to verify this right now, but I will address that in a
subsequent PR.
#3395 ended up introducing a change that added a required field when
deserializing `InitializeResult`, breaking Codex when used as an MCP
client, so the quick fix in #3436 was to make the new field `Optional`
with `skip_serializing_if = "Option::is_none"`, but that did not address
the problem that `mcp-types/generate_mcp_types.py` and
`mcp-types/src/lib.rs` are out of sync.
This PR gets things back to where they are in sync. It removes the
custom `mcp_types::McpClientInfo` type that was added to
`mcp-types/src/lib.rs` and forces us to use the generated
`mcp_types::Implementation` type. Though this PR also updates
`generate_mcp_types.py` to generate the additional `user_agent:
Optional<String>` field on `Implementation` so that we can continue to
specify it when Codex operates as an MCP server.
However, this also requires us to specify `user_agent: None` when Codex
operates as an MCP client.
We may want to introduce our own `InitializeResult` type that is
specific to when we run as a server to avoid this in the future, but my
immediate goal is just to get things back in sync.
# External (non-OpenAI) Pull Request Requirements
Currently, mcp server fail to start with:
```
🖐 MCP client for `<CLIENT>` failed to start: missing field `user_agent`
````
It isn't clear to me yet why this is happening. My understanding is that
this struct is simply added as a new field to the response but this
should fix it until I figure out the full story here.
<img width="714" height="262" alt="CleanShot 2025-09-10 at 13 58 59"
src="https://github.com/user-attachments/assets/946b1313-5c1c-43d3-8ae8-ecc3de3406fc"
/>
This PR improves two existing auth-related tests. They were failing when
run in an environment where an `OPENAI_API_KEY` env variable was
defined. The change makes them more resilient.
The previous config approach had a few issues:
1. It is part of the config but not designed to be used externally
2. It had to be wired through many places (look at the +/- on this PR
3. It wasn't guaranteed to be set consistently everywhere because we
don't have a super well defined way that configs stack. For example, the
extension would configure during newConversation but anything that
happened outside of that (like login) wouldn't get it.
This env var approach is cleaner and also creates one less thing we have
to deal with when coming up with a better holistic story around configs.
One downside is that I removed the unit test testing for the override
because I don't want to deal with setting the global env or spawning
child processes and figuring out how to introspect their originator
header. The new code is sufficiently simple and I tested it e2e that I
feel as if this is still worth it.
Adds support for `ArchiveConversation` in the JSON-RPC server that takes
a `(ConversationId, PathBuf)` pair and:
- verifies the `ConversationId` corresponds to the rollout id at the
`PathBuf`
- if so, invokes
`ConversationManager.remove_conversation(ConversationId)`
- if the `CodexConversation` was in memory, send `Shutdown` and wait for
`ShutdownComplete` with a timeout
- moves the `.jsonl` file to `$CODEX_HOME/archived_sessions`
---------
Co-authored-by: Gabriel Peal <gabriel@openai.com>
Adding the `rollout_path` to the `NewConversationResponse` makes it so a
client can perform subsequent operations on a `(ConversationId,
PathBuf)` pair. #3353 will introduce support for `ArchiveConversation`.
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with [ReviewStack](https://reviewstack.dev/openai/codex/pull/3352).
* #3353
* __->__ #3352
This PR does multiple things that are necessary for conversation resume
to work from the extension. I wanted to make sure everything worked so
these changes wound up in one PR:
1. Generate more ts types
2. Resume rollout history files rather than create a new one every time
it is resumed so you don't see a duplicate conversation in history for
every resume. Chatted with @aibrahim-oai to verify this
3. Return conversation_id in conversation summaries
4. [Cleanup] Use serde and strong types for a lot of the rollout file
parsing
We're trying to migrate from `session_id: Uuid` to `conversation_id:
ConversationId`. Not only does this give us more type safety but it
unifies our terminology across Codex and with the implementation of
session resuming, a conversation (which can span multiple sessions) is
more appropriate.
I started this impl on https://github.com/openai/codex/pull/3219 as part
of getting resume working in the extension but it's big enough that it
should be broken out.
When item ids are sent to Responses API it will load them from the
database ignoring the provided values. This adds extra latency.
Not having the mode to store requests also allows us to simplify the
code.
## Breaking change
The `disable_response_storage` configuration option is removed.
This PR introduces introduces a new
`OutgoingMessage::AppServerNotification` variant that is designed to
wrap a `ServerNotification`, which makes the serialization more
straightforward compared to
`OutgoingMessage::Notification(OutgoingNotification)`. We still use the
latter for serializing an `Event` as a `JSONRPCMessage::Notification`,
but I will try to get away from that in the near future.
With this change, now the generated TypeScript type for
`ServerNotification` is:
```typescript
export type ServerNotification =
| { "method": "authStatusChange", "params": AuthStatusChangeNotification }
| { "method": "loginChatGptComplete", "params": LoginChatGptCompleteNotification };
```
whereas before it was:
```typescript
export type ServerNotification =
| { type: "auth_status_change"; data: AuthStatusChangeNotification }
| { type: "login_chat_gpt_complete"; data: LoginChatGptCompleteNotification };
```
Once the `Event`s are migrated to the `ServerNotification` enum in Rust,
it should be considerably easier to work with notifications on the
TypeScript side, as it will be possible to `switch (message.method)` and
check for exhaustiveness.
Though we will probably need to introduce:
```typescript
export type ServerMessage = ServerRequest | ServerNotification;
```
and then we still need to group all of the `ServerResponse` types
together, as well.
# External (non-OpenAI) Pull Request Requirements
Before opening this Pull Request, please read the dedicated
"Contributing" markdown file or your PR may be closed:
https://github.com/openai/codex/blob/main/docs/contributing.md
If your PR conforms to our contribution guidelines, replace this text
with a detailed and high quality description of your changes.
## Summary
Follow-up to #3056
This PR updates the mcp-server interface for reading the config settings
saved by the user. At risk of introducing _another_ Config struct, I
think it makes sense to avoid tying our protocol to ConfigToml, as its
become a bit unwieldy. GetConfigTomlResponse was a de-facto struct for
this already - better to make it explicit, in my opinion.
This is technically a breaking change of the mcp-server protocol, but
given the previous interface was introduced so recently in #2725, and we
have not yet even started to call it, I propose proceeding with the
breaking change - but am open to preserving the old endpoint.
## Testing
- [x] Added additional integration test coverage
This PR does the following:
- divides user msgs into 3 categories: plain, user instructions, and
environment context
- Centralizes adding user instructions and environment context to a
degree
- Improve the integration testing
Building on top of #3123
Specifically this
[comment](https://github.com/openai/codex/pull/3123#discussion_r2319885089).
We need to send the user message while ignoring the User Instructions
and Environment Context we attach.
### Overview
This PR introduces the following changes:
1. Adds a unified mechanism to convert ResponseItem into EventMsg.
2. Ensures that when a session is initialized with initial history, a
vector of EventMsg is sent along with the session configuration. This
allows clients to re-render the UI accordingly.
3. Added integration testing
### Caveats
This implementation does not send every EventMsg that was previously
dispatched to clients. The excluded events fall into two categories:
• “Arguably” rolled-out events
Examples include tool calls and apply-patch calls. While these events
are conceptually rolled out, we currently only roll out ResponseItems.
These events are already being handled elsewhere and transformed into
EventMsg before being sent.
• Non-rolled-out events
Certain events such as TurnDiff, Error, and TokenCount are not rolled
out at all.
### Future Directions
At present, resuming a session involves maintaining two states:
• UI State
Clients can replay most of the important UI from the provided EventMsg
history.
• Model State
The model receives the complete session history to reconstruct its
internal state.
This design provides a solid foundation. If, in the future, more precise
UI reconstruction is needed, we have two potential paths:
1. Introduce a third data structure that allows us to derive both
ResponseItems and EventMsgs.
2. Clearly divide responsibilities: the core system ensures the
integrity of the model state, while clients are responsible for
reconstructing the UI.
#2747 encouraged me to audit our codebase for similar issues, as now I
am particularly suspicious that our flaky tests are due to a racy
deadlock.
I asked Codex to audit our code, and one of its suggestions was this:
> **High-Risk Patterns**
>
> All `send_*` methods await on a bounded
`mpsc::Sender<OutgoingMessage>`. If the writer blocks, the channel fills
and the processor task blocks on send, stops draining incoming requests,
and stdin reader eventually blocks on its send. This creates a
backpressure deadlock cycle across the three tasks.
>
> **Recommendations**
> * Server outgoing path: break the backpressure cycle
> * Option A (minimal risk): Change `OutgoingMessageSender` to use an
unbounded channel to decouple producer from stdout. Add rate logging so
floods are visible.
> * Option B (bounded + drop policy): Change `send_*` to try_send and
drop messages (or coalesce) when the queue is full, logging a warning.
This prevents processor stalls at the cost of losing messages under
extreme backpressure.
> * Option C (two-stage buffer): Keep bounded channel, but have a
dedicated “egress” task that drains an unbounded internal queue, writing
to stdout with retries and a shutdown timeout. This centralizes
backpressure policy.
So this PR is Option A.
Indeed, we previously used a bounded channel with a capacity of `128`,
but as we discovered recently with #2776, there are certainly cases
where we can get flooded with events.
That said, `test_shell_command_approval_triggers_elicitation` just
failed one one build when I put up this PR, so clearly we are not out of
the woods yet...
**Update:** I think I found the true source of the deadlock! See
https://github.com/openai/codex/pull/2876
POC code
```rust
use tokio::sync::mpsc;
use std::time::Duration;
#[tokio::main]
async fn main() {
println!("=== Test 1: Simulating original MCP server pattern ===");
test_original_pattern().await;
}
async fn test_original_pattern() {
println!("Testing the original pattern from MCP server...");
// Create channel - this simulates the original incoming_tx/incoming_rx
let (tx, mut rx) = mpsc::channel::<String>(10);
// Task 1: Simulates stdin reader that will naturally terminate
let stdin_task = tokio::spawn({
let tx_clone = tx.clone();
async move {
println!(" stdin_task: Started, will send 3 messages then exit");
for i in 0..3 {
let msg = format!("Message {}", i);
if tx_clone.send(msg.clone()).await.is_err() {
println!(" stdin_task: Receiver dropped, exiting");
break;
}
println!(" stdin_task: Sent {}", msg);
tokio::time::sleep(Duration::from_millis(300)).await;
}
println!(" stdin_task: Finished (simulating EOF)");
// tx_clone is dropped here
}
});
// Task 2: Simulates message processor
let processor_task = tokio::spawn(async move {
println!(" processor_task: Started, waiting for messages");
while let Some(msg) = rx.recv().await {
println!(" processor_task: Processing {}", msg);
tokio::time::sleep(Duration::from_millis(100)).await;
}
println!(" processor_task: Finished (channel closed)");
});
// Task 3: Simulates stdout writer or other background task
let background_task = tokio::spawn(async move {
for i in 0..2 {
tokio::time::sleep(Duration::from_millis(500)).await;
println!(" background_task: Tick {}", i);
}
println!(" background_task: Finished");
});
println!(" main: Original tx is still alive here");
println!(" main: About to call tokio::join! - will this deadlock?");
// This is the pattern from the original code
let _ = tokio::join!(stdin_task, processor_task, background_task);
}
```
---------
Co-authored-by: Michael Bolin <bolinfest@gmail.com>
- Introduce websearch end to complement the begin
- Moves the logic of adding the sebsearch tool to
create_tools_json_for_responses_api
- Making it the client responsibility to toggle the tool on or off
- Other misc in #2371 post commit feedback
- Show the query:
<img width="1392" height="151" alt="image"
src="https://github.com/user-attachments/assets/8457f1a6-f851-44cf-bcca-0d4fe460ce89"
/>
Adds custom `/prompts` to `~/.codex/prompts/<command>.md`.
<img width="239" height="107" alt="Screenshot 2025-08-25 at 6 22 42 PM"
src="https://github.com/user-attachments/assets/fe6ebbaa-1bf6-49d3-95f9-fdc53b752679"
/>
---
Details:
1. Adds `Op::ListCustomPrompts` to core.
2. Returns `ListCustomPromptsResponse` with list of `CustomPrompt`
(name, content).
3. TUI calls the operation on load, and populates the custom prompts
(excluding prompts that collide with builtins).
4. Selecting the custom prompt automatically sends the prompt to the
agent.
`test_shell_command_approval_triggers_elicitation()` is one of a number
of integration tests that we have observed to be flaky on GitHub CI, so
this PR tries to reduce the flakiness _and_ to provide us with more
information when it flakes. Specifically:
- Changed the command that we use to trigger the elicitation from `git
init` to `python3 -c 'import pathlib; pathlib.Path(r"{}").touch()'`
because running `git` seems more likely to invite variance.
- Increased the timeout to wait for the task response from 10s to 20s.
- Added more logging.
- added `uninlined_format_args` to `[workspace.lints.clippy]` in the
`Cargo.toml` for the workspace
- ran `cargo clippy --tests --fix`
- ran `just fmt`
## Summary
Adds a GetConfig request to the MCP Protocol, so MCP clients can
evaluate the resolved config.toml settings which the harness is using.
## Testing
- [x] Added an end to end test of the endpoint
this dramatically improves time to run `cargo test -p codex-core` (~25x
speedup).
before:
```
cargo test -p codex-core 35.96s user 68.63s system 19% cpu 8:49.80 total
```
after:
```
cargo test -p codex-core 5.51s user 8.16s system 63% cpu 21.407 total
```
both tests measured "hot", i.e. on a 2nd run with no filesystem changes,
to exclude compile times.
approach inspired by [Delete Cargo Integration
Tests](https://matklad.github.io/2021/02/27/delete-cargo-integration-tests.html),
we move all test cases in tests/ into a single suite in order to have a
single binary, as there is significant overhead for each test binary
executed, and because test execution is only parallelized with a single
binary.
Adds web_search tool, enabling the model to use Responses API web_search
tool.
- Disabled by default, enabled by --search flag
- When --search is passed, exposes web_search_request function tool to
the model, which triggers user approval. When approved, the model can
use the web_search tool for the remainder of the turn
<img width="1033" height="294" alt="image"
src="https://github.com/user-attachments/assets/62ac6563-b946-465c-ba5d-9325af28b28f"
/>
---------
Co-authored-by: easong-openai <easong@openai.com>
This can be the underlying logic in order to start a conversation from a
previous message. will need some love in the UI.
Base for building this: #2588
## Summary
- read the shell exec approval request's actual id instead of assuming
it is always 0
- use that id when validating and responding in the test
## Testing
- `cargo test -p codex-mcp-server
test_shell_command_approval_triggers_elicitation`
------
https://chatgpt.com/codex/tasks/task_i_68a6ab9c732c832c81522cbf11812be0
This PR adds a central `AuthManager` struct that manages the auth
information used across conversations and the MCP server. Prior to this,
each conversation and the MCP server got their own private snapshots of
the auth information, and changes to one (such as a logout or token
refresh) were not seen by others.
This is especially problematic when multiple instances of the CLI are
run. For example, consider the case where you start CLI 1 and log in to
ChatGPT account X and then start CLI 2 and log out and then log in to
ChatGPT account Y. The conversation in CLI 1 is still using account X,
but if you create a new conversation, it will suddenly (and
unexpectedly) switch to account Y.
With the `AuthManager`, auth information is read from disk at the time
the `ConversationManager` is constructed, and it is cached in memory.
All new conversations use this same auth information, as do any token
refreshes.
The `AuthManager` is also used by the MCP server's GetAuthStatus
command, which now returns the auth method currently used by the MCP
server.
This PR also includes an enhancement to the GetAuthStatus command. It
now accepts two new (optional) input parameters: `include_token` and
`refresh_token`. Callers can use this to request the in-use auth token
and can optionally request to refresh the token.
The PR also adds tests for the login and auth APIs that I recently added
to the MCP server.
This PR adds the following:
* A getAuthStatus method on the mcp server. This returns the auth method
currently in use (chatgpt or apikey) or none if the user is not
authenticated. It also returns the "preferred auth method" which
reflects the `preferred_auth_method` value in the config.
* A logout method on the mcp server. If called, it logs out the user and
deletes the `auth.json` file — the same behavior in the cli's `/logout`
command.
* An `authStatusChange` event notification that is sent when the auth
status changes due to successful login or logout operations.
* Logic to pass command-line config overrides to the mcp server at
startup time. This allows use cases like `codex mcp -c
preferred_auth_method=apikey`.
## Summary
Adds a `/mcp` command to list active tools. We can extend this command
to allow configuration of MCP tools, but for now a simple list command
will help debug if your config.toml and your tools are working as
expected.
Folds the top-level `shutdown()` function into a method of
`ShutdownHandle` and then simply stores `ShutdownHandle` on
`LoginServer` since the two fields it contains were always being used
together, anyway.
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with [ReviewStack](https://reviewstack.dev/openai/codex/pull/2396).
* #2399
* #2398
* __->__ #2396
* #2395
* #2394
* #2393
* #2389
https://github.com/openai/codex/pull/2373 introduced
`ServerOptions.login_timeout` and `spawn_timeout_watcher()` to use an
extra thread to manage the timeout for the login server. Now that we
have asyncified the login stack, we can use `tokio::time::timeout()`
from "outside" the login library to manage the timeout rather than
having to a commit to a specific "timeout" concept from within.
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with [ReviewStack](https://reviewstack.dev/openai/codex/pull/2395).
* #2399
* #2398
* #2396
* __->__ #2395
* #2394
* #2393
* #2389
