Adding the ability to resume conversations.
we have one verb `resume`.
Behavior:
`tui`:
`codex resume`: opens session picker
`codex resume --last`: continue last message
`codex resume <session id>`: continue conversation with `session id`
`exec`:
`codex resume --last`: continue last conversation
`codex resume <session id>`: continue conversation with `session id`
Implementation:
- I added a function to find the path in `~/.codex/sessions/` with a
`UUID`. This is helpful in resuming with session id.
- Added the above mentioned flags
- Added lots of testing
There are exactly 4 types of flaky tests in Windows x86 right now:
1. `review_input_isolated_from_parent_history` => Times out waiting for
closing events
2. `review_does_not_emit_agent_message_on_structured_output` => Times
out waiting for closing events
3. `auto_compact_runs_after_token_limit_hit` => Times out waiting for
closing events
4. `auto_compact_runs_after_token_limit_hit` => Also has a problem where
auto compact should add a third request, but receives 4 requests.
1, 2, and 3 seem to be solved with increasing threads on windows runner
from 2 -> 4.
Don't know yet why # 4 is happening, but probably also because of
WireMock issues on windows causing races.
We need to construct the history different when compact happens. For
this, we need to just consider the history after compact and convert
compact to a response item.
This needs to change and use `build_compact_history` when this #3446 is
merged.
No (intended) functional change.
This refactors the transcript view to hold a list of HistoryCells
instead of a list of Lines. This simplifies and makes much of the logic
more robust, as well as laying the groundwork for future changes, e.g.
live-updating history cells in the transcript.
Similar to #2879 in goal. Fixes#2755.
## 📝 Review Mode -- Core
This PR introduces the Core implementation for Review mode:
- New op `Op::Review { prompt: String }:` spawns a child review task
with isolated context, a review‑specific system prompt, and a
`Config.review_model`.
- `EnteredReviewMode`: emitted when the child review session starts.
Every event from this point onwards reflects the review session.
- `ExitedReviewMode(Option<ReviewOutputEvent>)`: emitted when the review
finishes or is interrupted, with optional structured findings:
```json
{
"findings": [
{
"title": "<≤ 80 chars, imperative>",
"body": "<valid Markdown explaining *why* this is a problem; cite files/lines/functions>",
"confidence_score": <float 0.0-1.0>,
"priority": <int 0-3>,
"code_location": {
"absolute_file_path": "<file path>",
"line_range": {"start": <int>, "end": <int>}
}
}
],
"overall_correctness": "patch is correct" | "patch is incorrect",
"overall_explanation": "<1-3 sentence explanation justifying the overall_correctness verdict>",
"overall_confidence_score": <float 0.0-1.0>
}
```
## Questions
### Why separate out its own message history?
We want the review thread to match the training of our review models as
much as possible -- that means using a custom prompt, removing user
instructions, and starting a clean chat history.
We also want to make sure the review thread doesn't leak into the parent
thread.
### Why do this as a mode, vs. sub-agents?
1. We want review to be a synchronous task, so it's fine for now to do a
bespoke implementation.
2. We're still unclear about the final structure for sub-agents. We'd
prefer to land this quickly and then refactor into sub-agents without
rushing that implementation.
Azure Responses API doesn't work well with store:false and response
items.
If store = false and id is sent an error is thrown that ID is not found
If store = false and id is not sent an error is thrown that ID is
required
Add detection for Azure urls and add a workaround to preserve reasoning
item IDs and send store:true
## Compact feature:
1. Stops the model when the context window become too large
2. Add a user turn, asking for the model to summarize
3. Build a bridge that contains all the previous user message + the
summary. Rendered from a template
4. Start sampling again from a clean conversation with only that bridge
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.
## Unified PTY-Based Exec Tool
Note: this requires to have this flag in the config:
`use_experimental_unified_exec_tool=true`
- Adds a PTY-backed interactive exec feature (“unified_exec”) with
session reuse via
session_id, bounded output (128 KiB), and timeout clamping (≤ 60 s).
- Protocol: introduces ResponseItem::UnifiedExec { session_id,
arguments, timeout_ms }.
- Tools: exposes unified_exec as a function tool (Responses API);
excluded from Chat
Completions payload while still supported in tool lists.
- Path handling: resolves commands via PATH (or explicit paths), with
UTF‑8/newline‑aware
truncation (truncate_middle).
- Tests: cover command parsing, path resolution, session
persistence/cleanup, multi‑session
isolation, timeouts, and truncation behavior.
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.
## Session snapshot
For POSIX shell, the goal is to take a snapshot of the interactive shell
environment, store it in a session file located in `.codex/` and only
source this file for every command that is run.
As a result, if a snapshot files exist, `bash -lc <CALL>` get replaced
by `bash -c <CALL>`.
This also fixes the issue that `bash -lc` does not source `.bashrc`,
resulting in missing env variables and aliases in the codex session.
## POSIX unification
Unify `bash` and `zsh` shell into a POSIX shell. The rational is that
the tool will not use any `zsh` specific capabilities.
---------
Co-authored-by: Michael Bolin <mbolin@openai.com>
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 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.
In this test, the ChatGPT token path is used, and the auth layer tries
to refresh the token if it thinks the token is “old.” Your helper writes
a fixed last_refresh timestamp that has now aged past the 28‑day
threshold, so the code attempts a real refresh against auth.openai.com,
never reaches the mock, and you end up with
received_requests().await.unwrap() being empty.
The gpt-oss models require reasoning with subsequent Chat Completions
requests because otherwise the model forgets why the tools were called.
This change fixes that and also adds some additional missing
documentation around how to handle context windows in Ollama and how to
show the CoT if you desire to.
We have two ways of loading conversation with a previous history. Fork
conversation and the experimental resume that we had before. In this PR,
I am unifying their code path. The path is getting the history items and
recording them in a brand new conversation. This PR also constraint the
rollout recorder responsibilities to be only recording to the disk and
loading from the disk.
The PR also fixes a current bug when we have two forking in a row:
History 1:
<Environment Context>
UserMessage_1
UserMessage_2
UserMessage_3
**Fork with n = 1 (only remove one element)**
History 2:
<Environment Context>
UserMessage_1
UserMessage_2
<Environment Context>
**Fork with n = 1 (only remove one element)**
History 2:
<Environment Context>
UserMessage_1
UserMessage_2
**<Environment Context>**
This shouldn't happen but because we were appending the `<Environment
Context>` after each spawning and it's considered as _user message_.
Now, we don't add this message if restoring and old conversation.
- added `uninlined_format_args` to `[workspace.lints.clippy]` in the
`Cargo.toml` for the workspace
- ran `cargo clippy --tests --fix`
- ran `just fmt`
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.
We want to send an aggregated output of stderr and stdout so we don't
have to aggregate it stderr+stdout as we lose order sometimes.
---------
Co-authored-by: Gabriel Peal <gpeal@users.noreply.github.com>
## Summary
GPT-5 introduced the concept of [custom
tools](https://platform.openai.com/docs/guides/function-calling#custom-tools),
which allow the model to send a raw string result back, simplifying
json-escape issues. We are migrating gpt-5 to use this by default.
However, gpt-oss models do not support custom tools, only normal
functions. So we keep both tool definitions, and provide whichever one
the model family supports.
## Testing
- [x] Tested locally with various models
- [x] Unit tests pass
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.
## Summary
We've experienced a bit of drift in system prompting for `apply_patch`:
- As pointed out in #2030 , our prettier formatting started altering
prompt.md in a few ways
- We introduced a separate markdown file for apply_patch instructions in
#993, but currently duplicate them in the prompt.md file
- We added a first-class apply_patch tool in #2303, which has yet
another definition
This PR starts to consolidate our logic in a few ways:
- We now only use
`apply_patch_tool_instructions.md](https://github.com/openai/codex/compare/dh--apply-patch-tool-definition?expand=1#diff-d4fffee5f85cb1975d3f66143a379e6c329de40c83ed5bf03ffd3829df985bea)
for system instructions
- We no longer include apply_patch system instructions if the tool is
specified
I'm leaving the definition in openai_tools.rs as duplicated text for now
because we're going to be iterated on the first-class tool soon.
## Testing
- [x] Added integration tests to verify prompt stability
- [x] Tested locally with several different models (gpt-5, gpt-oss,
o4-mini)
## Summary
Before we land #2243, let's start printing environment_context in our
preferred format. This struct will evolve over time with new
information, xml gives us a balance of human readable without too much
parsing, llm readable, and extensible.
Also moves us over to an Option-based struct, so we can easily provide
diffs to the model.
## Testing
- [x] Updated tests to reflect new format
## What? Why? How?
- When running on Windows, codex often tries to invoke bash commands,
which commonly fail (unless WSL is installed)
- Fix: Detect if powershell is available and, if so, route commands to
it
- Also add a shell_name property to environmental context for codex to
default to powershell commands when running in that environment
## Testing
- Tested within WSL and powershell (e.g. get top 5 largest files within
a folder and validated that commands generated were powershell commands)
- Tested within Zsh
- Updated unit tests
---------
Co-authored-by: Eddy Escardo <eddy@openai.com>
Codex created this PR from the following prompt:
> upgrade this entire repo to Rust 1.89. Note that this requires
updating codex-rs/rust-toolchain.toml as well as the workflows in
.github/. Make sure that things are "clippy clean" as this change will
likely uncover new Clippy errors. `just fmt` and `cargo clippy --tests`
are sufficient to check for correctness
Note this modifies a lot of lines because it folds nested `if`
statements using `&&`.
---
[//]: # (BEGIN SAPLING FOOTER)
Stack created with [Sapling](https://sapling-scm.com). Best reviewed
with [ReviewStack](https://reviewstack.dev/openai/codex/pull/2465).
* #2467
* __->__ #2465
Motivation: we have users who uses their API key although they want to
use ChatGPT account. We want to give them the chance to always login
with their account.
This PR displays login options when the user is not signed in with
ChatGPT. Even if you have set an OpenAI API key as an environment
variable, you will still be prompted to log in with ChatGPT.
We’ve also added a new flag, `always_use_api_key_signing` false by
default, which ensures you are never asked to log in with ChatGPT and
always defaults to using your API key.
https://github.com/user-attachments/assets/b61ebfa9-3c5e-4ab7-bf94-395c23a0e0af
After ChatGPT sign in:
https://github.com/user-attachments/assets/d58b366b-c46a-428f-a22f-2ac230f991c0
## Summary
We've been seeing a number of issues and reports with our synthetic
`apply_patch` tool, e.g. #802. Let's make this a real tool - in my
anecdotal testing, it's critical for GPT-OSS models, but I'd like to
make it the standard across GPT-5 and codex models as well.
## Testing
- [x] Tested locally
- [x] Integration test
