Replay pending client requests after `thread/resume` and emit resolved
notifications when those requests clear so approval/input UI state stays
in sync after reconnects and across subscribed clients.
Affected RPCs:
- `item/commandExecution/requestApproval`
- `item/fileChange/requestApproval`
- `item/tool/requestUserInput`
Motivation:
- Resumed clients need to see pending approval/input requests that were
already outstanding before the reconnect.
- Clients also need an explicit signal when a pending request resolves
or is cleared so stale UI can be removed on turn start, completion, or
interruption.
Implementation notes:
- Use pending client requests from `OutgoingMessageSender` in order to
replay them after `thread/resume` attaches the connection, using
original request ids.
- Emit `serverRequest/resolved` when pending requests are answered
or cleared by lifecycle cleanup.
- Update the app-server protocol schema, generated TypeScript bindings,
and README docs for the replay/resolution flow.
High-level test plan:
- Added automated coverage for replaying pending command execution and
file change approval requests on `thread/resume`.
- Added automated coverage for resolved notifications in command
approval, file change approval, request_user_input, turn start, and turn
interrupt flows.
- Verified schema/docs updates in the relevant protocol and app-server
tests.
Manual testing:
- Tested reconnect/resume with multiple connections.
- Confirmed state stayed in sync between connections.
Previously, clients would call `thread/start` with dynamic_tools set,
and when a model invokes a dynamic tool, it would just make the
server->client `item/tool/call` request and wait for the client's
response to complete the tool call. This works, but it doesn't have an
`item/started` or `item/completed` event.
Now we are doing this:
- [new] emit `item/started` with `DynamicToolCall` populated with the
call arguments
- send an `item/tool/call` server request
- [new] once the client responds, emit `item/completed` with
`DynamicToolCall` populated with the response.
Also, with `persistExtendedHistory: true`, dynamic tool calls are now
reconstructable in `thread/read` and `thread/resume` as
`ThreadItem::DynamicToolCall`.
Propagate client JSON-RPC errors for app-server request callbacks.
Previously a number of possible errors were collapsed to `channel
closed`. Now we should be able to see the underlying client error.
### Summary
This change stops masking client JSON-RPC error responses as generic
callback cancellation in app-server server->client request flows.
Previously, when the client responded with a JSON-RPC error, we removed
the callback entry but did not send anything to the waiting oneshot
receiver. Waiters then observed channel closure (for example, auth
refresh request canceled: channel closed), which hid the actual client
error.
Now, client JSON-RPC errors are forwarded through the callback channel
and handled explicitly by request consumers.
### User-visible behavior
- External auth refresh now surfaces real client JSON-RPC errors when
provided.
- True transport/callback-drop cases still report
canceled/channel-closed semantics.
### Example: client JSON-RPC error is now propagated (not masked as
"canceled")
When app-server asks the client to refresh ChatGPT auth tokens, it sends
a server->client JSON-RPC request like:
```json
{
"id": 42,
"method": "account/chatgptAuthTokens/refresh",
"params": {
"reason": "unauthorized",
"previousAccountId": "org-abc"
}
}
```
If the client cannot refresh and responds with a JSON-RPC error:
```
{
"id": 42,
"error": {
"code": -32000,
"message": "refresh failed",
"data": null
}
}
```
app-server now forwards that error through the callback path and
surfaces:
`auth refresh request failed: code=-32000 message=refresh failed`
Previously, this same case could be reported as:
`auth refresh request canceled: channel closed`
Took over the work that @aaronl-openai started here:
https://github.com/openai/codex/pull/10397
Now that app-server clients are able to set up custom tools (called
`dynamic_tools` in app-server), we should expose a way for clients to
pass in not just text, but also image outputs. This is something the
Responses API already supports for function call outputs, where you can
pass in either a string or an array of content outputs (text, image,
file):
https://platform.openai.com/docs/api-reference/responses/create#responses_create-input-input_item_list-item-function_tool_call_output-output-array-input_image
So let's just plumb it through in Codex (with the caveat that we only
support text and image for now). This is implemented end-to-end across
app-server v2 protocol types and core tool handling.
## Breaking API change
NOTE: This introduces a breaking change with dynamic tools, but I think
it's ok since this concept was only recently introduced
(https://github.com/openai/codex/pull/9539) and it's better to get the
API contract correct. I don't think there are any real consumers of this
yet (not even the Codex App).
Old shape:
`{ "output": "dynamic-ok", "success": true }`
New shape:
```
{
"contentItems": [
{ "type": "inputText", "text": "dynamic-ok" },
{ "type": "inputImage", "imageUrl": "data:image/png;base64,AAA" }
]
"success": true
}
```
## Summary
Add dynamic tool injection to thread startup in API v2, wire dynamic
tool calls through the app server to clients, and plumb responses back
into the model tool pipeline.
### Flow (high level)
- Thread start injects `dynamic_tools` into the model tool list for that
thread (validation is done here).
- When the model emits a tool call for one of those names, core raises a
`DynamicToolCallRequest` event.
- The app server forwards it to the client as `item/tool/call`, waits
for the client’s response, then submits a `DynamicToolResponse` back to
core.
- Core turns that into a `function_call_output` in the next model
request so the model can continue.
### What changed
- Added dynamic tool specs to v2 thread start params and protocol types;
introduced `item/tool/call` (request/response) for dynamic tool
execution.
- Core now registers dynamic tool specs at request time and routes those
calls via a new dynamic tool handler.
- App server validates tool names/schemas, forwards dynamic tool call
requests to clients, and publishes tool outputs back into the session.
- Integration tests
Ruslan Nigmatullin ·
Owen Lin ·
jif-oai ·