mirror of
https://github.com/pchuan98/codex.git
synced 2026-07-01 00:31:56 +08:00
Owen Lin
3516cb9751
## Why
Large MCP tool call outputs can make rollout JSONL files enormous. In
the session that motivated this change, the biggest JSONL records were:
- `event_msg/mcp_tool_call_end`
- `response_item/function_call_output`
both containing the same unbounded MCP payloads - just 3 MCP tool calls
that each were multi-hundred MBs 😱
This PR truncates both of those JSONL records.
## How
#### For `response_item/function_call_output`
Unified exec already bounds tool output before it is injected into
model-facing history, which also keeps the corresponding rollout
`response_item/function_call_output` records small.
MCP should follow the same pattern: truncate the model-facing tool
output at the tool-output boundary, while leaving code-mode/raw hook
consumers alone.
#### For `event_msg/mcp_tool_call_end`
`McpToolCallEnd` also needs its own bounded event copy because it is the
app-server/replay/UI event shape that backs `ThreadItem::McpToolCall`.
Unfortunately this is _not_ downstream of the `ToolOutput` trait.
## Model behavior
Model behavior is actually unchanged as a result of this PR.
Before this PR, MCP output was:
1. Converted to `FunctionCallOutput`.
2. Recorded into in-memory history.
3. Truncated by `ContextManager::record_items()` before later model
turns saw it.
After this branch, MCP output is truncated earlier, in
`McpToolOutput::response_payload()`, using the same helper. Then
`ContextManager::record_items()` sees an already-truncated output and
effectively has little/no additional work to do.
So the model should still see the same kind of truncated function-call
output. The practical difference is where truncation happens: earlier,
before rollout persistence/app-server emission can see the giant
payload.
## Verification
- `cargo test -p codex-core mcp_tool_output`
- `cargo test -p codex-core
mcp_tool_call::tests::truncate_mcp_tool_result_for_event`
- `cargo test -p codex-core
mcp_post_tool_use_payload_uses_model_tool_name_args_and_result`
- `just fmt`
- `just fix -p codex-core`
- `git diff --check`
3516cb9751
·
2026-04-30 16:30:43 +00:00
6,029 Commits
npm i -g @openai/codex
or brew install --cask codex
Codex CLI is a coding agent from OpenAI that runs locally on your computer.
If you want Codex in your code editor (VS Code, Cursor, Windsurf), install in your IDE.
If you want the desktop app experience, run
codex app or visit the Codex App page.
If you are looking for the cloud-based agent from OpenAI, Codex Web, go to chatgpt.com/codex.
Quickstart
Installing and running Codex CLI
Install globally with your preferred package manager:
# Install using npm
npm install -g @openai/codex
# Install using Homebrew
brew install --cask codex
Then simply run codex to get started.
You can also go to the latest GitHub Release and download the appropriate binary for your platform.
Each GitHub Release contains many executables, but in practice, you likely want one of these:
- macOS
- Apple Silicon/arm64:
codex-aarch64-apple-darwin.tar.gz - x86_64 (older Mac hardware):
codex-x86_64-apple-darwin.tar.gz
- Apple Silicon/arm64:
- Linux
- x86_64:
codex-x86_64-unknown-linux-musl.tar.gz - arm64:
codex-aarch64-unknown-linux-musl.tar.gz
- x86_64:
Each archive contains a single entry with the platform baked into the name (e.g., codex-x86_64-unknown-linux-musl), so you likely want to rename it to codex after extracting it.
Using Codex with your ChatGPT plan
Run codex and select Sign in with ChatGPT. We recommend signing into your ChatGPT account to use Codex as part of your Plus, Pro, Business, Edu, or Enterprise plan. Learn more about what's included in your ChatGPT plan.
You can also use Codex with an API key, but this requires additional setup.
Docs
This repository is licensed under the Apache-2.0 License.