Files
codex/codex-rs/rollout-trace
Channing Conger ef500f4d23 Move code mode behind an IPC host
Split the code-mode protocol and client from the V8-backed runtime so core and
app-server no longer link codex-code-mode in production. ThreadManager now
provisions durable code-mode sessions through a shared external host process,
while tests can still inject the in-process provider.

The IPC protocol uses a persistent stdin/stdout transport. Each frame is a
4-byte big-endian length followed by JSON, with a 16 MiB frame limit. Client
requests carry u64 request IDs so create, execute, wait, terminate, and shutdown
operations can be multiplexed over one process. Session IDs isolate durable
stored values. Execute returns an ExecutionStarted response immediately and an
asynchronous InitialResponse when the initial yield or completion is available.

Nested tool calls and notifications travel from the host back to the client as
delegate requests with their own IDs. Delegate responses, cancellation, and
cell-closed lifecycle messages use the same framed channel. Wire operations
encode errors as Result values. A dead connection fails pending operations,
cancels outstanding delegates, and lets the provider spawn a new host for later
sessions.

Build codex-code-mode-host with V8 pointer-compression sandbox support and add
it to canonical primary and app-server packages, legacy Linux and Windows
bundles, signing verification, installers, Python runtime packages, and release
CI for macOS, Linux, and Windows. The host is discovered next to the current
executable, through CODEX_CODE_MODE_HOST_PATH, or on PATH. OS-level seccomp or
seatbelt restrictions remain a follow-up to this cross-platform process split.

Benchmarks were run from release builds on Linux x86_64 with the V8 sandbox
profile and a text('ok') workload. Cold measurements used 30 samples, warm
session provisioning used 200, and warm command execution used 500. Values are
mean/p50/p95 in milliseconds:

- session startup: in-process 0.002/0.002/0.005, IPC 2.623/2.599/2.894
- fresh-session command: in-process 1.831/1.758/1.915, IPC 7.428/7.252/8.306
- warm session provisioning: in-process 0.002/0.002/0.003,
  IPC 0.471/0.463/0.581
- warm command: in-process 1.759/1.757/1.940, IPC 2.005/2.001/2.166

The steady-state median command overhead is approximately 0.244 ms. The median
fresh host plus first command cost is 7.252 ms.

Validation:

- 62/62 core code-mode integration tests passed against the external host
- focused protocol, client, runtime, host, tools, and trace tests passed
- Cargo and Bazel real-process host IPC tests passed
- 11/11 package builder tests passed
- Bazel lock verification, scoped Clippy fixes, and repository formatting passed
2026-06-10 14:38:41 -07:00
..
2026-06-10 14:38:41 -07:00
2026-06-10 14:38:41 -07:00

Rollout Trace

Privacy: Rollout tracing is not telemetry. Codex does not upload or report these traces; it writes local bundles only when CODEX_ROLLOUT_TRACE_ROOT is set. Those local bundles can contain prompts, responses, tool inputs/outputs, terminal output, and paths, so treat them as sensitive.

Rollout tracing is an opt-in diagnostic path for understanding what happened during a Codex session. It records raw runtime evidence into a local bundle on disk, then replays that bundle into a semantic graph that a debugger or UI can inspect.

The key design choice is: observe first, interpret later.

Hot-path Codex code does not try to build the final graph while the session is running. It writes ordered raw events and payload references. The offline reducer then decides which events became model-visible conversation, which events were runtime work, and how information moved between threads, tools, code cells, and terminal sessions.

What This Gives Us

Rollout traces make failures debuggable when the normal transcript is not enough. They preserve enough evidence to answer questions like:

  • Which model request produced this tool call?
  • Did this output come from the model-visible transcript, a code-mode runtime value, a terminal operation, or an agent notification?
  • Which code-mode exec cell issued a nested tool call?
  • Which terminal operation created or reused a running process?
  • Which multi-agent v2 tool call spawned, messaged, received from, or closed a child thread?

The reduced state.json is intentionally not just a transcript. It is a graph of model-visible conversation plus the runtime objects that explain how Codex got there.

System Shape

flowchart TD
    subgraph Runtime["codex-core runtime"]
        Protocol["protocol lifecycle\nthread start/end, turn start/end"]
        Inference["inference + compaction\nrequests, responses, checkpoints"]
        Tools["tool dispatch\ndirect model tools + code-mode nested tools"]
        CodeMode["code-mode runtime\nexec cells, yields, waits, termination"]
        Terminal["terminal runtime\nexec_command / write_stdin operations"]
        Agents["multi_agent_v2\nspawn, task delivery, result, close"]
    end

    Context["ThreadTraceContext\nroot/child no-op-capable producer"]
    Writer["TraceWriter\nassigns seq and writes payloads before events"]

    subgraph Bundle["trace bundle"]
        Manifest["manifest.json\ntrace_id, rollout_id, root_thread_id"]
        Events["trace.jsonl\nordered raw event spine"]
        Payloads["payloads/*.json\nlarge raw evidence"]
    end

    Reducer["replay_bundle\ndeterministic offline reducer"]

    subgraph State["state.json"]
        Threads["threads + turns"]
        Conversation["conversation_items\nwhat the model saw"]
        RuntimeObjects["inference_calls, tool_calls,\ncode_cells, terminals, compactions"]
        Edges["interaction_edges\nspawn, task, result, close"]
        RawRefs["raw_payload refs"]
    end

    Protocol --> Context
    Inference --> Context
    Tools --> Context
    CodeMode --> Context
    Terminal --> Context
    Agents --> Context

    Context --> Writer
    Writer --> Manifest
    Writer --> Payloads
    Writer --> Events

    Manifest --> Reducer
    Events --> Reducer
    Payloads --> Reducer

    Reducer --> Threads
    Reducer --> Conversation
    Reducer --> RuntimeObjects
    Reducer --> Edges
    Reducer --> RawRefs

The thread context is deliberately small and no-op capable. A root session starts one from CODEX_ROLLOUT_TRACE_ROOT; fresh spawned child threads derive their own context from the parent's context so the whole rollout tree shares one writer. Disabled contexts accept the same calls and record nothing.

Trace startup and writes are best-effort. Rollout tracing must never make a Codex session fail just because diagnostic recording failed. Core emits raw observations; this crate owns the bundle schema, trace-context APIs, writer, and reducer.

Bundle Layout

A trace bundle contains:

  • manifest.json: trace identity and bundle metadata.
  • trace.jsonl: append-only raw events ordered by writer-assigned seq.
  • payloads/*.json: raw requests, responses, tool inputs/results, runtime events, terminal output, compaction data, and protocol snapshots.
  • state.json: optional reducer output written by codex debug trace-reduce.

trace_id identifies this diagnostic artifact. rollout_id identifies the Codex rollout/session being observed. Keeping those separate lets us reason about the stored trace without confusing it with the product-level session identity.

To reduce a bundle:

codex debug trace-reduce <trace-bundle>

By default this writes <trace-bundle>/state.json. Rust callers can also call codex_rollout_trace::replay_bundle directly.

Raw Evidence vs Reduced Graph

flowchart LR
    Model["model-visible payloads\nrequests and response output items"]
    Runtime["runtime observations\ntool dispatch, terminal output, code-mode JSON"]
    RawPayloads["payloads/*.json\nexact evidence"]
    Reducer["reducer"]
    Conversation["ConversationItem\nwhat the model saw"]
    ToolCall["ToolCall\nruntime tool boundary"]
    CodeCell["CodeCell\nmodel-authored exec cell"]
    TerminalOperation["TerminalOperation\ncommand/write/poll"]
    InteractionEdge["InteractionEdge\ninformation flow"]

    Model --> RawPayloads
    Runtime --> RawPayloads
    RawPayloads --> Reducer

    Reducer --> Conversation
    Reducer --> ToolCall
    Reducer --> CodeCell
    Reducer --> TerminalOperation
    Reducer --> InteractionEdge

    CodeCell --> ToolCall
    ToolCall --> TerminalOperation
    ToolCall --> InteractionEdge
    Conversation --> InteractionEdge

This distinction is the reason the model has both raw payload references and semantic objects. A code-mode nested tool call, for example, has JSON input and output at the JavaScript runtime boundary, but the model-visible transcript only contains the surrounding exec custom tool call and its eventual output.

The reducer keeps those facts separate:

  • ConversationItem records what appeared in model-facing requests/responses.
  • ToolCall, CodeCell, TerminalOperation, InferenceCall, and Compaction record runtime/debug boundaries.
  • InteractionEdge records information flow between objects, such as a spawn_agent tool call delivering a task into a child thread.
  • RawPayloadRef points back to exact evidence when a viewer needs more detail than the reduced graph stores inline.

Multi-Agent v2

Multi-agent v2 child threads share the root trace writer. That means one root bundle reduces into one graph containing the parent thread, child threads, and the edges between them.

flowchart LR
    RootTool["root ToolCall\nspawn_agent / followup_task / send_message"]
    ChildInput["child ConversationItem\ninjected task/message"]
    ChildThread["child AgentThread"]
    ChildResult["child assistant ConversationItem\nresult message"]
    RootNotice["root ConversationItem\nsubagent notification"]
    CloseTool["root ToolCall\nclose_agent"]
    TargetThread["target AgentThread"]

    RootTool -- "spawn/task edge" --> ChildInput
    ChildInput --> ChildThread
    ChildThread --> ChildResult
    ChildResult -- "agent_result edge" --> RootNotice
    CloseTool -- "close_agent edge" --> TargetThread

Top-level independent threads still get independent bundles. Spawned child threads are different: they are part of the same rollout tree, so they belong in the same raw event log, payload directory, and reduced state.json.

Reducer Invariants

The reducer is strict where the raw evidence should be self-consistent:

  • raw events are replayed in seq order;
  • payload files must exist before events refer to them;
  • reduced object IDs are stable within one replay;
  • runtime events may be queued until the model-visible source or delivery target has been observed;
  • model-visible conversation is derived from model-facing payloads, not from runtime convenience output;
  • runtime payloads are evidence, not proof that the model saw the same bytes.

Those invariants let the reduced graph stay small while preserving a path back to the original evidence whenever a debugger needs to explain why an object or edge exists.