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codex/codex-rs/rollout-trace
cassirer-openai 89698ad1c3 [rollout-trace] Include x-request-id in rollout trace. (#20066)
## Why

Rollout traces need an identifier that can be used to correlate a Codex
inference with upstream Responses API, proxy, and engine logs. The
reduced trace model already exposed `upstream_request_id`, but it was
being populated from the Responses API `response.id`. That value is
useful for `previous_response_id` chaining, but it is not the transport
request id that upstream systems key on.

This PR separates those concepts so trace consumers can reliably answer
both questions:

- which Responses API response did this inference produce?
- which upstream request handled it?

## Structure

The change keeps the upstream request id at the same lifecycle level as
the provider stream:

- `codex-api` captures the `x-request-id` HTTP response header when the
SSE stream is created and exposes it on `ResponseStream`. Fixture and
websocket streams set the field to `None` because they do not have that
HTTP response header.
- `codex-core` carries that stream-level id into `InferenceTraceAttempt`
when recording terminal stream outcomes. Completed, failed, cancelled,
dropped-stream, and pre-response error paths all record the id when it
is available.
- `rollout-trace` now records both identifiers in raw terminal inference
events and response payloads: `response_id` for the Responses API
`response.id`, and `upstream_request_id` for `x-request-id`.
- The reducer stores both fields on `InferenceCall`. It also uses
`response_id` for `previous_response_id` conversation linking, which
removes the old accidental dependency on the misnamed
`upstream_request_id` field.
- Terminal inference reduction now consumes the full terminal payload
(`InferenceCompleted`, `InferenceFailed`, or `InferenceCancelled`) in
one place. That keeps status, partial payloads, response ids, and
upstream request ids consistent across success, failure, cancellation,
and late stream-mapper events.

## Why This Shape

`x-request-id` is a property of the HTTP/provider response envelope, not
an SSE event. Capturing it once in `codex-api` and plumbing it through
terminal trace recording avoids trying to infer the value from stream
contents, and it preserves the id even when the stream fails or is
cancelled after only partial output.

Keeping `response_id` separate from `upstream_request_id` also makes the
reduced trace model less surprising: `response_id` remains the
conversation-continuation id, while `upstream_request_id` is the
operational correlation id for upstream debugging.

## Validation

The PR updates trace and reducer coverage for:

- reading `x-request-id` from SSE response headers;
- storing the true upstream request id on completed inference calls;
- preserving upstream request ids for cancelled and late-cancelled
inference streams;
- keeping `previous_response_id` reconstruction tied to `response_id`
rather than transport request ids.
2026-04-28 21:11:17 +00:00
..

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.