Files
codex/codex-rs/rollout-trace
Celia Chen 172ab264bd fix: retry rejected previous-model compaction with selected model (#30319)
## Why

Pre-sampling compaction intentionally uses the previous turn's model
when the compaction compatibility hash changes or when switching to a
model with a smaller context window. This keeps compaction aligned with
the settings that produced the history, but it can block the next turn
when a resumed ChatGPT thread still references a model slug that has
since been retired. The Codex backend rejects that compaction request
before the user's currently selected model gets a chance to sample.

This PR lets those threads recover without changing previous-model
compaction behavior for API-key authentication or custom providers. It
is stacked on #31316, which is a behavior-preserving extraction of the
individual remote compaction attempts; this PR contains the fallback
behavior.

## What changed

- For automatic previous-model compaction, capture the selected model's
request context when using ChatGPT authentication with the OpenAI
provider and the selected model differs from the previous model.
- If the previous-model attempt returns an `InvalidRequest`, retry
compaction once with the selected model for both `/responses/compact`
and Responses Compaction V2.
- Complete history processing, lifecycle events, and token accounting
with the context of the model that successfully compacted the thread.
- If the fallback also fails, return the original previous-model error
so the retry does not change the user-visible failure.
- Record fallback attempts with reason, implementation, and outcome
telemetry.
- Leave API-key authentication, custom providers, same-model turns, and
non-`InvalidRequest` failures on their existing paths.

## Testing

- `just test -p codex-core -E 'test(pre_sampling_compact) |
test(model_unavailable_error)'` (10 tests)
- Added integration coverage for a resumed thread whose model was
renamed, a model downshift using Responses Compaction V2, and API-key
authentication with a custom provider.
2026-07-07 14:26:03 -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.