codex/codex-rs/rollout-trace

Rollout Trace

Privacy: Rollout tracing does not collect, upload, or report user data; it only writes local bundles when CODEX_ROLLOUT_TRACE_ROOT is set.

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, 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

    Recorder["RolloutTraceRecorder\nthin best-effort 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 --> Recorder
    Inference --> Recorder
    Tools --> Recorder
    CodeMode --> Recorder
    Terminal --> Recorder
    Agents --> Recorder

    Recorder --> Writer
    Writer --> Manifest
    Writer --> Payloads
    Writer --> Events

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

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

The recorder is deliberately small. It is enabled by CODEX_ROLLOUT_TRACE_ROOT and must never make a Codex session fail just because tracing failed. Core emits raw observations; this crate owns the bundle schema, writer API, 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.

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.