Run Teardown

Context totals and metadata finalization region

Stage context: This region performs the stage's visible bookkeeping work on context. It runs after prior stage-5 translations would have finished any earlier teardown work, and it focuses on exporting accumulated run data plus making a final _dump_trajectory() call. This is the first translated entry in the stage, so there are no translated sibling cross-references yet.

What this code does

This region copies accumulated rollout and token-accounting data from self into context. It merges main-chat data from self._chat with subagent data from self._subagent_rollout_details and self._subagent_metrics, builds context.metadata from several counters, and optionally includes self._chat.messages when self._store_all_messages is true. It then calls _dump_trajectory(); the visible product of the region is updated context fields plus that method call.

Interface · params / IO

(self, context, ...unseen...) -> unknown in this region

• params: self: ? — owner of chat, subagent accumulators, counters, and _dump_trajectory(); context: ? — external result object that receives rollout details, token totals, cost, and metadata
• reads: self._chat.rollout_details, self._subagent_rollout_details, self._chat.total_input_tokens, self._subagent_metrics.total_prompt_tokens, self._chat.total_output_tokens, self._subagent_metrics.total_completion_tokens, self._chat.total_cache_tokens, self._subagent_metrics.total_cached_tokens, self._chat.total_cost, self._subagent_metrics.total_cost_usd, self._n_episodes, self._api_request_times, self._summarization_count, self._store_all_messages, self._chat.messages
• returns: No return is visible in this region; its product is mutation of context and a call to _dump_trajectory().
• effects: writes context.rollout_details; writes context.n_input_tokens; writes context.n_output_tokens; writes context.n_cache_tokens; writes context.cost_usd; writes context.metadata; conditionally writes context.metadata["all_messages"]; calls self._dump_trajectory()

Execution flow

1. Set context.rollout_details to the concatenation of self._chat.rollout_details and self._subagent_rollout_details.
2. Compute aggregate token totals by adding self._chat totals to the matching fields on self._subagent_metrics, then store them in context.n_input_tokens, context.n_output_tokens, and context.n_cache_tokens.
3. Add self._chat.total_cost and self._subagent_metrics.total_cost_usd, then store that value in context.cost_usd only when the sum is greater than zero; otherwise store None.
4. Build context.metadata with n_episodes from self._n_episodes, api_request_times_msec from self._api_request_times, and summarization_count from self._summarization_count.
5. If self._store_all_messages is true, extend context.metadata with an all_messages entry taken from self._chat.messages.
6. Call self._dump_trajectory() after the context assignments.

Source

            context.rollout_details = (
                self._chat.rollout_details + self._subagent_rollout_details
            )

            # Include subagent metrics in context totals
            context.n_input_tokens = (
                self._chat.total_input_tokens
                + self._subagent_metrics.total_prompt_tokens
            )
            context.n_output_tokens = (
                self._chat.total_output_tokens
                + self._subagent_metrics.total_completion_tokens
            )
            context.n_cache_tokens = (
                self._chat.total_cache_tokens
                + self._subagent_metrics.total_cached_tokens
            )
            total_cost = self._chat.total_cost + self._subagent_metrics.total_cost_usd
            context.cost_usd = total_cost if total_cost > 0 else None
            context.metadata = {
                "n_episodes": self._n_episodes,
                "api_request_times_msec": self._api_request_times,
                "summarization_count": self._summarization_count,
            }
            if self._store_all_messages:
                context.metadata["all_messages"] = self._chat.messages

            # Dump trajectory to JSON
            self._dump_trajectory()

Non-obvious design decisions

• The region keeps subagent accounting separate until export time: it reads main-chat totals from self._chat and subagent totals from self._subagent_metrics, then combines them only when filling context. That preserves the distinction in the internal accumulators while still publishing one set of totals.
• The cost field uses an explicit total_cost > 0 check and writes None otherwise. This chooses a sentinel for zero-or-negative totals instead of always exposing a numeric value.
• The metadata dictionary is created in one assignment and then conditionally extended with all_messages under if self._store_all_messages:. That keeps the optional payload isolated behind one flag instead of mixing it into the base metadata shape.

Relations

• Callers: Terminus2.run region containing this snippet
• Core callees: self._dump_trajectory()
• Config / state sources: self._store_all_messages gates whether full chat history is exported; self._chat supplies rollout details, token totals, cost, and optional messages; self._subagent_metrics supplies subagent token and cost totals; self._subagent_rollout_details supplies additional rollout records
• Results to: context.rollout_details; context.n_input_tokens; context.n_output_tokens; context.n_cache_tokens; context.cost_usd; context.metadata; self._dump_trajectory() side effect

Wrapper for trajectory dump continuation index

Stage context: This unit is a thin wrapper around self._dump_trajectory_with_continuation_index(...). In this body, its only visible role is to supply self._summarization_count as the argument instead of requiring a caller to provide that value.

What this code does

_dump_trajectory reads self._summarization_count and passes it to self._dump_trajectory_with_continuation_index(...). It returns None and does not directly write any self attributes or registers. Any serialization or I/O behavior belongs to the callee, not to this wrapper body.

Interface · params / IO

(self) -> None

• params: self: ? — Bound Terminus2 instance that provides _summarization_count and _dump_trajectory_with_continuation_index
• reads: self._summarization_count, self._dump_trajectory_with_continuation_index (method lookup and invocation)
• returns: Returns None; the visible product of this body is the delegated call to self._dump_trajectory_with_continuation_index(self._summarization_count).
• effects: Invokes self._dump_trajectory_with_continuation_index(self._summarization_count); Writes no self fields or registers directly in this body

Execution flow

1. Read self._summarization_count from the instance.
2. Call self._dump_trajectory_with_continuation_index(...) with that count as the sole argument.
3. Return None after the delegated call completes.

Source

    def _dump_trajectory(self) -> None:
        """Dump trajectory data to JSON file following ATIF format."""
        self._dump_trajectory_with_continuation_index(self._summarization_count)

Non-obvious design decisions

• The wrapper hardcodes self._summarization_count as the continuation index instead of accepting an argument. That visible choice fixes the argument source at this call site and keeps callers from supplying a different index through this method.

Relations

• Callers: Terminus2 methods that want the default continuation index wrapper
• Core callees: Terminus2._dump_trajectory_with_continuation_index
• Config / state sources: self._summarization_count
• Results to: The delegated call to self._dump_trajectory_with_continuation_index(...)
• Related siblings: Terminus2.run calls _dump_trajectory() in the translated sibling synopsis

Serialize trajectory segment to JSON log

Stage context: This helper is the file-writing end of trajectory dumping. The sibling Terminus2._dump_trajectory supplies the continuation_index; this function turns current in-memory state into a validated Trajectory, chooses the output filename, and writes JSON under self.logs_dir.

What this code does

_dump_trajectory_with_continuation_index builds a Trajectory record for the current run state and writes it to a JSON file. It requires a live _context; otherwise it logs a warning and returns without writing anything. The written file includes final token and cost totals, agent metadata, the current steps, and optional continuation linkage when _linear_history and the continuation_index rules apply. It returns None; its visible product is the file write plus debug or error logging.

Interface · params / IO

(self, continuation_index: int) -> None

• params: continuation_index: int — Index that controls continuation metadata and the output filename
• reads: self._context, self._context.n_input_tokens, self._context.n_output_tokens, self._context.n_cache_tokens, self._context.cost_usd, self._parser_name, self._temperature, self._llm_kwargs, self._linear_history, self._summarization_count, self._session_id, self._model_name, self._trajectory_steps, self.logs_dir, self.logger, self.name(), self.version()
• returns: None; the real product is a trajectory JSON file written via Trajectory(...).to_json_dict(), format_trajectory_json(...), and f.write(...), or a logged warning/error when dumping does not proceed
• effects: logs a warning with self.logger.warning(...) and returns early when self._context is missing; writes a JSON file under self.logs_dir; logs a debug message with the chosen path after a successful write; logs an error message if opening, serializing, formatting, or writing raises inside the try block

Execution flow

1. It first checks self._context. If that attribute is falsy, it logs "No context available, skipping trajectory dump" through self.logger.warning(...) and returns.
2. It builds final_metrics from _context token and cost fields. The code preserves prompt and completion totals directly, uses _context.n_cache_tokens or 0 for cached tokens, and converts falsy _context.cost_usd values to None.
3. It assembles agent_extra starting with {"parser": self._parser_name}. It adds "temperature" only when self._temperature is not None, adds "llm_kwargs" only when self._llm_kwargs is truthy, and adds "continuation_index" only when both self._linear_history and continuation_index > 0 hold.
4. It initializes continued_trajectory_ref = None, then may replace it with f"trajectory.cont-{continuation_index + 1}.json" when self._linear_history is true and continuation_index < self._summarization_count.
5. It constructs a Trajectory with session_id=self._session_id, an Agent built from self.name(), self.version() or "unknown", self._model_name, and agent_extra, plus steps=self._trajectory_steps, the final_metrics, and the computed continued_trajectory_ref.
6. Before entering the try/except, it chooses the output path: trajectory.cont-{continuation_index}.json for linear-history continuation files (self._linear_history and continuation_index > 0), otherwise trajectory.json.
7. Inside the try, it opens the selected path for writing, serializes the model with trajectory.to_json_dict(), formats that JSON with format_trajectory_json(...), writes the resulting string, and logs a debug message. Any exception from that block is caught and logged with self.logger.error(...).

Source

    def _dump_trajectory_with_continuation_index(self, continuation_index: int) -> None:
        """Dump trajectory data to JSON file with specified continuation index.

        Args:
            continuation_index: The continuation index to use for filename and metadata.
                               For the initial trajectory, use 0.
                               For the first continuation, use 1, etc.
        """
        if not self._context:
            self.logger.warning("No context available, skipping trajectory dump")
            return

        # Construct the trajectory using Pydantic models for validation
        final_metrics = FinalMetrics(
            total_prompt_tokens=self._context.n_input_tokens,
            total_completion_tokens=self._context.n_output_tokens,
            total_cached_tokens=self._context.n_cache_tokens or 0,
            total_cost_usd=self._context.cost_usd if self._context.cost_usd else None,
        )

        agent_extra: dict[str, Any] = {
            "parser": self._parser_name,
        }
        if self._temperature is not None:
            agent_extra["temperature"] = self._temperature
        if self._llm_kwargs:
            agent_extra["llm_kwargs"] = self._llm_kwargs
        if self._linear_history and continuation_index > 0:
            agent_extra["continuation_index"] = continuation_index

        # Determine if this trajectory will be continued
        # In linear_history mode, when saving during summarization (i.e., continuation_index < _summarization_count),
        # this trajectory will have a continuation
        continued_trajectory_ref = None
        if self._linear_history and continuation_index < self._summarization_count:
            # This trajectory segment will be continued in the next file
            next_continuation_index = continuation_index + 1
            continued_trajectory_ref = f"trajectory.cont-{next_continuation_index}.json"

        trajectory = Trajectory(
            session_id=self._session_id,
            agent=Agent(
                name=self.name(),
                version=self.version() or "unknown",
                model_name=self._model_name,
                extra=agent_extra,
            ),
            steps=self._trajectory_steps,
            final_metrics=final_metrics,
            continued_trajectory_ref=continued_trajectory_ref,
        )

        # Determine trajectory filename based on continuation index
        if self._linear_history and continuation_index > 0:
            trajectory_path = (
                self.logs_dir / f"trajectory.cont-{continuation_index}.json"
            )
        else:
            trajectory_path = self.logs_dir / "trajectory.json"

        try:
            with open(trajectory_path, "w") as f:
                json_str = format_trajectory_json(trajectory.to_json_dict())
                f.write(json_str)
            self.logger.debug(f"Trajectory dumped to {trajectory_path}")
        except Exception as e:
            self.logger.error(f"Failed to dump trajectory: {e}")

Non-obvious design decisions

• The early if not self._context guard avoids emitting a partial file with no trial metrics source. The function chooses to skip the dump and record a warning instead of inventing defaults.
• The metric normalization is intentionally uneven: _context.n_cache_tokens or 0 forces a numeric cached-token count, while _context.cost_usd if self._context.cost_usd else None turns any falsy cost into None. That preserves the distinction the code makes between token counts and an absent-or-falsy cost field.
• The agent_extra rules are precise and not all truthiness-based. self._temperature uses is not None, so 0 is preserved as a meaningful configured value, while self._llm_kwargs must be truthy before the function includes it.
• Continuation metadata is split across two branches on purpose. The file gets its own continuation_index only for continuation files (continuation_index > 0), while continued_trajectory_ref appears only when the current segment should point to a later one (continuation_index < self._summarization_count under _linear_history).
• The broad try/except Exception wraps the actual file write and serialization path and converts failures into self.logger.error(...). This keeps trajectory-dump failures from escaping this helper.

Relations

• Callers: Terminus2._dump_trajectory
• Core callees: FinalMetrics(...); Agent(...); Trajectory(...); trajectory.to_json_dict(); format_trajectory_json(...); open(...); self.logger.warning(...); self.logger.debug(...); self.logger.error(...); self.name(); self.version()
• Config / state sources: self._parser_name; self._temperature; self._llm_kwargs; self._linear_history; self._model_name; self.logs_dir
• Results to: JSON file at self.logs_dir / "trajectory.json"; JSON file at self.logs_dir / f"trajectory.cont-{continuation_index}.json"; warning/debug/error log output through self.logger
• Related siblings: Terminus2._dump_trajectory: passes self._summarization_count as the continuation_index argument

stop remote asciinema and fetch local cast

Stage context: This region is the recording-shutdown part of TmuxSession.stop. It runs only when self._remote_asciinema_recording_path is set, and it handles the final remote-to-local cast transfer when self._local_asciinema_recording_path is also set.

What this code does

This region attempts to stop an active remote asciinema recording and, when configured, downloads the finished cast file to a local path. It reads self._remote_asciinema_recording_path to decide whether to do any recording teardown, uses self.send_keys(...) and self.environment.download_file(...), and may create the local parent directory for self._local_asciinema_recording_path. It returns no value; its visible product is the attempted recorder shutdown plus an optional local cast file.

Interface · params / IO

(self) -> None

• params: self: TmuxSession — session object that holds recording paths, logger, and environment access
• reads: self._remote_asciinema_recording_path, self._logger, self._local_asciinema_recording_path, self.environment
• returns: None; the real product is sending C-d, waiting briefly, and optionally downloading the remote cast file to the local filesystem.
• effects: logs Stopping recording. with self._logger.debug(...); awaits self.send_keys(keys=["C-d"], min_timeout_sec=0.1); imports asyncio inside the branch and awaits asyncio.sleep(0.5); may create self._local_asciinema_recording_path.parent with mkdir(parents=True, exist_ok=True); may download a file with self.environment.download_file(source_path=str(self._remote_asciinema_recording_path), target_path=self._local_asciinema_recording_path)

Execution flow

1. Check self._remote_asciinema_recording_path; if it is falsy, this region does nothing.
2. Log Stopping recording. and attempt recorder shutdown by awaiting self.send_keys(keys=["C-d"], min_timeout_sec=0.1).
3. Import asyncio inside this branch and await asyncio.sleep(0.5) to pause before any file transfer.
4. If self._local_asciinema_recording_path is set, create its parent directory with parents=True, exist_ok=True.
5. Still in that local-path branch, call self.environment.download_file(...) with source_path=str(self._remote_asciinema_recording_path) and target_path=self._local_asciinema_recording_path.

Source

        if self._remote_asciinema_recording_path:
            self._logger.debug("Stopping recording.")
            await self.send_keys(
                keys=["C-d"],
                min_timeout_sec=0.1,
            )

            # Wait a moment for the recording to finish writing
            import asyncio

            await asyncio.sleep(0.5)

            if self._local_asciinema_recording_path:
                self._local_asciinema_recording_path.parent.mkdir(
                    parents=True, exist_ok=True
                )
                # Ensure recording exists locally before merging markers
                await self.environment.download_file(
                    source_path=str(self._remote_asciinema_recording_path),
                    target_path=self._local_asciinema_recording_path,
                )

Non-obvious design decisions

• The code waits 0.5 seconds after sending C-d because the inline comment says it wants to give the recording time to finish writing before download.
• It creates the local parent directory only when self._local_asciinema_recording_path exists, so local filesystem setup stays tied to the download path branch.
• It converts self._remote_asciinema_recording_path with str(...) at the download_file call site, which matches the callee's source_path argument shape used here without changing how the path is stored on self.

Relations

• Callers: TmuxSession.stop
• Core callees: self.send_keys; asyncio.sleep; self.environment.download_file; Path.mkdir on self._local_asciinema_recording_path.parent
• Config / state sources: self._remote_asciinema_recording_path gates all work; self._local_asciinema_recording_path gates directory creation and download; self.environment supplies file transfer; self._logger supplies debug logging
• Results to: remote recording process via send_keys(keys=["C-d"], min_timeout_sec=0.1); local filesystem under self._local_asciinema_recording_path.parent; local cast file at self._local_asciinema_recording_path when download runs; debug log output