Goal
Produce a complete, reproducible record of a CodeAct reasoning session that captures every Python script executed, every interpreter response (stdout, stderr, exit code), every self-debug iteration with its root cause and fix, and every mapping from code output to strategic conclusion. This trace enables any engineerΓÇöwithout access to the original LLM sessionΓÇöto re-run all scripts in a fresh Python environment and obtain identical results, closing the reproducibility gap that plagues LLM-generated analysis.
The trace also serves as a quality signal: if a strategy conclusion cannot be traced to executable code that a human can re-run and verify, that conclusion is flagged as heuristic and treated with appropriate skepticism. By distinguishing computationally verified claims from model-generated prose, the trace gives strategy consumers a clear picture of what has been proven versus what has been suggested.
Implementation Details
The CodeAct framework, introduced by Wang et al. (2024) at ICML in "Executable Code Actions Elicit Better LLM Agents," establishes Python code as a unified action space for LLM agentsΓÇöreplacing JSON tool-call schemas, text-based action descriptions, and domain-specific DSLs with a single, general-purpose executable medium. The framework was evaluated across 17 large language models spanning open-source (Llama-2, Mistral, CodeLlama) and proprietary (GPT-3.5, GPT-4, Claude) families, with consistent findings: the code action space achieved up to 20% higher task success rates than text-based and JSON-based alternatives on complex agent benchmarks including ALFWorld, ScienceWorld, and WebArena. The performance gap widened as task complexity increased, suggesting that code's expressiveness advantage compounds when operations involve multi-step sequencing, conditional branching, mathematical computation, and error handlingΓÇöprecisely the characteristics of sophisticated Vidbyte strategy execution.
The CodeActInstruct dataset, comprising 7,000 multi-turn interaction trajectories, was constructed by prompting a teacher LLM (GPT-4) to generate diverse agent tasks and collecting the code-action sequences that successfully completed them. Each trajectory includes: the task specification, the model's Python code response, the interpreter's output (success or traceback), and the model's revision in response to errors. This dataset was used to fine-tune CodeActAgent variants from Llama-2 (7B and 13B) and Mistral (7B) base models, producing agents that could autonomously compose operationsΓÇöchaining library calls, handling edge cases with try-except blocks, and self-debugging based on interpreter feedback. The fine-tuned models demonstrated strong generalization to unseen tasks, suggesting that the code-as-action paradigm captures transferable reasoning patterns that text-based action spaces do not.
The Python interpreter loop is the framework's core execution mechanism. At each turn, the model emits a Python code block. The interpreter executes it in an isolated namespace with access only to the Python standard library (no pip-installed packages unless explicitly pre-authorized), captures stdout and stderr separately, records the exit code, and returns the complete execution transcript to the model. If execution fails (non-zero exit code or unhandled exception), the model receives the full traceback and generates a revised code block in the next turn. This loop continues until execution succeeds or a maximum iteration count is reached. The isolation constraintΓÇöstandard library onlyΓÇöis deliberate: it ensures reproducibility across environments and prevents the model from depending on obscure or unavailable packages whose behavior may vary across versions. For Vidbyte strategy execution, math, statistics, json, datetime, csv, re, collections, itertools, functools, typing, and dataclasses cover the vast majority of computational needs without external dependencies.
The control flow for reasoning structure is a critical design consideration. Rather than generating a single monolithic script that attempts to compute everything at once, CodeAct agents decompose complex tasks into sequences of smaller, independently executable code blocks. Each block accepts defined inputs (from previous blocks or user-provided parameters), performs a single well-scoped computation, and produces structured output that feeds into subsequent blocks. This modularity serves three purposes: it makes errors easier to isolate and debug (a traceback in block 3 does not require re-executing blocks 1 and 2), it enables partial reuse (the retention-curve block from one strategy can be imported into another), and it produces a natural trace structure where each code block corresponds to a reasoning step in the strategy algorithm. The trace maps each block to its position in the eight-step CodeAct reasoning algorithm, creating a bidirectional reference between the strategic logic and its computational implementation.
The CodeActAgent variants derived from Llama-2 and Mistral demonstrated particular strengths that inform this trace system's design. The Llama-2-based agents showed stronger mathematical reasoning, making them better suited for the quantitative domains (Retention Optimization, Monetization Strategy, Audience Growth). The Mistral-based agents showed stronger instruction-following and structural reasoning, making them better suited for the qualitative validation domains (Script Structure, Platform-Specific Formatting, Brand Narrative). While the trace system itself is model-agnosticΓÇöany LLM that can emit Python code can generate a traceΓÇöthe trace records which model produced each script, enabling downstream analysis of model-specific failure patterns and informing model selection for future strategy executions.
Intent
We run this trace because code-based reasoning is epistemically superior to prose-based reasoning in domains where conclusions depend on computation—the code either executes correctly and produces the claimed output, or it does not. Unlike natural-language reasoning chains, which can contain hidden leaps, unstated assumptions, and internally consistent but factually wrong arguments, Python scripts in an isolated interpreter produce deterministic, reproducible results that any engineer can independently verify. When a strategy conclusion is backed by executable code that a human can re-run and audit, the conclusion carries the weight of computational proof rather than rhetorical persuasion. The trace makes this distinction explicit: every computational claim is paired with its executable evidence, and every claim without such evidence is flagged as heuristic, preventing the strategy consumer from mistaking model-generated prose for verified analysis.
The trace also serves as a quality signal for the CodeAct reasoning process itself. By recording every failed execution alongside its root cause diagnosis and fix, the trace reveals the self-debugging competency of the model—how many iterations were needed, what types of errors occurred, and whether the fixes addressed root causes or merely symptoms. A session where the model required five attempts to get a retention-curve calculation right communicates different reliability than a session where every script executed correctly on the first attempt. For continuous improvement, this data feeds back into model selection, prompt optimization, and architectural decisions about which strategy domains genuinely benefit from CodeAct's computational approach versus those where prose reasoning is sufficient.
Algorithm for the Output Document
Restate the strategy question, constraints, evidence standard, and domain classification. Identify which elements of the strategy require computational support—quantitative analysis, simulation, statistical testing, data transformation—and which elements are qualitative judgments that will be flagged as heuristic. Produce a computational plan that maps each quantitative question to a sequence of independently executable Python scripts.
Set up the isolated execution environment. Record environment metadata: Python version, operating system, and standard library module versions. Initialize a clean namespace with access only to the Python standard library. Document any pre-authorized external dependencies with exact version numbers and justification. Create a directory structure for the trace output that will contain all scripts, execution transcripts, and the formatted trace document.
For each computational step in the plan, construct a self-contained Python script. Each script must import only standard library modules, define its inputs explicitly as function parameters or top-level variables, perform a single well-scoped computation, and produce structured output printed to stdout in a parseable format. Record the script in full in the trace with a step identifier that maps to the corresponding step in the CodeAct reasoning algorithm. No truncation, no summarization, no omission for brevity.
Execute each script in the isolated interpreter. Capture stdout, stderr, and exit code verbatim. Record the wall-clock execution duration in milliseconds. If the script writes to any filesystem location beyond the designated trace output directory, record the write operation and its justification. If the script makes any network call, record the endpoint, request parameters, response status, and justification.
If execution fails with a non-zero exit code or unhandled exception, initiate the self-debug loop. Record the full traceback or error message. Diagnose the root cause: is it a syntax error, a logical error, a missing import, a type mismatch, an unhandled edge case, or an environmental issue? Generate a revised script that addresses the root cause. Execute the revised script. Repeat until execution succeeds or a maximum iteration count (default 5) is reached. Record every iteration as a distinct entry showing the failing code, the error, the root cause diagnosis, and the revised code.
After all scripts have executed successfully, map each code output to the specific strategic conclusion it supports. For every computational output, identify the line range in the final strategy document where that output is cited as evidence, and record the bidirectional mapping in the trace. For every strategic conclusion that lacks supporting code output, flag it with a heuristic marker and note the reasoning type that was used instead—qualitative judgment, precedent-based reasoning, analogical reasoning, or model-generated prose.
Compose the revision log: a chronological summary of every self-debug iteration across the entire session. For each fix, record what failed (the script identifier and the error), why it failed (the root cause diagnosis), what was changed (a description of the revision), and whether the fix resolved the issue (confirmed by successful re-execution). The revision log enables downstream analysis of failure patterns—which error types are most common, which strategy domains trigger the most self-debug iterations, and which model families exhibit which error profiles.
Assemble the complete execution trace document. Structure it as valid markdown or JSON-LD with sections for Environment Metadata, Computational Plan, Script Executions (each with script code, execution transcript, and conclusion mapping), Revision Log, and Support Summary (ratio of computationally verified claims to heuristic claims). The trace must be self-contained: copying all scripts into a fresh directory and executing them in order must reproduce all results without modification.
Output Information
Write the execution trace as structured markdown in root memory/{question_name}.md before responding to the user. Include this scale note near the top of the file: "Scale: the trace expands or contracts with the session's actual computational demands—a session that executed 3 scripts receives a shorter trace than one that executed 15 scripts with 8 self-debug iterations. Every script execution, whether successful or failed, receives a full entry with code, transcript, and mapping." Use numbered entries for each script execution and each self-debug iteration so a reviewer can reference specific entries by number. Keep the trace public, inspectable, and complete—no code is omitted, no execution output is summarized, and no mapping is glossed over. After writing the file, respond with the path, the support summary (ratio of code-backed to heuristic claims), the number of self-debug iterations, and a brief statement of overall reproducibility confidence.
Things Not to Do
- Do not truncate or summarize Python scripts in the trace. A script excerpt or abbreviated listing destroys reproducibility—the reader must be able to copy the exact code and execute it. Record every script in full, including imports and any whitespace that affects execution.
- Do not omit failed executions from the trace. Failed executions are as informative as successful ones—they reveal what the model got wrong, how it diagnosed the error, and how it corrected the approach. A trace that only shows successful executions hides the self-debugging process and inflates the apparent reliability of the session.
- Do not confuse prose interpretation with computational output. When the model writes "the retention curve shows a 12% drop" but the Python output shows an 11.7% drop with a standard deviation of 3.2%, the trace must present both and flag the discrepancy. The trace's value comes from distinguishing what was computed from what was narrated.
- Do not allow scripts to produce side effects—filesystem writes, network calls, subprocess creation—beyond the designated trace output directory without explicit annotation and user permission. If a side effect is necessary for the computation, record it in the trace with full details and justification.
- Do not leave the conclusion-to-code mapping ambiguous. Every strategic conclusion that claims computational support must cite the specific script identifier and line range in the code output that produced the supporting numbers. Conclusions without this mapping must be explicitly flagged as heuristic.
- Do not omit environment metadata. A trace is only reproducible if the reader knows what Python version, operating system, and standard library versions were used. A trace that runs on Python 3.11 but produces different results on Python 3.12 due to a standard library change is a trace with a latent reproducibility bug.
- Do not treat the maximum self-debug iteration count as a hard limit that silently ends the trace. If the model reaches the maximum (default 5) without resolving the error, record all iterations and flag the unresolved error explicitly. The trace consumer must know which computations failed to converge and why.
- Do not present the trace as a substitute for human review. The trace is an audit tool—it enables review but does not perform it. The trace producer's responsibility is to make the trace complete, accurate, and self-contained; the consumer's responsibility is to verify.
Success Criteria
- Every Python script executed during the session is recorded in full in the trace, with no truncation or summarization of code content.
- The stdout, stderr, and exit code of every script execution are captured verbatim, including ANSI escape sequences if present.
- The trace is self-contained: copying all scripts from the trace into a fresh directory and running them in order reproduces all results without modification.
- Every self-debug iteration is recorded as a distinct entry showing the original failing code, the traceback or error message, the root cause diagnosis, and the revised code.
- Each strategic conclusion in the final output is mapped to the specific script and line range that produced the supporting computation (or marked as heuristic with no code mapping).
- The trace includes environment metadata: Python version, operating system, and stdlib module versions to aid reproducibility across environments.
- All scripts use only the Python standard library; any external dependency is listed with its exact version and a justification for its necessity.
- No script in the trace produces side effects (filesystem writes, network calls) beyond the designated trace output directory without explicit annotation and user permission.
- The trace differentiates between script output (computationally derived) and prose interpretation (model-generated) so consumers can distinguish verified results from narrative framing.
- The trace includes a revision log summarizing every self-debug fix: what failed, why it failed, what was changed, and whether the fix resolved the issue.
- Execution timing (wall-clock duration per script) is recorded to identify computational bottlenecks in the strategy pipeline.
- The trace is valid JSON-LD or structured markdown that can be ingested by automated validation pipelines without manual parsing.
Input
The output of a codeact-reasoning session: the full set of Python scripts executed, the interpreter transcripts for each execution (including failed attempts and their tracebacks), the strategy domain classification and parameters, and the final strategy conclusions. The trace generator consumes this structured output and produces the formatted execution record. No additional user input is required beyond the raw session output, though optional metadata (model identifier, session ID, user-provided tags) can be supplied for trace indexing.
1---2name: codeact-trace3description: Standalone execution trace that records every Python script, interpreter output, self-debug iteration, and code-to-conclusion mapping across an entire CodeAct reasoning session for full reproducibility.4---56## Goal78Produce a complete, reproducible record of a CodeAct reasoning session that captures every Python script executed, every interpreter response (stdout, stderr, exit code), every self-debug iteration with its root cause and fix, and every mapping from code output to strategic conclusion. This trace enables any engineerΓÇöwithout access to the original LLM sessionΓÇöto re-run all scripts in a fresh Python environment and obtain identical results, closing the reproducibility gap that plagues LLM-generated analysis.910The trace also serves as a quality signal: if a strategy conclusion cannot be traced to executable code that a human can re-run and verify, that conclusion is flagged as heuristic and treated with appropriate skepticism. By distinguishing computationally verified claims from model-generated prose, the trace gives strategy consumers a clear picture of what has been proven versus what has been suggested.1112## Implementation Details1314The CodeAct framework, introduced by Wang et al. (2024) at ICML in "Executable Code Actions Elicit Better LLM Agents," establishes Python code as a unified action space for LLM agentsΓÇöreplacing JSON tool-call schemas, text-based action descriptions, and domain-specific DSLs with a single, general-purpose executable medium. The framework was evaluated across 17 large language models spanning open-source (Llama-2, Mistral, CodeLlama) and proprietary (GPT-3.5, GPT-4, Claude) families, with consistent findings: the code action space achieved up to 20% higher task success rates than text-based and JSON-based alternatives on complex agent benchmarks including ALFWorld, ScienceWorld, and WebArena. The performance gap widened as task complexity increased, suggesting that code's expressiveness advantage compounds when operations involve multi-step sequencing, conditional branching, mathematical computation, and error handlingΓÇöprecisely the characteristics of sophisticated Vidbyte strategy execution.1516The CodeActInstruct dataset, comprising 7,000 multi-turn interaction trajectories, was constructed by prompting a teacher LLM (GPT-4) to generate diverse agent tasks and collecting the code-action sequences that successfully completed them. Each trajectory includes: the task specification, the model's Python code response, the interpreter's output (success or traceback), and the model's revision in response to errors. This dataset was used to fine-tune CodeActAgent variants from Llama-2 (7B and 13B) and Mistral (7B) base models, producing agents that could autonomously compose operationsΓÇöchaining library calls, handling edge cases with try-except blocks, and self-debugging based on interpreter feedback. The fine-tuned models demonstrated strong generalization to unseen tasks, suggesting that the code-as-action paradigm captures transferable reasoning patterns that text-based action spaces do not.1718The Python interpreter loop is the framework's core execution mechanism. At each turn, the model emits a Python code block. The interpreter executes it in an isolated namespace with access only to the Python standard library (no pip-installed packages unless explicitly pre-authorized), captures stdout and stderr separately, records the exit code, and returns the complete execution transcript to the model. If execution fails (non-zero exit code or unhandled exception), the model receives the full traceback and generates a revised code block in the next turn. This loop continues until execution succeeds or a maximum iteration count is reached. The isolation constraintΓÇöstandard library onlyΓÇöis deliberate: it ensures reproducibility across environments and prevents the model from depending on obscure or unavailable packages whose behavior may vary across versions. For Vidbyte strategy execution, `math`, `statistics`, `json`, `datetime`, `csv`, `re`, `collections`, `itertools`, `functools`, `typing`, and `dataclasses` cover the vast majority of computational needs without external dependencies.1920The control flow for reasoning structure is a critical design consideration. Rather than generating a single monolithic script that attempts to compute everything at once, CodeAct agents decompose complex tasks into sequences of smaller, independently executable code blocks. Each block accepts defined inputs (from previous blocks or user-provided parameters), performs a single well-scoped computation, and produces structured output that feeds into subsequent blocks. This modularity serves three purposes: it makes errors easier to isolate and debug (a traceback in block 3 does not require re-executing blocks 1 and 2), it enables partial reuse (the retention-curve block from one strategy can be imported into another), and it produces a natural trace structure where each code block corresponds to a reasoning step in the strategy algorithm. The trace maps each block to its position in the eight-step CodeAct reasoning algorithm, creating a bidirectional reference between the strategic logic and its computational implementation.2122The CodeActAgent variants derived from Llama-2 and Mistral demonstrated particular strengths that inform this trace system's design. The Llama-2-based agents showed stronger mathematical reasoning, making them better suited for the quantitative domains (Retention Optimization, Monetization Strategy, Audience Growth). The Mistral-based agents showed stronger instruction-following and structural reasoning, making them better suited for the qualitative validation domains (Script Structure, Platform-Specific Formatting, Brand Narrative). While the trace system itself is model-agnosticΓÇöany LLM that can emit Python code can generate a traceΓÇöthe trace records which model produced each script, enabling downstream analysis of model-specific failure patterns and informing model selection for future strategy executions.2324## Intent2526We run this trace because code-based reasoning is epistemically superior to prose-based reasoning in domains where conclusions depend on computation—the code either executes correctly and produces the claimed output, or it does not. Unlike natural-language reasoning chains, which can contain hidden leaps, unstated assumptions, and internally consistent but factually wrong arguments, Python scripts in an isolated interpreter produce deterministic, reproducible results that any engineer can independently verify. When a strategy conclusion is backed by executable code that a human can re-run and audit, the conclusion carries the weight of computational proof rather than rhetorical persuasion. The trace makes this distinction explicit: every computational claim is paired with its executable evidence, and every claim without such evidence is flagged as heuristic, preventing the strategy consumer from mistaking model-generated prose for verified analysis.2728The trace also serves as a quality signal for the CodeAct reasoning process itself. By recording every failed execution alongside its root cause diagnosis and fix, the trace reveals the self-debugging competency of the model—how many iterations were needed, what types of errors occurred, and whether the fixes addressed root causes or merely symptoms. A session where the model required five attempts to get a retention-curve calculation right communicates different reliability than a session where every script executed correctly on the first attempt. For continuous improvement, this data feeds back into model selection, prompt optimization, and architectural decisions about which strategy domains genuinely benefit from CodeAct's computational approach versus those where prose reasoning is sufficient.2930## Algorithm for the Output Document31321. Restate the strategy question, constraints, evidence standard, and domain classification. Identify which elements of the strategy require computational support—quantitative analysis, simulation, statistical testing, data transformation—and which elements are qualitative judgments that will be flagged as heuristic. Produce a computational plan that maps each quantitative question to a sequence of independently executable Python scripts.33342. Set up the isolated execution environment. Record environment metadata: Python version, operating system, and standard library module versions. Initialize a clean namespace with access only to the Python standard library. Document any pre-authorized external dependencies with exact version numbers and justification. Create a directory structure for the trace output that will contain all scripts, execution transcripts, and the formatted trace document.35363. For each computational step in the plan, construct a self-contained Python script. Each script must import only standard library modules, define its inputs explicitly as function parameters or top-level variables, perform a single well-scoped computation, and produce structured output printed to stdout in a parseable format. Record the script in full in the trace with a step identifier that maps to the corresponding step in the CodeAct reasoning algorithm. No truncation, no summarization, no omission for brevity.37384. Execute each script in the isolated interpreter. Capture stdout, stderr, and exit code verbatim. Record the wall-clock execution duration in milliseconds. If the script writes to any filesystem location beyond the designated trace output directory, record the write operation and its justification. If the script makes any network call, record the endpoint, request parameters, response status, and justification.39405. If execution fails with a non-zero exit code or unhandled exception, initiate the self-debug loop. Record the full traceback or error message. Diagnose the root cause: is it a syntax error, a logical error, a missing import, a type mismatch, an unhandled edge case, or an environmental issue? Generate a revised script that addresses the root cause. Execute the revised script. Repeat until execution succeeds or a maximum iteration count (default 5) is reached. Record every iteration as a distinct entry showing the failing code, the error, the root cause diagnosis, and the revised code.41426. After all scripts have executed successfully, map each code output to the specific strategic conclusion it supports. For every computational output, identify the line range in the final strategy document where that output is cited as evidence, and record the bidirectional mapping in the trace. For every strategic conclusion that lacks supporting code output, flag it with a heuristic marker and note the reasoning type that was used instead—qualitative judgment, precedent-based reasoning, analogical reasoning, or model-generated prose.43447. Compose the revision log: a chronological summary of every self-debug iteration across the entire session. For each fix, record what failed (the script identifier and the error), why it failed (the root cause diagnosis), what was changed (a description of the revision), and whether the fix resolved the issue (confirmed by successful re-execution). The revision log enables downstream analysis of failure patterns—which error types are most common, which strategy domains trigger the most self-debug iterations, and which model families exhibit which error profiles.45468. Assemble the complete execution trace document. Structure it as valid markdown or JSON-LD with sections for Environment Metadata, Computational Plan, Script Executions (each with script code, execution transcript, and conclusion mapping), Revision Log, and Support Summary (ratio of computationally verified claims to heuristic claims). The trace must be self-contained: copying all scripts into a fresh directory and executing them in order must reproduce all results without modification.4748## Output Information4950Write the execution trace as structured markdown in root memory/{question_name}.md before responding to the user. Include this scale note near the top of the file: "Scale: the trace expands or contracts with the session's actual computational demands—a session that executed 3 scripts receives a shorter trace than one that executed 15 scripts with 8 self-debug iterations. Every script execution, whether successful or failed, receives a full entry with code, transcript, and mapping." Use numbered entries for each script execution and each self-debug iteration so a reviewer can reference specific entries by number. Keep the trace public, inspectable, and complete—no code is omitted, no execution output is summarized, and no mapping is glossed over. After writing the file, respond with the path, the support summary (ratio of code-backed to heuristic claims), the number of self-debug iterations, and a brief statement of overall reproducibility confidence.5152## Things Not to Do5354- Do not truncate or summarize Python scripts in the trace. A script excerpt or abbreviated listing destroys reproducibility—the reader must be able to copy the exact code and execute it. Record every script in full, including imports and any whitespace that affects execution.55- Do not omit failed executions from the trace. Failed executions are as informative as successful ones—they reveal what the model got wrong, how it diagnosed the error, and how it corrected the approach. A trace that only shows successful executions hides the self-debugging process and inflates the apparent reliability of the session.56- Do not confuse prose interpretation with computational output. When the model writes "the retention curve shows a 12% drop" but the Python output shows an 11.7% drop with a standard deviation of 3.2%, the trace must present both and flag the discrepancy. The trace's value comes from distinguishing what was computed from what was narrated.57- Do not allow scripts to produce side effects—filesystem writes, network calls, subprocess creation—beyond the designated trace output directory without explicit annotation and user permission. If a side effect is necessary for the computation, record it in the trace with full details and justification.58- Do not leave the conclusion-to-code mapping ambiguous. Every strategic conclusion that claims computational support must cite the specific script identifier and line range in the code output that produced the supporting numbers. Conclusions without this mapping must be explicitly flagged as heuristic.59- Do not omit environment metadata. A trace is only reproducible if the reader knows what Python version, operating system, and standard library versions were used. A trace that runs on Python 3.11 but produces different results on Python 3.12 due to a standard library change is a trace with a latent reproducibility bug.60- Do not treat the maximum self-debug iteration count as a hard limit that silently ends the trace. If the model reaches the maximum (default 5) without resolving the error, record all iterations and flag the unresolved error explicitly. The trace consumer must know which computations failed to converge and why.61- Do not present the trace as a substitute for human review. The trace is an audit tool—it enables review but does not perform it. The trace producer's responsibility is to make the trace complete, accurate, and self-contained; the consumer's responsibility is to verify.6263## Success Criteria6465- Every Python script executed during the session is recorded in full in the trace, with no truncation or summarization of code content.66- The stdout, stderr, and exit code of every script execution are captured verbatim, including ANSI escape sequences if present.67- The trace is self-contained: copying all scripts from the trace into a fresh directory and running them in order reproduces all results without modification.68- Every self-debug iteration is recorded as a distinct entry showing the original failing code, the traceback or error message, the root cause diagnosis, and the revised code.69- Each strategic conclusion in the final output is mapped to the specific script and line range that produced the supporting computation (or marked as heuristic with no code mapping).70- The trace includes environment metadata: Python version, operating system, and stdlib module versions to aid reproducibility across environments.71- All scripts use only the Python standard library; any external dependency is listed with its exact version and a justification for its necessity.72- No script in the trace produces side effects (filesystem writes, network calls) beyond the designated trace output directory without explicit annotation and user permission.73- The trace differentiates between script output (computationally derived) and prose interpretation (model-generated) so consumers can distinguish verified results from narrative framing.74- The trace includes a revision log summarizing every self-debug fix: what failed, why it failed, what was changed, and whether the fix resolved the issue.75- Execution timing (wall-clock duration per script) is recorded to identify computational bottlenecks in the strategy pipeline.76- The trace is valid JSON-LD or structured markdown that can be ingested by automated validation pipelines without manual parsing.7778## Input7980The output of a codeact-reasoning session: the full set of Python scripts executed, the interpreter transcripts for each execution (including failed attempts and their tracebacks), the strategy domain classification and parameters, and the final strategy conclusions. The trace generator consumes this structured output and produces the formatted execution record. No additional user input is required beyond the raw session output, though optional metadata (model identifier, session ID, user-provided tags) can be supplied for trace indexing.
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