mid-conversation-effort-example changedbuild-with-claude/mid-conversation-effort-example
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2121The example is a single file. The constants control the effort level, the fan-out shape, and how often the mode refresher is re-sent. `MAX_CONCURRENT` caps how many subagents run at the same time (the PHP port is sequential and ignores it); `MAX_TOTAL_SUBTASKS` caps how many the model may queue in a single Workflow call. Splitting the two lets the model plan a large backlog without launching it all at once. The `DOC_TEST_MODE` check caps the loops to a single turn when that environment variable is set, so the automated docs harness can validate that the file compiles and finishes quickly without running the full orchestration; leave it unset when running the example yourself.
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23<CodeGroup>
23<CodeGroup exclude="shell:cURL, shell:CLI">
2424 ```python Python
2525 import atexit
2626 import concurrent.futures
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301301The reminders are short on purpose. They flip the mode and point at the tool description, where the heavyweight instructions live. The full text is sent once when the mode turns on, the refresher is re-sent only after several user turns, and the exit notice is sent once when the mode turns off.
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303<CodeGroup>
303<CodeGroup exclude="shell:cURL, shell:CLI">
304304 ```python Python
305305 MODE_ENTER = (
306306 "Orchestration mode is on: optimize for the most exhaustive, correct answer rather than "
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401401The Workflow tool carries the real behavioral contract: the opt-in rule, the standing consent that applies while the mode is on, granularity guidance for sizing the fan-out, and the quality patterns the model can reach for (a verification wave, a completeness critic, multiphase sequencing). Subagents also get a `report_findings` tool so their results come back as structured JSON instead of prose, and the bash tool is the Anthropic-defined `bash_20250124` tool run locally.
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403<CodeGroup>
403<CodeGroup exclude="shell:cURL, shell:CLI">
404404 ```python Python
405405 WORKFLOW_TOOL = {
406406 "name": "Workflow",
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887887The bash handler runs the requested command with a timeout, captures combined stdout and stderr, and truncates the result so a runaway command can't flood the context window. Commands run in the directory you launch the example from, so pointing it at a project means starting it there; when `DOC_TEST_MODE` is set, the harness instead gives bash a small throwaway fixture directory that is removed on exit. There is no sandbox here: the command runs with the permissions of the process that launched the example. For clarity this example runs each call in a fresh subshell rather than maintaining the persistent session the `bash_20250124` contract describes; a production agent should back the tool with a long-lived shell so that working directory, environment, and the `restart` action behave as documented.
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889<CodeGroup>
889<CodeGroup exclude="shell:cURL, shell:CLI">
890890 ```python Python
891891 # Run bash where the example was launched. In DOC_TEST_MODE the docs harness
892892 # points it at a throwaway fixture directory instead, removed on exit.
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14341434Each workflow subtask becomes its own small agent loop with the bash tool, running at the same effort as the main loop. A per-request timeout bounds each API call so a dropped connection degrades one subagent instead of stalling the whole run.
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1436<CodeGroup>
1436<CodeGroup exclude="shell:cURL, shell:CLI">
14371437 ```python Python
14381438 def run_subagent(model: str, prompt: str) -> str:
14391439 """One subagent: a small nested agent loop with the bash tool plus report_findings.
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19781978A fan-out that spawns dozens of subagents is expensive to restart from scratch. A small content-addressed journal makes it idempotent: before dispatching a subagent, look up the SHA-256 of its prompt in a local JSON file, and return the recorded result if one exists. Interrupt the run, rerun it, and only the subtasks that never finished are recomputed. The journal deduplicates across runs, not within a single fan-out wave; delete the journal file to start fresh.
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1980<CodeGroup>
1980<CodeGroup exclude="shell:cURL, shell:CLI">
19811981 ```python Python
19821982 _journal_lock = threading.Lock()
19831983
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22692269The fan-out accepts up to `MAX_TOTAL_SUBTASKS` prompts, runs them through the journal with at most `MAX_CONCURRENT` in flight (sequential in the PHP port), and isolates failures so one broken subagent degrades to an error string instead of ending the run. Once the first wave finishes, a second wave reuses the same subagent path to try to refute each result: every verifier re-derives the claims from the source, defaulting to refuted when uncertain. Both the original result and its verdict are returned to the orchestrator so it can weigh them together.
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2271<CodeGroup>
2271<CodeGroup exclude="shell:cURL, shell:CLI">
22722272 ```python Python
22732273 def normalize_subtasks(raw) -> list[str]:
22742274 """Accept the subtasks input in whatever shape the model emits: an array, the array
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38283828 The bash tool in this example runs model-written commands directly on your machine with no sandbox, and the fan-out runs several of those agents in parallel. Run it in a directory and environment you are comfortable exposing, and add sandboxing before adapting it for anything beyond local experimentation.
38293829</Warning>
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3831<CodeGroup>
3831<CodeGroup exclude="shell:cURL, shell:CLI">
38323832 ```python Python
38333833 if __name__ == "__main__":
38343834 task = (
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