Construir um modo de orquestração

import atexit
import concurrent.futures
import hashlib
import json
import os
import shutil
import subprocess
import sys
import tempfile
import threading

import anthropic

client = anthropic.Anthropic()

MODEL = "claude-opus-4-8"
EFFORT = "xhigh"

SYSTEM_PROMPT = "You are a helpful general-purpose agent. Answer the user's request directly."

REQUEST_TIMEOUT_SECONDS = 600
BASH_TIMEOUT_SECONDS = 60
TOOL_RESULT_MAX_CHARS = 8000
MAX_CONCURRENT = 10
DOC_TEST_MODE = bool(os.environ.get("DOC_TEST_MODE"))
MAX_TOTAL_SUBTASKS = 2 if DOC_TEST_MODE else 200
MAX_SUBAGENT_TURNS = 1 if DOC_TEST_MODE else 15
MAX_MAIN_TURNS = 1 if DOC_TEST_MODE else 30
TURNS_BETWEEN_REFRESHERS = 10
JOURNAL_PATH = os.environ.get("ORCH_JOURNAL") or "orchestration_journal.json"

MODE_ENTER = (
    "Orchestration mode is on: optimize for the most exhaustive, correct answer rather than "
    "the fastest one. Use the Workflow tool on every substantive task, sized to the problem's "
    "natural decomposition rather than the maximum the tool allows. See the Workflow tool's "
    "description for standing consent, granularity guidance, and quality patterns. Work solo "
    "only on conversational or trivial turns."
)
MODE_REFRESH = (
    "Orchestration mode is still on. Use the Workflow tool; see its standing consent section."
)
MODE_EXIT = (
    "Orchestration mode is off. The Workflow tool's standard opt-in rule applies again."
)

WORKFLOW_TOOL = {
    "name": "Workflow",
    "description": (
        "Orchestrate a multi-agent workflow: split a large task into independent subtasks "
        "and run them as parallel agents, then collect their results.\n\n"
        "Opt-in: only use this tool when the user explicitly asks for a workflow, or when a "
        "system message confirms that orchestration mode is on.\n\n"
        "Quality patterns: adversarial verification (a second wave of agents checks the first "
        "wave's findings against the source), a completeness critic (one agent hunts for what "
        "the others missed), and multi-phase sequencing (understand, design, implement, and "
        "review as separate workflow calls, reading results between phases). A useful default "
        "is hybrid: scout inline first to discover the work-list, then fan out over it.\n\n"
        "Granularity: scope each subtask to a distinct concern, component, or question rather "
        "than per line or per file section. Scale the count to what the user asked for: a "
        "focused review of a module of a few hundred lines rarely needs more than about ten "
        "subtasks; a broad audit of a large codebase can justify more.\n\n"
        "Standing consent: while a system message confirms orchestration mode is on, that "
        "opt-in is standing. Author and run a workflow for every substantive task by default, "
        "and lean toward verifying findings adversarially. Work solo only on conversational "
        "turns or trivial mechanical edits. When a system message says the mode is off, "
        "revert to the opt-in rule above."
    ),
    "input_schema": {
        "type": "object",
        "properties": {
            "subtasks": {
                "type": "array",
                "items": {"type": "string"},
                "description": "Independent subtask prompts to run as parallel agents",
            }
        },
        "required": ["subtasks"],
    },
}

BASH_TOOL = {"type": "bash_20250124", "name": "bash"}

REPORT_TOOL = {
    "name": "report_findings",
    "description": (
        "Report the final findings for your subtask. Call this exactly once, when you are "
        "done investigating; it ends your task."
    ),
    "input_schema": {
        "type": "object",
        "properties": {
            "summary": {"type": "string", "description": "Two or three sentences of synthesis"},
            "findings": {
                "type": "array",
                "items": {
                    "type": "object",
                    "properties": {
                        "claim": {"type": "string", "description": "The finding, one sentence"},
                        "evidence": {
                            "type": "string",
                            "description": "How it was verified (file, line, or command output)",
                        },
                        "severity": {"type": "string", "enum": ["high", "medium", "low", "info"]},
                    },
                    "required": ["claim", "evidence", "severity"],
                },
            },
        },
        "required": ["summary", "findings"],
    },
}

# Run bash where the example was launched. In DOC_TEST_MODE the docs harness
# points it at a throwaway fixture directory instead, removed on exit.
if DOC_TEST_MODE:
    WORK_DIR = tempfile.mkdtemp(prefix="orchestration-")
    atexit.register(shutil.rmtree, WORK_DIR, ignore_errors=True)
    with open(os.path.join(WORK_DIR, "sample.py"), "w") as fixture:
        fixture.write(
            "def fib(n):\n"
            "    return n if n < 2 else fib(n - 1) + fib(n - 2)\n\n"
            "print(fib(10))\n"
        )
else:
    WORK_DIR = os.getcwd()


def run_bash(command: str) -> tuple[str, bool]:
    """Run a shell command and return (output, is_error). No sandbox: example code only."""
    print(f"[bash] {command}", file=sys.stderr)
    try:
        proc = subprocess.run(
            ["bash", "-c", command],
            cwd=WORK_DIR,
            capture_output=True,
            text=True,
            errors="replace",
            timeout=BASH_TIMEOUT_SECONDS,
        )
    except subprocess.TimeoutExpired:
        return f"command timed out after {BASH_TIMEOUT_SECONDS}s", True
    output = (proc.stdout + proc.stderr).strip() or "(no output)"
    if len(output) > TOOL_RESULT_MAX_CHARS:
        output = output[:TOOL_RESULT_MAX_CHARS] + f"\n(truncated at {TOOL_RESULT_MAX_CHARS} chars)"
    if proc.returncode != 0:
        output = f"(exit code {proc.returncode})\n{output}"
    return output, proc.returncode != 0


def handle_bash_block(block) -> tuple[str, bool]:
    if block.input.get("restart") is True:
        return "Shell restarted.", False
    command = block.input.get("command")
    if not isinstance(command, str) or not command:
        return "bash error: no command was provided.", True
    return run_bash(command)

def run_subagent(model: str, prompt: str) -> str:
    """One subagent: a small nested agent loop with the bash tool plus report_findings.
    Subagents inherit the main loop's effort level."""
    subagent_system = (
        "You are one agent in a larger parallel fan-out, assigned a single subtask. "
        "Investigate it directly, using bash to check facts rather than guessing, and finish "
        "by calling report_findings exactly once. Return findings, not narration."
    )
    messages = [{"role": "user", "content": prompt}]
    for _ in range(MAX_SUBAGENT_TURNS):
        with client.messages.stream(
            model=model,
            max_tokens=64000,
            system=subagent_system,
            thinking={"type": "adaptive"},
            output_config={"effort": EFFORT},
            tools=[BASH_TOOL, REPORT_TOOL],
            messages=messages,
            timeout=REQUEST_TIMEOUT_SECONDS,
        ) as stream:
            response = stream.get_final_message()
        messages.append({"role": "assistant", "content": response.content})
        if response.stop_reason == "pause_turn":
            continue
        if response.stop_reason != "tool_use":
            text = "".join(block.text for block in response.content if block.type == "text")
            if response.stop_reason == "max_tokens":
                text += "\n\n(warning: subagent response was truncated at max_tokens)"
            return text
        tool_results = []
        report = None
        for block in response.content:
            if block.type != "tool_use":
                continue
            if block.name == "report_findings":
                report = json.dumps(block.input, indent=2)
                output, is_error = "Findings recorded.", False
            elif block.name == "bash":
                output, is_error = handle_bash_block(block)
            else:
                output, is_error = f"unknown tool: {block.name}", True
            tool_results.append(
                {
                    "type": "tool_result",
                    "tool_use_id": block.id,
                    "content": output,
                    "is_error": is_error,
                }
            )
        if report is not None:
            return report
        messages.append({"role": "user", "content": tool_results})
    return "(subagent hit the turn limit before finishing)"

_journal_lock = threading.Lock()


def _load_journal() -> dict:
    try:
        with open(JOURNAL_PATH) as file:
            return json.load(file) or {}
    except (OSError, json.JSONDecodeError):
        return {}


def journaled(prompt: str, compute) -> str:
    """Return a cached result for this exact prompt, or compute and persist it. This
    makes the fan-out resumable: interrupt the run, rerun it, and only the subtasks
    that never finished are recomputed. Delete the journal file to start fresh."""
    key = hashlib.sha256(prompt.encode()).hexdigest()
    cached = _load_journal().get(key)
    if cached is not None:
        print(f"[journal] cache hit for {key[:12]}", file=sys.stderr)
        return cached
    result = compute()
    try:
        with _journal_lock:  # fan-out writes from many threads
            journal = _load_journal()
            journal[key] = result
            temp = f"{JOURNAL_PATH}.tmp"
            with open(temp, "w") as file:
                json.dump(journal, file)
            os.replace(temp, JOURNAL_PATH)  # atomic on POSIX and Windows
    except OSError as error:  # the journal is best-effort; never discard a computed result
        print(f"[journal] write failed: {error}", file=sys.stderr)
    return result

def normalize_subtasks(raw) -> list[str]:
    """Accept the subtasks input in whatever shape the model emits: an array, the array
    JSON-encoded as a single string, or a newline-separated list."""
    if isinstance(raw, str):
        try:
            raw = json.loads(raw)
        except json.JSONDecodeError:
            raw = raw.splitlines() if "\n" in raw else [raw]
    if not isinstance(raw, list):
        return []
    return [task.strip() for task in raw if isinstance(task, str) and task.strip()]


def verify_prompt_for(subtask: str, result: str) -> str:
    return (
        "Adversarially verify the subagent result below: try to REFUTE it. Re-derive the "
        "claims yourself with bash rather than trusting the result, and look for evidence "
        "that contradicts them. Default to refuted if uncertain. Call report_findings with "
        "summary 'refuted: <why>' or 'confirmed: <why>', citing the file:line or command "
        "output that decided it.\n\n"
        f"Subtask: {subtask}\n\nResult to verify:\n{result}"
    )


def run_workflow(model: str, raw_subtasks) -> tuple[str, bool]:
    """Run subtasks as parallel subagents, then run a second verification wave over
    the results, and return both. MAX_TOTAL_SUBTASKS bounds how many the model can
    queue; MAX_CONCURRENT bounds how many run at once."""
    all_subtasks = normalize_subtasks(raw_subtasks)
    subtasks = all_subtasks[:MAX_TOTAL_SUBTASKS]
    dropped = len(all_subtasks) - len(subtasks)
    if not subtasks:
        return "Workflow error: no usable subtasks were provided.", True
    print(f"[workflow] fanning out {len(subtasks)} agents", file=sys.stderr)

    def run_one(prompt: str) -> str:
        try:
            return journaled(prompt, lambda: run_subagent(model, prompt))
        except Exception as error:  # isolation boundary: one bad subagent should not end the run
            return f"(subagent failed: {type(error).__name__}: {error})"

    with concurrent.futures.ThreadPoolExecutor(max_workers=MAX_CONCURRENT) as pool:
        results = list(pool.map(run_one, subtasks))
        print(f"[workflow] verifying {len(results)} results", file=sys.stderr)
        verify_prompts = [verify_prompt_for(task, result) for task, result in zip(subtasks, results)]
        verdicts = list(pool.map(run_one, verify_prompts))

    joined = "\n\n".join(
        f"[agent {index + 1}: {task}]\n{result}\n\n[verify {index + 1}]\n{verdict}"
        for index, (task, result, verdict) in enumerate(zip(subtasks, results, verdicts))
    )
    if dropped > 0:
        joined = (
            f"(note: {dropped} subtasks beyond MAX_TOTAL_SUBTASKS={MAX_TOTAL_SUBTASKS} were not "
            "run; rerun them in a follow-up Workflow call)\n\n" + joined
        )
    return joined, False

class ModeAgent:
    """An agent loop whose orchestration mode is toggled with mid-conversation system messages."""

    def __init__(self, model: str, mode_on: bool = True):
        self.model = model
        self.mode_on = mode_on
        self.messages: list[dict] = []
        self._mode_announced = False
        self._exit_pending = False
        self._turns_since_reminder = 0

    def set_mode(self, mode_on: bool) -> None:
        """Turn the mode on or off. The notice is delivered with the next user turn."""
        if mode_on == self.mode_on:
            return
        if not mode_on:
            if self._mode_announced:
                self._exit_pending = True
        else:
            self._exit_pending = False
        self.mode_on = mode_on

    def _due_system_messages(self) -> list[dict]:
        """System messages owed on this turn: an exit notice, the full mode text on entry,
        or a one-line refresher every TURNS_BETWEEN_REFRESHERS user turns."""
        due = []
        if self._exit_pending:
            self._exit_pending = False
            self._mode_announced = False
            due.append({"role": "system", "content": MODE_EXIT})
        if self.mode_on:
            if not self._mode_announced:
                self._mode_announced = True
                self._turns_since_reminder = 0
                due.append({"role": "system", "content": MODE_ENTER})
            elif self._turns_since_reminder >= TURNS_BETWEEN_REFRESHERS:
                self._turns_since_reminder = 0
                due.append({"role": "system", "content": MODE_REFRESH})
        return due

    def turn(self, user_input: str) -> str:
        # Mid-conversation system messages follow the user turn they apply to, which keeps
        # the cached prefix ahead of them untouched.
        self.messages.append({"role": "user", "content": user_input})
        self.messages.extend(self._due_system_messages())
        self._turns_since_reminder += 1

        for _ in range(MAX_MAIN_TURNS):
            with client.messages.stream(
                model=self.model,
                max_tokens=64000,
                system=SYSTEM_PROMPT,  # static for the whole session
                thinking={"type": "adaptive"},
                output_config={"effort": EFFORT},
                tools=[WORKFLOW_TOOL, BASH_TOOL],
                messages=self.messages,
                timeout=REQUEST_TIMEOUT_SECONDS,
            ) as stream:
                response = stream.get_final_message()
            self.messages.append({"role": "assistant", "content": response.content})

            if response.stop_reason == "pause_turn":
                continue
            if response.stop_reason != "tool_use":
                text = "".join(block.text for block in response.content if block.type == "text")
                if response.stop_reason == "max_tokens":
                    # Drop the truncated assistant message so later turns don't build on it.
                    self.messages.pop()
                    text += "\n\n(warning: response was truncated at max_tokens)"
                return text

            tool_results = []
            for block in response.content:
                if block.type != "tool_use":
                    continue
                if block.name == "Workflow":
                    output, is_error = run_workflow(self.model, block.input.get("subtasks", []))
                elif block.name == "bash":
                    output, is_error = handle_bash_block(block)
                else:
                    output, is_error = f"unknown tool: {block.name}", True
                tool_results.append(
                    {
                        "type": "tool_result",
                        "tool_use_id": block.id,
                        "content": output,
                        "is_error": is_error,
                    }
                )
            self.messages.append({"role": "user", "content": tool_results})
        return "(hit the main loop turn limit before finishing)"

if __name__ == "__main__":
    task = (
        sys.argv[1]
        if len(sys.argv) > 1
        else "Explore the current directory, then give a thorough review: what it does, "
        "code-quality issues, and concrete improvements."
    )
    agent = ModeAgent(MODEL)
    print(agent.turn(task))
    agent.set_mode(False)
    print(agent.turn("Briefly summarize what you found above, no fan-out needed."))