Task Decomposition Agent — Python (vollständige Implementierung mit LLM)

examples/
└── agent-patterns/
    └── task-decomposition-agent/
        └── python/
            ├── main.py           # Plan -> Execute -> Combine
            ├── llm.py            # planner + final synthesis
            ├── gateway.py        # policy boundary: plan validation + tool execution control
            ├── tools.py          # deterministic tools (Anna/Max, US, USD)
            └── requirements.txt

git clone https://github.com/AgentPatterns-tech/agentpatterns.git
cd agentpatterns

cd examples/agent-patterns/task-decomposition-agent/python
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

export OPENAI_API_KEY="sk-..."
# optional:
# export OPENAI_MODEL="gpt-4.1-mini"
# export OPENAI_TIMEOUT_SECONDS="60"

python main.py

from __future__ import annotations

from typing import Any

MANAGERS = {
    42: {"id": 42, "name": "Anna", "region": "US", "team": "Retail East"},
    7: {"id": 7, "name": "Max", "region": "US", "team": "Retail West"},
}

SALES_DATA = {
    "2026-04": [
        {"day": "2026-04-01", "gross_usd": 5200.0, "orders": 120},
        {"day": "2026-04-02", "gross_usd": 4890.0, "orders": 113},
        {"day": "2026-04-03", "gross_usd": 6105.0, "orders": 141},
        {"day": "2026-04-04", "gross_usd": 5580.0, "orders": 127},
        {"day": "2026-04-05", "gross_usd": 6420.0, "orders": 149},
    ]
}

REFUND_DATA = {
    "2026-04": [
        {"day": "2026-04-01", "refunds_usd": 140.0},
        {"day": "2026-04-02", "refunds_usd": 260.0},
        {"day": "2026-04-03", "refunds_usd": 210.0},
        {"day": "2026-04-04", "refunds_usd": 590.0},
        {"day": "2026-04-05", "refunds_usd": 170.0},
    ]
}


def get_manager_profile(manager_id: int) -> dict[str, Any]:
    manager = MANAGERS.get(manager_id)
    if not manager:
        return {"error": f"manager {manager_id} not found"}
    return {"manager": manager}


def fetch_sales_data(month: str) -> dict[str, Any]:
    rows = SALES_DATA.get(month)
    if not rows:
        return {"error": f"sales data for {month} not found"}
    return {"month": month, "currency": "USD", "daily_sales": rows}


def fetch_refund_data(month: str) -> dict[str, Any]:
    rows = REFUND_DATA.get(month)
    if not rows:
        return {"error": f"refund data for {month} not found"}
    return {"month": month, "currency": "USD", "daily_refunds": rows}


def calculate_monthly_kpis(month: str) -> dict[str, Any]:
    sales_rows = SALES_DATA.get(month)
    refund_rows = REFUND_DATA.get(month)
    if not sales_rows or not refund_rows:
        return {"error": f"kpi inputs for {month} not found"}

    gross_sales = sum(row["gross_usd"] for row in sales_rows)
    total_refunds = sum(row["refunds_usd"] for row in refund_rows)
    total_orders = sum(row["orders"] for row in sales_rows)
    net_sales = gross_sales - total_refunds
    refund_rate = (total_refunds / gross_sales) if gross_sales else 0.0

    top_day = max(sales_rows, key=lambda row: row["gross_usd"])["day"]

    return {
        "month": month,
        "currency": "USD",
        "gross_sales_usd": round(gross_sales, 2),
        "refunds_usd": round(total_refunds, 2),
        "net_sales_usd": round(net_sales, 2),
        "orders": total_orders,
        "refund_rate": round(refund_rate, 4),
        "top_sales_day": top_day,
    }


def detect_risk_signals(month: str) -> dict[str, Any]:
    refund_rows = REFUND_DATA.get(month)
    if not refund_rows:
        return {"error": f"refund data for {month} not found"}

    high_refund_day = max(refund_rows, key=lambda row: row["refunds_usd"])
    warnings: list[str] = []

    if high_refund_day["refunds_usd"] >= 500:
        warnings.append(
            f"Refund spike detected on {high_refund_day['day']}: {high_refund_day['refunds_usd']} USD"
        )

    if not warnings:
        warnings.append("No critical risk signals detected for this month.")

    return {
        "month": month,
        "currency": "USD",
        "risk_warnings": warnings,
        "peak_refund_day": high_refund_day,
    }

from __future__ import annotations

import hashlib
import json
from dataclasses import dataclass
from typing import Any, Callable


class StopRun(Exception):
    def __init__(self, reason: str):
        super().__init__(reason)
        self.reason = reason


@dataclass(frozen=True)
class Budget:
    max_plan_steps: int = 6
    max_execute_steps: int = 8
    max_tool_calls: int = 8
    max_seconds: int = 60


def _stable_json(value: Any) -> str:
    if value is None or isinstance(value, (bool, int, float, str)):
        return json.dumps(value, ensure_ascii=True, sort_keys=True)
    if isinstance(value, list):
        return "[" + ",".join(_stable_json(item) for item in value) + "]"
    if isinstance(value, dict):
        parts = []
        for key in sorted(value):
            parts.append(
                json.dumps(str(key), ensure_ascii=True) + ":" + _stable_json(value[key])
            )
        return "{" + ",".join(parts) + "}"
    return json.dumps(str(value), ensure_ascii=True)


def args_hash(args: dict[str, Any]) -> str:
    raw = _stable_json(args or {})
    return hashlib.sha256(raw.encode("utf-8")).hexdigest()[:12]


def validate_plan_action(
    action: Any, *, max_plan_steps: int, allowed_tools: set[str]
) -> list[dict[str, Any]]:
    if not isinstance(action, dict):
        raise StopRun("invalid_plan:not_object")

    kind = action.get("kind")
    if kind == "invalid":
        raise StopRun("invalid_plan:non_json")
    if kind != "plan":
        raise StopRun("invalid_plan:bad_kind")

    allowed_top_keys = {"kind", "steps"}
    if set(action.keys()) - allowed_top_keys:
        raise StopRun("invalid_plan:extra_keys")

    steps = action.get("steps")
    if not isinstance(steps, list) or not steps:
        raise StopRun("invalid_plan:missing_steps")
    if len(steps) < 3:
        raise StopRun("invalid_plan:min_steps")
    if len(steps) > max_plan_steps:
        raise StopRun("invalid_plan:max_steps")

    normalized: list[dict[str, Any]] = []
    seen_ids: set[str] = set()

    for index, step in enumerate(steps, start=1):
        if not isinstance(step, dict):
            raise StopRun(f"invalid_plan:step_{index}_not_object")

        allowed_step_keys = {"id", "title", "tool", "args"}
        if set(step.keys()) - allowed_step_keys:
            raise StopRun(f"invalid_plan:step_{index}_extra_keys")

        step_id = step.get("id")
        if not isinstance(step_id, str) or not step_id.strip():
            raise StopRun(f"invalid_plan:step_{index}_missing_id")
        if step_id in seen_ids:
            raise StopRun("invalid_plan:duplicate_step_id")
        seen_ids.add(step_id)

        title = step.get("title")
        if not isinstance(title, str) or not title.strip():
            raise StopRun(f"invalid_plan:step_{index}_missing_title")

        tool = step.get("tool")
        if not isinstance(tool, str) or not tool.strip():
            raise StopRun(f"invalid_plan:step_{index}_missing_tool")
        tool = tool.strip()
        if tool not in allowed_tools:
            raise StopRun(f"invalid_plan:tool_not_allowed:{tool}")

        args = step.get("args", {})
        if args is None:
            args = {}
        if not isinstance(args, dict):
            raise StopRun(f"invalid_plan:step_{index}_bad_args")

        normalized.append(
            {
                "id": step_id.strip(),
                "title": title.strip(),
                "tool": tool,
                "args": args,
            }
        )

    return normalized


class ToolGateway:
    def __init__(
        self,
        *,
        allow: set[str],
        registry: dict[str, Callable[..., dict[str, Any]]],
        budget: Budget,
    ):
        self.allow = set(allow)
        self.registry = registry
        self.budget = budget
        self.tool_calls = 0
        self.seen_calls: set[str] = set()

    def call(self, name: str, args: dict[str, Any]) -> dict[str, Any]:
        self.tool_calls += 1
        if self.tool_calls > self.budget.max_tool_calls:
            raise StopRun("max_tool_calls")

        if name not in self.allow:
            raise StopRun(f"tool_denied:{name}")

        tool = self.registry.get(name)
        if tool is None:
            raise StopRun(f"tool_missing:{name}")

        signature = f"{name}:{args_hash(args)}"
        if signature in self.seen_calls:
            raise StopRun("loop_detected")
        self.seen_calls.add(signature)

        try:
            return tool(**args)
        except TypeError as exc:
            raise StopRun(f"tool_bad_args:{name}") from exc
        except Exception as exc:
            raise StopRun(f"tool_error:{name}") from exc

from __future__ import annotations

import json
import os
from typing import Any

from openai import APIConnectionError, APITimeoutError, OpenAI

MODEL = os.getenv("OPENAI_MODEL", "gpt-4.1-mini")
LLM_TIMEOUT_SECONDS = float(os.getenv("OPENAI_TIMEOUT_SECONDS", "60"))


class LLMTimeout(Exception):
    pass


class LLMEmpty(Exception):
    pass


PLAN_SYSTEM_PROMPT = """
You are a task decomposition planner.
Return only one JSON object in this exact shape:
{
  "kind": "plan",
  "steps": [
    {"id": "step_1", "title": "...", "tool": "...", "args": {...}}
  ]
}

Rules:
- Create 3 to 6 steps.
- Use only tools from available_tools.
- Keep args minimal and valid.
- Do not add extra keys.
- Do not output markdown.
""".strip()

FINAL_SYSTEM_PROMPT = """
You are a reporting assistant.
Write a short final summary in English for a US business audience.
Include: manager name, month, gross sales (USD), refunds (USD), net sales (USD), refund rate (%), and key risk note.
""".strip()

TOOL_CATALOG = [
    {
        "name": "get_manager_profile",
        "description": "Get manager profile by manager_id",
        "args": {"manager_id": "integer"},
    },
    {
        "name": "fetch_sales_data",
        "description": "Get daily gross sales for a month",
        "args": {"month": "string in YYYY-MM"},
    },
    {
        "name": "fetch_refund_data",
        "description": "Get daily refund values for a month",
        "args": {"month": "string in YYYY-MM"},
    },
    {
        "name": "calculate_monthly_kpis",
        "description": "Calculate gross/refunds/net/order KPIs for a month",
        "args": {"month": "string in YYYY-MM"},
    },
    {
        "name": "detect_risk_signals",
        "description": "Detect risk warnings for a month",
        "args": {"month": "string in YYYY-MM"},
    },
]


def _get_client() -> OpenAI:
    api_key = os.getenv("OPENAI_API_KEY")
    if not api_key:
        raise EnvironmentError(
            "OPENAI_API_KEY is not set. Run: export OPENAI_API_KEY='sk-...'"
        )
    return OpenAI(api_key=api_key)


def create_plan(goal: str, max_plan_steps: int) -> dict[str, Any]:
    payload = {
        "goal": goal,
        "max_plan_steps": max_plan_steps,
        "available_tools": TOOL_CATALOG,
    }

    client = _get_client()
    try:
        completion = client.chat.completions.create(
            model=MODEL,
            temperature=0,
            timeout=LLM_TIMEOUT_SECONDS,
            response_format={"type": "json_object"},
            messages=[
                {"role": "system", "content": PLAN_SYSTEM_PROMPT},
                {"role": "user", "content": json.dumps(payload, ensure_ascii=True)},
            ],
        )
    except (APITimeoutError, APIConnectionError) as exc:
        raise LLMTimeout("llm_timeout") from exc

    text = completion.choices[0].message.content or "{}"
    try:
        return json.loads(text)
    except json.JSONDecodeError:
        return {"kind": "invalid", "raw": text}


def compose_final_answer(goal: str, history: list[dict[str, Any]]) -> str:
    payload = {
        "goal": goal,
        "history": history,
    }

    client = _get_client()
    try:
        completion = client.chat.completions.create(
            model=MODEL,
            temperature=0,
            timeout=LLM_TIMEOUT_SECONDS,
            messages=[
                {"role": "system", "content": FINAL_SYSTEM_PROMPT},
                {"role": "user", "content": json.dumps(payload, ensure_ascii=True)},
            ],
        )
    except (APITimeoutError, APIConnectionError) as exc:
        raise LLMTimeout("llm_timeout") from exc

    text = completion.choices[0].message.content or ""
    text = text.strip()
    if not text:
        raise LLMEmpty("llm_empty")
    return text

from __future__ import annotations

import json
import time
from typing import Any

from gateway import Budget, StopRun, ToolGateway, args_hash, validate_plan_action
from llm import LLMEmpty, LLMTimeout, compose_final_answer, create_plan
from tools import (
    calculate_monthly_kpis,
    detect_risk_signals,
    fetch_refund_data,
    fetch_sales_data,
    get_manager_profile,
)

GOAL = (
    "Prepare an April 2026 monthly sales summary for manager_id=42 in USD. "
    "Use step-by-step decomposition. Include gross sales, refunds, net sales, refund rate, and one risk note."
)

# max_execute_steps here limits plan length (number of planned steps), not runtime loop iterations.
BUDGET = Budget(max_plan_steps=6, max_execute_steps=8, max_tool_calls=8, max_seconds=60)

TOOL_REGISTRY = {
    "get_manager_profile": get_manager_profile,
    "fetch_sales_data": fetch_sales_data,
    "fetch_refund_data": fetch_refund_data,
    "calculate_monthly_kpis": calculate_monthly_kpis,
    "detect_risk_signals": detect_risk_signals,
}

ALLOWED_TOOLS = {
    "get_manager_profile",
    "fetch_sales_data",
    "fetch_refund_data",
    "calculate_monthly_kpis",
    "detect_risk_signals",
}


def run_task_decomposition(goal: str) -> dict[str, Any]:
    started = time.monotonic()
    trace: list[dict[str, Any]] = []
    history: list[dict[str, Any]] = []

    gateway = ToolGateway(allow=ALLOWED_TOOLS, registry=TOOL_REGISTRY, budget=BUDGET)

    try:
        raw_plan = create_plan(goal=goal, max_plan_steps=BUDGET.max_plan_steps)
    except LLMTimeout:
        return {
            "status": "stopped",
            "stop_reason": "llm_timeout",
            "llm_phase": "plan",
            "trace": trace,
            "history": history,
        }

    try:
        steps = validate_plan_action(
            raw_plan,
            max_plan_steps=BUDGET.max_plan_steps,
            allowed_tools=ALLOWED_TOOLS,
        )
    except StopRun as exc:
        return {
            "status": "stopped",
            "stop_reason": exc.reason,
            "raw_plan": raw_plan,
            "trace": trace,
            "history": history,
        }

    if len(steps) > BUDGET.max_execute_steps:
        return {
            "status": "stopped",
            "stop_reason": "max_execute_steps",
            "plan": steps,
            "trace": trace,
            "history": history,
        }

    for step_no, step in enumerate(steps, start=1):
        elapsed = time.monotonic() - started
        if elapsed > BUDGET.max_seconds:
            return {
                "status": "stopped",
                "stop_reason": "max_seconds",
                "plan": steps,
                "trace": trace,
                "history": history,
            }

        tool_name = step["tool"]
        tool_args = step["args"]

        try:
            observation = gateway.call(tool_name, tool_args)
            trace.append(
                {
                    "step_no": step_no,
                    "step_id": step["id"],
                    "tool": tool_name,
                    "args_hash": args_hash(tool_args),
                    "ok": True,
                }
            )
        except StopRun as exc:
            trace.append(
                {
                    "step_no": step_no,
                    "step_id": step["id"],
                    "tool": tool_name,
                    "args_hash": args_hash(tool_args),
                    "ok": False,
                    "stop_reason": exc.reason,
                }
            )
            return {
                "status": "stopped",
                "stop_reason": exc.reason,
                "plan": steps,
                "trace": trace,
                "history": history,
            }

        history.append(
            {
                "step_no": step_no,
                "plan_step": step,
                "observation": observation,
            }
        )

    try:
        answer = compose_final_answer(goal=goal, history=history)
    except LLMTimeout:
        return {
            "status": "stopped",
            "stop_reason": "llm_timeout",
            "llm_phase": "finalize",
            "plan": steps,
            "trace": trace,
            "history": history,
        }
    except LLMEmpty:
        return {
            "status": "stopped",
            "stop_reason": "llm_empty",
            "llm_phase": "finalize",
            "plan": steps,
            "trace": trace,
            "history": history,
        }

    return {
        "status": "ok",
        "stop_reason": "success",
        "answer": answer,
        "plan": steps,
        "trace": trace,
        "history": history,
    }


def main() -> None:
    result = run_task_decomposition(GOAL)
    print(json.dumps(result, indent=2, ensure_ascii=False))


if __name__ == "__main__":
    main()

openai==2.21.0

{
  "status": "ok",
  "stop_reason": "success",
  "answer": "In April 2026, under manager Anna's leadership, gross sales were $28,195, refunds were $1,370, net sales were $26,825, and refund rate was 4.86%.",
  "plan": [
    {"id": "step_1", "tool": "fetch_sales_data", "args": {"month": "2026-04"}},
    {"id": "step_2", "tool": "fetch_refund_data", "args": {"month": "2026-04"}},
    {"id": "step_3", "tool": "calculate_monthly_kpis", "args": {"month": "2026-04"}},
    {"id": "step_4", "tool": "detect_risk_signals", "args": {"month": "2026-04"}},
    {"id": "step_5", "tool": "get_manager_profile", "args": {"manager_id": 42}}
  ],
  "trace": [
    {"step_no": 1, "step_id": "step_1", "tool": "fetch_sales_data", "args_hash": "...", "ok": true},
    {"step_no": 2, "step_id": "step_2", "tool": "fetch_refund_data", "args_hash": "...", "ok": true},
    {"step_no": 3, "step_id": "step_3", "tool": "calculate_monthly_kpis", "args_hash": "...", "ok": true},
    {"step_no": 4, "step_id": "step_4", "tool": "detect_risk_signals", "args_hash": "...", "ok": true},
    {"step_no": 5, "step_id": "step_5", "tool": "get_manager_profile", "args_hash": "...", "ok": true}
  ],
  "history": [{...}]
}