DeepAgents Deep Research
子 Agent 集成分析

# ── 参数配置 ──
max_concurrent_research_units = 3
max_researcher_iterations = 3
current_date = datetime.now().strftime("%Y-%m-%d")

# ── Orchestrator 的 system_prompt（两段拼接）──
INSTRUCTIONS = (
    RESEARCH_WORKFLOW_INSTRUCTIONS            # 研究流程规范
    + "\n\n" + "=" * 80 + "\n\n"
    + SUBAGENT_DELEGATION_INSTRUCTIONS.format(  # 并发调度规范
        max_concurrent_research_units=3,
        max_researcher_iterations=3,
    )
)

# ── SubAgent 定义（TypedDict 格式）──
research_sub_agent = {
    "name":          "research-agent",
    "description":   "Delegate research to the sub-agent researcher.",
    "system_prompt": RESEARCHER_INSTRUCTIONS.format(date=current_date),
    "tools":         [tavily_search, think_tool],
}

# ── 创建主 agent ──
agent = create_deep_agent(
    model=init_chat_model("anthropic:claude-sonnet-4-5-20250929", temperature=0.0),
    tools=[tavily_search, think_tool],
    system_prompt=INSTRUCTIONS,
    subagents=[research_sub_agent],
)

class SubAgent(TypedDict):
    name:          str              # 唯一标识，task() 调用时使用
    description:   str              # 主 agent 据此决定何时委派
    system_prompt: str              # subagent 的完整系统提示
    tools:         NotRequired[list]  # 不填则继承主 agent 工具
    model:         NotRequired[str]  # 可使用不同模型（如 Haiku 降本）
    middleware:    NotRequired[list]
    interrupt_on:  NotRequired[dict]
    permissions:   NotRequired[list]

归属：research-agent subagent system_prompt

# your_agent.py
from datetime import datetime
from langchain.chat_models import init_chat_model
from deepagents import create_deep_agent

# 复用 deep_research 的 prompts 和 tools
from research_agent.prompts import (
    RESEARCHER_INSTRUCTIONS,
    RESEARCH_WORKFLOW_INSTRUCTIONS,
    SUBAGENT_DELEGATION_INSTRUCTIONS,
)
from research_agent.tools import tavily_search, think_tool

# ① 你自己的领域 prompt
YOUR_DOMAIN_INSTRUCTIONS = """
You are a [your role description].

Your primary responsibilities:
- [Domain task A]
- [Domain task B]

When research is needed, delegate to the "research-agent" subagent
via the task() tool. Never conduct web searches yourself.
"""

# ② 整合：领域 prompt + 研究调度规范
MAIN_INSTRUCTIONS = (
    YOUR_DOMAIN_INSTRUCTIONS
    + "\n\n" + "=" * 80 + "\n\n"
    + RESEARCH_WORKFLOW_INSTRUCTIONS
    + "\n\n" + "=" * 80 + "\n\n"
    + SUBAGENT_DELEGATION_INSTRUCTIONS.format(
        max_concurrent_research_units=3,
        max_researcher_iterations=3,
    )
)

# ③ SubAgent 定义（与原示例完全相同）
research_sub_agent = {
    "name":          "research-agent",
    "description":   "Conducts web research on a single topic. "
                     "Provide one focused research task at a time.",
    "system_prompt": RESEARCHER_INSTRUCTIONS.format(
        date=datetime.now().strftime("%Y-%m-%d")
    ),
    "tools": [tavily_search, think_tool],
    # 可选：用更轻量模型降低成本
    # "model": "anthropic:claude-haiku-4-5-20251001",
}

# ④ 创建你的主 agent
agent = create_deep_agent(
    model=init_chat_model("anthropic:claude-sonnet-4-6", temperature=0.0),
    tools=[
        your_domain_tool_1,   # 你自己的工具
        your_domain_tool_2,
        tavily_search,        # 主 agent 也持有，供 subagent 继承
        think_tool,
    ],
    system_prompt=MAIN_INSTRUCTIONS,
    subagents=[research_sub_agent],
)

# 中间层：deep-researcher subagent（扮演原始 agent.py 的角色）
deep_researcher_subagent = {
    "name":        "deep-researcher",
    "description": "Conducts comprehensive deep research with multiple "
                   "parallel sub-researchers. Use for complex, multi-"
                   "faceted research requiring exhaustive coverage.",
    "system_prompt": (
        RESEARCH_WORKFLOW_INSTRUCTIONS
        + "\n\n" + "=" * 80 + "\n\n"
        + SUBAGENT_DELEGATION_INSTRUCTIONS.format(
            max_concurrent_research_units=3,
            max_researcher_iterations=3,
        )
    ),
    "tools": [tavily_search, think_tool],
}

# 主 agent：只包含自己的领域职责
agent = create_deep_agent(
    model=init_chat_model("anthropic:claude-sonnet-4-6"),
    tools=[your_domain_tool_1, your_domain_tool_2],
    system_prompt=YOUR_DOMAIN_INSTRUCTIONS,
    subagents=[deep_researcher_subagent],  # 注意：无 research-agent
)

def build_main_instructions(
    domain_prompt: str,
    max_concurrent: int = 3,
    max_iterations: int = 3,
) -> str:
    """将领域 prompt 与研究调度规范合并为完整的主 agent system_prompt。"""
    separator = "\n\n" + "=" * 80 + "\n\n"
    return separator.join([
        domain_prompt.strip(),
        RESEARCH_WORKFLOW_INSTRUCTIONS.strip(),
        SUBAGENT_DELEGATION_INSTRUCTIONS.format(
            max_concurrent_research_units=max_concurrent,
            max_researcher_iterations=max_iterations,
        ).strip(),
    ])


def build_research_subagent(
    model_override: str | None = None,
) -> dict:
    """创建标准 research-agent subagent 定义。"""
    subagent = {
        "name":          "research-agent",
        "description":   "Conducts web research on a single focused topic.",
        "system_prompt": RESEARCHER_INSTRUCTIONS.format(
            date=datetime.now().strftime("%Y-%m-%d")
        ),
        "tools": [tavily_search, think_tool],
    }
    if model_override:
        subagent["model"] = model_override
    return subagent