创业公司想法验证器

此工作流程通过以下方式帮助创业者验证他们的创业想法：

1. 阐明和完善核心业务概念
2. 与现有解决方案进行原创性评估
3. 定义清晰的使命和目标
4. 进行全面的市场研究和分析

这有什么用？

• 在投入资源之前，获得对您的创业想法的客观反馈 • 了解您的总可用市场和目标细分市场 • 验证关于市场机会和竞争的假设 • 定义清晰的使命和目标以指导执行

谁应该使用？

• 创业者和初创公司创始人 • 产品经理和业务战略家 • 创新团队 • 进行初步筛选的天使投资人和风险投资人

用例示例：

• 新产品/服务验证 • 市场机会评估 • 竞争分析 • 商业模式验证 • 目标客户细分 • 使命/愿景完善

快速入门：

1. 安装依赖项： pip install openai agno

2. 设置环境变量：

   • export OPENAI_API_KEY=“xxx”

3. 运行： python startup_idea_validator.py

该工作流程将通过人工智能驱动的分析和研究，引导您完成创业想法的验证。利用这些见解来完善您的概念和业务计划！ """

startup_idea_validator.py

import json
from typing import Iterator, Optional

from agno.agent import Agent
from agno.models.openai import OpenAIChat
from agno.storage.sqlite import SqliteStorage
from agno.tools.googlesearch import GoogleSearchTools
from agno.utils.log import logger
from agno.utils.pprint import pprint_run_response
from agno.workflow import RunEvent, RunResponse, Workflow
from pydantic import BaseModel, Field


class IdeaClarification(BaseModel):
    originality: str = Field(..., description="Originality of the idea.")
    mission: str = Field(..., description="Mission of the company.")
    objectives: str = Field(..., description="Objectives of the company.")


class MarketResearch(BaseModel):
    total_addressable_market: str = Field(
        ..., description="Total addressable market (TAM)."
    )
    serviceable_available_market: str = Field(
        ..., description="Serviceable available market (SAM)."
    )
    serviceable_obtainable_market: str = Field(
        ..., description="Serviceable obtainable market (SOM)."
    )
    target_customer_segments: str = Field(..., description="Target customer segments.")


class StartupIdeaValidator(Workflow):
    idea_clarifier_agent: Agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        instructions=[
            "Given a user's startup idea, its your goal to refine that idea. ",
            "Evaluates the originality of the idea by comparing it with existing concepts. ",
            "Define the mission and objectives of the startup.",
        ],
        add_history_to_messages=True,
        add_datetime_to_instructions=True,
        response_model=IdeaClarification,
        debug_mode=False,
    )

    market_research_agent: Agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        tools=[GoogleSearchTools()],
        instructions=[
            "You are provided with a startup idea and the company's mission and objectives. ",
            "Estimate the total addressable market (TAM), serviceable available market (SAM), and serviceable obtainable market (SOM). ",
            "Define target customer segments and their characteristics. ",
            "Search the web for resources if you need to.",
        ],
        add_history_to_messages=True,
        add_datetime_to_instructions=True,
        response_model=MarketResearch,
        debug_mode=False,
    )

    competitor_analysis_agent: Agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        tools=[GoogleSearchTools()],
        instructions=[
            "You are provided with a startup idea and some market research related to the idea. ",
            "Identify existing competitors in the market. ",
            "Perform Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis for each competitor. ",
            "Assess the startup’s potential positioning relative to competitors.",
        ],
        add_history_to_messages=True,
        add_datetime_to_instructions=True,
        markdown=True,
        debug_mode=False,
    )

    report_agent: Agent = Agent(
        model=OpenAIChat(id="gpt-4o-mini"),
        instructions=[
            "You are provided with a startup idea and other data about the idea. ",
            "Summarise everything into a single report.",
        ],
        add_history_to_messages=True,
        add_datetime_to_instructions=True,
        markdown=True,
        debug_mode=False,
    )

    def get_idea_clarification(self, startup_idea: str) -> Optional[IdeaClarification]:
        try:
            response: RunResponse = self.idea_clarifier_agent.run(startup_idea)

            # Check if we got a valid response
            if not response or not response.content:
                logger.warning("Empty Idea Clarification response")
            # Check if the response is of the expected type
            if not isinstance(response.content, IdeaClarification):
                logger.warning("Invalid response type")

            return response.content

        except Exception as e:
            logger.warning(f"Failed: {str(e)}")

        return None

    def get_market_research(
        self, startup_idea: str, idea_clarification: IdeaClarification
    ) -> Optional[MarketResearch]:
        agent_input = {"startup_idea": startup_idea, **idea_clarification.model_dump()}

        try:
            response: RunResponse = self.market_research_agent.run(
                json.dumps(agent_input, indent=4)
            )

            # Check if we got a valid response
            if not response or not response.content:
                logger.warning("Empty Market Research response")

            # Check if the response is of the expected type
            if not isinstance(response.content, MarketResearch):
                logger.warning("Invalid response type")

            return response.content

        except Exception as e:
            logger.warning(f"Failed: {str(e)}")

        return None

    def get_competitor_analysis(
        self, startup_idea: str, market_research: MarketResearch
    ) -> Optional[str]:
        agent_input = {"startup_idea": startup_idea, **market_research.model_dump()}

        try:
            response: RunResponse = self.competitor_analysis_agent.run(
                json.dumps(agent_input, indent=4)
            )

            # Check if we got a valid response
            if not response or not response.content:
                logger.warning("Empty Competitor Analysis response")

            return response.content

        except Exception as e:
            logger.warning(f"Failed: {str(e)}")

        return None

    def run(self, startup_idea: str) -> Iterator[RunResponse]:
        logger.info(f"Generating a startup validation report for: {startup_idea}")

        # Clarify and quantify the idea
        idea_clarification: Optional[IdeaClarification] = self.get_idea_clarification(
            startup_idea
        )

        if idea_clarification is None:
            yield RunResponse(
                event=RunEvent.workflow_completed,
                content=f"Sorry, could not even clarify the idea: {startup_idea}",
            )
            return

        # Do some market research
        market_research: Optional[MarketResearch] = self.get_market_research(
            startup_idea, idea_clarification
        )

        if market_research is None:
            yield RunResponse(
                event=RunEvent.workflow_completed,
                content="Market research failed",
            )
            return

        competitor_analysis: Optional[str] = self.get_competitor_analysis(
            startup_idea, market_research
        )

        # Compile the final report
        final_response: RunResponse = self.report_agent.run(
            json.dumps(
                {
                    "startup_idea": startup_idea,
                    **idea_clarification.model_dump(),
                    **market_research.model_dump(),
                    "competitor_analysis_report": competitor_analysis,
                },
                indent=4,
            )
        )

        yield RunResponse(
            content=final_response.content, event=RunEvent.workflow_completed
        )


# Run the workflow if the script is executed directly
if __name__ == "__main__":
    from rich.prompt import Prompt

    # Get idea from user
    idea = Prompt.ask(
        "[bold]What is your startup idea?[/bold]\n✨",
        default="A marketplace for Christmas Ornaments made from leather",
    )

    # Convert the idea to a URL-safe string for use in session_id
    url_safe_idea = idea.lower().replace(" ", "-")

    startup_idea_validator = StartupIdeaValidator(
        description="Startup Idea Validator",
        session_id=f"validate-startup-idea-{url_safe_idea}",
        storage=SqliteStorage(
            table_name="validate_startup_ideas_workflow",
            db_file="tmp/agno_workflows.db",
        ),
    )

    final_report: Iterator[RunResponse] = startup_idea_validator.run(startup_idea=idea)

    pprint_run_response(final_report, markdown=True)

使用方法

1. 创建虚拟环境

   打开 Terminal 并创建一个 python 虚拟环境。

   Mac

   python3 -m venv .venv
   source .venv/bin/activate
   
2. 安装库

   pip install openai agno
   
3. 运行工作流程

   python startup_idea_validator.py