First working implementation of the program
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import os
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from pydantic import BaseModel, Field as PydanticField # Alias it to avoid conflicts
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from crewai.tools import tool
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from typing import List, Dict, Any
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from shared_config import verbose, logger
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from crewai import Agent, Task, Crew, Process, LLM, TaskOutput, TaskOutput
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from crewai.tools import tool
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from langchain_community.tools import DuckDuckGoSearchRun
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from datetime import datetime
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import os
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###########################################################################################
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#Define a callback function to log CrewAI step task
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###########################################################################################
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def log_task_to_file(output:TaskOutput):
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"""Callback function to append task details into the log file."""
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formatted_str = f"""
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{'\n'}CrewAI Task Info
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{"=" * 50}
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Timestamp: {datetime.now()}
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Task Description: {output.description}
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Agent Assigned: {output.agent}
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{"-" * 30}
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Raw Output:{'\n'}
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{output.raw}{'\n'}
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{"=" * 50}
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"""
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logger.info(formatted_str)
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def create_crew(config):
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# ==========================================
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# 1. DEFINE CUSTOM TOOLS & OUTPUT SCHEMAS
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# ==========================================
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# Simple search tool for the Research Agent
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@tool("web_search")
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def web_search_tool(query: str) -> str:
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"""Search the web for current events, news, or factual information."""
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ddg = DuckDuckGoSearchRun()
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return ddg.invoke(query)
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class ResearchEvidence(BaseModel):
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source_url: str = PydanticField(..., description="The source URL of the evidence.")
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claim: str = PydanticField(..., description="The factual finding or data point.")
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context: str = PydanticField(..., description="The context or time frame of this data point.")
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class ResearchOutputSchema(BaseModel):
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competitors: List[Dict[str, Any]] = PydanticField(..., description="List of competitors with pricing, features, and target audience.")
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market_trends: List[str] = PydanticField(..., description="Key macro trends observed.")
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evidence_citations: List[ResearchEvidence] = PydanticField(..., description="Valid source citations for facts collected.")
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# ---------------------------------------------------------
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#Create llm instance
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# ---------------------------------------------------------
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if config.provider == "OpenAI":
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llm = LLM(
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model=config.model,
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api_key=os.getenv("OPENAI_API_KEY"),
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temperature=config.temperature,
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max_completion_tokens=config.max_tokens,
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)
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else:
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llm = LLM(
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model=f"ollama/{config.model}",
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api_key="ollama",
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base_url="http://localhost:11434",
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temperature=config.temperature
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)
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logger.info(f"""LLM Model: {config.model}, Provider: {config.provider}, Temperature: {config.temperature}""")
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# ---------------------------------------------------------
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# 1. Agent Definitions
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# ---------------------------------------------------------
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# 1. Orchestrator & Documenter (Manager)
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head_planner = Agent(
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role='Head Planner and Orchestrator',
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goal="Synthesize intermediary outputs and assemble the final master GTM document.",
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backstory="A precise compiler and documentarian...",
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#goal='Orchestrate the GTM workflow, delegate tasks to specialized agents, and synthesize all insights into a flawless final GTM document.',
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#backstory='You are a Principal Operations Strategy Director. You excel at managing cross-functional teams, ensuring strict alignment, and formatting messy raw data into executive-ready corporate documentation.',
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allow_delegation=False,
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verbose=verbose,
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llm=llm
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)
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# 2. researcher
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researcher_agent = Agent(
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role="Market Research Specialist",
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goal="Gather raw competitive intelligence, target audience signals, and pricing tiers with structural citations.",
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backstory="An elite OSINT researcher who tracks digital signals. You ignore marketing fluff and extract verifiable numbers, product capabilities, and structural URLs.",
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#role='Research Agent',
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#goal='Gather comprehensive market data, identify industry trends, and map out the competitive landscape.',
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#backstory='You are an elite Market Research Analyst. You are an expert at mining deep web data, extracting customer pain points, and finding precise statistical facts from modern industry reports.',
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verbose=verbose,
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tools=[web_search_tool],
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use_system_prompt=False,
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llm=llm
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)
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# 3. analyst
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analyst_agent = Agent(
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role="Strategic Business Analyst",
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goal="Transform unstructured research data into rigorous market models, SWOT matrixes, and 4P/7P frameworks.",
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backstory="A former MBB consultant who specializes in corporate strategy. You find patterns in chaotic data, flag market gaps, and synthesize pricing elasticities.",
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#role='Analyst Agent',
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#goal='Synthesize raw market data into structured frameworks like SWOT, 4P, 7P, and target user personas.',
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#backstory='You are a data-driven Strategic Analyst. You look past surface-level facts to find underlying market gaps, evaluate competitive positioning, and model risks.',
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verbose=verbose,
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llm=llm
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)
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# 4. strategist
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strategist_agent = Agent(
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role="Go-To-Market (GTM) Strategist",
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goal="Draft the final actionable GTM blueprint including ICP maps, messaging layers, and launch milestone phases.",
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backstory="A legendary growth marketing executive. You turn raw analytical data into high-converting messaging matrixes, channel frameworks, and scalable product launch schedules.",
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#role='Strategy Agent',
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#goal='Formulate the actionable Go-To-Market blueprint, positioning, pricing strategies, and launch timelines.',
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#backstory='You are a veteran GTM Growth Strategist. You specialize in crafting commercial launch playbooks, positioning products uniquely against rivals, and designing user-acquisition loops.',
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verbose=verbose,
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llm=llm
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)
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# ---------------------------------------------------------
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# 2. Task Definitions
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# ---------------------------------------------------------
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#Fetch User query from config
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query = config.user_query
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# Task 1: Research Agent gathers raw intelligence
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research_task = Task(
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description=(
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"Search the web for the top 3-5 players in the target market: '{query}'. "
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"Extract pricing tiers, core feature modules, and target customer profiles. "
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"Every single metric or claim MUST have a URL source tracked inside the JSON structure. "
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"Do not fabricate any data and do not make assumptions. Only include verifiable facts using working URLs."
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"If you cannot find a specific metric, leave it blank and do not fabricate data."
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),
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expected_output="A structured JSON file matching the schema with validated competitive rows and strict citation tracking.",
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output_json=ResearchOutputSchema,
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agent=researcher_agent
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)
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# Task 2: Analyst Agent creates comparative structures
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analysis_task = Task(
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description=(
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"Review the structured JSON output provided by the Research Agent. "
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"Construct a detailed markdown competitive landscape table and a pricing matrix. "
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"Perform a comprehensive SWOT, 4P, and 7P analysis based strictly on the evidence compiled."
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),
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expected_output="A deep-dive analytical report featuring clean markdown tables, a SWOT quadrant, and an exhaustive 4P/7P matrix analysis.",
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agent=analyst_agent
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)
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# Task 3: Strategy Agent plans market entry
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strategy_task = Task(
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description=(
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"Take the analytical matrices and SWOT outputs to build a comprehensive GTM document. "
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"Define 2 distinct Ideal Customer Profiles (ICPs). Outline the core value proposition. "
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"Create a messaging framework (Hook, Problem, Solution) per ICP. "
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"Identify high-ROI acquisition channels and detail a 30-60-90 day milestone launch plan."
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),
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expected_output="An actionable, comprehensive market entry strategy blueprint divided into logical execution phases.",
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agent=strategist_agent
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)
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# Task 4: Head Planner synthesizes, formats, and exports
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compilation_task = Task(
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description=(
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"Synthesize the outputs from the Research, Analyst, and Strategy agents into a seamless, high-caliber Master GTM Report. "
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"Include all research data tables, citations, SWOT/7P strategic matrices, and execution timelines. "
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"Include a title that cleary identifies the target market and a subtitle that highlights the core value proposition. "
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),
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expected_output="Final master GTM document.",
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context=[research_task, analysis_task, strategy_task],
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agent=head_planner
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)
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# ---------------------------------------------------------
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# 3. Crew Setup
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# ---------------------------------------------------------
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crew= Crew(
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agents=[researcher_agent, analyst_agent, strategist_agent, head_planner],
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tasks=[research_task, analysis_task, strategy_task, compilation_task],
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process=Process.sequential, # Tasks execute in exact sequence
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#manager_agent=head_planner
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task_callback=log_task_to_file,
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verbose=verbose
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)
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logger.info(f"Crew setup complete with {len(crew.agents)} agents and {len(crew.tasks)} tasks.")
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return crew
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