Implementation Blueprint

# Ensure Python 3.10+ is installed
python3 --version

# Create a virtual environment
python3 -m venv crewai-env
source crewai-env/bin/activate

# Install crewAI and standard tools
pip install crewai langchain-openai duckduckgo-search

export OPENAI_API_KEY="sk-your-openai-api-key"

from crewai import Agent, Task, Crew, Process
from langchain_community.tools import DuckDuckGoSearchRun

# 1. Define Tools
search_tool = DuckDuckGoSearchRun()

# 2. Define Agents
researcher = Agent(
    role='Senior Research Analyst',
    goal='Uncover cutting-edge developments in AI and data science',
    backstory="""You work at a leading tech think tank.
    Your expertise lies in identifying emerging trends.
    You have a knack for dissecting complex data and presenting actionable insights.""",
    verbose=True,
    allow_delegation=False,
    tools=[search_tool]
)

writer = Agent(
    role='Tech Content Strategist',
    goal='Craft compelling content on tech advancements',
    backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
    You transform complex concepts into compelling narratives.""",
    verbose=True,
    allow_delegation=True # The writer can ask the researcher for more info
)

# 3. Define Tasks
task1 = Task(
    description="""Conduct a comprehensive analysis of the latest advancements in AI agents in 2024.
    Identify key trends, breakthrough technologies, and potential industry impacts.""",
    expected_output="Full analysis report in bullet points",
    agent=researcher
)

task2 = Task(
    description="""Using the insights provided, develop an engaging blog post that highlights the most significant AI agent advancements.
    Your post should be informative yet accessible, catering to a tech-savvy audience. Use markdown formatting.""",
    expected_output="A 4 paragraph markdown blog post",
    agent=writer
)

# 4. Instantiate the Crew
crew = Crew(
    agents=[researcher, writer],
    tasks=[task1, task2],
    process=Process.sequential # Tasks execute in order
)

# 5. Kickoff the execution
print("Starting the Crew orchestration...")
result = crew.kickoff()

print("######################")
print("FINAL RESULT:")
print(result)

from langchain_community.llms import Ollama

# Configure local LLM
ollama_llm = Ollama(model="mistral")

# Pass it to the agent
local_agent = Agent(
    role='Local Data Analyst',
    goal='Analyze local CSV files securely',
    backstory="You process sensitive data that cannot leave the internal network.",
    llm=ollama_llm,  # Override the default OpenAI model here
    verbose=True
)

pip install fastapi uvicorn pydantic

from fastapi import FastAPI
from pydantic import BaseModel
# import your crew definition logic here...

app = FastAPI(title="Company AI Agents API")

class ResearchRequest(BaseModel):
    topic: str

@app.post("/api/research")
async def run_research_crew(request: ResearchRequest):
    # Dynamically inject the requested topic into the task description
    task1.description = f"Conduct comprehensive research on: {request.topic}."
    
    # Run the crew synchronously (In a real production environment, use Celery/BackgroundTasks for long running crews)
    result = crew.kickoff()
    
    return {"topic": request.topic, "result": result}

# Run with: uvicorn api:app --host 0.0.0.0 --port 8000