Building Production Multi-Agent Systems with CrewAI, LangChain & LlamaIndex

Single-prompt LLM applications are rapidly being replaced by autonomous multi-agent architectures that collaborate, delegate tasks, and execute complex business logic autonomously.

1. Implementing a CrewAI Multi-Agent Workflow

from crewai import Agent, Task, Crew, Process
from langchain_openai import ChatOpenAI

# Initialize LLM
llm = ChatOpenAI(model="gpt-4o", temperature=0.2)

# Define Senior Researcher Agent
researcher = Agent(
    role='Senior AI Researcher',
    goal='Uncover groundbreaking developments in AI frameworks',
    backstory='You are a seasoned analyst at Guide AI Hub specializing in AI trends.',
    verbose=True,
    llm=llm
)

# Define Senior Editor Agent
editor = Agent(
    role='Technical Content Editor',
    goal='Synthesize technical research into clean, actionable markdown guides',
    backstory='You review technical documentation and enforce high quality standards.',
    verbose=True,
    llm=llm
)

# Define Execution Tasks
task1 = Task(description='Research recent updates in CrewAI v0.50+', agent=researcher)
task2 = Task(description='Write a technical summary article based on findings', agent=editor)

# Form the Crew
crew = Crew(
    agents=[researcher, editor],
    tasks=[task1, task2],
    process=Process.sequential
)

result = crew.kickoff()
print(result)