Multi-Agent Systems — CrewAI, AutoGen, LangGraph — Prompt Engineering & AI Tools

🤖 Multi-Agent কী?

একটি agent-এর বদলে একাধিক specialized agent মিলে কাজ করে। প্রতিটি agent-এর নিজস্ব role, tool ও expertise।

🎯 কেন Multi-Agent?

Single agent সব কাজে expert হতে পারে না
Division of labor — better quality
Parallel execution — faster
Specialized prompt per role
Easier to debug ও maintain

🛠️ Popular Frameworks

CrewAI — easy, role-based, Python
AutoGen (Microsoft) — conversational pattern
LangGraph — state machine, production
Swarm (OpenAI) — lightweight handoff

💻 CrewAI Example

from crewai import Agent, Task, Crew

researcher = Agent(
    role="Senior Researcher",
    goal="Find latest AI trends 2025",
    backstory="Expert AI analyst",
    tools=[search_tool]
)

writer = Agent(
    role="Tech Writer",
    goal="Write engaging blog posts",
    backstory="10 years tech writing"
)

task1 = Task(description="Research top 5 AI trends", agent=researcher)
task2 = Task(description="Write 1500-word blog", agent=writer)

crew = Crew(agents=[researcher, writer], tasks=[task1, task2])
result = crew.kickoff()

🏗️ LangGraph Pattern

from langgraph.graph import StateGraph, END

def researcher_node(state): ...
def writer_node(state): ...
def reviewer_node(state): ...

graph = StateGraph(State)
graph.add_node("research", researcher_node)
graph.add_node("write", writer_node)
graph.add_node("review", reviewer_node)
graph.add_edge("research", "write")
graph.add_conditional_edges("review",
    lambda s: "write" if s["needs_revision"] else END
)

🎓 মূল কথা ⭐

Multi-agent = team of specialists। Start simple — 2-3 agent। Add complexity only when needed। Production-এ debugging, observability critical।