AI Orchestration Frameworks: Building Complex AI Workflows

Complex AI applications require orchestration of multiple models, tools, and workflows. Let’s explore the frameworks.

Orchestration Options

# AutoGen for multi-agent orchestration
from autogen import AssistantAgent, UserProxyAgent

assistant = AssistantAgent(
    name="DataAnalyst",
    llm_config={"model": "gpt-4o"},
    system_message="You analyze data and provide insights."
)

user_proxy = UserProxyAgent(
    name="User",
    human_input_mode="NEVER",
    code_execution_config={"work_dir": "coding"}
)

user_proxy.initiate_chat(assistant, message="Analyze the sales trends")

# LangGraph for workflow orchestration
from langgraph.graph import StateGraph

def analyze_node(state):
    # Analysis logic
    return {"analysis": result}

def decide_next(state):
    if state["needs_more_data"]:
        return "fetch_data"
    return "complete"

workflow = StateGraph(State)
workflow.add_node("analyze", analyze_node)
workflow.add_conditional_edges("analyze", decide_next)

app = workflow.compile()
result = app.invoke({"query": "Analyze sales"})

Choose orchestration frameworks based on workflow complexity and team expertise.