Multi-Agent Orchestration: LangChain vs CrewAI vs AutoGen for Enterprise Deployments

from crewai import Agent, Task, Crew, Process
from crewai.tools import BaseTool
from langchain_openai import ChatOpenAI
from typing import Optional
import os

# Initialize LLM
llm = ChatOpenAI(model="gpt-4o", temperature=0.1)

# --- Tool Definitions ---
class VendorDatabaseTool(BaseTool):
    name: str = "vendor_database_lookup"
    description: str = "Look up vendor profiles, ratings, and historical performance from the enterprise vendor database."

    def _run(self, vendor_name: str) -> str:
        # In production: query your ERP/vendor DB
        return f"Vendor {vendor_name}: Rating 4.2/5, 23 past contracts, avg delivery 3.2 days, compliance score 94%"

class CompliancePolicyTool(BaseTool):
    name: str = "compliance_policy_lookup"
    description: str = "Retrieve applicable compliance policies and procurement rules for a given category or spend threshold."

    def _run(self, category: str, spend_amount: float) -> str:
        # In production: query your policy knowledge base
        if spend_amount > 50000:
            return f"Category {category}: Requires 3 competitive bids, CFO sign-off above $50k, legal review for contracts >$100k"
        return f"Category {category}: Single-source allowed below $50k, manager approval required"

class PurchaseOrderTool(BaseTool):
    name: str = "create_purchase_order"
    description: str = "Create a draft purchase order in the ERP system. Requires vendor name, amount, and justification."

    def _run(self, vendor: str, amount: float, justification: str) -> str:
        # In production: call ERP API
        po_number = f"PO-2026-{hash(vendor) % 10000:04d}"
        return f"Draft PO {po_number} created for {vendor} at ${amount:,.2f}. Awaiting approval workflow."

# --- Agent Definitions ---
procurement_analyst = Agent(
    role="Senior Procurement Analyst",
    goal="Analyze procurement requests, validate vendor options, and ensure policy compliance",
    backstory=(
        "You are an experienced procurement analyst with deep knowledge of enterprise sourcing "
        "policies, vendor evaluation frameworks, and spend management. You always verify compliance "
        "requirements before recommending any vendor or spend commitment."
    ),
    tools=[VendorDatabaseTool(), CompliancePolicyTool()],
    llm=llm,
    verbose=True,
    max_iter=5
)

vendor_evaluator = Agent(
    role="Vendor Evaluation Specialist",
    goal="Score and rank vendors against requirements using objective criteria",
    backstory=(
        "You specialize in vendor due diligence and comparative analysis. You evaluate vendors "
        "on price, reliability, compliance history, and strategic fit. Your recommendations are "
        "data-driven and defensible to finance and legal stakeholders."
    ),
    tools=[VendorDatabaseTool()],
    llm=llm,
    verbose=True,
    max_iter=5
)

decision_maker = Agent(
    role="Procurement Decision Manager",
    goal="Synthesize analysis and vendor evaluations into a final procurement recommendation with full justification",
    backstory=(
        "You are responsible for final procurement decisions. You balance cost, risk, compliance, "
        "and strategic supplier relationships. Your output is a structured recommendation that "
        "business stakeholders and finance can act on immediately."
    ),
    tools=[PurchaseOrderTool()],
    llm=llm,
    verbose=True,
    max_iter=3
)

# --- Task Definitions ---
def create_procurement_crew(request: str, category: str, budget: float) -> Crew:
    analyze_request = Task(
        description=(
            f"Analyze the following procurement request and identify compliance requirements:\n"
            f"Request: {request}\nCategory: {category}\nBudget: ${budget:,.2f}\n"
            f"Retrieve applicable policies and identify 2-3 candidate vendors from the database."
        ),
        expected_output="A structured analysis including: applicable policies, compliance requirements, and a list of candidate vendors with initial assessment.",
        agent=procurement_analyst
    )

    evaluate_vendors = Task(
        description=(
            f"Using the candidate vendors identified, perform a detailed evaluation for the {category} "
            f"procurement at ${budget:,.2f}. Score each vendor on: pricing competitiveness, "
            f"delivery reliability, compliance score, and strategic fit. Rank and justify your top choice."
        ),
        expected_output="A vendor scorecard with ranked recommendations and a clear justification for the top-ranked vendor.",
        agent=vendor_evaluator,
        context=[analyze_request]
    )

    create_recommendation = Task(
        description=(
            "Based on the compliance analysis and vendor evaluation, create a final procurement "
            "recommendation. If the top vendor is appropriate, create a draft purchase order. "
            "Include risk flags and any required approval steps."
        ),
        expected_output="A final procurement recommendation memo with: recommended vendor, justification, compliance sign-off checklist, and draft PO number if created.",
        agent=decision_maker,
        context=[analyze_request, evaluate_vendors]
    )

    return Crew(
        agents=[procurement_analyst, vendor_evaluator, decision_maker],
        tasks=[analyze_request, evaluate_vendors, create_recommendation],
        process=Process.hierarchical,
        manager_llm=llm,
        verbose=True
    )

def main():
    crew = create_procurement_crew(
        request="Office furniture for new engineering floor — 40 workstations with ergonomic chairs",
        category="Office Equipment & Furniture",
        budget=75000.0
    )
    result = crew.kickoff()
    print("\n=== PROCUREMENT DECISION ===")
    print(result)

if __name__ == "__main__":
    main()