Enterprise Tech Stack Selection

The Framework Decision Is Not the Most Important Decision

Teams often spend weeks debating LangGraph vs AutoGen vs CrewAI. The framework choice matters, but the infrastructure decisions underneath it matter more. A well-architected system with a mediocre framework beats a poorly-architected system with the perfect framework every time.

Start with infrastructure. Then choose a framework.

Infrastructure Layer: Required Regardless of Framework

These components are non-negotiable for any enterprise MAS:

flowchart TD
    subgraph App["Application Layer"]
        FW["Framework\nLangGraph / AutoGen / CrewAI"]
    end
    subgraph Infra["Infrastructure Layer, Framework-Agnostic"]
        PS[("Persistent State\nPostgreSQL / Redis")]
        MQ["Message Queue\nKafka / Redis Streams"]
        SM["Secrets Manager\nHashiCorp Vault / AWS SM"]
        OBS["Observability\nTraces + Metrics + Logs"]
    end
    FW --> PS
    FW --> MQ
    FW --> SM
    FW --> OBS
    style FW fill:#EEF0F7,stroke:#6366F1,color:#0F172A
    style PS fill:#f0fdf9,stroke:#0D9488,color:#0F172A
    style MQ fill:#f0fdf9,stroke:#0D9488,color:#0F172A
    style SM fill:#f0fdf9,stroke:#0D9488,color:#0F172A
    style OBS fill:#f0fdf9,stroke:#0D9488,color:#0F172A

Persistent State, Agent runs fail. Pods restart. LLM APIs timeout. Without persistent state, a 45-minute multi-agent workflow that fails at step 38 restarts from scratch. PostgreSQL with LangGraph’s AsyncPostgresSaver or Redis for ephemeral state are standard choices.

Message Queue, Synchronous agent-to-agent calls don’t scale and create tight coupling. A queue decouples producers from consumers, handles backpressure, and enables async agent communication.

Secrets Manager, API keys for 6 different LLM providers, 3 databases, and 2 vector stores cannot live in environment variables. Vault or cloud-native secrets managers rotate credentials and audit access.

Observability, You cannot debug what you cannot see. Distributed traces (OpenTelemetry), structured logs (JSON to a log aggregator), and metrics (Prometheus) are required from day one.

Framework Comparison

# Decision matrix: score each criterion for your specific use case
# Scale: 1 (poor) to 5 (excellent)

framework_comparison = {
    "LangGraph": {
        "control_flow_flexibility":  5,  # arbitrary graph topology, conditional edges, cycles
        "state_management":          5,  # built-in checkpointing, time-travel, state reducers
        "learning_curve":            3,  # graph model takes time to internalize
        "conversation_support":      3,  # possible but not the primary paradigm
        "production_maturity":       5,  # used in production at scale, extensive docs
        "debugging_tooling":         5,  # LangSmith integration is class-leading
        "best_for": "Complex stateful workflows with dynamic control flow",
    },
    "AutoGen": {
        "control_flow_flexibility":  3,  # conversation-driven, less graph-like
        "state_management":          3,  # improving but not LangGraph's level
        "learning_curve":            4,  # conversation model is intuitive
        "conversation_support":      5,  # purpose-built for multi-agent conversation
        "production_maturity":       4,  # v0.4 is significantly improved
        "debugging_tooling":         3,  # improving, less mature than LangGraph
        "best_for": "Conversational multi-agent collaboration, code generation teams",
    },
    "CrewAI": {
        "control_flow_flexibility":  3,  # role-based flow, less flexible than LangGraph
        "state_management":          3,  # basic, improving in newer versions
        "learning_curve":            5,  # fastest to get started
        "conversation_support":      4,  # Crew conversation model is natural
        "production_maturity":       4,  # large community, well-documented
        "debugging_tooling":         3,  # crew traces, but less depth than LangSmith
        "best_for": "Role-based teams with well-defined task sequences",
    },
}

LangGraph: When to Choose It

LangGraph is the right choice when your workflow has:

• Conditional routing based on intermediate results
• Cycles (retry logic, self-correction loops)
• Checkpointing requirements (long-running, resumable workflows)
• Human-in-the-loop at specific decision points

# LangGraph's core strength: routing decisions are Python functions, not config
# This lets you implement arbitrarily complex control flow

from langgraph.graph import StateGraph, END
from typing import Literal

def route_after_validation(state: dict) -> Literal["fix_errors", "proceed", "escalate"]:
    """Routing logic that reads computed state and returns the next node name."""
    if state["validation_errors"] and state["retry_count"] < 3:
        return "fix_errors"      # loop back for automatic retry
    elif state["validation_errors"] and state["retry_count"] >= 3:
        return "escalate"        # too many retries, route to human review
    else:
        return "proceed"         # clean result, move to next stage

builder = StateGraph(dict)
builder.add_node("validate", validate_output)
builder.add_node("fix_errors", fix_validation_errors)
builder.add_node("proceed", proceed_to_next_stage)
builder.add_node("escalate", escalate_to_human)

# add_conditional_edges calls route_after_validation with current state after each validate run
builder.add_conditional_edges("validate", route_after_validation)
builder.add_edge("fix_errors", "validate")    # retry loop: fix → re-validate

AutoGen: When to Choose It

AutoGen 0.4’s AgentChat model excels when agents need to converse freely to solve a problem, the solution emerges from dialogue rather than a predefined graph.

# AutoGen 0.4: conversational multi-agent pattern
# Agents collaborate via structured messages; no explicit graph required

from autogen_agentchat.agents import AssistantAgent, UserProxyAgent
from autogen_agentchat.teams import SelectorGroupChat
from autogen_ext.models import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(model="gpt-5.6-sol")

# Each agent has a specialized role defined by its system_message
researcher = AssistantAgent(
    name="Researcher",
    model_client=model_client,
    # System message defines the agent's persona and constraints
    system_message="You research topics and return factual summaries with citations.",
)

critic = AssistantAgent(
    name="Critic",
    model_client=model_client,
    # Critic agent reviews the researcher's output and identifies gaps
    system_message="You critique research for accuracy, completeness, and logical gaps.",
)

# SelectorGroupChat uses an LLM to decide which agent speaks next
# This enables emergent conversation flow without a hardcoded sequence
team = SelectorGroupChat(
    participants=[researcher, critic],
    model_client=model_client,
    termination_condition=lambda msgs: len(msgs) > 10,  # stop after 10 exchanges
)

CrewAI: When to Choose It

CrewAI is fastest to prototype when your system maps naturally to roles and sequential tasks.

# CrewAI 0.80+: role-based team with explicit task sequence
from crewai import Agent, Task, Crew, Process

# Agents are defined by their role, goal, and backstory
# CrewAI uses these as system prompts to specialize each agent
analyst = Agent(
    role="Senior Financial Analyst",
    goal="Analyze financial data and identify key trends and risks",
    backstory="15 years experience in investment banking. Expert in ratio analysis and DCF modeling.",
    verbose=True,
)

writer = Agent(
    role="Financial Report Writer",
    goal="Transform analysis into clear, executive-ready reports",
    backstory="Former Bloomberg journalist. Specializes in making complex finance accessible.",
)

# Tasks define the expected output and which agent is responsible
analysis_task = Task(
    description="Analyze Q3 financials for {company}. Focus on: revenue growth, margins, cash flow.",
    expected_output="Structured analysis with key ratios, trends, and 3 risk factors.",
    agent=analyst,                # this task is assigned to the analyst agent
)

# Process.sequential ensures tasks run in order: analyst first, then writer
crew = Crew(
    agents=[analyst, writer],
    tasks=[analysis_task, report_task],
    process=Process.sequential,
)

The Hybrid Approach: Framework + Custom Glue

Most enterprise systems end up with a framework at the core plus custom glue code for enterprise-specific requirements. This is normal and expected.

# Pattern: use LangGraph for orchestration, custom code for enterprise integration
# The framework handles state + routing; your code handles auth, audit, and compliance

class EnterpriseAgentOrchestrator:
    def __init__(self, graph, audit_logger, secret_manager):
        self.graph = graph               # LangGraph compiled graph
        self.audit = audit_logger        # custom audit trail for compliance
        self.secrets = secret_manager    # HashiCorp Vault client

    async def run(self, task: dict, user_context: dict) -> dict:
        # Pre-run: enterprise concerns that frameworks don't handle
        await self.audit.log_task_start(task, user_context)   # SOC 2 audit trail
        api_keys = await self.secrets.get_batch([              # rotate keys at runtime
            "openai_key", "anthropic_key", "database_url"
        ])

        # Inject enterprise config into the graph run
        config = {
            "configurable": {
                "thread_id": task["task_id"],
                "user_id": user_context["user_id"],
                "api_keys": api_keys,          # passed to nodes via config, not state
            }
        }

        result = await self.graph.ainvoke(task, config=config)

        # Post-run: more enterprise concerns
        await self.audit.log_task_complete(task["task_id"], result)
        return result

Choose your framework based on your primary workflow pattern. Build the infrastructure layer first. The framework is just the top slice.