Orchestration Showdown: Graphs vs Conversations vs Roles vs Raw Loops

The framework you choose shapes how you think about the problem.

Four philosophies dominate agent orchestration: graphs (LangGraph), conversations (AutoGen), role-playing teams (CrewAI), and raw loops (custom). Each imposes a mental model. Each has trade-offs. Choose wrong and you'll fight the framework instead of shipping.

This isn't a feature comparison. It's architecture.

The Paradigm Shift

Traditional software: deterministic instruction execution. You define control flow explicitly—loops, conditionals, function calls. The code does exactly what you wrote.

Agent software: probabilistic intent resolution. An LLM decides what to do next. Your job is to bound that decision space, manage state across steps, and recover when things go wrong.

Orchestration frameworks are the operating system for this new paradigm. They manage agent lifecycles, state persistence, and the bridge between probabilistic reasoning and deterministic execution.

Four Philosophies

LangGraph: The State Machine

LangGraph models agents as directed graphs. Nodes are compute units. Edges define control flow. State flows through the graph, modified at each node.

User Input → Agent Node → [Conditional Edge] → Tool Node → Agent Node → Output
                              ↑___________________________|

Key concepts:

• Nodes are functions, not autonomous agents. A "Researcher Agent" might be three nodes: generate_query, execute_search, synthesize_results. Decomposition, not monoliths.
• Edges are control flow. Normal edges are deterministic. Conditional edges are where the LLM's probabilistic output gets routed—a classifier decides the next step.
• Cyclic graphs enable loops. Reason → Act → Observe → Reason. Real problem-solving is iterative, not linear.
• State schema is the single source of truth. TypedDict or Pydantic. If it's not in the schema, it doesn't exist.

Philosophy: explicit control, no magic. You define every possible path. The graph compels architectural discipline.

AutoGen: The Conversation

AutoGen treats orchestration as dialogue between agents. The primitive is the message, not the state.

Coder Agent ←→ Reviewer Agent ←→ Executor Agent
                    ↓
            GroupChat Manager

Key concepts:

• Agents are conversationalists. They send() and receive() messages. System capabilities emerge from interaction.
• GroupChat Manager acts as message bus. When one agent speaks, the manager broadcasts and decides who speaks next.
• Speaker selection is the orchestration engine. Auto (LLM picks), round-robin, or custom FSM.
• Evolution to event-driven: newer versions adopt the Actor Model—loosely coupled agents reacting to events asynchronously.

Philosophy: emergence through dialogue. Define the participants; let conversation structure emerge. Works well for open-ended research. Less well for rigid business logic.

CrewAI: The Role-Playing Team

CrewAI anthropomorphizes the code. Agents have Roles, Goals, and Backstories. Teams are organized like corporate hierarchies.

Manager Agent
    ├── Researcher (Role: "Senior Analyst", Backstory: "20 years experience...")
    ├── Writer (Role: "Content Strategist", ...)
    └── Editor (Role: "Quality Reviewer", ...)

Key concepts:

• Persona as prompt engineering. Defining a detailed backstory steers the LLM's latent space without manual prompt tuning.
• Sequential process: linear pipeline. Task A output → Task B input. Simple, deterministic.
• Hierarchical process: a Manager agent analyzes tasks, plans execution, delegates to workers. Implicit orchestration.
• Flows: recent addition bringing graph-like structure for when the "team" metaphor breaks down.

Philosophy: anthropomorphize the code. Think in terms of job descriptions, not functions. Fast to prototype, but the Manager is a black box.

Custom/DIY: The Router and Loop

A significant fraction of production systems bypass frameworks entirely.

while True:
    response = llm.call(messages)
    if response.tool_calls:
        result = execute_tool(response.tool_calls[0])
        messages.append(result)
    else:
        return response.content

Key concepts:

• ReAct loop: while loop + tool calls. Send prompt, check for tools, execute, append result, repeat. That's it.
• Router pattern: single LLM call classifies intent, dispatches to specific handler. Flat architecture, low latency.
• No framework overhead: no abstraction layers, no magic, no dependency churn.

Philosophy: control over abstraction. When you can see every line of code, you can debug and optimize everything. The trade-off is you build everything yourself.

State Management: The Differentiator

How a framework handles state determines whether it's production-ready or prototype-only.

LangGraph's checkpointing is production-grade. Every node execution creates an immutable state snapshot. You can query history, load a past checkpoint, modify state, and fork execution. "Time travel" for debugging production failures.

AutoGen's state is the conversation. Long-running chats get expensive (context stuffing). Newer versions add explicit save_state() / load_state(), but it's bolted on, not foundational.

CrewAI has memory layers (short-term, long-term, entity), but they're designed for recall, not replay. Flow state persistence is less granular than LangGraph's step-by-step checkpointing.

Production Reality

Observability

LangGraph + LangSmith: best-in-class. Every node, every edge transition, every state mutation—traced and visualized. You can see exactly which path the agent took and inspect state at each step.

AutoGen + OpenTelemetry: industry-standard distributed tracing. Integrates with Jaeger, Arize Phoenix. Good for microservices shops.

CrewAI: integrates with AgentOps and LangTrace, but the hierarchical delegation happens inside framework prompts. Tracing why the Manager made a decision is harder.

Latency

CrewAI (high overhead): Agents often "think about thinking" before acting. Planning steps, chain-of-thought preambles. A simple tool call might take 9+ seconds due to this overhead.

LangGraph (low overhead): Graph traversal is compiled Python—near-instant. The only latency is state persistence (database write) and LLM inference.

Custom (zero overhead): As fast as your LLM provider allows.

Cost

OpenAI Assistants API: convenient but dangerous. The server manages threads by re-feeding entire conversation history. Developers have limited control over summarization. Token costs balloon as conversations grow.

AutoGen GroupChat: every message broadcasts to every agent. Five agents, ten turns = geometric token consumption.

LangGraph: granular state design lets you discard irrelevant history, summarize before passing to nodes, implement "ephemeral state" patterns. You control what each node sees.

The Convergence

The industry is converging on graphs.

Microsoft Agent Framework (announced late 2025) merges AutoGen + Semantic Kernel:

• Adopts graph-based workflows (validating LangGraph's philosophy)
• Adds checkpointing (validating the persistence pattern)
• Combines AutoGen's orchestration with Semantic Kernel's enterprise connectors

Model Context Protocol (MCP) is standardizing tool integration, commoditizing the "connector" layer. Competition shifts to orchestration intelligence and observability.

The pattern: explicit control flow is winning over probabilistic interaction. "Chat-based" orchestration (loops, lack of control) and "role-based" orchestration (opacity) are losing to state machines.

Decision Framework

The Verdict

Graphs are winning. Explicit beats probabilistic for production. "Agency" is not magic—it's an engineering pattern.

LangGraph is the right choice for anything complex or production-bound. The graph model forces architectural discipline. Checkpointing makes recovery and debugging tractable. LangSmith gives you visibility.

CrewAI ships fast for prototypes and creative workflows. Know its limits: latency overhead, debugging opacity, implicit orchestration that can fail mysteriously.

AutoGen excels at research and coding swarms where the solution path isn't known. The conversation metaphor works for emergence, less for reliability.

Custom is underrated. For a customer support bot or data extraction pipeline, a 50-line Python script beats any framework. No magic, no dependencies, no surprises.

The framework you choose is the mental model you adopt. Choose the one that matches how you actually think about your problem.