Agent Swarm vs Anthropic Workflows vs LangGraph: Which Multi-Agent Architecture Should You Use?

The phrase "Agent swarm" is everywhere right now. But behind the hype, there are fundamentally different approaches. What does it mean? What do I do with it? How can I use it within my context to meet my needs?

Here are some of our thoughts:

What Is a "Multi-Agent" System?

Think of it like a team of specialists instead of one generalist.

Single agent: One agent that does research, code, emails, calendar—everything.

Multi-agent: A researcher agent, a coder agent, and an assistant agent......—each doing what they're best at, handing off work.

Why? Because AI models work better with focused tasks. A model with 50 tools gets confused, whereas, one model with 5 specialized tools performs better.

1. OpenAI Swarm

A lightweight framework for handing off conversations between agents.

Mental model: A customer service call where you get transferred between departments.

Best for: Customer service bots, simple routing, prototyping multi-agent ideas quickly.

Limitations: Stateless, linear flow only, sequential. Marked as experimental. Please note that, although it is not marked as parallel, you can technically run multiple swarm instances to achieve that.

2. Anthropic's Agent Patterns

Not a framework—a philosophy. Their key insight: "Start simple. Only add complexity when you need it."

They distinguish between Workflows (pre-defined code paths, you control flow) and Agents (dynamic, model-driven, LLM controls flow).

The 5 workflow patterns: Prompt Chaining, Routing, Parallelization, Orchestrator-Workers, and Evaluator-Optimizer.

Best for: Teams who want design patterns rather than a framework dependency. Works with any model in theory (because it is from Anthropic and we haven't seen demos on other models yet).

3. LangGraph Multi-Agent

Build agents as graphs—nodes are agents, edges are communication paths. Two main patterns:

Collaboration: All agents share the same message history. Everyone sees everything. Good for tight collaboration, can get noisy.

Hierarchical (Supervisor): A "boss" agent delegates to "worker" agents with private workspaces. Only final outputs are shared.

Best for: When you need full control over orchestration in production system, state management, and deployment.

4. DeepAgents (LangChain/LangGraph)

A developer framework you run and control, built on LangChain + LangGraph. Ships with planning, a filesystem backend, and sub-agent spawning out of the box.

The security model is explicit: "trust the LLM" means you enforce safety at the tool and sandbox level, not by hoping the model behaves.

Best for: Production agent systems where you need full control over orchestration, tooling, memory, and deployment. If you're building agents into your product, this is the path.

Tradeoff: You own the ops. So that can be powerful, but you take the risk.

5. Kimi Agent Swarm (Moonshot / Kimi 2.5)

A model-native capability where the system auto-creates and coordinates a swarm for you (they claimed up to ~100 sub-agents and ~1,500 tool calls.)

You don't predefine the workflow or subagents. You describe the goal; Kimi figures out the decomposition and parallelism.

Best for: Big, messy tasks that benefit from aggressive parallelism without you designing the graph. "Research a ton of sources and synthesize" or "build this whole thing end-to-end."

Tradeoff: Less explicit control and inspectability. You're buying into Kimi's orchestration style.

6. Claude Code Agent Teams

A product feature inside Claude Code that spawns multiple Claude agents in parallel, with a lead agent coordinating, assigning subtasks, and merging results.

Token costs scale with teammates because each one is effectively its own session with its own context.

Best for: Codebase work—reviews, refactors, multi-area changes where parallel agents each take a slice and a lead merges the results.

Tradeoff: Purpose-built for coding workflows. If your use case is business process automation or research, this isn't the tool.

7. MetaSwarm

An open-source multi-agent orchestrator specifically for Claude Code https://dsifry.github.io/metaswarm/ , coordinating specialized agents across a full dev workflow (issue → implementation → review gates → PR).

It includes a "self-improving" git-native knowledge base—the system learns from the codebase as it works.

Best for: Teams already using Claude Code who want structured, opinionated multi-agent dev workflows without building the orchestration themselves.

How They Compare

Quick Decision Guide

• Building agents into your product? → DeepAgents / LangGraph
• Maximum parallel horsepower, minimal setup? → Kimi Agent Swarm
• Primary use case is coding? → Claude Code Agent Teams or MetaSwarm
• Want patterns, not dependencies? → Anthropic's approach
• Just need simple routing? → OpenAI Swarm

Watch the Costs

Multi-agent parallelism gets expensive fast. Every sub-agent is burning tokens, and coordination overhead adds up.

• Claude explicitly notes that team costs scale with number and duration of teammates
• Kimi's auto-spawning can surprise you if you're not watching
• Framework-based approaches give you control but require careful design

Start Simple

Before any multi-agent framework, ask:

1. Can a single well-prompted LLM do this?
2. Can prompt chaining solve it?
3. Do I actually need multiple agents?

As Anthropic says: "Agents trade latency and cost for better task performance. Consider when this tradeoff makes sense."

The Bottom Line

"Agent swarm" isn't one thing—it's a spectrum from fully-controlled frameworks to fully-automated orchestration. The right choice depends on how much control you need, where your data can live, how much you're willing to spend on coordination overhead, and whether your use case is coding-specific or general.

Pick the approach that matches your constraints, not the one with the best demo.