Compare LangGraph, a low-level framework for building stateful agents, with Agent Swarm, an operating system for AI work you self-host under MIT.
The real comparison is not which abstraction is nicer. It is whether you want to build an agent system or run a persistent agent team.
LangGraph is a low-level orchestration framework for building stateful agents in Python or TypeScript. Agent Swarm is an open-source operating system for AI work: a lead agent delegates goals to workers such as Claude Code or Codex, with isolated containers, shared memory, and review gates. The key difference is build versus run: LangGraph gives you primitives to code your own agent system, while Agent Swarm is a running system you hand work to. Choose LangGraph when your product needs bespoke agent logic in code. Choose Agent Swarm when you want a standing AI team that remembers prior work, runs recurring tasks on a schedule, and self-hosts free under MIT.
LangGraph is a Python and TypeScript library (MIT) that you embed in your own application. It does not abstract prompts or architecture, and you can always understand what your agent will do next by looking at the current node. When the deliverable is code you own, a framework is the right shape.
LangGraph lets you mix deterministic, hand-coded steps with LLM-driven agentic steps in the same graph. Teams with complex tasks bespoke to their needs get precise control over orchestration that higher-level tools hide.
LangGraph ships durable execution for long-running agents, interrupts for human-in-the-loop, and short-term working memory plus long-term persistent memory as library features. If you are prepared to operate the deployment around them, the primitives are strong.
Agent Swarm is already an operating system for work: a lead agent receives tasks from Slack, repositories, issue trackers, email, or the API, then delegates to workers with priority queues, dependencies, and progress reporting. You assign goals instead of designing graphs.
Agent Swarm keeps persistent memory and identity for every agent, with a reusable skill system and a shared searchable filesystem. The swarm recalls prior decisions and failed approaches instead of starting cold on each run.
Schedules, DAG workflows, and human-in-the-loop approval nodes are built in. Agent Swarm self-hosts free under MIT with Docker Compose or Helm, with no license fee and no enforced worker cap, and workers run Claude Code, Codex, opencode, pi-mono, Devin, or ACP-compatible agents.
A useful comparison says where each tool actually wins. agent-swarm.dev is for a persistent, owned operating team; the alternative wins when its shape fits your job better.
If you are shipping an agent inside your own product, an MIT library you embed is the right tool. Agent Swarm is an operated system, not an SDK; it does not replace framework code in your application.
As of 2026-10-02, LangGraph has 42,630 GitHub stars against Agent Swarm's 851, plus LangSmith Studio, a dedicated IDE for visualizing and debugging agents. Agent Swarm is the younger project by a wide margin.
Agent Swarm is open source under MIT and the fastest way to judge it is a real task. The bunx @desplega.ai/agent-swarm onboard wizard collects credentials, generates the compose files, starts the stack, and verifies health. Hand the swarm a job from Slack or GitHub and compare that with scaffolding the same job as a LangGraph graph.
LangGraph is a low-level framework for building stateful agents in code; Agent Swarm is an open-source operating system for AI work that you run as a standing team. They answer different needs: pick LangGraph to build an agent system, pick Agent Swarm to have one already running. If your goal is a working AI team rather than an agent codebase, Agent Swarm replaces the build-it-yourself path.
The langgraph library is MIT-licensed and free to use. The Agent Server package (langgraph-api) is source-available under Elastic-2.0, not an OSI license: running it in production needs a license key, and LangChain staff state that full self-hosting of production workloads requires an Enterprise license (as of 2026-10-02). Agent Swarm self-hosts free under MIT with no license fee.
Yes. A product team can ship agent features built with LangGraph while Agent Swarm runs the company's operations around it: coding, research, review, and recurring reports arriving from Slack, GitHub, Linear, or email. Agent Swarm's workers run Claude Code, Codex, and other harnesses; it does not ask you to rewrite application code.
Choose LangGraph when you need to write and own bespoke agent logic in Python or TypeScript, with explicit control over every step. Choose Agent Swarm when you want persistent workers, shared memory, schedules, and review loops already running on infrastructure you control.
This page compares product categories and operating models from public product documentation and repositories. We do not claim the tools are interchangeable.
See how agent-swarm.dev stacks up against the rest.
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