Comparison
Agentlas vs LangGraph: which should you use?
Short answer: choose Agentlas if you want to describe a recurring job in plain language and get a working agent team back with no code. Choose LangGraph if your team wants full code-level control over a hand-wired, graph-based agent workflow in Python.
Agentlas vs LangGraph, side by side
| LangGraph | Agentlas | |
|---|---|---|
| What it is | A low-level Python library for wiring explicit state graphs between agent nodes. | A builder that generates the agent team for you from a plain-language description. |
| Setup | You write the graph: nodes, edges, conditional routing, and a recursion_limit, by hand. | Describe the recurring job in one sentence, answer up to 6 clarifying questions. |
| Topology | Whatever graph shape you wire — peer-to-peer, cyclic, or hierarchical, your call. | An auto-generated top-down org chart: a coordinator delegates to specialists. |
| Loop protection | You set recursion_limit and design conditional edges yourself. | Loop guards are injected into every published agent automatically — no config. |
| Audience | Experienced Python engineers who want low-level graph control. | Non-coders, founders, and small teams — no Python or graph wiring required. |
| Output | A Python module you host, deploy, and maintain. | A portable .claude/ folder that runs on Claude Code, Codex, Gemini CLI, Cursor, or Manus. |
| Security review | Bring your own — no built-in scan before you deploy a graph. | A 9-category scan blocks publish on leaked keys, unsafe shell, and credential exfiltration. |
| Where it runs | Wherever you host your Python process. | The AI account you already pay for — no Agentlas-hosted runtime. |
When to choose which
Choose Agentlas if
- You want to describe a recurring job in one sentence and get a reviewable agent team back.
- You are not a Python developer, or don't want to hand-wire a state graph for this job.
- You want loop protection and a security scan built in, not something you assemble yourself.
- You want the output to run on Claude Code, Codex, or Cursor without hosting anything yourself.
Choose LangGraph if
- Your team wants explicit, low-level control over conditional branching and custom state.
- The workflow is genuinely cyclic or non-hierarchical in a way a top-down team can't model.
- You're comfortable writing and hosting the Python module yourself.
Frequently asked questions
Is Agentlas a replacement for LangGraph?
Not for every use case. LangGraph is a low-level graph library for developers who want to hand-wire exactly how agents pass control to each other. Agentlas sits upstream of that decision — it decides what agents you need and generates the team from one sentence. If you specifically want graph-level control in Python, LangGraph is the right layer; if you want a working agent team without writing that graph yourself, Agentlas is.
Agentlas vs LangGraph — what's the actual difference?
LangGraph gives you a state graph you wire by hand, node by node, with a recursion_limit you set yourself. Agentlas auto-generates a top-down team (a coordinator plus specialists) from a plain-language description, injects loop guards automatically, and outputs portable markdown instead of a Python module you host.
Does Agentlas have the same loop risk as a hand-wired LangGraph?
Agentlas avoids the most common loop source by design — the org chart it generates is top-down only, so agents don't pass work back and forth peer-to-peer. On top of that, every published agent ships with loop guards (retry caps, tool-call caps, a 5xx circuit breaker). In LangGraph, avoiding loops is entirely on you: setting recursion_limit and designing conditional edges carefully.
Can a non-developer use LangGraph instead of Agentlas?
Not easily. LangGraph assumes you're comfortable writing Python and reasoning about a state graph. Agentlas is built for the opposite case: describing a recurring job in plain language and getting a reviewable agent team back, with no code.
Does Agentlas do everything LangGraph does?
No — LangGraph gives fine-grained control over conditional branching, custom state, and complex cyclic graphs that a from-a-sentence generator won't match. Agentlas trades that low-level control for speed and accessibility: an opinionated top-down team, generated automatically, that a non-developer can review and ship.
Where does an Agentlas-built team run compared to a LangGraph app?
A LangGraph app is a Python module you host and maintain yourself. Agentlas outputs a portable .claude/ folder that runs on Claude Code, Codex CLI, Gemini CLI, Cursor, or Manus — using the AI account you already pay for, with no Agentlas-hosted runtime in between.
Build your first agent team free
Describe the recurring work in one sentence. No code, no credit card to start.
More: What is an AI agent team builder? · Agentlas vs CrewAI · Security model