Comparison
Agentlas vs LangGraph: which should you use?
Short answer: choose LangGraph when your team needs framework-level control over a graph application. Choose Agentlas when the agent itself must be an owned, portable asset that non-developers can build and run in Desktop, then keep private or distribute deliberately.
Agentlas vs LangGraph, side by side
| LangGraph | Agentlas | |
|---|---|---|
| What it is | A framework for building stateful, graph-based agent applications. | An Agent OS for building, borrowing, owning, restoring, and running portable agent packages. |
| Primary surface | Application code and the development tools your team chooses. | Agentlas Desktop for visual creation and execution, plus the existing Agentlas Terminal. |
| Control model | Explicit nodes, state, edges, branching, persistence, and application behavior. | Editable agent/team architecture, tools, permissions, memory rules, handoffs, and host bindings. |
| Runtime | The Python application and infrastructure you operate. | A supported local or self-managed host chosen by the user; Agent Cloud does not run the model. |
| Portable identity | Defined by your repository, deployment, and versioning design. | Package hash, owner scope, local install state, Cloud revision, and restore receipts are explicit. |
| Distribution | Your normal code, package, or service delivery pipeline. | Public Hub for borrowed assets and private Agent Cloud for owner-only package restore. |
| Best fit | Developers who need graph-level control in an application. | Users who need the agent itself to stay portable, inspectable, and reusable across supported hosts. |
When to choose which
Choose Agentlas if
- You want a signed visual Desktop surface for building and running agents.
- You need private Agent Cloud restore across supported computers without moving local credentials.
- You want public Hub discovery and borrowing to remain separate from private ownership.
- You need package identity and runtime receipts instead of a chat-only configuration.
Choose LangGraph if
- Your team wants explicit, low-level control over conditional branching and custom state.
- The workflow needs graph-specific branching, persistence, or state semantics.
- You're comfortable implementing and operating the application code.
Frequently asked questions
Is Agentlas a replacement for LangGraph?
Not at the same layer. LangGraph is for implementing graph-based applications. Agentlas is for treating the agent as an owned package with a Desktop runtime, public Hub distribution, and private Agent Cloud restore.
Agentlas vs LangGraph — what's the actual difference?
LangGraph exposes nodes, state, edges, persistence, and execution flow in code. Agentlas exposes asset ownership, architecture, tools, permissions, memory, runtime receipts, Hub visibility, and Cloud restore through a packaged product surface.
Can Agentlas represent agent teams?
Yes. Desktop can create and edit coordinated teams with explicit roles and handoffs. A visible architecture is only the start: production readiness still depends on real runtime results, cancellation, recovery, permissions, and receipts.
Can a non-developer use LangGraph instead of Agentlas?
LangGraph is intended for application development. Agentlas Desktop provides a visual build and run path for users who do not want to begin by implementing a graph application in code.
Does Agentlas do everything LangGraph does?
No. LangGraph offers application-level graph primitives and custom state control. Agentlas focuses on the lifecycle of a portable agent asset and a non-developer-friendly local runtime. Complex framework code remains a developer responsibility.
Where do both products execute?
A LangGraph application runs in infrastructure the developer operates. An Agentlas package runs on the supported local or self-managed host the user selects. Private Agent Cloud stores and restores bytes; it is not the execution runtime.
Try the Agentlas product layer
Build and run locally in Desktop, or inspect public agents and teams in the Hub.
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