OpenHands Review

The OpenHands docs describe several product surfaces. Agent Canvas is the open-source browser client and control center for agent conversations and automations, and it can run as an all-in-one stack or connect to local, self-hosted, Cloud, or Enterprise backends. The Software Agent SDK is a Python library for building code-focused agents, and Agent Server exposes execution, conversations, tools, and workspaces through REST and WebSocket APIs. OpenHands Cloud is a managed service that handles hosted execution, integrations, collaboration, access controls, usage reporting, and budget management, and OpenHands Enterprise provides commercial capabilities for licensed self-hosting or managed deployment. A CLI built on the SDK is maintained primarily for stability, and a community-supported Sandbox Server creates and manages sandboxed environments that host the Agent Server. OpenAlternative lists use cases such as scanning repositories and patching security issues, reviewing pull requests, migrating COBOL to Java with tests, and investigating production errors.

The README gives several ways to run it. Without a sandbox you install with npm install -g @openhands/agent-canvas and run agent-canvas, which needs Node.js 24+ and uv. With Docker you run a single container that mounts a projects folder and publishes port 8000, or start with OH_CONVERSATION_RUNTIME=docker agent-canvas so each conversation gets its own container, and you can also run from source with npm install and npm run dev. The UI is at localhost:8000 (or localhost:8000/canvas for the Docker image). Safety notes matter: the no-sandbox mode gives the agent full access to your filesystem, local listeners bind to loopback only, exposing the UI on a LAN or publicly requires an API key and the self-hosting guide, Docker per-conversation isolation covers conversation execution but not the whole Canvas service, existing local conversations are not converted to Docker, and conversations sharing a host workspace share its files. The docs say you can bring any LLM, and do not list specific model requirements.

You give an agent a task through Agent Canvas, the CLI, or an integration, and it works inside its sandbox and reports back, with Cloud adding collaboration, access controls, and budget management. The docs we read do not cover benchmark results, recommended models, or specific GitHub, GitLab, and Jira integration details. This section reflects the docs and repository README; we did not run OpenHands.

• Agent Canvas, an open-source browser client for agent conversations and automations
• Python Software Agent SDK and an Agent Server with REST and WebSocket APIs
• Docker sandboxes per conversation, with a community-supported Sandbox Server
• CLI built on the SDK
• Works with any LLM
• OpenHands Cloud for hosted execution and budgets, and Enterprise for licensed self-hosting
• Use cases from security patching and PR review to COBOL-to-Java migration

• Designed for repo-wide, parallel automation rather than single prompts
• A real SDK and API surface for building your own agents, not just a UI
• Model-agnostic and MIT licensed, with a large community of about 91,000 stars
• Clear isolation options, from no sandbox to Docker per conversation

• The no-sandbox mode exposes your whole filesystem to the agent, and per-conversation Docker isolation does not cover the whole Canvas service
• The Local GUI is deprecated and several surfaces (CLI, Sandbox Server) are maintained differently, so the product map is more complex than it looks
• Each public repository has its own license, so check the exact one before reusing code
• Results depend on the model and the task, and parallel runs consume model credits
• Cloud and Enterprise pricing is not published on the pages we reviewed

The software is free under the MIT license, and you pay for model usage and infrastructure when self-hosting. OpenHands Cloud and Enterprise are commercial, and the pages we reviewed list no prices. The OpenAlternative page also carries an affiliate VPS offer, which is not OpenHands pricing.

Claude Code and Codex are vendor coding agents, usually driven interactively by one developer, and Devin and Zencoder are commercial autonomous agents. OpenHands leans toward autonomous, repo-wide jobs run in parallel in sandboxes, with an SDK and APIs for integrating into your own pipelines. Choose it when you want agents to take whole tasks and open pull requests on your own infrastructure, and a terminal agent such as OpenCode or Pi when you want a hands-on pairing experience.

• Engineering teams automating repetitive repo-wide work such as security patching and migrations
• Organizations that need agents running on their own infrastructure
• Developers who want to build custom coding agents on a Python SDK
• Teams that want a managed Cloud option alongside open source

• Developers who want a light interactive assistant in the editor
• Teams without the ability to review agent output carefully
• Anyone who cannot run Docker sandboxes safely