OpenHands vs Claude Code: open source sandbox vs terminal agent

OpenHands and Claude Code are both autonomous AI coding agents. Both take tasks described in plain language, navigate a codebase, run your tools, and work through problems without you driving every step. But their designs pull in different directions: OpenHands runs agents in a sandboxed runtime that supports many model providers, can browse the web, and runs self-hosted on your infrastructure. Claude Code runs as a terminal process with full access to your local machine, dispatches multiple tasks in parallel via git worktrees, and builds on Anthropic's model releases first.

Which one fits depends on whether you need model flexibility and deployment control, or terminal-native autonomy with a flat-rate cost model.

OpenHands: multi-model, sandboxed, open source

OpenHands (formerly OpenDevin, All Hands AI) is an MIT-licensed open source project with around 88,000 GitHub stars. The current release is v1.24.0 (September 25, 2026).

Originally Docker-based, the project has moved to Agent Canvas as its primary interface: a browser-based control center where agents run as persistent workers, not one-off chat sessions. The architecture is Agent Canvas (browser client), a Software Agent SDK, Agent Server, and Automation Server for scheduled and event-driven runs. A CLI remains supported. The underlying runtime uses the Agent Client Protocol (ACP), an open JSON-RPC standard, which means you can drive the same workflows from different surfaces.

OpenHands supports more than 10 model providers: Claude, GPT-6, Gemini, Grok, DeepSeek, and local models via LM Studio or Ollama. Switching providers is a configuration change. For teams that route different task types to different models by capability or cost, that flexibility is built in at the framework level.

The agent can also browse the web: navigate sites, fill forms, read documentation, interact with browser-based tools. This is part of the architecture, not a plugin. For tasks that require research, checking live documentation, or interacting with external services as part of the build, the browser capability changes what the agent can do. Claude Code has no native equivalent.

Self-hosted via the MIT source, OpenHands costs only the model API calls you make. The cloud option (openhands.dev) offers a free tier capped at 10 conversations per day, and an Enterprise plan with RBAC, SAML/SSO, centralized billing, and an Agent Control Plane.

Where OpenHands is strong:

• Model choice. Any major provider, including local models. If cost control means mixing models by task type, OpenHands supports it natively. Claude Code is Anthropic-only.
• Browser agent. The agent can navigate the web as part of a task: research, reading live documentation, filling forms. Built in at the architecture level.
• Self-hosted, data-sovereign. Run entirely on your infrastructure with your own API keys. For regulated environments or teams with strict data residency requirements, this is the right option.
• Free for self-hosting. The MIT license means the tool itself costs nothing. You pay only for the model API calls you make.
• Team features in the enterprise track. RBAC, centralized billing, and the Agent Control Plane address multi-user coordination that Claude Code has no equivalent of today.

Where OpenHands adds friction:

• More setup steps. Agent Canvas is a capable interface, but getting from zero to a working agent session takes more configuration than running npm install -g @anthropic-ai/claude-code in a terminal.
• No native parallel dispatch for coding. OpenHands runs conversations, not git worktrees. Running multiple coding agents on multiple branches independently at the same time requires more orchestration on your part.
• First-party model capabilities lag. Claude Code gets Anthropic's model improvements first. If you use Claude through OpenHands, you have the same model but not the same first-party optimizations and new agentic capabilities that land in Claude Code directly.
• Cloud free tier is capped. Ten conversations per day hits limits fast on real project work. Beyond that, you are self-hosting or on enterprise pricing.

Claude Code: terminal-native, autonomous, first-party

Claude Code is Anthropic's own agent, built and shipped alongside their model releases. It installs from the terminal, runs as a process with full access to your local machine's file system, tools, and shell, and works through tasks autonomously: reading the codebase, running your build toolchain, catching test failures, and looping until the task is done.

Because Anthropic builds Claude Code directly, capabilities land here first. New Claude models, subagent delegation, and new agentic features are in Claude Code before any third-party tool can integrate them. It is the reference runtime for how Anthropic's models work as agents, and it ships near-daily with incremental improvements.

For parallel work, the designed pattern is git worktrees: one agent per worktree, each on an isolated branch, running a different task independently. You start several tasks, leave them running, come back to multiple finished branches and review the diffs. There is no coordination overhead between instances. Running agents in parallel on separate work is the built-in workflow, not a workaround. For a look at how this plays out in practice, the Claude Code worktree workflow covers setup, merge sequencing, and where the ceiling is.

Claude Code pricing runs through Anthropic: Pro at $20/month or Max at $100-200/month covers most usage, or you pay direct API costs. One subscription, predictable monthly charge, no per-conversation metering.

Where Claude Code is strong:

• Terminal depth. Full host access: your build system, test runner, package manager, shell - exactly as they are, with no sandbox abstraction between the agent and your tools. If the agent needs to start a dev server and inspect the output, it does.
• Autonomous end-to-end execution. Describe the task; come back to a finished branch. The agent builds context, runs tests, fixes failures, commits. You set the task and the agent resolves it.
• Native parallel worktrees. One agent per branch, dispatched independently, no coordination overhead. Several tasks running at once is the designed pattern. OpenHands has no direct equivalent for code-focused parallel work.
• First-party capability access. New Claude models and agentic features land here before any other tool. If you want the leading edge of what Claude can do as a coding agent, this is the right runtime.
• Predictable flat-rate cost. $20-200/month regardless of how many agent runs you do within the plan. For teams running many tasks, the cost predictability matters more than per-API metering.

Where Claude Code adds friction:

• Model lock-in. Claude only. No routing to other providers, no cost optimization across model tiers, no local model option.
• No sandboxed environment. The agent runs in your terminal with full local access. This is correct for most development tasks, but it is not the right fit for research workflows or external tasks that need browser access or an isolated execution environment.
• No team features today. No shared workspaces, no centralized billing, no RBAC. It is a single-user tool. For multi-user coordination, it has nothing comparable to OpenHands Enterprise.
• Subscription required. No self-hosting option and no meaningful free tier. OpenHands is free to self-host with your own API keys.

Which to pick

The two tools are often complementary rather than mutually exclusive. Teams using Claude Code for parallel coding work sometimes route research tasks or documentation queries to OpenHands where the browser agent matters. The choice is not always binary. For a broader look at how different agentic coding patterns hold up at scale, which agentic coding patterns actually scale maps the tradeoffs by codebase size and task type.

The layer neither provides

Both OpenHands and Claude Code make it faster to execute on a coding task. What neither addresses is the process around the execution: deciding on direction before the agent runs, a visual step where you verify the proposed approach before any code is written, a review gate that checks the output before it merges. Whether you are using OpenHands with multiple models in a sandboxed environment or Claude Code running autonomously in your terminal, the agent executes what you aim it at. The question of what to aim at, in what order, with what verification at each stage, stays with you.

defract operates at that layer: a structured lifecycle (story, design, architecture, implementation, review, release) that runs on top of Claude Code, with a visual design stage and enforced review gates where agents check each other's work before you sign off. It is not a replacement for either tool compared here - it is an orchestration layer that sits on top of Claude Code specifically. If the gap between "the agent ran" and "the right thing shipped" sounds familiar, why AI coding agents need hard stage boundaries covers what creates that gap and what closes it.