Devin AI vs OpenHands: Comparing Sandbox Security and Model Control

Two coding agents pointed at the same repository will happily overwrite each other's work, and neither will notice. The fix is not a smarter agent, it is a shared place to put work where the second one can see what the first one did.

The evolution of artificial intelligence coding tools has moved rapidly from simple autocomplete extensions to autonomous systems. Early tools focused on line-by-line completion, leaving the developer to run, test, and debug the code manually. Modern autonomous coding agents can design multi-step plans, execute commands in a shell, modify files, and test their changes.

Unlike traditional autocomplete extensions that act as passive assistants, autonomous agents operate in loops. They write a block of code, attempt to compile it, inspect the compiler errors, and revise the code based on the feedback. This execution cycle requires the agent to have direct access to a development environment, including a filesystem and a shell.

This shift introduces two leading systems in the autonomous space: Devin AI and OpenHands.

The choice between Devin AI and OpenHands centers on proprietary convenience versus open-source transparency and control over coding environments.

Devin AI is a commercial, closed-source product built by Cognition. It is designed to act as a managed, autonomous software colleague. OpenHands is licensed under the MIT License and developed in the open by All-Hands-AI.

Many developer guides use the outdated name OpenDevin. The project was renamed to OpenHands in mid-2024 to reflect its broader community focus on collaborative agent environments. The open-source core has accumulated over 80,800 stars on GitHub as of August 2026. This comparison examines the technical trade-offs between their execution sandboxes, model routing mechanisms, and data containment strategies.

When evaluating these tools, teams must decide whether their priority is a ready-to-run SaaS platform or a fully custom execution stack. A closed platform eliminates setup steps, but it introduces network dependencies and vendor limits. An open alternative requires configuration work, but it returns the source inspection, security boundaries, and model selections to your control.

• Cline Security Model and Safe Agent PracticesCline Enterprise documents four security-by-design claims: client-side execution, no data exfiltration, no codebase...
• Cline vs Cursor: Comparing the Top AI Coding ToolsAccording to the JetBrains AI Pulse Survey, 18% of developers report using Cursor at work, while GitHub Copilot...
• Devin AI Open Source Status: Commercial SaaS vs. Open AlternativesWhile Devin AI is a proprietary closed-source platform developed by Cognition, several open-source alternatives exist....
• How to Manage Files Generated by Devin AIDevin AI generates dozens to hundreds of files per session, but its workspace resets between runs. This guide covers...
• How to Try Devin AI for FreeA Devin AI free trial is Free at $0 per month with limited agent usage, not a short coupon clock. Pro starts at $20 per...
• Devin AI Agent: How the Autonomous Engineer WorksCognition's Nubank case study reports an 8x engineering-time efficiency gain and over 20x cost savings when Devin...

More on this subject: Devin (103 guides)

An autonomous agent requires a safe environment to run code, install packages, and test applications. Without a strict execution boundary, an agent could run a destructive command, wipe out local files, or compromise system keys.

Devin AI and OpenHands solve this safety problem using different sandboxing designs.

Devin AI runs entirely in a sandboxed hosted environment managed by Cognition. When a developer starts a session, the system provisions an isolated cloud virtual machine, often called a Devbox. This hosted environment contains a full operating system shell, a code editor, a web browser, and developer tools. The core reasoning system, or the brain, remains in the cloud of Cognition. The execution of shell commands, network fetches, and application tests happens inside the Devbox. For developers who want to route Devin's actions through their own infrastructure, Cognition provides Devin Outposts. This mechanism connects Devin's cloud sessions to local virtual machines or private clusters.

OpenHands relies on a containerized execution sandbox on your own hardware or server. By default, OpenHands runs its agent controller in a Python process that starts execution tasks inside a dedicated Docker container. The agent has full access to the terminal and tools inside the container, but it cannot access the files on your host machine unless they are explicitly mounted in the workspace directory.

The difference in sandboxing is a trade-off between convenience and oversight.

Devin AI manages the virtual machine lifecycle, which reduces configuration time. The developer does not need to download container images or manage local resources. The system is ready to run immediately in the cloud. However, this managed convenience requires trusting a vendor with code execution and network traffic.

OpenHands puts the environment configuration in the hands of the developer. Running the sandbox locally or self-hosting it on a private virtual machine allows platform teams to inspect the container image, set firewall rules, and monitor CPU usage. If an agent tries to execute a malicious script, the impact is confined to the Docker container, which can be destroyed and recreated immediately. The team must manage the container lifecycle and resource limits, which increases operational effort.

In production environments, teams often extend the default Docker sandbox of OpenHands with hardened runtimes like gVisor or Kata Containers. These tools restrict the system calls an agent can make to the host kernel, adding a layer of defense against container escape exploits. Devin's hosted environment relies on proprietary infrastructure security to enforce this separation, moving the defense-in-depth responsibility from your internal staff to the provider.

Network containment is a critical concern. In an open-source sandbox, you can block all outbound traffic except for approved API hosts, preventing the agent from sending code context to unknown destinations. A hosted service manages these rules on its side, meaning your team cannot directly audit or enforce network policies at the network layer.

The intelligence of a coding agent depends on the underlying large language model. How an agent connects to these models dictates the operating cost, the quality of code planning, and the privacy of prompt data.

OpenHands is model-agnostic. It routes all model queries through LiteLLM, a library that standardizes calls to dozens of provider backends. Developers configure their own API keys, or bring your own key, to use commercial APIs such as Claude 3.5 Sonnet, GPT-4o, or Gemini 1.5 Pro. Alternatively, developers can configure OpenHands to use local models running via Ollama or LM Studio. This setup allows complete data containment, as prompts never leave the local network. The cost of running OpenHands is simply the cost of the model tokens consumed, with no platform markup.

Devin AI operates as a unified SaaS platform where Cognition manages model routing. While Devin uses specialized models tuned for agent workflows, the developer does not manage the underlying API keys directly. Cognition charges for Devin usage using Agent Compute Units, which combine virtual machine runtime, bandwidth, and model inference into a single billable metric. Devin has also integrated with tools like Windsurf to align its cloud-based agent runs with interactive in-IDE workflows, but the model decisions and billing remain managed by Cognition.

This model routing difference shapes how teams evaluate tool costs.

OpenHands allows teams to swap models instantly by changing a configuration string, helping them optimize for speed or cost depending on the task. Developers can use cheap local models for simple debugging and switch to larger frontier models for complex architectural changes.

Devin AI offers a tuned, cohesive reasoning model that requires no configuration. The platform manages context length, tool usage, and prompt formats, ensuring a consistent execution quality. The trade-off is higher costs and a lack of visibility into the exact prompt payloads sent to the model.

Agent loops are token-heavy. An agent that reads a codebase, runs tests, reads the output, and iterates will consume hundreds of thousands of tokens per task. The bring your own key approach of OpenHands lets teams use their existing enterprise contracts and volume discounts with LLM providers. Devin's unit-based pricing wraps these costs together, which simplifies billing but can result in a higher cost per execution hour.

Using local open-weights models also mitigates the risk of API deprecation or service outages. If a commercial provider changes its model performance or deprecates an API endpoint, a self-hosted agent pointing to a local model continues to operate. A managed service binds your workflows to the provider's active model lineup, meaning updates on their end can shift the agent's behavior without warning.

Autonomous coding agents do not work in isolation. An agent needs to hand its work to other agents or human developers for review, testing, and final integration. Storing code files on local drives or raw cloud buckets can lead to version conflicts and lost context when multiple agents collaborate.

Engineering teams need shared workspaces to manage this handoff. While local storage and standard cloud drives can store code files, they lack the tools needed for human-agent collaboration.

Intelligent workspaces help bridge this gap by providing a persistent coordination layer. When developers deploy coding agents, they can use Fastio workspaces to organize files, track changes, and share context.

Fastio provides shared workspaces where agents and human team members collaborate on the same files. In this environment, files are indexed automatically upon arrival when Intelligence is enabled. Agents use the Fastio Model Context Protocol, or remote MCP server, to search the workspace, read documentation, and write code, while humans monitor progress through the web interface. This unified search combines exact matching with semantic retrieval, allowing agents to find code snippets by meaning rather than filename.

To protect code quality during handoffs, Fastio tracks a complete version history for every file. If an agent makes a mistake or writes broken code, developer teams can restore previous versions. Developers can also use Collaborative Notes to co-edit project guidelines or documentation in real time, with cursors visible to both humans and agents.

When a contract agent finishes setting up a codebase, the agent can transfer ownership of the organization to the client via a claim link. The agent can retain administrative access while handing control of the files to the human team.

Consider a scenario where an OpenHands agent is tasked with refactoring a series of legacy Python endpoints. The agent completes the changes, runs the unit test suite inside its Docker container, and gets a passing result. However, the developer team has not yet reviewed the code. Instead of copying the code files through email or zip files, the agent writes the refactored files directly to a shared Fastio workspace. Fastio's auto-indexing service processes the code instantly. A second agent, or a developer starting an interactive session, can run semantic queries like 'find endpoints affected by the refactoring' or 'summarize recent changes in the codebase' without needing to pull the code locally first. The workspace becomes the persistent intelligence substrate that preserves the execution context between multiple runs. The humans review the modifications through the visual files history, verifying that the agent's work conforms to internal conventions before deploying the changes.

The platform operates on a paid subscription model. Every organization begins with a 14-day free trial. Plans include Starter, Business, and Enterprise. Credits are metered for AI operations at 1 credit per 100 tokens, allowing teams to scale their workspace intelligence based on active usage. You can view these subscription options on our pricing page and learn more about our agent features in the storage for agents guide.

Coordinate your coding agents in secure workspaces

Establish a persistent workspace for your OpenHands and Devin AI agent runs, complete with version history and a remote MCP server endpoint. Every organization starts with a 14-day free trial.

Start 14-Day Trial

Deploying OpenHands on a local workstation requires Docker and a model provider key. This guide walks through configuring the sandbox, setting up the API connection, and running the agent canvas.

First, establish the directory structure for your projects. OpenHands will only access directories located within the projects folder path to maintain sandbox isolation. Create the directories using the terminal:

mkdir -p $HOME/openhands_projects $HOME/.openhands

Next, start the OpenHands Docker container. This command mounts your projects directory to the internal projects path of the container, maps port 8000, and mounts the configuration folder. Run the container:

docker run -it --rm \
  -p 8000:8000 \
  -v "$HOME/.openhands:/home/openhands/.openhands" \
  -v "$HOME/openhands_projects:/projects" \
  ghcr.io/openhands/agent-canvas:1.13.0

Once the container starts, open your browser and navigate to http://localhost:8000/canvas to access the Agent Canvas interface.

To configure a model, open the settings menu in the interface. Under the LLM settings, specify the model provider and API key. OpenHands uses LiteLLM routing conventions. For example, to use Anthropic Claude 3.5 Sonnet, set the model name to:

anthropic/claude-3-5-sonnet

Provide your Anthropic API key in the credential field. If you prefer to run a local model via Ollama, set the model name to:

ollama/llama3

Set the API base URL to point to your local machine, ensuring the container can resolve the host network path.

To connect your local OpenHands agent to your Fastio workspace, configure the agent to use the remote Fastio MCP server. Follow the details in our remote MCP documentation to set up the connection headers:

url: https://mcp.fast.io/mcp/code
headers:
  Authorization: Bearer YOUR_FASTIO_API_KEY

The OpenHands agent can now read guidelines, fetch reference code, and write its output files directly back to your secure Fastio workspace.

If the Docker container fails to connect to a local Ollama instance, ensure the host network interface is accessible. On macOS and Windows, containers access the host services using the host address host.docker.internal. When starting the container, verify that your local service is configured to listen on all interfaces, and check the container log output to diagnose connection blocks.

When configuring a project workspace inside the OpenHands container, verify that your user ID and group ID match the container's execution permissions. By default, OpenHands runs actions as a user named 'openhands' with UID 1000. If your host files are owned by root or a different user ID, you might encounter permission denied errors during file write operations. To prevent this, you can set the environment variables SANDBOX_USER_ID or pass the corresponding docker run flags to match your local system user permissions. Proper alignment of file permissions ensures that the agent's workspace modification tasks execute without authorization blocks.

Selecting between Devin AI and OpenHands requires weighing development velocity against security compliance and control. This comparison outlines the core differences between the two systems across key dimensions.

• Licensing and Source Code. OpenHands uses the MIT license for its core code, allowing developers to inspect the source, write custom tools, and self-host the agent server. Devin AI is a proprietary product with a closed codebase, requiring developers to work within the feature set and interfaces provided by Cognition.
• Execution Sandbox. OpenHands executes all code changes, terminal commands, and browser tests within a local Docker container managed by the developer. Devin AI runs code inside a cloud-hosted Devbox managed by Cognition, with option to use Devin Outposts to route execution to private servers.
• Model Integration. OpenHands is model-agnostic, routing requests through LiteLLM to support bring your own key configurations for dozens of providers and local models. Devin AI manages model selection and inference, charging users through Agent Compute Units that combine VM usage and tokens.
• Deployment Options. OpenHands can run locally, in Docker, on private virtual machines, or on the hosted OpenHands Cloud. Devin AI operates as a hosted SaaS platform, though enterprise customers can configure private network access.
• Cost and Budgeting. OpenHands is free to run, with teams only paying for the model tokens consumed through their own API keys. Devin AI requires a paid subscription and usage billing based on compute units.

Platform teams should choose Devin AI when they want an immediate, tuned coding agent with no local infrastructure setup or configuration work. This choice is best when convenience outweighs the need for source inspection or private model routing.

Teams should choose OpenHands when they require source code auditing, strict data containment using local models, and custom container configurations. OpenHands is also the right fit for teams that want to avoid vendor lock-in and manage their own API keys.

Compliance requirements can also dictate this choice. If your firm must follow data location guidelines, sending code out of your network to a hosted service is a compliance risk. OpenHands lets you keep all execution and model processing inside your certified cloud boundaries, while Devin requires specific network configuration to achieve a similar isolation level.