Documentation Index
Fetch the complete documentation index at: https://docs.openreward.ai/llms.txt
Use this file to discover all available pages before exploring further.
What are Sandboxes?
Sandboxes are isolated execution environments. They are temporary, disposable computers that are spin up when needed and shut down when finished. When you run AI-generated code, sandboxes provide:- Isolation: Code runs in its own container, separated from your system
- Security: Malicious code can’t access your files or network
- Reproducibility: Each sandbox starts fresh with a clean state
- Scalability: Spin up hundreds of sandboxes simultaneously
Sandboxes in Environments
Environments can be powerful with custom tools alone, but sandboxes unlock a new level of capability by giving agents access to a full computer environment. Sandboxes enable agents to:- Execute code: Run Python, JavaScript, shell scripts, and more
- Use command-line tools: Install packages, run git commands, process files
- Test solutions: Write code, run it, debug errors, and iterate
- Access file systems: Read, write, and manipulate files across multiple tool calls
- Write Python code to solve an equation
- Execute the code in a sandbox
- Get the result back
- Submit the final answer
Sandbox Providers
ORS environments work with any sandbox provider.** OpenReward provides a sandbox solution that plays nicely with mounting agent filesystems. We show how to use this in the Sandboxes documentation, as well as the Building agentic environments tutorial. But ORS environments are built to be vendor-agnostic, and work just as well with standard sandbox providers in the community. Some examples include:- Using E2B: See Using E2B Sandboxes
- Using Daytona: See Using Daytona Sandboxes
- Using Modal: See Using Modal Sandboxes

