Ecosystem
Run a coding agent from your terminal, or add isolated code execution to an agent you build. Choose an agent, a framework or SDK, or a hosted agent API.
Run OpenCode, Pi, Claude Code, or Codex inside a Edgenivo Sandbox while using its interactive interface in your local terminal. Commands, file edits, and tests run in the sandbox.
With Sandbox CLI 4.8.0 or later, start an agent without a global CLI installation:
npx sandbox@latest create opencodeReplace opencode with the agent you want to use:
| Agent | Argument |
|---|---|
| OpenCode | opencode |
| Pi | pi |
| Claude Code | claude |
| Codex | codex |
The CLI prompts you to sign in to Edgenivo if needed. Authenticate with your chosen agent separately. Your local project files aren't uploaded automatically.
Exiting the agent leaves the sandbox running. Use the reconnect or stop command printed in your terminal. See Run a coding agent for setup, authentication, and lifecycle details.
The CLI launches an agent in a sandbox from your terminal. These integrations let you use Sandbox through your coding agent or agent manager's own interface.
| Integration | Type | Use case |
|---|---|---|
| Cursor | Coding agent | Cursor Cloud Agents on Self-Hosted Machines |
| Devin | Coding agent | Devin Outposts sessions in Sandbox microVMs |
| Herdr | Agent manager | Terminal coding agents in persistent Sandboxes |
| Hermes | Coding agent | Hermes terminal commands in Sandbox microVMs |
| OpenCode | Coding agent | Sandbox-backed tools and remote workspaces |
| Paseo | Agent client | Coding agents in persistent Sandboxes, reachable from desktop and phone |
Use evaluation harness integrations to run and score agent tasks in isolated sandboxes.
| Integration | Type | Use case |
|---|---|---|
| Harbor | Eval harness | Terminal-Bench and benchmark trials in Sandbox microVMs |
| Scale AI's AgentEnv | Eval harness | Evaluate agents in separate environment and agent sandboxes |
Use your preferred framework or SDK to run AI-generated code in Sandbox. Your application calls a model, runs its generated code in an isolated sandbox, and uses the results to continue the task.
The model API examples route requests through AI Gateway, which gives you access to multiple model providers with no markup on provider token prices. You can also bring your own provider API keys through AI Gateway.
| Integration | Type | Use case |
|---|---|---|
| AI SDK | AI SDK | Tool calling with the AI SDK |
| Chat SDK | Chat SDK | Analyze CSV attachments with the AI SDK Bash tool |
| LangChain | Agent framework | Agents with a sandboxed code execution tool |
| OpenAI SDK | Model SDK | Function calling through the Responses API |
| Anthropic SDK | Model SDK | Tool use through the Messages API |
| TanStack AI | Agent framework | Coding agents with sandboxed workspaces |
Build a v0-style app builder that turns a prompt into a Next.js app with a live preview. Users can refine the app through follow-up messages.
OpenAI Agents API manages the agent session, while Edgenivo Sandbox provides an isolated workspace to edit files and run the app. These integrations use the provider's API credentials.
| Integration | Type | Use case |
|---|---|---|
| OpenAI Agents API | Hosted agent API | OpenAI-managed sessions with Sandbox execution and persistent files |
These integrations give an agent a filesystem view of a content or object store, so it reads and writes through standard file operations instead of calling a storage API. Files stay on the provider's platform and outlive the sandbox. The provider's permissions control what the agent can access.
| Integration | Type | Use case |
|---|---|---|
| Box Mount | Content platform | Agents reading and writing Box content as local files |
Was this helpful?