Best code execution APIs for AI agents in 2026

Choosing a code execution API for AI agents starts with the tasks your agent needs to perform, such as analyzing data, running scripts, installing dependencies, testing generated code, or starting application previews. Your application needs to submit work, interpret the response, and recover when execution or the connection fails.

This guide compares code execution APIs for AI agents from Northflank, E2B, Modal, Vercel, and Blaxel, explaining where each platform fits and how its API handles execution, results, streaming, state, and recovery. It also covers supported SDKs and the programming languages you can use to call each API, isolation, deployment options, and cost considerations.

A code execution API is the interface your application calls to run code and retrieve results. A sandbox is the isolated environment where that code runs. The services below expose execution APIs backed by sandboxes, but the two terms describe different parts of the integration.

A command interface starts a process, such as a test runner, package installer, or development server. Your application handles its output and eventual exit status. A code interpreter interface executes snippets in a language context and can retain variables between calls. An agent analyzing a dataset may need an interpreter; an agent building a repository usually needs command and file operations.

Agent-oriented SDKs connect these operations to an agent’s tool calls: create an environment, execute code, return results, and continue or clean up. The useful distinction is how much execution and lifecycle handling the SDK provides for your workflow.

The SDK language is another separate choice. A Python client can execute JavaScript or shell commands when the remote environment contains those runtimes. Check both the client library and the image or interpreter your code will use.

Compare the execution call and its response before choosing an environment. A completed result, a running process handle, and an interpreter result require different logic in your agent’s tool handler.

The SDK language and result format affect integration; isolation describes the execution environment behind the API. Network access and credentials still need controls appropriate to the task.

The five platforms below provide code execution APIs for different AI-agent workflows, including running commands, interpreting code, using GPUs, building application previews, and managing processes across requests. Each combines code execution with environment management, but their interfaces and retained state differ.

Northflank provides a code execution API that AI-agent applications can call to run AI-generated code in isolated sandboxes, install dependencies, test changes, and start development servers. GPU-backed sandboxes also support agent tasks that need accelerated computation.

• SDKs for agent tool calls: JavaScript and Python SDKs let an application execute code, receive results, and manage sandbox lifecycles across an agent’s tool calls.
• Streamed output and execution results: The command execution API returns progress and completion results to guide an agent’s next step. Interactive sessions also accept input while commands run.
• Workspace continuity and file persistence: Successive calls can use the same workspace. Pausing preserves attached-volume files, but stops processes and discards ephemeral files; commands need restarting after resume.
• Isolation: On Northflank Cloud, CPU sandboxes run in microVMs and GPU sandboxes use gVisor, with isolation enabled automatically.
• Deployment options: Northflank Sandboxes run on managed infrastructure or through bring your own cloud (BYOC), with Northflank as the control plane, provisioning and managing Kubernetes for agent sandboxes in your VPC. A sandbox can host the agent itself or the code it executes. Your own cloud requires a supported isolation runtime. Orchestration metadata is handled separately from execution.
• Team collaboration and API access: Roles and permissions let teams manage agent environments together while controlling resource access through the dashboard and API.

Best for: AI agents that need a code execution API for scripts, tests, and development servers, with control over where their sandboxes run.

E2B provides a code interpreter API that AI-agent applications can call from Python or JavaScript/TypeScript. It suits analysis workflows where an agent submits code, examines the result, and builds on earlier calculations.

• Variables retained between calls: Code contexts retain variables and imports, allowing an agent to load a dataset once and explore it across several calls. Restarting the context clears that state.
• Results: Its streaming interface separates execution logs, runtime errors, and results such as charts, tables, and text.
• Memory and filesystem persistence: Normal pause preserves memory and files, while filesystem-only pause does not retain variables in memory. Workflows that depend on earlier calculations need a way to reconstruct them when that state is lost.
• Isolation: Each sandbox uses a Firecracker-based microVM with its own kernel for agent code execution.

Best for: AI agents that need a stateful code interpreter API for iterative data analysis and rich results.

Modal’s sandbox execution API lets AI-agent applications run generated code and interact with it while it executes. GPU access makes it relevant to agents whose tasks include accelerated computation.

• Interactive process execution: The command execution interface lets an application stream output, send input, or wait for a finished result. This suits agents following a longer computation or working with a process that needs further input.
• Python, JavaScript/TypeScript, and Go SDKs: Python is Modal’s primary application language. Its JavaScript/TypeScript and Go SDKs also support sandboxes, though they remain in beta without full Python feature parity.
• State and recovery for longer tasks: A running sandbox retains its working environment, with continuous lifetime capped at 24 hours. GPU workloads can also be preempted. Saved files or checkpoints let an agent continue in a replacement environment instead of repeating all its work.
• Isolation: Standard sandboxes use gVisor. VM Sandboxes provide a separate Linux kernel in beta and currently support CPU workloads only.

Best for: AI agents that need a process execution API with interactive input and output, particularly for GPU-backed tasks.

Vercel provides command and file operations for AI coding agents through Vercel Sandbox, with JavaScript/TypeScript and Python SDKs. Its execution model fits the progression from generating an application to building it and showing a working preview.

• Build commands and preview servers: The command API can wait for a completed result or let work continue in the background. Applications can stream logs and retrieve the final exit status, giving an agent feedback on a build while a preview server remains available.
• Execution tracking across requests: A command identifier lets the application retrieve an earlier execution. That helps it follow ongoing work across requests, although the application still needs to determine when a preview server is ready.
• Filesystem persistence across sessions: Persistent sandbox sessions retain repository changes and installed files through filesystem snapshots, while background services need restarting. Snapshots expire 30 days after last use by default; the 45-minute Hobby and 24-hour Pro/Enterprise limits apply to individual running sessions.
• Isolation: Each sandbox runs in a Firecracker microVM with its own filesystem and network.

Best for: AI coding agents that need a command execution API to build applications and provide interactive previews.

Blaxel, now part of Baseten, provides a process API for AI agents whose tasks span multiple client requests. An application can start a named process, disconnect, and later return to check its progress.

• Process status and streamed logs: The process execution API supports TypeScript, Python, and Go integrations, with operations for checking status, streaming logs, and waiting for completion. Named processes help an application inspect earlier work before starting a replacement.
• Background execution controls: Waiting for a response and keeping execution active are separate choices. Keep-alive prevents standby while a process runs; without it, a disconnected task can be suspended. A client wait timeout alone does not necessarily stop the process.
• Memory and filesystem persistence: The standby lifecycle retains memory and files while suspending execution. An agent can return to its working state, although connections to databases or other external services may need restoring.
• Isolation: Each sandbox runs in its own microVM with a separate kernel.

Best for: AI agents that need a process execution API for tasks they revisit across requests, with control over whether work continues during a disconnect.

A backend tool handler connects the model to the execution API. It authorizes tasks, keeps provider credentials outside generated code, creates or reconnects to environments, and returns results to the agent.

Save environment and process identifiers where available. Return status, bounded output, command exit codes or interpreter errors, and artifact references in a consistent format. Stderr alone does not mean failure.

A request timeout does not necessarily stop remote execution. Check the original task before retrying work with side effects. Save required files before cleanup, and stop, pause, or delete environments when work finishes.

Choose the service whose response and recovery model fits your agent’s workflow, then validate it with a representative task. Include a failing command, an interrupted connection, a long-running process, file retrieval, and cleanup. Check concurrent sandbox limits and environment-creation rates separately, using the plan you would actually deploy. Verify that your dependencies, file operations, and any agent-framework integration fit the execution environment.

Northflank is the default recommendation for teams that need a code execution API for AI agents to run AI-generated scripts, test code changes, and launch application previews in isolated sandboxes. For an interpreter-heavy workflow, prioritize context and rich-result handling; for builds and servers, prioritize process identifiers, streaming, readiness, and termination.

Measure time to useful output with your real dependencies. Test fresh creation and resume separately; they measure different stages of execution. Compare cost per completed agent task, including setup, execution, retries, and retained storage. The decisive question is whether your application can determine what happened and continue safely when a call fails.

A built-in code interpreter can suit an AI agent whose tasks fit its supported languages, packages, and session limits. Northflank, E2B, Modal, Vercel, and Blaxel provide separate code execution APIs for AI agents, with different approaches to commands, interpreter sessions, and background work. A separate API is useful when your application needs execution or deployment controls beyond the built-in interpreter.

AI agents can run languages such as Python, JavaScript, TypeScript, and Bash through code execution APIs, depending on the provider and environment. Command execution APIs from Northflank, Modal, and Vercel can run code using runtimes installed in the sandbox. The available runtimes and dependencies determine which languages a particular environment supports.

The code execution APIs covered here run AI-generated code inside isolated environments. Northflank Cloud uses microVMs for CPU sandboxes and gVisor for GPU sandboxes; E2B and Vercel use Firecracker-based microVMs. Modal’s standard sandboxes use gVisor, and Blaxel uses microVMs. These boundaries restrict access to surrounding infrastructure, but network permissions, credentials, and resource limits still need controls appropriate to the task.

Yes, but files and in-memory state persist differently. Northflank retains attached-volume files across pause, while its running processes stop. E2B retains variables within interpreter contexts and preserves memory on normal pause; Blaxel preserves memory and files during standby while suspending execution. The right code execution API depends on whether the agent needs saved files, variables, or process state for its next call.

Yes, with a provider that supports customer-cloud deployment. Northflank offers bring your own cloud (BYOC), acting as the control plane to run agent sandboxes inside your cloud account and VPC. Its own-cloud setup requires a supported isolation runtime, and orchestration metadata is handled separately from the execution environment.

For the infrastructure and lifecycle decisions behind the API:

• Code execution sandboxes for AI agents
• Persistent versus ephemeral AI sandboxes
• AI-agent sandbox security checklist
• Deploying an AI agent from sandbox to production