Indexable

Sandbox infrastructure for safe, forkable AI agent execution.

Indexable provides sandbox infrastructure for AI agents, enabling developers to give agents safe, isolated execution environments with the ability to fork and snapshot full environment state — including files, processes, memory, and databases — at any point during agent execution. This capability addresses one of the core reliability and safety challenges in agentic AI: agents that take real actions in real environments can make mistakes that are difficult or impossible to reverse. Indexable’s fork and snapshot model allows agents to explore actions, roll back to known-good states, and execute in isolated environments without risk of unintended side effects in production systems. It targets SMB and mid-market development teams building agents that need more than simple code sandboxing and differentiates through the depth and completeness of its environment isolation — covering the full stack rather than just the file system.

Compliance

SOC 2

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Key Features

Full-environment sandboxing: Provides isolated execution environments for agents that encompass files, running processes, memory state, and databases — not just code execution — enabling agents to interact with a complete environment safely.
Environment forking: Allows agents to fork the current environment state and explore different execution paths in parallel or sequentially, with the ability to abandon branches without affecting the base environment.
State snapshots: Enables point-in-time snapshots of full environment state, allowing agents to roll back to a known-good state if an action produces unexpected results or an error occurs mid-task.
Database isolation: Includes database state in the sandboxed environment, enabling agents that interact with databases to do so safely without risking data corruption or unintended writes to production systems.
Usage-based pricing: Charges based on actual sandbox usage rather than flat subscriptions, making it accessible for teams with variable agent workloads.

Use Cases

For teams building agents that interact with complex software environments: A development team building an agent that needs to run code, modify files, and interact with databases uses Indexable to give it a full sandboxed environment where mistakes are isolated and reversible, rather than executing directly in a production or development system.
For AI coding agents that need to explore multiple solution paths: A coding agent tackling a complex engineering task forks the environment to try different implementation approaches in parallel, comparing results and rolling back unsuccessful paths without any effect on the main codebase.

Pricing MODELS

Usage-based

Pricing Summary

Indexable uses a usage-based pricing model with custom enterprise pricing for larger deployments. Specific pricing is not publicly listed — teams contact the company or review current documentation for details.