Test agent integrations against isolated stateful API simulations
Archal provides testing sandboxes for AI agents and software integrations. Instead of connecting a test run to a real Slack workspace, issue tracker or payment-related account, developers can connect it to a stateful simulation managed by Archal. The aim is to make repeated tests easier to isolate and compare without leaving changed records in shared test accounts.
A sandbox contains one or more environments, each representing a supported external service. Actions can change that environment, and later calls can observe those changes during the same run. This is important for agents that must create an item, update it and then query the result, because a sequence of unrelated static mock responses cannot fully represent that behavior. Environments can be reset to a declared baseline so another attempt begins from the same conditions.
The environments expose REST or MCP interfaces using familiar request paths and payload shapes. The catalog includes services such as GitHub, Linear, Slack and Supabase, with other environments documented separately. Archal explicitly describes these as independently built simulations, not the providers' live services. Coverage differs by environment and operation, so teams should consult the supported capability profile before interpreting a passing test as complete compatibility with an upstream API.
Official use cases include integration tests, coding-agent harnesses and reinforcement-learning rollouts. Request, response, mutation, timing and usage information can be inspected together, helping a developer understand the sequence that led to a failure. Billing is based on each environment's active duration, and failed provisioning and cold-start time are excluded according to the homepage. New accounts receive introductory credits in two stages. The current site states that paid continuation remains disabled during its safety rollout, which is a material planning constraint for teams considering sustained testing. A small supported scenario is therefore the appropriate starting point before building a larger evaluation dependency.
