Rystic

Rystic provides local, stateful simulators for popular software and service APIs. It gives developers a controlled substitute for live systems such as Slack, GitHub, Linear, Stripe, PayPal, Okta, Resend, Gmail, Google Drive, Jira, Notion, Shopify, and S3. The main idea is straightforward. Tests point at a simulator on localhost instead of creating real accounts, consuming live rate limits, or leaving test data in a vendor workspace. The simulators run on a laptop or in continuous integration and preserve the effects of earlier calls, so a sequence can behave like an actual service rather than a set of isolated canned responses. Local runs have no vendor rate limits, which helps when a test suite needs to repeat the same flow many times.

State is both persistent within a run and settable before it begins. A team can seed the users, channels, repositories, issues, payments, files, or other service objects needed for a scenario, then let an application or agent interact with them through normal API patterns. Every later request sees what earlier requests changed. Rystic's documentation also covers reset and determinism, snapshots, test cases, webhooks, provider actions, timelines, multi-simulator environments, network profiles, and fault injection. That makes it useful for repeatable integration tests as well as exploratory runs where the path isn't known in advance.

The product is especially aimed at software agents, which often cross several services during one task. A support agent might read a Slack conversation, inspect a GitHub issue and a Linear ticket, check billing in Stripe, and draft a reply based on the combined context. Testing that flow against production services is slow and risky. With Rystic, the surrounding world can be assembled locally, including the starting state and failure conditions. The documentation includes guidance for grading agent runs, connecting coding agents, intercepting vendor calls, running shared simulators, and building integration tests in CI.

Coverage goes deeper than a single generic mock endpoint. The public documentation has simulator-specific sections for behavior such as GitHub pull requests, branch protection, workflows, reviews, merge queues, and GraphQL; Slack events, search, threads, identities, interactivity, and MCP; Stripe subscriptions, invoices, Connect, Checkout, test clocks, payouts, disputes, and dashboards; plus detailed surfaces for the other catalog services. Some simulators include a verified user interface, so browser-driven tests can interact with a visual product surface as well as an API. Feature depth varies by service, and the catalog clearly marks several future additions as coming soon.

Rystic says it verifies simulators daily by sending the same requests to the live API and the simulator before releases, then publishing agreement percentages. That is a more concrete maintenance posture than simply promising compatibility. It doesn't mean every request or edge case is reproduced, and the visible percentages vary by service. It does give buyers a way to judge how closely the implemented behavior tracks the current vendor API. For test design, that transparency matters because a simulator is only valuable when teams know which production assumptions it can safely exercise.

The free plan includes GitHub, Linear, and Slack for one seat and ten simulator hours per month. It includes every scenario, settable state, latency injection, and fault injection, but not verified user interfaces. Pro costs $89.99 per seat each month, includes the full catalog, verified interfaces, and $100 in usage credit per seat, with simulator time above the pool charged per minute. Its 14-day trial requires a card. Enterprise is custom priced for agent evaluations and reinforcement learning environments at scale, with unlimited seats, local and CI use, custom builds, fully offline operation, and resale inside those environments.

Rystic fits engineering teams building integrations, autonomous agents, migration tools, or evaluation harnesses that need realistic external systems without production dependencies. Its strengths are reproducibility, stateful behavior, a broad catalog, and tooling for failures that are hard to trigger safely against live services. The main tradeoffs are cost once a team needs the full catalog, incomplete parity with the vendors it models, and the learning required to seed good scenarios. Teams also need to confirm that the exact API surface their software uses appears in the relevant simulator documentation before treating it as a production-faithful test bed.