Keystroke vs TryCase: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Keystroke and TryCase — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Keystroke

Freemium

Single platform for building, deploying, and running internal AI agents and multi-step AI systems with memory, tools, and triggers.

Agents with memory: build agents that keep memory, a persistent file system, and code execution to handle real, stateful work.
AI systems: compose agents, workflows, triggers, and human-approval steps into durable multi-step automations.
1,000+ integrations: connect any tool via built-in integrations, custom APIs, or MCP servers.
Company brain: teams share context, agents, and skills so knowledge and automation compound across the organization.
Triggers & schedules: run agents on webhooks, schedules, or event triggers with replay-safe workflows.
Repo-based deploy: deploy agents from your own repo, or build them alongside a coding agent like Cursor or Claude Code.
Usage controls: set spend limits, get alerts, and stop runs before you go over budget.
Enterprise controls: RBAC, agent guardrails, and enterprise-grade security in the Organization tier.

Ops team automates inbox triage, meeting-notes review, and follow-up drafting with agents that trigger on Granola/Slack events.
Engineering team ships internal agents from their own repo with the same review workflow they use for production code.
Sales team stands up a durable pipeline of triggers and workflows for CRM updates, brief generation, and outbound drafting.
Central AI team gives every department a shared workspace of agents, skills, and context instead of one-off scripts.
Startup founder combines multiple agents into an AI system that runs core internal processes without hiring more staff.
Regulated org deploys agents under RBAC and guardrails with human-approval steps on high-risk actions.

Paid

An AI QA agent that opens your app on every pull request and posts a verdict, captioned video and screenshot back to GitHub.

PR-Triggered Runs: Connecting a repository is enough - every pull request marked ready for review starts a test run with no pipeline config.
Journey Selection From Diff: TryCase reads the changed code and chooses which user flows are actually affected rather than replaying a whole suite.
Disposable Linux Environments: Each run gets a fresh environment with terminal and browser control, so state from earlier runs never leaks in.
Video and Screenshot Evidence: Results arrive as a captioned recording plus a screenshot commented on the PR, showing exactly what the app did.
Bring Your Own AI: Connect Codex through an existing ChatGPT subscription or supply an OpenRouter key and pay your provider directly for inference.
Agent Skills: Packaged skills teach Claude, Codex, Cursor and other compatible agents to drive TryCase environments without manual setup.
Parallel Workers: Up to twelve workers per bot run journeys concurrently, with testing time tracked separately for setup, the primary bot and each worker.
Usage-Based Hour Pools: Monthly plans grant a shared pool of end-to-end testing hours across setup, PRs and retries, with no automatic overage charges.

Pre-Merge Verification: Confirm a checkout or signup flow still works before approving a pull request, without pulling the branch locally.
Visual Regression Review: Catch layout and rendering breakage that unit tests pass over by watching the recorded walkthrough.
Agent-Written Code Review: Require an AI coding agent to return screenshots and recordings proving its change runs, not just a diff.
Suite-Free E2E Coverage: Give a small team end-to-end coverage without staffing the maintenance of a Playwright or Cypress suite.
Demo Clips From Branches: Reuse the captioned videos as short product demos of a feature still sitting on a branch.
Release Triage: Scan verdicts across several open PRs to decide which changes are safe to batch into a release.