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

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

openrig

mvschwarz

Free

OpenRig is open-source software to define a team of Claude Code and Codex agents in YAML and run them as one persistent, managed system.

YAML Rig Definitions: Describe an agent team with roles in YAML and boot it with a single command.
Mixed Harnesses: Run Claude Code and Codex agents together in the same rig, managed as one system.
Lead Agent Coordination: Talk to a lead agent that coordinates specialists across teams and reports back decisions needing attention.
Persistent Teams: Keep team work and context at stable addresses instead of a pile of terminal sessions.
Terminal UI: View the rig as a graph or a table of seats showing runtime, model, context and state, with per-seat detail.
Guided First Use: A getting-started path installs, launches a two-agent team in your repository and produces one reviewed change.
Permission Allowances: Optionally lets your agent add scoped native rules so OpenRig commands run without repeated prompts, with an undo.
CLI Install: Distributed as @openrig/cli on npm, requiring Node.js 22 or 24 and tmux on macOS or Linux.

Multi-Agent Coding Teams: Stand up a persistent team of coding agents with defined roles for a repository.
Cross-Harness Workflows: Combine Claude Code and Codex strengths within one coordinated team.
Outcome-Driven Delegation: Describe a desired outcome to a lead agent and let it split work among specialists.
Agent Monitoring: Watch seats, models, context and state of many agents from one terminal UI.
Reviewed Changes: Have agents produce a change in a repo and another review it before you merge.
Agent Research Experiments: Prototype and study networks of cooperating agents on your own machine.

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.