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

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

Hexis

Bevelites GmbH

Freemium

Open-source git-backed control plane for enterprise AI agents — context, skills, tools and permissions as files in your own repo, served over MCP.

Git-backed source of truth: Context, skills, tools, permissions, and agent identities live as Markdown/YAML files in your own repository — reviewed, branched, and diffed like any other code.
Typed knowledge with provenance: Every fact is a typed node with source, owner, and last verification date, compiled into a graph you can traverse, mass-update, and dashboard.
Markdown skills, not prompt fragments: Procedures live as readable Markdown files that domain owners can review, instead of prompt snippets buried in a vendor config.
Tool manifests with vaulted secrets: Declare each tool once, hold secrets in a vault, and set file-level access rules that say which agent may read which file and call which endpoint.
Per-agent identity: Each agent is a named actor with its own credentials and scope — no shared service accounts, and every action is attributable to a specific agent.
Any runtime via MCP or UTCP: Serves the same governed surface to Claude Code, Cursor, ChatGPT, opencode, background agents, in-platform agents, and self-hosted models.
Open-source Apache-friendly release: Hexis is the OSS core; Bevel (paid) adds hosted services, integrations, and enterprise support on top.

Enterprises running AI agents across multiple vendors (Claude, Cursor, ChatGPT) who want context and skills defined once in their own infrastructure instead of re-uploaded per vendor.
Procurement, GTM, and RFI teams building specialised agents (e.g., tender desks, campaign agents) on top of a shared reusable knowledge layer with provenance.
Platform teams that need per-agent identities and file-level access controls so every tool call is attributable and destructive endpoints are gated by review.
Companies wary of vendor lock-in who want a portable spec (MCP/UTCP) so they can switch agent runtimes without rebuilding context, skills, and tool wiring.
Engineering leaders who want the AI 'operating manual' reviewed in git diffs and change requests instead of edited inside a black-box vendor console.
Open-source-first teams evaluating a control plane locally with Hexis before committing to Bevel's hosted platform for larger deployments.

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.