EvalFox continuously scores your AI agents across quality, safety, and performance — catching regressions before they reach users and giving your team a shared ground truth on agent health.
Works with every agentic framework
No infrastructure to manage. No code changes required. Just point EvalFox at your agent and get a full quality picture instantly.
Paste your agent's endpoint URL and configure auth headers. EvalFox auto-detects the protocol — REST, SSE, WebSocket, or OpenAI-compatible — and fires a live probe in seconds.
Upload your PRD or system prompt and EvalFox auto-generates test cases and judging criteria matched to your requirements. Or build tests manually — single-turn or full multi-turn conversations.
EvalFox evaluates every run across quality, safety, and performance dimensions. Regressions trigger instant alerts. Every score ships with LLM judge reasoning you can inspect and trust.
Built for the full evaluation lifecycle — from first prototype to production monitoring.
Paste any agent URL. Configure request shape and response path with a visual builder. No SDK, no proxy, no backend changes — just a URL and you're evaluating.
Upload your product requirements doc. EvalFox parses it and generates happy-path, edge-case, negative, adversarial, and multi-turn tests — plus a matched set of judging criteria.
Every score comes with a chain-of-thought explanation from a configurable judge model. Understand exactly why a test passed or failed, not just that it did.
Baseline pinning, automated comparison on every new run, and immediate alerts via Slack, email, or webhook the moment a dimension drops below threshold.
GitHub Actions, GitLab CI, and CircleCI plugins that trigger a full test suite on every push and block merges when the score falls below your quality bar.
Schedule runs every 15 minutes or on deploy. In proxy mode, EvalFox scores a sample of real production traffic — a live quality signal with no synthetic overhead.
EvalFox scores every agent response across quality, reliability, safety, and performance — each dimension tracked independently so you know exactly what shifted, and why.
Usage-based overages, transparent judge model cost pass-through, no surprises.