Comparisons / DeepEval
BayesCore vs DeepEval
DeepEval helps engineers test whether their LLM pipeline is working. BayesCore is the runtime itself — it tracks belief state per agent and refuses to proceed when confidence is too low. Testing and runtime guardrails are complementary, but they are not the same thing.
| Feature | BayesCore | DeepEval |
|---|---|---|
| Where it operates | Inside the runtime — gates agent steps before they execute | Outside — tests LLM outputs after they are produced |
| Primary user | Knowledge workers using agents to do real work | AI engineers writing and testing pipelines |
| Interface | Desktop app + MCP connections (no code required) | Python SDK, CLI, CI/CD integration |
| Uncertainty model | Beta-Bernoulli belief state per agent, gates execution | LLM-as-judge metrics — no persistent uncertainty model |
| Prevents bad outputs | Yes — agent pauses or escalates before acting | No — catches failures after they occur |
| Reproducibility | Locked scoring formula — consistent across runs | Non-deterministic — temperature variance in judge calls |
| MCP tool connections | Yes — any MCP server, tools auto-register in pipelines | No native MCP support |
| Audit trail | Per-step trace with gate decision and confidence at runtime | Test results and metric scores per eval run |
| Works offline | Yes — bundled Phi-3 Mini | Requires LLM API calls |
| Pricing | Free web tool / $149 one-time | Open source / Confident AI SaaS |