Comparisons
BayesCore vs Everything
Every other agent runtime — LangChain, CrewAI, AutoGen, Claude Projects — has no principled way to handle uncertainty. Agents hallucinate confidently and the pipeline proceeds. BayesCore's wedge: agents that know what they don't know.
The most popular agent framework — and the clearest illustration of the problem.
LangChain chains LLM calls together with no uncertainty model. If a step produces a wrong answer, the chain proceeds anyway. BayesCore tracks belief state per agent and stops the pipeline when confidence is insufficient.
Multi-agent coordination without calibration is organized hallucination.
CrewAI coordinates agents working as a team. But agents trust each other blindly — one hallucination propagates through the whole crew. BayesCore verifies every step before the next agent receives it.
ChatGPT will always give you an answer. BayesCore won't, if it shouldn't.
ChatGPT is RLHF-tuned to be helpful — which means it produces confident output even when uncertain. BayesCore surfaces uncertainty explicitly: CLARIFY when evidence is thin, ESCALATE when confidence is too low to act.
Eval tooling tells you what went wrong. Runtime guardrails prevent it.
Braintrust evaluates LLM outputs after the agent has already run. BayesCore gates steps before they execute. Both have a role — but only one stops the mistake before it happens.
Testing your pipeline is not the same as making your pipeline trustworthy.
DeepEval is a Python framework for testing LLM pipelines after the fact. BayesCore is the runtime itself — belief state, confidence gate, and adversarial verification built into every execution.