MIT-licensed Framework for LLMs, RAGs, Chatbots testing. Configurable via YAML and integrable into CI pipelines for automated testing.

Library for Microsoft Bot Framework Chatbot unit testing

Sub-second RAG regression testing. Define golden questions, detect lost chunks in CI. pytest for your RAG pipeline.

A Python library to connect and interact with chatbots.

An automated approach for exploring and testing conversational agents using large language models. TRACER discovers chatbot functionalities, generates user profiles, and creates comprehensive test suites for conversational AI systems.

pytest lab for testing LLMs: RAG eval, red teaming, guardrails, drift monitoring — 14 modules, 382 tests, zero API calls needed

An open-source framework for robust, LLM-powered testing and tracing of conversational AI applications.

A plug & play framework for generative ai projects to be tested & automated

UI for persona-api

A Node.js testing framework for ChatBots

QA framework for testing conversational AI systems (LLM agents, chatbots, voice assistants) with workflow validation and regression checks

Quality auditor for AI chatbots. Analyzes your conversation logs to show where the bot is underperforming.

Modular, extensible QA framework for evaluating AI chatbots — built for CI/CD pipelines, model comparison, and continuous quality monitoring.

Structural fingerprinting for conversational AI · SVD, Jensen-Shannon divergence, silhouette analysis · Black-box characterization of LLMs and chatbots

Automated testing framework for AI chatbots with Train/Assisted/Auto modes, LangSmith integration, and intelligent failure diagnosis

GenAI Chatbot Testing Framework using Python & Pytest | Validates intent, context, prompt variations, hallucination risks, safety, and response quality scoring

Lightweight evaluation lab for LLM chatbots — quality metrics, regression tests, and model comparison harness in pytest.