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.

EvalBot — local-first chatbot security & quality evaluation (FastAPI + Next.js). Evaluate chatbot answers against your own docs & guidelines with ML/NLP + AI-judge scoring. Apache-2.0.

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

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.

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

A framework for testing LLM-based chatbots in regulated industries (telco, banking, insurance). Covers hallucination detection, prompt injection resistance, response quality scoring and regression testing.

A Node.js testing framework for ChatBots

UI for persona-api

Bilingual portfolio project for evaluating chatbot helpfulness, accuracy, tone, safety, and instruction following.

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

LLM-powered testing workbench for n8n chatbots with client review portal

A dataset-driven LLM evaluation harness for testing behavioral reliability across OpenAI and Claude. Uses invariant-based assertions, suite organization, and CLI filtering for multi-model comparison.

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