DeepEval vs OpenCompass

DeepEval is a free, open-source framework for building evaluation pipelines to test AI systems. Its Pytest-native evaluations run in CI/CD or as Python scripts, and the framework lists more than 50 research-backed metrics, including checks for hallucination, faithfulness, answer relevancy, summarization, toxicity and bias. It evaluates text, images and audio, including conversational and voice interactions, using methods such as G-Eval, DAG, QAG and JevEval. DeepEval can generate synthetic goldens from a knowledge base and model conversations across user personas. It also traces agent steps so they can be graded and inspected in a terminal or test runner. Integrations cover agent frameworks including LangChain, Pydantic AI, OpenAI Agents, LangGraph, LlamaIndex and CrewAI, alongside a range of model providers. By default, it sends basic telemetry to PostHog, excluding personally identifiable information and stored results; users can opt out with an environment variable. The maker describes DeepEval OS as for pre-production testing, with results in local files and an engineer-owned test runner. Enterprise capabilities are offered through Confident AI Evals, with self-hosted or maker-cloud deployment options.

OpenCompass is a free, open-source Python platform for evaluating large language models across knowledge, reasoning, coding, science, language, long-context and safety datasets. It describes support for models from OpenAI, Anthropic, Gemini, Qwen, GLM, DeepSeek and other providers, along with more than 100 datasets. Evaluations can use zero-shot, few-shot or chain-of-thought methods with standard or dialogue prompt templates. The system supports task division, distributed evaluation and data-parallel work across multiple GPUs. Configuration files record experiments, and results can be reported in real time. Its modular design can be extended with models, datasets, task-division strategies and cluster-management systems. OpenCompass evaluates API models as well as open-source models; installation documentation lists LMDeploy and vLLM inference backends and an API option. A VLMEvalKit integration supports multimodal dataset loading, OpenAI-compatible inference and VLMEvalKit metrics. It is intended for self-hosted use under the Apache-2.0 license. The README recommends Python 3.12, while pyext-backed code-evaluation datasets require Python 3.10. Some datasets and agent evaluations need extra dependencies that may conflict.