DeepEval

OpenAI Evals is an open-source framework for evaluating large language models and systems built with them, including tool-using agents and prompt chains. It includes a registry of benchmark evals, supports custom model-graded and private evals based on a user's data, and uses a completion-function protocol for advanced workflows. The package can be installed with pip and run locally with an OpenAI API key; the repository also documents optional result logging to Snowflake and configuration through the OpenAI Dashboard.

Pydantic Evals is a code-first Python framework from Pydantic for evaluating LLM applications and agents. It organizes evaluations into datasets of cases, tasks, experiments, and evaluators: each case runs against a task, and evaluators score its outputs or tool-call trajectory using deterministic checks, custom logic, LLM judges, or span-based analysis of OpenTelemetry traces. Evaluations can run over defined datasets or sampled live production traffic, with results available in Python reports, the terminal, or Pydantic Logfire for visualization and comparison. The package is distributed as `pydantic-evals`; Logfire integration is optional.

Azure AI Evaluation SDK is an evaluation framework for Microsoft Foundry users. It is used to test model behavior against application-specific scenarios rather than relying only on general benchmarks, including string-check evaluations and LLM-as-a-judge assessments.

ai-evaluation-framework is an open-source framework by Dreamers Inc. for benchmarking model-based solutions by accuracy, p95 latency, and estimated cost against user-defined ground-truth cases. Each task specifies fields and a scoring rule per field, including exact identifiers, normalized dates, numeric tolerances, containment, token overlap, and expected-missing values; cases can also provide alternate accepted renderings. It runs models from OpenAI, Anthropic, or an OpenAI-compatible gateway, compares results in a table, emits detailed JSON reports, and caches responses by model, task, and case so scoring rules can be changed without another model call. The repository uses document-field extraction as its worked example but accepts arbitrary text inputs such as OCR output, transcripts, and PDF text. A separate feedback server collects thumbs-up or thumbs-down responses with required comments, stores append-only JSONL data, and exposes statistics, exports, and candidate cases for human review; it does not train models. The repository includes tests using scripted mock models and is licensed under Apache-2.0.

GuideLLM is an open-source platform for benchmarking and evaluating real-world LLM inference deployments. It simulates end-to-end interactions with OpenAI-compatible and vLLM-native servers, using real or synthetic text and multimodal datasets and configurable execution profiles such as synchronous, concurrent, throughput, constant-rate, Poisson, and sweep workloads. It measures SLO-oriented latency and token statistics, including time to first token, inter-token latency, and end-to-end behavior, then produces console, JSON, CSV, or HTML reports for deployment tuning, capacity planning, and regression tracking. The tool provides both a CLI and API, with multiprocessing, threading, asynchronous execution, and Hugging Face, file, synthetic, or custom data sources.