Production-grade AI evaluation, prompt management & observability SDK. Automated evaluations with sub-100ms guardrails. No human-in-the-loop required. Python + TypeScript.
Die Hauptfunktionen von future-agi/futureagi-sdk sind: Observability and Evaluation.
Open-Source-Alternativen zu future-agi/futureagi-sdk sind unter anderem: aavetis/azure-openai-logger — "Batteries included" logging solution for your Azure OpenAI instance. deepchecks/deepchecks — Deepchecks is a machine learning model validation framework and MLOps testing library. It serves as an AI data quality… evidentlyai/evidently — Evidently is an AI observability platform and evaluation framework designed to quantify the performance of machine… fiddler-labs/fiddler-auditor — Fiddler Auditor is a tool to evaluate language models. future-agi/traceai — Open Source AI Tracing Framework built on Opentelemetry for AI Applications and Frameworks. aashirpersonal/semantic-coverage — Automated detection of knowledge gaps and blind spots in RAG vector stores.
"Batteries included" logging solution for your Azure OpenAI instance.
Deepchecks is a machine learning model validation framework and MLOps testing library. It serves as an AI data quality suite and performance evaluator designed to verify the integrity and performance of models and datasets from research through production. The project functions as a model monitoring tool for tracking data drift and performance degradation in production environments. It allows for the creation of custom validation suites and utilizes a pluggable check architecture to automate quality checks within continuous integration pipelines. The framework covers a broad range of capabil
Evidently is an AI observability platform and evaluation framework designed to quantify the performance of machine learning models and large language models. It functions as a monitoring tool for detecting data drift and quality degradation in tabular datasets, while providing a specialized analyzer for the faithfulness and correctness of retrieval augmented generation systems. The project distinguishes itself through an evaluation framework that utilizes judge models and custom rubrics to score language model outputs. It includes tools for iterative prompt optimization and the generation of
Automated detection of knowledge gaps and blind spots in RAG vector stores.