Ultimate Automated evaluations Solutions for Everyone

Discover all-in-one Automated evaluations tools that adapt to your needs. Reach new heights of productivity with ease.

Automated evaluations

  • Open-source observability tool for enhancing LLM applications.
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    What is Langtrace AI?
    Langtrace offers a comprehensive suite of features that helps developers monitor and enhance their large language model applications. It utilizes OpenTelemetry standards for compatibility, allowing the collection of traces from various sources and offering insights into performance metrics. This tool assists in identifying trends, anomalies, and areas for improvement, thereby making applications more efficient and reliable. It empowers teams to establish automated evaluations and feedback loops, significantly streamlining the development and enhancement processes of LLM applications.
  • WorFBench is an open-source benchmark framework evaluating LLM-based AI agents on task decomposition, planning, and multi-tool orchestration.
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    What is WorFBench?
    WorFBench is a comprehensive open-source framework designed to assess the capabilities of AI agents built on large language models. It offers a diverse suite of tasks—from itinerary planning to code generation workflows—each with clearly defined goals and evaluation metrics. Users can configure custom agent strategies, integrate external tools via standardized APIs, and run automated evaluations that record performance on decomposition, planning depth, tool invocation accuracy, and final output quality. Built‐in visualization dashboards help trace each agent’s decision path, making it easy to identify strengths and weaknesses. WorFBench’s modular design enables rapid extension with new tasks or models, fostering reproducible research and comparative studies.
  • QueryCraft is a toolkit for designing, debugging, and optimizing AI agent prompts, with evaluation and cost analysis capabilities.
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    What is QueryCraft?
    QueryCraft is a Python-based prompt engineering toolkit designed to streamline the development of AI agents. It enables users to define structured prompts through a modular pipeline, connect seamlessly to multiple LLM APIs, and conduct automated evaluations against custom metrics. With built-in logging of token usage and costs, developers can measure performance, compare prompt variations, and identify inefficiencies. QueryCraft also includes debugging tools to inspect model outputs, visualize workflow steps, and benchmark across different models. Its CLI and SDK interfaces allow integration into CI/CD pipelines, supporting rapid iteration and collaboration. By providing a comprehensive environment for prompt design, testing, and optimization, QueryCraft helps teams deliver more accurate, efficient, and cost-effective AI agent solutions.
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