Semi Agent provides a modular architecture for building AI agents that can plan, execute actions, and remember context over time. It integrates with popular language models, supports tool definitions for custom functionality, and maintains conversational or task-oriented memory. Developers can define step-by-step plans, connect external APIs or scripts as tools, and leverage built-in logging to debug and optimize agent behavior. Its open-source design and Python basis allow easy customization, extensibility, and integration into existing pipelines.

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.

Cekura, previously known as Vocera, offers cutting-edge testing and evaluation services for AI voice agents. Designed for organizations that utilize AI-driven customer interactions, Cekura ensures seamless and efficient agent performance. Using workflows, personas, and real audio, Cekura helps simulate countless scenarios to evaluate and fine-tune voice agents against custom metrics. Additionally, it provides actionable insights, real-time monitoring, and instant notifications for optimal agent performance.