Compare Adaline vs. Agenta

Rapidly refine your work and deploy with assurance. To ensure confident deployment, assess your prompts using a comprehensive evaluation toolkit that includes context recall, LLM as a judge, latency metrics, and additional tools. Let us take care of intelligent caching and sophisticated integrations to help you save both time and resources. Engage in swift iterations of your prompts within a collaborative environment that accommodates all leading providers, supports variables, offers automatic versioning, and more. Effortlessly create datasets from actual data utilizing Logs, upload your own as a CSV file, or collaboratively construct and modify within your Adaline workspace. Monitor usage, latency, and other important metrics to keep track of your LLMs' health and your prompts' effectiveness through our APIs. Regularly assess your completions in a live environment, observe how users interact with your prompts, and generate datasets by transmitting logs via our APIs. This is the unified platform designed for iterating, evaluating, and overseeing LLMs. If your performance declines in production, rolling back is straightforward, allowing you to review how your team evolved the prompt over time while maintaining high standards. Moreover, our platform encourages a seamless collaboration experience, which enhances overall productivity across teams.

Collaborate effectively on prompts and assess LLM applications with assurance using Agenta, a versatile platform that empowers teams to swiftly develop powerful LLM applications. Build an interactive playground linked to your code, allowing the entire team to engage in experimentation and collaboration seamlessly. Methodically evaluate various prompts, models, and embeddings prior to launching into production. Share a link to collect valuable human feedback from team members, fostering a collaborative environment. Agenta is compatible with all frameworks, such as Langchain and Lama Index, as well as model providers, including OpenAI, Cohere, Huggingface, and self-hosted models. Additionally, the platform offers insights into the costs, latency, and chain of calls associated with your LLM application. Users can create straightforward LLM apps right from the user interface, but for those seeking to develop more tailored applications, coding in Python is necessary. Agenta stands out as a model-agnostic tool that integrates with a wide variety of model providers and frameworks, though it currently only supports an SDK in Python. This flexibility ensures that teams can adapt Agenta to their specific needs while maintaining a high level of functionality.