Agenta
Agenta is an open-source LLMOps platform that helps AI teams build and ship reliable LLM applications. Developers and subject matter experts work together to experiment with prompts, run evaluations, and debug production issues. The platform addresses a common problem: LLMs are unpredictable, and most teams lack the right processes. Prompts get scattered across tools. Teams work in silos and deploy without validation. When things break, debugging feels like guesswork. Agenta centralizes your LLM development workflow: Experiment: Compare prompts and models side by side. Track version history and debug with real production data. Evaluate: Replace guesswork with automated evaluations. Integrate LLM-as-a-judge, built-in evaluators, or your own code. Observe: Trace every request to find failure points. Turn any trace into a test with one click. Monitor production with live evaluations.
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Agenta is an open-source LLMOps platform designed to assist AI teams in building and deploying reliable LLM applications. It facilitates collaboration between developers and subject matter experts to experiment with prompts, run evaluations, and debug production issues.
Agenta centralizes the LLM development workflow with three main features: Experiment, which allows users to compare prompts and models side by side while tracking version history; Evaluate, which replaces guesswork with automated evaluations using built-in evaluators or custom code; and Observe, which enables tracing of every request to identify failure points and monitor production with live evaluations.
Agenta addresses the common issues faced by AI teams, such as the unpredictability of LLMs, scattered prompts across various tools, siloed team workflows, and the challenges of debugging when things go wrong. It provides a structured approach to streamline the LLM development process.
Currently, there are no user-generated pros and cons available for Agenta. However, its open-source nature and focus on centralizing LLM development workflows are potential advantages, while the lack of user feedback may be a consideration for new users.