Compare Langfuse vs. Weavel

Langfuse is a free and open-source LLM engineering platform that helps teams to debug, analyze, and iterate their LLM Applications. Observability: Incorporate Langfuse into your app to start ingesting traces. Langfuse UI : inspect and debug complex logs, user sessions and user sessions Langfuse Prompts: Manage versions, deploy prompts and manage prompts within Langfuse Analytics: Track metrics such as cost, latency and quality (LLM) to gain insights through dashboards & data exports Evals: Calculate and collect scores for your LLM completions Experiments: Track app behavior and test it before deploying new versions Why Langfuse? - Open source - Models and frameworks are agnostic - Built for production - Incrementally adaptable - Start with a single LLM or integration call, then expand to the full tracing for complex chains/agents - Use GET to create downstream use cases and export the data

Introducing Ape, the pioneering AI prompt engineer, designed with advanced capabilities such as tracing, dataset curation, batch testing, and evaluations. Achieving a remarkable 93% score on the GSM8K benchmark, Ape outperforms both DSPy, which scores 86%, and traditional LLMs, which only reach 70%. It employs real-world data to continually refine prompts and integrates CI/CD to prevent any decline in performance. By incorporating a human-in-the-loop approach featuring scoring and feedback, Ape enhances its effectiveness. Furthermore, the integration with the Weavel SDK allows for automatic logging and incorporation of LLM outputs into your dataset as you interact with your application. This ensures a smooth integration process and promotes ongoing enhancement tailored to your specific needs. In addition to these features, Ape automatically generates evaluation code and utilizes LLMs as impartial evaluators for intricate tasks, which simplifies your assessment workflow and guarantees precise, detailed performance evaluations. With Ape's reliable functionality, your guidance and feedback help it evolve further, as you can contribute scores and suggestions for improvement. Equipped with comprehensive logging, testing, and evaluation tools for LLM applications, Ape stands out as a vital resource for optimizing AI-driven tasks. Its adaptability and continuous learning mechanism make it an invaluable asset in any AI project.