langfuse and phoenix
These are competitors offering overlapping LLM observability and evaluation capabilities, though Langfuse provides additional features like prompt management and playground while Phoenix focuses more narrowly on observability and evals.
About langfuse
langfuse/langfuse
🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23
This platform helps AI application developers build, test, and improve their large language model (LLM) powered products. It takes data from your LLM application's usage and provides tools for debugging, evaluating performance, and managing prompts. The end users are developers, machine learning engineers, and product managers working on AI applications.
About phoenix
Arize-ai/phoenix
AI Observability & Evaluation
This tool helps AI practitioners understand and improve their Large Language Model (LLM) applications. You input your LLM's interactions and performance metrics, and it provides insights into how well your models are working and where they might be going wrong. It's for anyone building, evaluating, or maintaining LLM-powered applications, such as AI product managers, machine learning engineers, and data scientists.
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