LLM Application Evaluation - Deepeval enables component-level evaluation and tracing of LLM applications, addressing the need to identify issues within retrievers, tool calls, or the LLM itself [1] - The "@observe" decorator allows tracing of individual LLM components like tools, retrievers, and generators [2] - Metrics can be attached to each component for detailed analysis [2] - Deepeval provides a visual breakdown of component performance [2] Open Source and Data Control - Deepeval is a 100% open-source tool with over 8.5 thousand stars [2] - Users can self-host Deepeval to maintain control over their data [2] Ease of Use - Implementing Deepeval requires only 3 lines of code [1] - No refactoring of existing code is needed [1]

Core Functionality - DeepEval provides open-source tracing for LLM applications using a Python decorator `@observe` [1] - The solution enables component-level evaluations of LLM apps, addressing issues within retrievers, tool calls, or the LLM itself [1] - It allows attaching different metrics to each component of the LLM application [1] - DeepEval offers a visual breakdown of the performance of each component [1] Open Source and Hosting - DeepEval is 100% open-source with over 8,500 stars [2] - The solution can be self-hosted, ensuring data privacy [2]