UpTrain
StaleDescription
An evaluation and monitoring tool for LLM applications that checks response quality, context relevance, factuality, and user feedback for agent systems.
Key Features
- LLM response quality evaluation with automated scoring across multiple dimensions
- Context relevance checking to verify retrieved information matches queries
- Factuality verification to detect hallucinations and unsupported claims
- User feedback integration for continuous improvement of agent outputs
- One-click evaluation dashboard for visualizing evaluation results over time
- Support for evaluating multi-step agent workflows end-to-end
Use Cases
π‘ Monitor and improve LLM-powered customer support agents in production
π‘ Evaluate prompt engineering iterations before deploying to users
π‘ Detect quality regressions in retrieval-augmented generation pipelines
π‘ Benchmark different LLM providers for specific agent tasks
Categories
Quick Start
Install via `pip install uptrain`. Initialize an UpTrain evaluation object, define your checks (response quality, context relevance, factuality), and run evaluations against your LLM outputs. Results appear in a local dashboard for analysis.