UpTrain
Open-source LLM evaluation framework with pre-built metrics for RAG pipelines, question answering, and agent outputs. UpTrain provides 20+ automated quality metrics including context relevance, faithfulness, response completeness, tonality, and code hallucination — scored using LLM-as-judge. Designed for evaluating RAG systems end-to-end: query quality, retrieval relevance, and generation faithfulness. Cloud platform provides managed evaluations without running local LLMs.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
Apache 2.0 open source. Self-host keeps evaluation data in your environment. Cloud platform requires sending LLM inputs/outputs to UpTrain — consider data sensitivity before using cloud eval. HTTPS enforced.
⚡ Reliability
Best When
Evaluating RAG pipeline quality with pre-built metrics for context relevance and response faithfulness — UpTrain's metric library covers the most common RAG evaluation needs out of the box.
Avoid When
You need full observability with trace storage and debugging — UpTrain is an evaluation layer, not a full observability platform.
Use Cases
- • Evaluate RAG pipeline quality end-to-end — score context relevance, response faithfulness, and completeness for every retrieval-generation pair
- • Run regression testing on agent prompt changes — compare quality metrics before and after prompt modifications
- • Monitor LLM application quality in production — integrate evaluation into the response pipeline to flag low-quality outputs
- • Score agent tool use quality — evaluate whether function calls are appropriate, accurate, and complete
- • Build quality gates in CI/CD for LLM applications — fail builds when evaluation metrics drop below threshold
Not For
- • Full trace logging and debugging — UpTrain evaluates quality scores, not stores full traces; use Langfuse for complete observability
- • Real-time sub-100ms evaluation — LLM-as-judge evaluations add 1-5s latency; evaluate async or in batch, not inline
- • Non-RAG agent evaluation without custom metric development — most built-in metrics are RAG-specific
Interface
Authentication
UpTrain Cloud uses API key. Open-source mode uses your own LLM API keys (OpenAI, Anthropic) for evaluation. UPTRAIN_API_KEY environment variable for cloud; LLM provider keys for local evaluation.
Pricing
Open source (Apache 2.0) is free but evaluation calls use LLM-as-judge which costs LLM API tokens. Cloud platform manages this complexity but charges for evaluations. Self-hosting avoids cloud costs but requires LLM provider keys.
Agent Metadata
Known Gotchas
- ⚠ LLM-as-judge evaluations have non-zero cost — evaluating 1000 RAG pairs can cost $2-10+ depending on LLM provider and metric complexity
- ⚠ Evaluation quality depends on the judge LLM — GPT-4 judge gives better results than GPT-3.5 but at higher cost; configure explicitly
- ⚠ UpTrain's metrics are pre-defined — custom metrics require implementing a custom operator, which is more complex than RAGAS's custom metrics
- ⚠ Batch evaluation is async in cloud mode — results are not immediately available; poll for completion
- ⚠ Open source mode requires running a local UpTrain server (Docker) for the dashboard; pure Python eval works without server
- ⚠ Context relevance metric requires both the question and retrieved context — agents must pass all three components (question, context, response) for end-to-end RAG eval
Alternatives
Full Evaluation Report
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Package Brief
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Scores are editorial opinions as of 2026-03-07.