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ScriptsMay 11, 2026·2 min de lectura

TruLens — Evaluate and Track LLM Apps

Instrument LLM apps and run systematic evals for RAG quality and regressions to find failure modes fast. Combine tracing and scorecards in one workflow.

Agent Toolkit
Agent Toolkit · Community
Introducción

Instrument LLM apps and run systematic evals for RAG quality and regressions to find failure modes fast. Combine tracing and scorecards in one workflow.

  • Best for: RAG/agent builders who want measurable quality (before/after) instead of vibe-checking prompts
  • Works with: Python, LLM app frameworks (LangChain/RAG pipelines), notebooks + CI-friendly eval runs
  • Setup time: 15 minutes

Quantitative Notes

  • Setup time ~15 minutes (install + one quickstart notebook or script)
  • GitHub stars + forks (verified): see Source & Thanks
  • Start with 10–50 eval cases to catch regressions early (then scale up)

Practical Notes

Treat evals like unit tests: freeze a small, representative dataset, define 2–4 core metrics, and make them run on every change that touches prompts/retrieval/tooling. When a score drops, inspect traces for which step (retrieval, reasoning, formatting) caused the regression.

Safety note: Avoid optimizing for a single metric—use a small metric set (quality + safety) and review traces for overfitting.

FAQ

Q: Is it only for RAG? A: No. It’s useful for any LLM app: chatbots, agents, tool callers, and prompt workflows.

Q: How do I use it in CI? A: Export eval cases as data, run scoring on each PR, and fail the build on threshold drops.

Q: What should I measure first? A: Start with retrieval relevance + groundedness for RAG, then add task success and safety checks.

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Fuente y agradecimientos

GitHub: https://github.com/truera/trulens Owner avatar: https://avatars.githubusercontent.com/u/51224128?v=4 License (SPDX): MIT GitHub stars (verified via api.github.com/repos/truera/trulens): 3,305 GitHub forks (verified via api.github.com/repos/truera/trulens): 274

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