TruLens for LLMs

TruLens instruments your AI agent with OpenTelemetry, scores every step with benchmarked LLM judges, and tells you which version to ship.

trulens.org
TruLens for LLMs homepage screenshot

About TruLens for LLMs

TruLens for LLMs provides tracing and evaluation for AI agents, including RAG workflows, MCP apps and summarization. It uses OpenTelemetry instrumentation and records per-step latency, inputs, outputs, tokens and cost, then applies LLM judges that explain their scores.

It can track multi-turn conversations, allow judges to be adjusted with rubrics, examples and score ranges, and compare app versions by average metric values to review quality and cost differences. Instrumentation is described for plain Python, LangGraph and LlamaIndex.

Key features

  • Trace agent steps with latency, inputs, outputs, tokens and cost
  • Score each step with LLM judges that include explanations
  • Track and evaluate complete multi-turn conversations
  • Customize judges with rubrics, examples and score ranges
  • Compare app versions by average metric values in leaderboard
  • Auto-instrument LangGraph and LlamaIndex or instrument plain Python
Best for: Developers building and iterating on agents, RAG systems, MCP apps and summarization workflows who need step-level traces and scores.
Good for
  • Records per-step latency, inputs, outputs, tokens and cost
  • Judge scores include explanations that point to the cause
  • Judges can be aligned to a domain with private data, rubrics and examples

TruLens for LLMs at a glance

Type
Online tool
Pricing
Not confirmed. Check the site
Platforms
Web
Open source
Not stated
Language
English
Last checked
Oct 5, 2026 · Online when checked
Listed
Oct 5, 2026

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TruLens for LLMs FAQ

What types of workloads can TruLens evaluate?
Agents, RAG, MCP apps and summarization, with metrics such as tool selection, groundedness, tool calling and comprehensiveness.
Which frameworks can it instrument?
Plain Python code and frameworks including LangGraph and LlamaIndex, using OpenTelemetry.
How can judges be adapted to a specific domain?
By using private data with a custom rubric, few-shot examples, and an adjusted score range.
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