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EvaluationProduction Ready
trulens
by truera
Evaluate and track LLMs and AI agents with observability and custom metrics
Python
Updated Feb 11, 2026
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What It Does
Provides evaluation, tracking, and observability for LLM experiments and AI agents. Captures model inputs/outputs, custom metrics, and traces to let you measure agent behavior over time. Offers configurable dashboards, explainability hooks, and integrations so evaluations plug into existing LLM toolchains. This supports Human-in-the-Loop workflows and aligns with the Model Context Protocol (MCP) for tool interoperability.
Why It Matters
As agents interact and delegate, reproducible evaluation and historical tracking are essential to judge reliability and failure modes. Trulens gives teams a consistent place to record agent runs, compute custom quality metrics, and inspect behavior — turning ad-hoc testing into continuous agent evaluation. That historical signal is critical for building agent track records and trustable RepKit-style workflows Agent Registry Pattern.
Ideal For
Teams running LLM experiments or multi-agent workflows who need reproducible evaluation, observability, and explainability for agent behavior. Supports an Agent Loop approach to ongoing coordination.
Use Cases
- Instrument agent conversations to log interactions and compute reproducible metrics
- Run continuous evaluation pipelines that compare agent versions and track regressions
- Inspect agent failure modes with traces and explainability hooks for postmortem analysis
Works With
langchainopenaihuggingface
Topics
agent-evaluationagentopsai-agentsai-monitoringai-observabilityevalsexplainable-mlllm-evalllm-evaluationllmops+3 more
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Keywords
agent-evaluationagent track recordcontinuous agent evaluationagent reliability