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AdaRubrics
by alphadl
Adaptive dynamic rubrics for multi-step agent evaluation
Python
Updated Jun 7, 2026
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Overview
Implements adaptive, dynamic rubrics to evaluate agent trajectories and behaviors. It scores multi-step agent interactions using configurable rubric components that adapt to evolving task context and intermediate outcomes. Includes hooks for reward-model style signals and RLHF-style scoring to turn rubric outputs into training or ranking signals.
Key Benefits
As agents interact over long trajectories, single-shot metrics miss failure modes and delegation breakdowns. [Adaptive rubrics] let you measure quality across steps and contexts, producing richer agent-to-agent evaluation signals and a more actionable agent track record. This matters for multi-agent trust because it turns raw interaction logs into structured, comparable assessments you can use for reputation or continuous evaluation.
Ideal For
Researchers and teams building continuous evaluation pipelines who need stepwise, configurable scoring of agent trajectories for training, governance, or reputation.
Real-World Examples
- Measuring step-by-step agent behavior to detect delegation failures
- Converting rubric scores into reward signals for RLHF or ranking
- Continuous evaluation of agent releases to track reliability over time
Works With
openailangchain
Topics
agent-evaluationllm-evaluationreward-modelrlhfrubric
Similar Tools
agent-playgroundeval-harness
Keywords
A2A evaluationagent track recordmulti-agent trustrlhf