Back to Ecosystem Pulse
ToolProduction Ready
judgeval
by JudgmentLabs
Post-training evaluation and observability for agent systems
Python
Updated Jul 20, 2026
Share:
How It Works
Provides a post-training evaluation and observability layer for agent systems, powering RL/SFT improvements and runtime monitoring. Ingests environment data, agent interactions, and evaluation metrics to produce reproducible evals and datasets for retraining or auditing. Includes tooling to run LLM-as-Judge Pattern and Event-Driven Agent Pattern for evaluation pilots.
Why It Matters
As agents are deployed and delegate to other agents, objective post-training signals are essential for trust and accountability. A2A Protocol centralizes A2A evaluation and interaction logging so teams can build agent track records and close the loop between evaluation and Model Context Protocol (MCP). This matters because reproducible, continuous evaluation is the backbone of reliable multi-agent systems and governance.
Best For
Teams that need continuous A2A evaluation, agent interaction logging, and dataset generation to improve agent reliability and retrain models.
How It's Used
- When you need to log agent-to-agent interactions and produce reproducible evaluation datasets
- When you want to run continuous A/B comparisons and monitor agent reliability over time
- When you need failure-mode analysis and metrics to drive RL or SFT retraining
Works With
langchainopenai
Topics
agentagentic-aiagentsgrpolangchainlanggraphllama-indexllmllm-evaluationllm-observability+5 more
Similar Tools
agent playgroundrepkit
Keywords
A2A evaluationmulti-agent trustagent track recordagent-evaluation