EvalView

Application Monitoring & Observability

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This system provides extensive regression testing and behavior monitoring for AI agents, enabling developers to detect and prevent performance issues, regressions, and unintended behavioral changes after model updates or tool modifications. Its key features include baseline snapshot comparisons, detailed diffing of tool calls and outputs, multi-layered scoring mechanisms such as semantic similarity and large language model-based quality assessments, and seamless integration with continuous integration workflows, dashboards, and alerting platforms. It addresses problems like silent failures, output degradation, tool miscalls, and unexpected drift in multi-turn conversations, making it ideal for AI developers, machine learning engineers, and teams deploying complex conversational agents or AI services in production environments.

EvalView can be found in Application Monitoring & Observability categories.

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EvalView Main Features

• Skill Validation
  It checks if your agent meets specific skill requirements like Claude Skill or OpenAI Codex.
• Easy Setup and Use
  You can start testing quickly with simple commands like pip install and evalview init.
• Four Scoring Layers
  EvalView uses tool checks, code checks, meaning understanding, and AI judge to score behaviors.
• Statistical Testing
  It uses math tests to measure how reliable your agent's responses are with metrics like pass@k.
• Live Traffic Capture
  EvalView can listen to real user interactions through a proxy to test actual usage.
• Integration with CI/CD
  EvalView works with GitHub Actions and pull requests to check your agent after updates.
• Golden Baseline Storage
  saves the original behavior as a standard for future comparison.
• Multiple Reference Baselines
  It can compare your agent to up to 5 different examples for harder tests.
• Baseline Regression Detection
  EvalView saves a perfect version of your agent's behavior and detects any changes from it.
• Detects Different Change Types
  It finds when behavior is identical, tools change, output shifts, or the overall score drops.
• Multi-turn Conversation Testing
  EvalView tests conversations that have multiple turns and scores each part.
• Tool Call and Parameter Diffing
  It compares the tools called and their settings to find differences between behaviors.
• Test Generation from Live Agents
  It can create test cases based on conversations happening with your live agent.
• Production Monitoring with Alerts
  EvalView watches your agent in real use and sends alerts via Slack if needed.

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