{"version":"1.0","type":"card","id":"abf80d19-21a5-40a9-919d-184da4850d25","url":"https://stacklist.com/card/abf80d19-21a5-40a9-919d-184da4850d25","title":"Agentic or Tool Use - Ragas","source_url":"https://docs.ragas.io/en/stable/concepts/metrics/available_metrics/agents/","note":"This page provides an evaluation framework for assessing AI applications through various metrics. It outlines the available metrics related to agentic or tool use, helping developers understand and improve their AI systems.","image":{"url":"https://ucarecdn.com/6bcaeb28-f2a6-49e3-828f-b286d94ff6f3/","alt":"Agentic or Tool Use - Ragas","width":1280,"height":800},"stack":{"id":"29e01ac8-6202-4abf-ab14-a0613026186b","title":"AI Agent Evaluation Frameworks and Benchmarks","url":"https://stacklist.com/c/technology/stack/29e01ac8-6202-4abf-ab14-a0613026186b"},"created_at":"2026-07-02T09:48:50.463Z","updated_at":null,"aco":{"summary":"Ragas Agentic or Tool Use Metrics documentation describes evaluation dimensions for AI agent and tool-use workflows, focusing on the TopicAdherence metric that measures an AI system's ability to stay within predefined domains. The metric computes precision, recall, and F1 score using reference topics and user input, with a detailed Python code example demonstrating evaluation of conversational interactions.","tags":["ragas","agentic-metrics","topic-adherence","llm-evaluation","tool-use","conversational-ai","precision-recall"],"key_entities":[{"name":"Ragas","type":"technology","confidence":0.95},{"name":"TopicAdherence","type":"concept","confidence":0.95},{"name":"Agentic Metrics","type":"concept","confidence":0.85},{"name":"OpenAI","type":"technology","confidence":0.9},{"name":"gpt-4o-mini","type":"technology","confidence":0.9},{"name":"Precision-Recall-F1","type":"concept","confidence":0.85},{"name":"Albert Einstein","type":"person","confidence":0.8},{"name":"AsyncOpenAI","type":"technology","confidence":0.75}],"classification":"reference","language":"en","confidence":0.85,"provenance":{"model":"claude-opus-4-6","tool":"@stacklist/mcp-server@2.0.0","confidence":0.85,"timestamp":"2026-07-02T09:49:02.518Z"},"token_counts":{"approximate":5446,"cl100k":5118},"content_hash":"sha256:747a75f8a218fcce8d1ec46cfc136684c3536a74afcf3ab99d9f59555bd3132f","acp_version":"0.2","body_available":true,"body_tokens":5446,"visibility":"public","agent_accessible":true,"status":"final"},"_links":{"self":"/api/public/card/abf80d19-21a5-40a9-919d-184da4850d25.json","html":"https://stacklist.com/card/abf80d19-21a5-40a9-919d-184da4850d25","md":"/api/public/card/abf80d19-21a5-40a9-919d-184da4850d25.md","stack_json":"/api/public/stack/29e01ac8-6202-4abf-ab14-a0613026186b.json"}}