Open Issues Need Help
View All on GitHubAI Summary: This issue requests adding explicit type hints to all fields within the `DatasetCase` dataclass located in `src/agentunit/datasets/base.py`. The task involves reviewing the file, applying appropriate Python types like `str` or `list[str]`, and ensuring `from __future__ import annotations` is present. Successful completion requires all fields to be hinted, and both `ruff check` and existing tests to pass.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.
AgentUnit is a pytest-inspired evaluation harness for autonomous agents and retrieval-augmented generation (RAG) workflows. It helps you describe repeatable scenarios, connect them to your agent stack, and score results with both heuristic and LLM-backed metrics.