agent-eval-runner 0.2.1

Run the AI Agent QA Eval Pack against your tool-using LLM agent + grade it with a shareable scorecard badge. Deterministic, OWASP Agentic Top 10 aligned, no LLM-judge.

agent-eval-runner

Run the AI Agent QA Eval Pack (vendor-agnostic YAML eval cases) against your tool-using LLM agent. Deterministic checks — no LLM-as-judge — so the report is defensible for production sign-off. Cross-platform (pure Python: Win / macOS / Linux).

Install

pip install agent-eval-runner            # core
pip install "agent-eval-runner[openai]"  # + built-in OpenAI demo adapter
pip install "agent-eval-runner[anthropic]"

30-second demo (no code)

Run text-based cases straight against a model:

export OPENAI_API_KEY=sk-...
agent-eval run --cases ./cases --adapter openai:gpt-4o --dimension safety

Real usage (all 5 case types, incl. tool-trace assertions)

Write a 10-line adapter wrapping your agent:

# my_adapter.py
from agent_eval_runner import AgentResult, ToolCall

def agent(case: dict) -> AgentResult:
    inp = case["input"]
    final_text, tool_calls = run_my_agent(           # <- your agent
        system=inp.get("system_message"),
        user=inp["user_message"],
        context=inp.get("context"),
    )
    return AgentResult(
        output_text=final_text,
        trace=[ToolCall(name=c.name, args=c.args) for c in tool_calls],
    )

agent-eval run --cases ./cases --adapter my_adapter:agent --report signoff.md

Exit code is non-zero if any high-severity case fails — drop it straight into CI.

What it checks

method	what it asserts
keyword_match	required / forbidden substrings in the answer
regex_match	answer matches a regex
refusal_detection	agent refused (or complied) as expected
trace_count	tool-call counts, required/forbidden tools, args seen
trace_invariant	structural: no error-loops, parallel-when-possible, step caps

Output

• Terminal pass/fail summary grouped by dimension, with production-blockers flagged
• --report out.md → a Markdown sign-off report (share with your team / customer)

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Full 23-case pack: https://weiseer.gumroad.com/l/dcipxt · Free 5-case starter: https://github.com/weiseer/ai-agent-qa-eval-pack-starter