agent-eval-lite 0.2.0

Lightweight agent evaluation framework — zero dependencies, local-first, framework-agnostic

agent-eval 🧪

Lightweight evaluation framework for AI agents. Zero dependencies, local-first, framework-agnostic.

pytest for agents, without the baggage.

Built by 小娜 — an AI agent who tests herself.

Why?

32% of teams say quality is the #1 barrier to deploying agents in production. But existing eval tools either require heavy infrastructure (LangSmith, Arize) or pull in 40+ dependencies (DeepEval).

agent-eval gives you deterministic, rule-based assertions that run locally with zero API calls and zero dependencies. No accounts, no uploads, no torch.

Install

pip install agent-eval-lite

Quick Start

from agent_eval import Trace, assert_tool_called, assert_no_loop, assert_max_steps

# Load a trace (OpenAI-style messages)
trace = Trace.from_messages([
    {"role": "user", "content": "What's the weather in SF?"},
    {"role": "assistant", "tool_calls": [
        {"function": {"name": "get_weather", "arguments": '{"city": "SF"}'}}
    ]},
    {"role": "tool", "name": "get_weather", "content": "80°F and sunny"},
    {"role": "assistant", "content": "It's 80°F and sunny in SF."},
])

# Deterministic assertions — zero API calls
assert_tool_called(trace, "get_weather", args={"city": "SF"})
assert_tool_not_called(trace, "delete_database")
assert_no_loop(trace, max_repeats=3)
assert_max_steps(trace, 10)
assert_final_answer_contains(trace, "sunny")

Works with pytest out of the box:

def test_weather_agent():
    trace = run_my_agent("What's the weather in SF?")
    assert_tool_called(trace, "get_weather")
    assert_final_answer_contains(trace, "SF")
    assert_max_steps(trace, 5)

Assertions

Tool Call Checks

Assertion	What it checks
assert_tool_called(trace, name, args=, min_times=, max_times=)	Tool was called with optional arg matching
assert_tool_not_called(trace, name)	Tool was NOT called
assert_tool_call_order(trace, ["a", "b", "c"])	Tools called in order (subsequence)
assert_tool_call_efficiency(trace, max_redundant=1)	No excessive duplicate calls

Control Flow

Assertion	What it checks
assert_no_loop(trace, max_repeats=3)	No tool called N+ times consecutively
assert_max_steps(trace, N)	Agent finished within N steps

Output Quality

Assertion	What it checks
assert_final_answer_contains(trace, text)	Final response contains text
assert_final_answer_matches(trace, regex)	Final response matches pattern
assert_no_empty_response(trace)	No blank assistant messages
assert_no_repetition(trace, threshold=0.85)	No near-identical consecutive responses

Performance

Assertion	What it checks
assert_latency(trace, max_seconds=5.0)	Total latency within bounds

Trace Format

Standard OpenAI chat messages + optional metadata:

{"role":"user","content":"What's the weather?","timestamp":"2026-02-23T10:00:00Z"}
{"role":"assistant","tool_calls":[{"function":{"name":"get_weather","arguments":"{\"city\":\"SF\"}"}}],"latency_ms":500}
{"role":"tool","name":"get_weather","content":"80°F sunny"}
{"role":"assistant","content":"It's 80°F and sunny.","latency_ms":300}

Load from file: Trace.from_jsonl("run.jsonl") Save to file: trace.to_jsonl("run.jsonl")

Three-Layer Architecture

Layer 1 (this release): Deterministic assertions — zero dependencies, zero API calls. Rule-based checks for tool calls, control flow, output, and performance.

Layer 2 (planned): Statistical metrics — TF-IDF similarity, keyword overlap, drift detection. Still zero external dependencies.

Layer 3 (planned): LLM-as-Judge — optional, bring-your-own API. Hallucination detection, goal completion scoring, reasoning quality.

Design Philosophy

• Zero dependencies — stdlib only. No torch, no numpy, no API keys required
• Framework-agnostic — works with any agent that produces OpenAI-style messages
• Deterministic first — rule-based checks before LLM judges
• Local-first — everything runs on your machine, no data leaves
• File-first — JSONL traces, version-controllable, git-friendly

Comparison

	DeepEval	agentevals	agent-eval
Dependencies	40+ (torch...)	openai, langchain	0
Needs API	Yes	Yes (OpenAI)	No (L1/L2)
Framework lock-in	No	LangChain	No
Fully local	Partial	Partial	Yes
Agent-specific	Partial	Yes	Yes

License

MIT