agent-eval-lite 0.5.0

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 xiaona-ai.

Why?

32% of teams say quality is the #1 barrier to deploying agents in production. Existing eval tools often need hosted infrastructure or heavy dependency stacks.

agent-eval gives deterministic assertions and LLM-as-judge evaluation — all with zero dependencies.

Install

pip install agent-eval-lite

Features

• Deterministic assertions for tool use, control flow, output, and latency
• LLM-as-judge evaluation (v0.4) — 4 built-in judges + custom G-Eval, zero external deps
• Cost tracking — both for agent traces and judge calls themselves
• Consistency reports — multi-run comparison with threshold checks
• Safety assertions — sensitive data detection and prompt injection leak patterns
• CLI — show, diff, stats, cost, consistency, judge with JSON output

Quick Start

Deterministic Assertions

from agent_eval import (
    Trace,
    assert_tool_called,
    assert_tool_not_called,
    assert_no_loop,
    assert_max_steps,
    assert_final_answer_contains,
)

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."},
])

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")

LLM-as-Judge (v0.4.0) 🆕

from agent_eval import JudgeProvider, judge_goal_completion, judge_faithfulness

# Works with ANY OpenAI-compatible API — zero external dependencies
provider = JudgeProvider(
    api_key="your-key",
    base_url="https://api.openai.com/v1",  # or any compatible endpoint
    model="gpt-4o",
)

# Goal completion judge
result = judge_goal_completion(
    provider,
    goal="Find the weather in San Francisco",
    output="It's 80°F and sunny in SF.",
)
print(result.passed)     # True
print(result.reasoning)  # "The agent directly answered..."

# Faithfulness judge (hallucination detection)
result = judge_faithfulness(
    provider,
    context="The API returned: 80°F, sunny, San Francisco",
    output="It's 80°F and sunny in SF.",
)
print(result.passed)             # True
print(result.unsupported_claims) # []

# Every judge call tracks its own cost
print(result.judge_cost.total_tokens)       # 150
print(result.judge_cost.estimated_cost_usd) # 0.00125 (with pricing table)

Custom Judge (G-Eval Style)

from agent_eval import create_custom_judge, Rubric

# Binary (pass/fail)
conciseness_judge = create_custom_judge(
    criteria="Response must be concise and under 100 words",
    binary=True,
)
result = conciseness_judge(provider=provider, input="Summarize X", output="X is Y.")

# Likert (1-5 scale) with custom rubric
helpfulness_judge = create_custom_judge(
    criteria="How helpful is the response?",
    evaluation_steps=[
        "Check if the response addresses the user's question",
        "Check if actionable steps are provided",
        "Evaluate tone and clarity",
    ],
    rubric=[
        Rubric(1, "Not helpful at all"),
        Rubric(3, "Somewhat helpful but missing key info"),
        Rubric(5, "Extremely helpful and actionable"),
    ],
)
result = helpfulness_judge(provider=provider, input="How do I deploy?", output="Run docker push...")
print(result.score)      # 0.75 (normalized 0-1)
print(result.raw_score)  # 4 (original 1-5)

Multi-step Faithfulness (v0.5)

# Thorough mode: 3-step pipeline (claims extraction → verification → aggregation)
result = judge_faithfulness(
    provider, context="...", output="...",
    mode="thorough",  # default is "fast" (single-call)
)
# 4-level classification: supported / contradicted / fabricated / idk
# "idk" (benign gaps like common knowledge) don't count as unfaithful

Jury Mode — Multi-Model Voting (v0.5)

from agent_eval import JudgeJury, JudgeProvider, judge_faithfulness

# Create providers with different models
jury = JudgeJury([
    JudgeProvider(api_key="k1", base_url="https://provider1/v1", model="claude-sonnet-4-6"),
    JudgeProvider(api_key="k2", base_url="https://provider2/v1", model="grok-4.1-fast"),
    JudgeProvider(api_key="k3", base_url="https://provider3/v1", model="gpt-5.2"),
])

# Run any judge across all models — majority vote
verdict = jury.judge(judge_faithfulness, context="...", output="...")
print(verdict.passed)           # True/False (majority vote)
print(verdict.agreement_ratio)  # 0.67 - 1.0
print(verdict.unanimous)        # True if all agree
print(verdict.total_cost)       # Aggregated token costs

Trajectory Quality Judge

from agent_eval import judge_trajectory

trajectory = [
    {"role": "user", "content": "Book a flight to Tokyo"},
    {"role": "assistant", "content": "", "tool_calls": [{"function": {"name": "search_flights"}}]},
    {"role": "tool", "name": "search_flights", "content": "Found 3 flights"},
    {"role": "assistant", "content": "", "tool_calls": [{"function": {"name": "book_flight"}}]},
    {"role": "tool", "name": "book_flight", "content": "Booked!"},
    {"role": "assistant", "content": "Your flight to Tokyo is booked!"},
]

result = judge_trajectory(provider, trajectory=trajectory)
print(result.score)      # 0.75-1.0 for efficient trajectories
print(result.raw_score)  # 4 or 5

Works with pytest:

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)

def test_agent_quality():
    trace = run_my_agent("What's the weather in SF?")
    provider = JudgeProvider(api_key=os.environ["JUDGE_API_KEY"])
    result = judge_goal_completion(provider, goal="Get SF weather", output=trace.final_response.text_content)
    assert result.success

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

Cost and Budget (v0.3)

Assertion	What it checks
assert_total_tokens(trace, max_tokens)	Total usage tokens within budget
assert_total_cost(trace, max_usd, pricing, strict=False)	Total USD cost within budget
assert_tokens_per_step(trace, max_avg)	Avg tokens per assistant step
assert_cost_efficiency(trace, max_cost_per_tool_call, pricing)	Cost per tool call

Consistency (v0.3)

API	What it checks
ConsistencyReport(traces)	Pairwise consistency metrics
assert_consistency(...)	Threshold checks for tool/answer/step variance

Safety (v0.3)

Assertion	What it checks
assert_no_sensitive_data(trace, patterns, roles=None)	Detects regex matches in message content
assert_no_injection_leak(trace, system_prompt, min_chunk_words=5)	Detects verbatim system prompt leakage

LLM-as-Judge (v0.4) 🆕

Judge	What it evaluates	Output
judge_goal_completion(provider, goal, output)	Did the agent complete the goal?	pass/fail
judge_trajectory(provider, trajectory, reference=)	Trajectory quality and efficiency	1-5 score
judge_faithfulness(provider, context, output, mode=)	Is the output grounded in context?	pass/fail + claims
judge_reasoning(provider, reasoning, expected=)	Reasoning chain quality	1-5 score
create_custom_judge(criteria, steps=, rubric=, binary=)	Custom G-Eval criteria	configurable
JudgeJury(providers).judge(judge_fn, ...)	Multi-model voting (v0.5)	aggregated verdict

Key differentiators:

• 🏆 Zero dependencies — uses urllib.request from stdlib (competitors need openai/httpx/langchain)
• 🏆 Judge cost tracking — every judge call reports token usage and estimated cost
• 🏆 Any provider — works with any OpenAI-compatible API endpoint
• CoT evaluation steps and rubric anchoring (G-Eval methodology)

CLI

# Trace inspection
agent-eval show run.jsonl [--json]
agent-eval diff baseline.jsonl current.jsonl [--json] [--fail-on-regression]
agent-eval stats run.jsonl [--json]

# Cost & consistency
agent-eval cost run.jsonl --max-tokens 5000 --max-usd 0.05 --pricing pricing.json
agent-eval consistency run1.jsonl run2.jsonl run3.jsonl --min-tool-consistency 0.8

# LLM-as-judge (v0.4) 🆕
agent-eval judge run.jsonl --judge-type goal --api-key $KEY --model gpt-4o
agent-eval judge run.jsonl --judge-type trajectory --api-key $KEY --json
agent-eval judge run.jsonl --judge-type faithfulness --context "ground truth text"
agent-eval judge run.jsonl --judge-type custom --criteria "Response is helpful" --binary

Set JUDGE_API_KEY env var or pass --api-key. Use --base-url for non-OpenAI providers.

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,"usage":{"prompt_tokens":100,"completion_tokens":50,"model":"gpt-4o"}}
{"role":"tool","name":"get_weather","content":"80°F sunny"}
{"role":"assistant","content":"It's 80°F and sunny.","latency_ms":300}

Architecture

Layer	What	Dependencies
Deterministic (v0.1-0.3)	Assertions, cost, consistency, safety	Zero
LLM-as-Judge (v0.4)	Semantic evaluation via any LLM API	Zero (urllib)
Statistical (planned)	Drift detection, similarity metrics	Zero

Design Philosophy

• Zero dependencies: stdlib only — even LLM judges use urllib
• Framework-agnostic: works with any OpenAI-style trace
• Deterministic first: assertions before judges
• Local-first: no required data upload
• Cost-aware: track both agent and evaluation costs

Comparison

	DeepEval	agentevals	judges	agent-eval
Dependencies	40+ (torch...)	langchain	openai+instructor	0
Needs API	Yes	Yes	Yes	Optional (judge only)
Framework lock-in	No	LangChain	No	No
Fully local	Partial	No	No	Yes (deterministic)
Judge cost tracking	No	No	No	Yes
Zero-dep LLM judge	No	No	No	Yes (urllib)
Multi-model jury	No	No	No	Yes (v0.5)
Public benchmarks	No	No	No	FaithBench κ=0.68

Benchmark Results

We evaluate our faithfulness judge against FaithBench (Bao et al., NAACL 2025) — a human-annotated benchmark of 750 challenging summarization hallucinations where SOTA detectors disagree.

Faithfulness Judge on FaithBench (v0.5.0)

FaithBench selects the hardest cases where SOTA detectors disagree — a deliberately adversarial benchmark.

Judge Model	Provider	Accuracy	F1	Cohen's κ	Notes
Claude Sonnet 4.6	huan666	83%	0.83	0.68	Best balance
GPT-5.2	wcgio	77%	0.76	0.55	Slightly lenient
Claude Sonnet 4.6 (100 samples)	sorai	71%	0.69	0.42	Larger sample
DeepSeek v3.2	huan666	70%	0.47	0.31	Too strict
Grok 4.1 Fast	wcgio	70%	0.40	0.29	Too strict

Key findings:

• Cohen's κ = 0.68 (substantial agreement) with Claude Sonnet 4.6 — competitive with dedicated hallucination detectors
• Models show different bias directions: grok/deepseek are too strict (high FN), gpt-5.2 is lenient (high FP)
• Jury mode (v0.5) exploits these complementary biases via multi-model voting
• Our single-call prompt achieves this at 1/3 the cost of multi-step pipelines

Two evaluation modes (v0.5):

• mode="fast" (default): Single-call 4-level NLI classification — best accuracy/cost ratio
• mode="thorough": 3-step pipeline (claims extraction → per-claim verification → aggregation) — better explainability, 4-level per-claim verdicts (supported/contradicted/fabricated/idk)

Reproduce: python benchmarks/run_standard_benchmark.py --dataset faithbench --model claude-sonnet-4-6 --samples 100

License

MIT