checkllm

The most comprehensive LLM evaluation framework. The pytest of LLM testing.

pip install checkllm

def test_my_llm(check):
    output = my_llm("What is Python?")
    check.contains(output, "programming language")
    check.no_pii(output)
    check.hallucination(output, context="Python is a programming language created by Guido van Rossum.")

That's it. No setup, no boilerplate. The check fixture works in any pytest test.

Why checkllm?

• Zero learning curve -- if you know pytest, you know checkllm. Just add a check parameter.
• 39 free deterministic checks run instantly with zero API calls. No API key needed to start.
• 72 LLM-as-judge metrics -- hallucination, faithfulness, trajectory, per-turn, dual-judge, and more.
• 151 red team vulnerability types with 25 attack strategies -- the most comprehensive adversarial testing suite available.
• 17 compliance frameworks -- OWASP LLM/API/Agentic Top 10, MITRE ATLAS, EU AI Act, ISO 42001, HIPAA, GDPR, and more.
• Same checks everywhere -- use them in tests, CI, and production guardrails.

Quickstart

Install

pip install checkllm
checkllm init --use-case rag  # generates a tailored test file

1. Deterministic checks (free, instant)

def test_basic_quality(check):
    output = my_llm("Summarize this article.")

    check.contains(output, "key finding")
    check.max_tokens(output, limit=200)
    check.no_pii(output)
    check.is_json(output)
    check.gleu(output, reference="Expected summary text.", threshold=0.5)
    check.chrf(output, reference="Expected summary text.", threshold=0.4)
    check.latency_check(start_time, end_time, max_ms=3000)
    check.cost_check(input_tokens=500, output_tokens=200, model="gpt-4o", max_cost=0.05)

2. LLM-as-judge (deeper evaluation)

def test_rag_quality(check):
    output = my_rag("What causes climate change?")
    context = retrieve_context("climate change")

    check.hallucination(output, context=context)
    check.faithfulness(output, context=context)
    check.relevance(output, query="What causes climate change?")
    check.toxicity(output)

3. Fluent chaining

def test_with_chaining(check):
    output = my_llm("Explain quantum physics simply.")

    check.that(output) \
        .contains("quantum") \
        .max_tokens(200) \
        .has_no_pii() \
        .scores_above("relevance", 0.8, query="quantum physics")

4. Production guardrails

from checkllm import Guard, CheckSpec

guard = Guard(checks=[
    CheckSpec(check_type="no_pii"),
    CheckSpec(check_type="max_tokens", params={"limit": 500}),
    CheckSpec(check_type="toxicity"),
])

result = guard.validate(llm_output)
if not result.valid:
    result.raise_on_failure()

5. YAML-based evaluation

# checkllm.yaml
description: "Customer support chatbot evaluation"
judge:
  backend: openai
  model: gpt-4o

prompts:
  - "You are a helpful support agent. Answer: {{query}}"

tests:
  - vars:
      query: "How do I return an item?"
    assert:
      - type: contains
        value: "return policy"
      - type: relevance
        threshold: 0.8
      - type: no_pii
      - type: max_tokens
        value: 500

settings:
  budget: 5.0

checkllm eval-yaml checkllm.yaml

How checkllm compares

Independent benchmark, not just feature counts. On the public competitor leaderboard (docs/benchmarks/competitor-comparison.md) checkllm holds rank 1 on every published row against DeepEval and promptfoo: halubench/hallucination 0.783, ragtruth/hallucination 0.663, ragtruth/faithfulness 0.754, ragtruth/context_relevance 0.565, and truthfulqa/answer_relevancy 0.546 (ROC-AUC, gpt-4o-mini judge, 200 source rows per slice). Methodology is in docs/benchmarks/methodology.md; raw scores ship in benchmarks/competitor_comparison/.

Feature comparison

Feature	checkllm	DeepEval	Ragas	promptfoo
pytest native	Yes	Wrapper	No	No
Free deterministic checks	39	Limited	Limited	Yes
LLM-as-judge metrics	72	~50	~40	Custom
Red team vulnerability types	151	40+	0	100+
Attack strategies	25	10+	0	30+
Compliance frameworks	17	3	0	10+
Multi-provider judges	15+ backends	13+	~6	50+
Consensus judging	7 strategies	No	Dual-judge	No
Production guardrails	Built-in	No	No	API
Cost control & budgets	Built-in	No	No	Caching
Knowledge Graph synthesis	Full pipeline	No	Yes	No
Multilingual prompts	20 languages	No	Yes	No
Prompt optimization	4 algorithms	4	2	No
YAML config evaluation	Yes	No	No	Yes
Streaming evaluation	Token-by-token	No	No	No
Regression detection	Statistical (p-values)	No	No	No
DPO export	Yes	No	No	No
Telemetry / phoning home	None	PostHog + Sentry	None	Telemetry
Independence	Fully independent	YC-backed	YC-backed	OpenAI-owned

All metrics by category

RAG Evaluation (14 metrics)

hallucination faithfulness faithfulness_hhem context_relevance context_entity_recall contextual_precision contextual_recall answer_completeness groundedness nonllm_context_precision nonllm_context_recall quoted_spans_alignment nv_context_relevance nv_response_groundedness

General Quality (12 metrics)

relevance coherence fluency consistency correctness factual_correctness sentiment toxicity bias summarization nv_answer_accuracy prompt_alignment

Completeness & Instruction Following (5 metrics)

response_completeness instruction_following instruction_completeness conversation_completeness topic_adherence

Agent & Tool Evaluation (12 metrics)

task_completion tool_accuracy tool_call_f1 plan_adherence plan_quality step_efficiency knowledge_retention goal_accuracy trajectory_goal_success trajectory_tool_sequence trajectory_step_count trajectory_tool_args_match

Per-Turn Conversation (3 metrics)

turn_relevancy turn_faithfulness turn_coherence

Multimodal (6 metrics)

image_relevance image_helpfulness image_coherence text_to_image image_editing image_reference

Structured Output (4 metrics)

code_correctness sql_equivalence comparative_quality datacompy_score

Role & Safety (3 metrics)

role_adherence role_violation non_advice

MCP & Tool-Specific (3 metrics)

mcp_use mcp_task_completion multi_turn_mcp_use

Specialized (3 metrics)

g_eval noise_sensitivity rubric

Deterministic Checks (39, zero API cost)

contains not_contains starts_with ends_with regex exact_match exact_match_strict min_tokens max_tokens min_words max_words min_chars max_chars min_sentences max_sentences is_json json_schema is_xml is_yaml is_html no_pii language readability similarity bleu rouge_l meteor gleu chrf latency_check cost_check string_distance perplexity is_valid_python is_url has_url word_count char_count sentence_count

Red teaming & adversarial testing

from checkllm.redteam import RedTeamer, VulnerabilityType
from checkllm.redteam_strategies import StrategyType

red = RedTeamer()
report = await red.scan(
    target=my_llm_function,
    vulnerability_types=[
        VulnerabilityType.PROMPT_INJECTION,
        VulnerabilityType.JAILBREAK,
        VulnerabilityType.PII_LEAKAGE,
        VulnerabilityType.DATA_EXFILTRATION,
    ],
    strategies=[StrategyType.BASE64, StrategyType.CRESCENDO, StrategyType.PERSONA],
    attacks_per_type=5,
)
print(report.summary())
print(report.risk_summary())  # CVSS severity breakdown

151 vulnerability types across 12 categories: prompt injection, jailbreak, PII leakage, harmful content, encoding attacks, privilege escalation, agentic AI attacks, brand & reputation, industry compliance, and more.

25 attack strategies: BASE64, ROT13, HEX, LEETSPEAK, MORSE, HOMOGLYPH, CRESCENDO (multi-turn escalation), JAILBREAK_TREE, JAILBREAK_META, JAILBREAK_COMPOSITE, BEST_OF_N, PERSONA, HYPOTHETICAL, ROLEPLAY, LAYER (composable chaining), and more.

Coding agent security

from checkllm.redteam_coding_agents import CodingAgentScanner

scanner = CodingAgentScanner(judge=judge)
report = await scanner.scan(target=my_coding_agent)
# Tests: repo prompt injection, sandbox escape, secret leakage, verifier sabotage

Compliance frameworks

from checkllm.compliance_frameworks import ComplianceScanner, ComplianceFramework

scanner = ComplianceScanner(judge=judge)
report = await scanner.scan(
    target=my_llm,
    frameworks=[
        ComplianceFramework.OWASP_LLM_TOP10,
        ComplianceFramework.OWASP_AGENTIC_TOP10,
        ComplianceFramework.EU_AI_ACT,
        ComplianceFramework.HIPAA,
    ],
)
print(report.summary())

17 frameworks: OWASP LLM Top 10, OWASP API Top 10, OWASP Agentic Top 10, MITRE ATLAS, EU AI Act, ISO 42001, NIST AI RMF, NIST CSF, HIPAA, GDPR, PCI-DSS, SOC2, ISO 27001, COPPA, FERPA, CCPA, DoD AI Ethics.

Knowledge Graph test generation

from checkllm.knowledge_graph import KGTestGenerator, EntityExtractor, SimilarityBuilder

gen = KGTestGenerator(judge=judge)
samples = await gen.generate(
    documents=["doc1 text...", "doc2 text..."],
    num_samples=50,
    synthesizers={"single_hop": 0.4, "multi_hop_abstract": 0.3, "multi_hop_specific": 0.3},
    personas=5,
)
cases = gen.to_cases(samples)

Build a knowledge graph from your documents, then generate diverse test cases with single-hop, multi-hop abstract, and multi-hop specific queries. Supports persona variation, query styles (web search, misspelled, conversational), and configurable complexity.

Multilingual evaluation

from checkllm.multilingual import PromptAdapter, detect_language

adapter = PromptAdapter(judge=judge)
translated = await adapter.adapt(template=my_prompt, target_language="ja")
adapter.save_translations("translations/ja.json")

lang = detect_language("Esto es un texto en espanol.")  # "es"

Supports 20+ languages with automatic prompt adaptation. Language detection uses Unicode character-range analysis with LLM fallback.

Prompt optimization

from checkllm.optimize import create_optimizer

optimizer = create_optimizer("miprov2", judge=judge)  # or "genetic", "copro", "simba"
result = await optimizer.optimize(
    prompt="Summarize this document.",
    test_cases=my_test_cases,
    metric_fn=my_metric,
    num_candidates=10,
)
print(f"Improved from {result.initial_score:.2f} to {result.best_score:.2f}")

Four optimization algorithms: Genetic (evolutionary), MIPROv2 (instruction + demonstration), COPRO (failure-driven iterative), SIMBA (similarity-based adaptation).

Multi-provider judges

from checkllm import create_judge

judge = create_judge("openai", model="gpt-4o")
judge = create_judge("anthropic", model="claude-sonnet-4-6")
judge = create_judge("gemini", model="gemini-2.0-flash")
judge = create_judge("ollama", model="llama3.1")       # Free, local
judge = create_judge("litellm", model="any-model")     # 100+ models
judge = create_judge("deepseek")
judge = create_judge("groq")
judge = create_judge("fireworks")

Auto-detection: set OPENAI_API_KEY, ANTHROPIC_API_KEY, or have Ollama running -- checkllm picks the best judge automatically.

Consensus judging

from checkllm import ConsensusJudge

judges = [("gpt4", gpt4_judge), ("claude", claude_judge), ("gemini", gemini_judge)]
consensus = ConsensusJudge(judges, strategy="majority")  # or mean, median, unanimous, min, max, weighted

Cost control

checkllm estimate tests/              # See costs before running
checkllm run tests/ --budget 5.0      # Cap spend at $5
checkllm run tests/ --dry-run         # Estimate without executing

Configuration

# pyproject.toml
[tool.checkllm]
judge_backend = "auto"
judge_model = "gpt-4o"
default_threshold = 0.8
budget = 10.0
cache_enabled = true
engine = "auto"

CLI

Command	Description
checkllm init	Scaffold a project (--use-case, --ci)
checkllm run	Run tests (--budget, --dry-run, --snapshot)
checkllm eval-yaml	Run YAML-based evaluation
checkllm estimate	Estimate costs before running
checkllm watch	Re-run on file changes
checkllm report	Generate HTML report
checkllm snapshot	Save baseline for regression detection
checkllm diff	Compare snapshots
checkllm history	View run history and trends
checkllm list-metrics	Show all available checks and metrics
checkllm cache	Manage judge response cache
checkllm dashboard	Launch web dashboard

Framework integrations

# LangChain
from checkllm.integrations.langchain import CheckllmCallbackHandler
chain.invoke(input, config={"callbacks": [CheckllmCallbackHandler(checks=["no_pii"])]})

# CrewAI
from checkllm.integrations.crewai import CheckllmCrewCallback

# OpenAI Agents SDK
from checkllm.integrations.openai_agents import CheckllmRunHandler

# Claude Agent SDK
from checkllm.integrations.claude_agents import CheckllmAgentHandler

# PydanticAI
from checkllm.integrations.pydantic_ai import CheckllmResultValidator

# LlamaIndex
from checkllm.integrations.llama_index import CheckllmCallbackHandler

Custom metrics

from checkllm import metric, CheckResult

@metric("brevity")
def brevity_check(output: str, max_words: int = 50, **kwargs) -> CheckResult:
    words = len(output.split())
    return CheckResult(
        passed=words <= max_words,
        score=min(1.0, max_words / max(words, 1)),
        reasoning=f"{words} words (limit: {max_words})",
        cost=0.0, latency_ms=0, metric_name="brevity",
    )

License

MIT