pauly4010-evalgate-sdk 2.1.0

EvalGate Python SDK — CI for AI behavior. Traces, evaluations, assertions, and regression gates for LLM apps.

pauly4010-evalgate-sdk

Evaluation infrastructure for AI systems. Trace, test, and judge every LLM call — in five lines of Python.

Versioning: This package uses the same version as the TypeScript SDK (@evalgate/sdk). The Python SDK jumped from 1.0.0 → 1.9.x → 2.0.0 to align with TypeScript; both SDKs now share the same major.minor version going forward.

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Stop LLM regressions before they reach production. EvalGate gives you assertions, test suites, tracing, and CI regression gates — with no infrastructure to manage and no lock-in.

Install

pip install pauly4010-evalgate-sdk                        # Core
pip install "pauly4010-evalgate-sdk[openai]"              # + OpenAI tracing
pip install "pauly4010-evalgate-sdk[anthropic]"           # + Anthropic tracing
pip install "pauly4010-evalgate-sdk[all]"                 # Everything

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Quickstart (30 seconds)

No API key needed for local assertions:

from evalgate_sdk import expect

result = expect("The capital of France is Paris.").to_contain("Paris")
print(result.passed)  # True

Ready to send traces to the platform? Add an API key:

from evalgate_sdk import AIEvalClient, CreateTraceParams

client = AIEvalClient(api_key="sk-...")
trace = await client.traces.create(CreateTraceParams(name="chat-quality"))

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Why EvalGate?

LLMs don't fail like traditional software — they drift silently. A prompt tweak or model swap can quietly degrade output quality, and you won't notice until users complain. EvalGate turns evaluations into CI gates so regressions never reach production.

What you get	How it works
20+ assertions	expect(output).to_contain("Paris"), .to_not_contain_pii(), .to_have_sentiment("positive")
Test suites	Define cases, run them, get pass/fail + scores
Workflow tracing	Track multi-agent handoffs, decisions, and costs
OpenAI / Anthropic	Drop-in tracing wrappers — one line to instrument
Regression gates	Block deploys when eval scores drop
Snapshot testing	Save and compare outputs over time
CLI	evalgate run, evalgate gate, evalgate ci

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Assertions

20+ built-in checks for LLM output quality, safety, and structure:

from evalgate_sdk import expect

# Content
expect("The capital of France is Paris.").to_contain("Paris")
expect("Hello World").to_not_contain_pii()
expect("Thank you for your help.").to_be_professional()

# Sentiment
expect("Great product!").to_have_sentiment("positive")

# Structure
expect('{"name": "Alice"}').to_be_valid_json()
expect(0.95).to_be_between(0.0, 1.0)
expect("Hello world").to_have_length(min=5, max=100)

Standalone functions are also available:

from evalgate_sdk import contains_keywords, has_no_toxicity, matches_pattern

assert contains_keywords("quick brown fox", ["quick", "fox"])
assert has_no_toxicity("Thank you for your help.")
assert matches_pattern("abc-123", r"\w+-\d+")

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Test Suites

from evalgate_sdk import create_test_suite
from evalgate_sdk.types import TestSuiteCase, TestSuiteConfig

suite = create_test_suite("safety-checks", TestSuiteConfig(
    evaluator=my_llm_function,
    test_cases=[
        TestSuiteCase(name="greeting", input="Hello", expected_output="Hi there!"),
        TestSuiteCase(name="pii-check", input="Describe yourself",
                      assertions=[{"type": "not_contains_pii"}]),
    ],
))

result = await suite.run()
print(f"{result.passed_count}/{result.total} passed")

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OpenAI Integration

Trace every OpenAI call with one line:

from openai import AsyncOpenAI
from evalgate_sdk import AIEvalClient
from evalgate_sdk.integrations.openai import trace_openai

traced = trace_openai(AsyncOpenAI(), AIEvalClient.init())
response = await traced.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Explain gravity"}]
)
# Automatically traced with latency, tokens, and output

Or run a batch eval with built-in assertions:

from evalgate_sdk import openai_chat_eval, OpenAIChatEvalCase

result = await openai_chat_eval(
    name="chat-quality",
    model="gpt-4",
    cases=[
        OpenAIChatEvalCase(
            input="Explain gravity in one sentence.",
            assertions=[{"type": "contains_keywords", "value": ["gravity", "force"]}],
        ),
    ],
)
print(f"{result.passed_count}/{result.total} passed — score: {result.score:.2f}")

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Anthropic Integration

from anthropic import AsyncAnthropic
from evalgate_sdk import AIEvalClient
from evalgate_sdk.integrations.anthropic import trace_anthropic

traced = trace_anthropic(AsyncAnthropic(), AIEvalClient.init())
response = await traced.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain gravity"}]
)

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Workflow Tracing

Track multi-agent systems end-to-end — handoffs, decisions, and cost:

from evalgate_sdk import AIEvalClient, WorkflowTracer
from evalgate_sdk.types import HandoffType, CostCategory, RecordCostParams

client = AIEvalClient.init()
tracer = WorkflowTracer(client)

await tracer.start_workflow("research-pipeline")
span = await tracer.start_agent_span("researcher", {"query": "AI trends"})
await tracer.end_agent_span(span, {"findings": "..."})

await tracer.record_handoff("researcher", "writer", handoff_type=HandoffType.DELEGATION)
await tracer.record_cost(RecordCostParams(
    agent_name="researcher", category=CostCategory.LLM_INPUT, amount=0.05, tokens=1500
))

await tracer.end_workflow()
print(f"Total cost: ${tracer.get_total_cost():.2f}")

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Regression Gates

Block deployments when eval scores drop:

from evalgate_sdk import evaluate_regression, to_pass_gate

report = evaluate_regression(current_results, baseline)
assert to_pass_gate(report), f"Regression detected: {report.summary}"

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CLI

The Python CLI is available as evalgate (install with pip install "pauly4010-evalgate-sdk[cli]"):

evalgate init                    # Scaffold eval config
evalgate run --dir ./evals       # Run all evaluations
evalgate gate --baseline b.json  # Regression gate
evalgate ci                      # Run + gate (CI mode)
evalgate doctor                  # Check setup
evalgate discover                # Find eval files
evalgate explain                 # Root cause analysis on last failure

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Reliability

Feature	Detail
Python	3.9, 3.10, 3.11, 3.12, 3.13
Dependencies	Only httpx + pydantic — nothing else
Async	Native async/await throughout; sync wrappers available
Type hints	Full py.typed — works with mypy and Pyright
Errors	Structured: RateLimitError, AuthenticationError, NetworkError, ValidationError
Rate handling	Built-in RateLimiter with configurable tiers
Caching	RequestCache with TTL and LRU eviction
Batching	batch_process() with concurrency control
Pagination	Async PaginatedIterator with cursor support

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API Reference

Module	Methods
client.traces	create, list, get, update, delete, create_span, list_spans
client.evaluations	create, get, list, update, delete, create_test_case, list_test_cases, create_run, list_runs, get_run
client.llm_judge	evaluate, create_config, list_configs, list_results, get_alignment
client.annotations	create, list, tasks.create, tasks.list, tasks.get, tasks.items.create, tasks.items.list
client.developer	get_usage, get_usage_summary, api_keys.*, webhooks.*

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Examples

See the examples/python/ directory for runnable scripts and notebooks:

• OpenAI Eval — Trace and evaluate OpenAI chat completions
• RAG Eval — Evaluate retrieval-augmented generation pipelines
• Agent Eval — Test and trace multi-agent workflows

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No Lock-in

rm .evalgate/config.json

No account cancellation. No data export. Your local assertions keep working.

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Links

Platform · GitHub · TypeScript SDK

License

MIT