Evaluations

Overview

The OpenLIT SDK provides server-side evaluations via openlit.eval() (Python) and openlit.eval() (JS/TS). Evaluations use the same engine, rules, contexts, and custom eval types configured in the OpenLIT dashboard — working identically for development (offline) and production (online) stages.

Quick Start

Run your first offline evaluation in 3 lines of code.

Batch Evaluation

Evaluate multiple prompt/response pairs concurrently.

Attributes & Rules

Auto-resolve OTel attributes for context-aware evaluations.

Offline Evaluations

Offline evaluations run on the OpenLIT server using the same evaluation engine as online/auto evaluations. The SDK sends your prompt and response to the server, which runs LLM-as-judge evaluation and returns structured results.

Prerequisites

A running OpenLIT instance with evaluation configured in the dashboard.
An OpenLIT API key (create one in the dashboard under Settings > API Keys).

Quick Start

Python
TypeScript / JavaScript

import openlit

# Option 1: Configure once via init()
openlit.init(
    openlit_url="http://localhost:3000",
    openlit_api_key="openlit-xxxxx",
)

# Run evaluation
result = openlit.eval(
    prompt="What is the capital of France?",
    response="The capital of France is Lyon.",
    contexts=["Paris is the capital and largest city of France."],
)

# Use in assertions
assert result.passed, f"Evaluation failed: {result.failed_evals}"

# Option 2: Pass credentials directly (overrides init/env vars)
result = openlit.eval(
    prompt="Explain quantum computing",
    response="Quantum computers use qubits...",
    openlit_url="http://localhost:3000",
    openlit_api_key="openlit-xxxxx",
)

import openlit, { isPassed, getFailedEvals } from 'openlit';

// Option 1: Configure once via init()
openlit.init({
  openlitUrl: 'http://localhost:3000',
  openlitApiKey: 'openlit-xxxxx',
});

// Run evaluation
const result = await openlit.eval({
  prompt: 'What is the capital of France?',
  response: 'The capital of France is Lyon.',
  contexts: ['Paris is the capital and largest city of France.'],
});

// Use in assertions
console.log(result.success);       // true
console.log(isPassed(result));     // false — hallucination detected
console.log(getFailedEvals(result)); // [{ type: 'hallucination', ... }]

// Option 2: Pass credentials directly (overrides init/env vars)
const result = await openlit.eval({
  prompt: 'Explain quantum computing',
  response: 'Quantum computers use qubits...',
  openlitUrl: 'http://localhost:3000',
  openlitApiKey: 'openlit-xxxxx',
});

You can also configure via environment variables:

export OPENLIT_URL="http://localhost:3000"
export OPENLIT_API_KEY="openlit-xxxxx"

`openlit.eval()` / `openlit.eval({})` Parameters

Python
TypeScript / JavaScript

Parameter	Type	Description	Default
`prompt`	`str`	The user prompt sent to the LLM. Required.	—
`response`	`str`	The LLM’s response to evaluate. Required.	—
`contexts`	`list[str]`	Ground truth context for the evaluation.	`None`
`eval_types`	`list[str]`	Specific eval types to run (e.g. `["hallucination", "toxicity"]`). Runs all enabled types if omitted.	`None`
`attributes`	`dict`	Trace attributes for rule engine matching (overrides auto-resolved attributes).	`None`
`threshold_score`	`float`	Score threshold for verdict determination.	`0.5`
`store_results`	`bool`	Whether to store results in the OpenLIT database.	`True`
`run_id`	`str`	Identifier to group related evaluations.	`None`
`metadata`	`dict`	Custom key-value metadata to attach to results.	`None`
`openlit_api_key`	`str`	API key (overrides `init()` and env var).	`None`
`openlit_url`	`str`	Server URL (overrides `init()` and env var).	`None`
`print_results`	`bool`	Print formatted summary to terminal.	`True`

Parameter	Type	Description	Default
`prompt`	`string`	The user prompt sent to the LLM. Required.	—
`response`	`string`	The LLM’s response to evaluate. Required.	—
`contexts`	`string[]`	Ground truth context for the evaluation.	`undefined`
`evalTypes`	`string[]`	Specific eval types to run (e.g. `["hallucination", "toxicity"]`). Runs all enabled types if omitted.	`undefined`
`attributes`	`Record<string, string \| number \| boolean>`	Trace attributes for rule engine matching (overrides auto-resolved attributes).	`undefined`
`thresholdScore`	`number`	Score threshold for verdict determination.	`0.5`
`storeResults`	`boolean`	Whether to store results in the OpenLIT database.	`true`
`runId`	`string`	Identifier to group related evaluations.	`undefined`
`metadata`	`Record<string, string>`	Custom key-value metadata to attach to results.	`undefined`
`openlitApiKey`	`string`	API key (overrides `init()` and env var).	`undefined`
`openlitUrl`	`string`	Server URL (overrides `init()` and env var).	`undefined`
`printResults`	`boolean`	Print formatted summary to terminal.	`true`

Result Object

openlit.eval() returns an OfflineEvalResult with these properties:

Property	Type	Description
`success`	`bool`	Whether the evaluation completed without errors.
`passed`	`bool`	`True` if no evaluation types returned a “yes” verdict.
`evaluations`	`list[OfflineEvaluation]`	Individual evaluation results per type.
`failed_evals`	`list[OfflineEvaluation]`	Evaluations that returned a “yes” verdict.
`context_applied`	`ContextInfo`	Information about rule-matched context.
`metadata`	`dict`	Model, run ID, token usage, and cost metadata.
`error`	`str`	Error message if `success` is `False`.

Each OfflineEvaluation contains:

Field	Type	Description
`type`	`str`	The evaluation type (e.g. “hallucination”).
`score`	`float`	The evaluation score (0.0 to 1.0).
`verdict`	`str`	”yes” if detected, “no” otherwise.
`classification`	`str`	Category of the detection or “none”.
`explanation`	`str`	Brief explanation of the evaluation result.

Selecting Evaluation Types

Run specific evaluation types instead of all enabled ones:

Python
TypeScript / JavaScript

result = openlit.eval(
    prompt="Discuss workplace equality",
    response="Older workers can't learn new tech.",
    eval_types=["bias", "toxicity"],
)

const result = await openlit.eval({
  prompt: 'Discuss workplace equality',
  response: "Older workers can't learn new tech.",
  evalTypes: ['bias', 'toxicity'],
});

Discover Available Types

Python
TypeScript / JavaScript

types = openlit.get_eval_types()
for t in types:
    print(f"{t.id}: {t.label} (custom={t.is_custom}, enabled={t.enabled})")

const types = await openlit.getEvalTypes();
for (const t of types) {
  console.log(`${t.id}: ${t.label} (custom=${t.isCustom}, enabled=${t.enabled})`);
}

Batch Evaluation

Evaluate multiple prompt/response pairs concurrently:

Python
TypeScript / JavaScript

dataset = [
    {
        "prompt": "What is 2+2?",
        "response": "2+2 equals 4.",
        "contexts": ["Basic arithmetic."],
    },
    {
        "prompt": "Who wrote Hamlet?",
        "response": "Hamlet was written by Charles Dickens.",
    },
    {
        "prompt": "Describe gravity",
        "response": "Gravity is the force of attraction between masses.",
        "eval_types": ["hallucination"],
    },
]

batch_result = openlit.eval_batch(
    dataset=dataset,
    eval_types=["hallucination", "toxicity"],
    max_concurrent=5,
)

print(f"Pass rate: {batch_result.pass_rate:.0%}")
assert batch_result.all_passed

import openlit, { isAllPassed, getPassRate } from 'openlit';

const batchResult = await openlit.evalBatch({
  dataset: [
    {
      prompt: 'What is 2+2?',
      response: '2+2 equals 4.',
      contexts: ['Basic arithmetic.'],
    },
    {
      prompt: 'Who wrote Hamlet?',
      response: 'Hamlet was written by Charles Dickens.',
    },
    {
      prompt: 'Describe gravity',
      response: 'Gravity is the force of attraction between masses.',
      evalTypes: ['hallucination'],
    },
  ],
  evalTypes: ['hallucination', 'toxicity'],
  maxConcurrent: 5,
});

console.log(`Pass rate: ${(getPassRate(batchResult) * 100).toFixed(0)}%`);
console.log(`All passed: ${isAllPassed(batchResult)}`);

`openlit.eval_batch()` Parameters

Parameter	Type	Description	Default
`dataset`	`list[dict]`	List of items with `prompt` and `response` keys. Required.	—
`eval_types`	`list[str]`	Default eval types (can be overridden per item).	`None`
`attributes`	`dict`	Default attributes (can be overridden per item).	`None`
`threshold_score`	`float`	Default threshold score.	`0.5`
`store_results`	`bool`	Store all results in the database.	`True`
`run_id`	`str`	Group all batch evaluations under this ID. Auto-generated if omitted.	`None`
`max_concurrent`	`int`	Maximum number of concurrent evaluations.	`5`
`print_results`	`bool`	Print aggregate summary to terminal.	`True`

Automatic Attribute Resolution

The SDK automatically resolves trace attributes for rule engine matching, enabling context-aware evaluations without extra configuration. The resolution order (last wins):

OTEL_RESOURCE_ATTRIBUTES environment variable
OTEL_SERVICE_NAME environment variable
OPENLIT_ENVIRONMENT / OTEL_DEPLOYMENT_ENVIRONMENT environment variable
openlit.init() configuration (application_name, environment)
Explicit attributes parameter (highest priority)

Python
TypeScript / JavaScript

import openlit

# These are auto-detected for rule matching:
openlit.init(
    application_name="my-chatbot",
    environment="staging",
)

# Rules configured in the dashboard for service.name="my-chatbot"
# and deployment.environment="staging" will automatically match.
result = openlit.eval(
    prompt="Hello",
    response="Hi there!",
)

# Override auto-resolved attributes:
result = openlit.eval(
    prompt="Hello",
    response="Hi there!",
    attributes={
        "service.name": "different-service",
        "custom.tag": "experiment-v2",
    },
)

import openlit from 'openlit';

// These are auto-detected for rule matching:
openlit.init({
  applicationName: 'my-chatbot',
  environment: 'staging',
});

// Rules configured in the dashboard for service.name="my-chatbot"
// and deployment.environment="staging" will automatically match.
const result = await openlit.eval({
  prompt: 'Hello',
  response: 'Hi there!',
});

// Override auto-resolved attributes:
const result2 = await openlit.eval({
  prompt: 'Hello',
  response: 'Hi there!',
  attributes: {
    'service.name': 'different-service',
    'custom.tag': 'experiment-v2',
  },
});

CI/CD Integration

Use offline evaluations in your test suite or CI pipeline:

Python
TypeScript / JavaScript

import openlit
import pytest

def test_no_hallucination():
    result = openlit.eval(
        prompt="What year did WW2 end?",
        response="World War 2 ended in 1945.",
        eval_types=["hallucination"],
        print_results=False,
    )
    assert result.passed, f"Hallucination detected: {result.failed_evals}"

def test_batch_quality():
    dataset = load_test_cases()  # your test data
    result = openlit.eval_batch(
        dataset=dataset,
        print_results=False,
    )
    assert result.pass_rate >= 0.95, f"Pass rate too low: {result.pass_rate:.0%}"

import openlit, { isPassed, getFailedEvals, isAllPassed, getPassRate } from 'openlit';
import { describe, test, expect } from 'vitest'; // or jest

describe('LLM quality', () => {
  test('no hallucination', async () => {
    const result = await openlit.eval({
      prompt: 'What year did WW2 end?',
      response: 'World War 2 ended in 1945.',
      evalTypes: ['hallucination'],
      printResults: false,
    });
    expect(isPassed(result)).toBe(true);
  });

  test('batch quality', async () => {
    const result = await openlit.evalBatch({
      dataset: loadTestCases(),
      printResults: false,
    });
    expect(getPassRate(result)).toBeGreaterThanOrEqual(0.95);
  });
});

Configuration Precedence

For openlit_api_key and openlit_url, the resolution order is:

Explicit function parameter (highest priority)
openlit.init() configuration
OPENLIT_API_KEY / OPENLIT_URL environment variables

Deploy OpenLIT

Deployment options for scalable LLM monitoring infrastructure

Online Evaluations

Get started with evaluating your LLM responses in 2 simple steps on OpenLIT

Destinations

Send telemetry to Datadog, Grafana, New Relic, and other observability stacks

Zero-code observability with the OpenLIT Controller

Discover and instrument LLM traffic across Kubernetes, Docker, and Linux using eBPF — no code changes required.

Getting Started

Core Features

Integrations

Destinations

Overview

Quick Start

Batch Evaluation

Attributes & Rules

Offline Evaluations

Prerequisites

Quick Start

`openlit.eval()` / `openlit.eval({})` Parameters

Result Object

Selecting Evaluation Types

Discover Available Types

Batch Evaluation

`openlit.eval_batch()` Parameters

Automatic Attribute Resolution

CI/CD Integration

Configuration Precedence

Deploy OpenLIT

Online Evaluations

Destinations

Zero-code observability with the OpenLIT Controller

Getting Started

Core Features

Integrations

Destinations

Documentation Index

​Overview

Quick Start

Batch Evaluation

Attributes & Rules

​Offline Evaluations

​Prerequisites

​Quick Start

​openlit.eval() / openlit.eval({}) Parameters

​Result Object

​Selecting Evaluation Types

​Discover Available Types

​Batch Evaluation

​openlit.eval_batch() Parameters

​Automatic Attribute Resolution

​CI/CD Integration

​Configuration Precedence

Deploy OpenLIT

Online Evaluations

Destinations

Zero-code observability with the OpenLIT Controller

Overview

Offline Evaluations

Prerequisites

Quick Start

`openlit.eval()` / `openlit.eval({})` Parameters

Result Object

Selecting Evaluation Types

Discover Available Types

Batch Evaluation

`openlit.eval_batch()` Parameters

Automatic Attribute Resolution

CI/CD Integration

Configuration Precedence