Openevals NPM | npm.io

⚖️ OpenEvals

Much like tests in traditional software, evals are a hugely important part of bringing LLM applications to production. The goal of this package is to help provide a starting point for you to write evals for your LLM applications, from which you can write more custom evals specific to your application.

If you are looking for evals specific to evaluating LLM agents, please check out agentevals.

Quickstart

To get started, install openevals:

npm install openevals @langchain/core

This quickstart will use an evaluator powered by OpenAI's o3-mini model to judge your results, so you'll need to set your OpenAI API key as an environment variable:

export OPENAI_API_KEY="your_openai_api_key"

Once you've done this, you can run your first eval:

import { createLLMAsJudge, CORRECTNESS_PROMPT } from "openevals";

const correctnessEvaluator = createLLMAsJudge({
  prompt: CORRECTNESS_PROMPT,
  model: "openai:o3-mini",
});

const inputs = "How much has the price of doodads changed in the past year?"
// These are fake outputs, in reality you would run your LLM-based system to get real outputs
const outputs = "Doodads have increased in price by 10% in the past year."
const referenceOutputs = "The price of doodads has decreased by 50% in the past year."

// When calling an LLM-as-judge evaluator, parameters are formatted directly into the prompt
const evalResult = await correctnessEvaluator({
  inputs,
  outputs,
  referenceOutputs,
});

console.log(evalResult);

{
    key: 'score',
    score: false,
    comment: '...'
}

By default, LLM-as-judge evaluators will return a score of True or False. See the LLM-as-judge section for more information on how to customize the scoring, model, and prompt!

Installation
Evaluators
Python Async Support
LangSmith Integration
- Pytest or Vitest/Jest
- Evaluate

Installation

You can install openevals like this:

npm install openevals @langchain/core

For LLM-as-judge evaluators, you will also need an LLM client. By default, openevals will use LangChain chat model integrations and comes with langchain_openai installed by default. However, if you prefer, you may use the OpenAI client directly:

npm install openai

It is also helpful to be familiar with some evaluation concepts.

Evaluators

LLM-as-judge

One common way to evaluate an LLM app's outputs is to use another LLM as a judge. This is generally a good starting point for evals.

This package contains the createLLMAsJudge function, which takes a prompt and a model as input, and returns an evaluator function that handles formatting inputs, parsing the judge LLM's outputs into a score, and LangSmith tracing and result logging.

To use the createLLMAsJudge function, you need to provide a prompt and a model. For prompts, LangSmith has some prebuilt prompts in the openevals.prompts module that you can use out of the box. Here's an example:

import { createLLMAsJudge, CORRECTNESS_PROMPT } from "openevals";

const correctnessEvaluator = createLLMAsJudge({
  prompt: CORRECTNESS_PROMPT,
  model: "openai:o3-mini",
});

Note that CORRECTNESS_PROMPT is a simple f-string that you can log and edit as needed for your specific use case:

console.log(CORRECTNESS_PROMPT);

You are an expert data labeler evaluating model outputs for correctness. Your task is to assign a score based on the following rubric:

<Rubric>
  A correct answer:
  - Provides accurate and complete information
  ...
<input>
{inputs}
</input>

<output>
{outputs}
</output>
...

By convention, we generally suggest sticking to inputs, outputs, and referenceOutputs as the names of the parameters for LLM-as-judge evaluators, but these will be directly formatted into the prompt so you can use any variable names you want.

Prebuilt prompts

Correctness

openevals includes a prebuilt prompt for createLLMAsJudge that scores the correctness of an LLM's output. It takes inputs, outputs, and optionally, referenceOutputs as parameters.

import { createLLMAsJudge, CORRECTNESS_PROMPT } from "openevals";

const correctnessEvaluator = createLLMAsJudge({
  prompt: CORRECTNESS_PROMPT,
  feedbackKey: "correctness",
  model: "openai:o3-mini",
});

const inputs = "How much has the price of doodads changed in the past year?"
const outputs = "Doodads have increased in price by 10% in the past year."
const referenceOutputs = "The price of doodads has decreased by 50% in the past year."

const evalResult = await correctnessEvaluator({
  inputs,
  outputs,
  referenceOutputs,
});

console.log(evalResult);

{
    key: 'correctness',
    score: false,
    comment: '...'
}

Conciseness

openevals includes a prebuilt prompt for createLLMAsJudge that scores the conciseness of an LLM's output. It takes inputs and outputs as parameters.

import { createLLMAsJudge, CONCISENESS_PROMPT } from "openevals";

const concisenessEvaluator = createLLMAsJudge({
  prompt: CONCISENESS_PROMPT,
  feedbackKey: "conciseness",
  model: "openai:o3-mini",
});

const inputs = "How is the weather in San Francisco?"
const outputs = "Thanks for asking! The current weather in San Francisco is sunny and 90 degrees."

const evalResult = await concisenessEvaluator({
  inputs,
  outputs,
});

console.log(evalResult);

{
    key: 'conciseness',
    score: false,
    comment: '...'
}

Hallucination

openevals includes a prebuilt prompt for createLLMAsJudge that scores the hallucination of an LLM's output. It takes inputs, outputs, and optionally, context as parameters.

import { createLLMAsJudge, HALLUCINATION_PROMPT } from "openevals";

const hallucinationEvaluator = createLLMAsJudge({
  prompt: HALLUCINATION_PROMPT,
  feedbackKey: "hallucination",
  model: "openai:o3-mini",
});

const inputs = "What is a doodad?"
const outputs = "I know the answer. A doodad is a kitten."
const context = "A doodad is a self-replicating swarm of nanobots. They are extremely dangerous and should be avoided at all costs. Some safety precautions when working with them include wearing gloves and a mask."

const evalResult = await hallucinationEvaluator({
  inputs,
  outputs,
  context,
});

console.log(evalResult);

{
    key: 'hallucination',
    score: false,
    comment: '...'
}

Customizing prompts

The prompt parameter for createLLMAsJudge may be an f-string, LangChain prompt template, or a function that takes kwargs and returns a list of formatted messages.

Though we suggest sticking to conventional names (inputs, outputs, and referenceOutputs) as prompt variables, you can also require additional variables. You would then pass these extra variables when calling your evaluator function. Here's an example:

import { createLLMAsJudge } from "openevals";

const MY_CUSTOM_PROMPT = `
Use the following context to help you evaluate for hallucinations in the output:

<context>
{context}
</context>

<input>
{inputs}
</input>

<output>
{outputs}
</output>
`;

const customPromptEvaluator = createLLMAsJudge({
  prompt: MY_CUSTOM_PROMPT,
  model: "openai:o3-mini",
});

const inputs = "What color is the sky?"
const outputs = "The sky is red."

const evalResult = await customPromptEvaluator({
  inputs,
  outputs,
});

For convenience, the following options are also available:

system: a string that sets a system prompt for the judge model by adding a system message before other parts of the prompt.
fewShotExamples: a list of example dicts that are appended to the end of the prompt. This is useful for providing the judge model with examples of good and bad outputs. The required structure looks like this:

const fewShotExamples = [
  {
    inputs: "What color is the sky?",
    outputs: "The sky is red.",
    reasoning: "The sky is red because it is early evening.",
    score: 1,
  }
]

These will be appended to the end of the final user message in the prompt.

Customizing the model

There are a few ways you can customize the model used for evaluation. You can pass a string formatted as PROVIDER:MODEL (e.g. model=anthropic:claude-3-5-sonnet-latest) as the model, in which case the package will attempt to import and initialize a LangChain chat model instance. This requires you to install the appropriate LangChain integration package installed. Here's an example:

npm install @langchain/anthropic

import { createLLMAsJudge, CORRECTNESS_PROMPT } from "openevals";

const anthropicEvaluator = createLLMAsJudge({
  prompt: CORRECTNESS_PROMPT,
  model: "anthropic:claude-3-5-sonnet-latest",
});

You can also directly pass a LangChain chat model instance as judge. Note that your chosen model must support structured output:

import { createLLMAsJudge, CORRECTNESS_PROMPT } from "openevals";
import { ChatAnthropic } from "@langchain/anthropic";

const anthropicEvaluator = createLLMAsJudge({
  prompt: CORRECTNESS_PROMPT,
  judge: new ChatAnthropic({ model: "claude-3-5-sonnet-latest", temperature: 0.5 }),
});

This is useful in scenarios where you need to initialize your model with specific parameters, such as temperature or alternate URLs if using models through a service like Azure.

Finally, you can pass a model name as model and a judge parameter set to an OpenAI client instance:

npm install openai

import { OpenAI } from "openai";
import { createLLMAsJudge, CORRECTNESS_PROMPT } from "openevals";

const openaiEvaluator = createLLMAsJudge({
  prompt: CORRECTNESS_PROMPT,
  model: "gpt-4o-mini",
  judge: new OpenAI(),
});

Customizing output scores

There are two fields you can set to customize the output of your evaluator:

continuous: a boolean that sets whether the evaluator should return a float score somewhere between 0 and 1 instead of a binary score. Defaults to False.
choices: a list of floats that sets the possible scores for the evaluator.

These parameters are mutually exclusive. When using either of them, you should make sure that your prompt is grounded in information on what specific scores mean - the prebuilt ones in this repo do not have this information!

For example, here's an example of how to define a less harsh definition of correctness that only penalizes incorrect answers by 50% if they are on-topic:

import { createLLMAsJudge } from "openevals";

const MY_CUSTOM_PROMPT = `
You are an expert data labeler evaluating model outputs for correctness. Your task is to assign a score based on the following rubric:

<Rubric>
  Assign a score of 0, .5, or 1 based on the following criteria:
  - 0: The answer is incorrect and does not mention doodads
  - 0.5: The answer mentions doodads but is otherwise incorrect
  - 1: The answer is correct and mentions doodads
</Rubric>

<input>
{inputs}
</input>

<output>
{outputs}
</output>

<reference_outputs>
{reference_outputs}
</reference_outputs>
`;

const customEvaluator = createLLMAsJudge({
  prompt: MY_CUSTOM_PROMPT,
  choices: [0.0, 0.5, 1.0],
  model: "openai:o3-mini",
});

const result = await customEvaluator({
  inputs: "What is the current price of doodads?",
  outputs: "The price of doodads is $10.",
  reference_outputs: "The price of doodads is $15.",
});

console.log(result);

{
    'key': 'score',
    'score': 0.5,
    'comment': 'The provided answer mentioned doodads but was incorrect.'
}

Finally, if you would like to disable justifications for a given score, you can set useReasoning: False when creating your evaluator.

Extraction and tool calls

Two very common use cases for LLMs are extracting structured output from documents and tool calling. Both of these require the LLM to respond in a structured format. This package provides a prebuilt evaluator to help you evaluate these use cases, and is flexible to work for a variety of extraction/tool calling use cases.

You can use the createJsonMatchEvaluator evaluator in two ways: 1. To perform an exact match of the outputs to reference outputs 2. Using LLM-as-a-judge to evaluate the outputs based on a provided rubric.

Note that this evaluator may return multiple scores based on key and aggregation strategy, so the result will be an array of scores rather than a single one.

Evaluating structured output with exact match

Use exact match evaluation when there is a clear right or wrong answer. A common scenario is text extraction from images or PDFs where you expect specific values.

import { createJsonMatchEvaluator } from "openevals";
import { OpenAI } from "openai";

const outputs = [
    {a: "Mango, Bananas", b: 2},
    {a: "Apples", b: 2, c: [1,2,3]},
]
const reference_outputs = [
    {a: "Mango, Bananas", b: 2},
    {a: "Apples", b: 2, c: [1,2,4]},
]

const client = new OpenAI();

const evaluator = createJsonMatchEvaluator({
    // How to aggregate feedback keys in each element of the list: "average", "all", or None
    // "average" returns the average score. "all" returns 1 only if all keys score 1; otherwise, it returns 0. None returns individual feedback chips for each key
    aggregator="all",
    // Remove if evaluating a single structured output. This aggregates the feedback keys across elements of the list. Can be "average" or "all". Defaults to "all". "all" returns 1 if each element of the list is 1; if any score is not 1, it returns 0. "average" returns the average of the scores from each element. 
    list_aggregator="average",
    // The keys to ignore during evaluation. Any key not passed here or in `rubric` will be evaluated using an exact match comparison to the reference outputs
    exclude_keys=["a"],
    // The provider and name of the model to use
    judge: client,
    model: "openai:o3-mini",
})

// Invoke the evaluator with the outputs and reference outputs
const result = await evaluator({
    outputs,
    reference_outputs,
})

console.log(result)

For the first element, "b" will be 1 and the aggregator will return a score of 1 For the second element, "b" will be 1, "c" will be 0 and the aggregator will return a score of 0 Therefore, the list aggregator will return a final score of 0.5.

{
    'key': 'json_match:all',
    'score': 0.5,
    'comment': None,
}

Evaluating structured output with LLM-as-a-Judge

Use LLM-as-a-judge to evaluate structured output or tools calls when the criteria is more subjective (for example the output is a kind of fruit or mentions all the fruits).

import { createJsonMatchEvaluator } from "openevals";
import { OpenAI } from "openai";

const outputs = [
    {a: "Mango, Bananas", b: 2},
    {a: "Apples", b: 2, c: [1,2,3]},
]
const reference_outputs = [
    {a: "Bananas, Mango", b: 2},
    {a: "Apples, Strawberries", b: 2},
]

const client = new OpenAI();

const evaluator = createJsonMatchEvaluator({
    // How to aggregate feedback keys in each element of the list: "average", "all", or None
    // "average" returns the average score. "all" returns 1 only if all keys score 1; otherwise, it returns 0. None returns individual feedback chips for each key
    aggregator="average",
    // Remove if evaluating a single structured output. This aggregates the feedback keys across elements of the list. Can be "average" or "all". Defaults to "all". "all" returns 1 if each element of the list is 1; if any score is not 1, it returns 0. "average" returns the average of the scores from each element. 
    list_aggregator="all",
    // The criteria for the LLM judge to use for each key you want evaluated by the LLM
    rubric={
        a: "Does the answer mention all the fruits in the reference answer?"
    },
    // The keys to ignore during evaluation. Any key not passed here or in `rubric` will be evaluated using an exact match comparison to the reference outputs
    exclude_keys=["c"],
    // The provider and name of the model to use
    judge: client,
    model: "openai:o3-mini",
    // Whether to use reasoning to reason about the keys in `rubric`. Defaults to True
    useReasoning: true
})

// Invoke the evaluator with the outputs and reference outputs
const result = await evaluator({
    outputs,
    reference_outputs,
})

console.log(result)

For the first element, "a" will be 1 since both Mango and Bananas are in the reference output, "b" will be 1 and "d" will be 0. The aggregator will return an average score of 0.6. For the second element, "a" will be 0 since the reference output doesn't mention all the fruits in the output, "b" will be 1. The aggregator will return a score of 0.5. Therefore, the list aggregator will return a final score of 0.

{
    'key': 'json_match:a',
    'score': 0,
    'comment': None
}

Other

This package also contains prebuilt evaluators for calculating common metrics such as Levenshtein distance, exact match, etc. You can import and use them as follows:

Exact match

import { exactMatch } from "openevals";

const outputs = { a: 1, b: 2 };
const referenceOutputs = { a: 1, b: 2 };
const result = exactMatch(outputs, referenceOutputs);

console.log(result);

{
    key: "equal",
    score: true,
}

Levenshtein distance

import { levenshteinDistance } from "openevals";

const outputs = "The correct answer";
const referenceOutputs = "The correct answer";
const result = levenshteinDistance(outputs, referenceOutputs);

console.log(result);

{
    key: "levenshtein_distance",
    score: 0,
}

Embedding similarity

This evaluator uses a LangChain embeddings client directly and calculates distance between two strings using cosine similarity.

import { createEmbeddingSimilarityEvaluator } from "openevals";
import { OpenAIEmbeddings } from "@langchain/openai";

const evaluator = createEmbeddingSimilarityEvaluator({
  embeddings: new OpenAIEmbeddings({ model: "text-embedding-3-small" }),
});

const result = await evaluator(
    outputs="The weather is nice!",
    referenceOutputs="The weather is very nice!",
);

console.log(result);

{
    key: "embedding_similarity",
    score: 0.9147273943905653,
}

Agent evals

If you are building an agent, the evals in this repo are useful for evaluating specific outputs from your agent against references.

However, if you want to get started with more in-depth evals that take into account the entire trajectory of an agent, please check out the agentevals package.

Python Async Support

All openevals evaluators support Python asyncio. As a convention, evaluators that use a factory function will have async put immediately after create_ in the function name (for example, create_async_llm_as_judge), and evaluators used directly will end in async (e.g. exact_match_async).

Here's an example of how to use the create_async_llm_as_judge evaluator asynchronously:

from openevals.llm import create_async_llm_as_judge

evaluator = create_async_llm_as_judge(
    prompt="What is the weather in {inputs}?",
    model="openai:o3-mini",
)

result = await evaluator(inputs="San Francisco")

If you are using the OpenAI client directly, remember to pass in AsyncOpenAI as the judge parameter:

from openai import AsyncOpenAI

evaluator = create_async_llm_as_judge(
    prompt="What is the weather in {inputs}?",
    judge=AsyncOpenAI(),
    model="o3-mini",
)

result = await evaluator(inputs="San Francisco")

LangSmith Integration

For tracking experiments over time, you can log evaluator results to LangSmith, a platform for building production-grade LLM applications that includes tracing, evaluation, and experimentation tools.

LangSmith currently offers two ways to run evals: a pytest (Python) or Vitest/Jest integration and the evaluate function. We'll give a quick example of how to run evals using both.

Pytest or Vitest/Jest

First, follow these instructions to set up LangSmith's pytest runner, or these to set up Vitest or Jest, setting appropriate environment variables:

export LANGSMITH_API_KEY="your_langsmith_api_key"
export LANGSMITH_TRACING="true"

Then, set up a file named test_correctness.eval.ts with the following contents:

import * as ls from "langsmith/vitest";
// import * as ls from "langsmith/jest";

import { createLLMAsJudge, CORRECTNESS_PROMPT } from "openevals";

const correctnessEvaluator = createLLMAsJudge({
  prompt: CORRECTNESS_PROMPT,
  feedbackKey: "correctness",
  model: "openai:o3-mini",
});


ls.describe("Correctness", () => {
  ls.test("incorrect answer", {
    inputs: {
      question: "How much has the price of doodads changed in the past year?"
    },
    referenceOutputs: {
      answer: "The price of doodads has decreased by 50% in the past year."
    }
  }, async ({ inputs, referenceOutputs }) => {
    const outputs = "Doodads have increased in price by 10% in the past year.";
    ls.logOutputs({ answer: outputs });

    await correctnessEvaluator({
      inputs,
      outputs,
      referenceOutputs,
    });
  });
});

Note that when creating the evaluator, we've added a feedback_key parameter. This will be used to name the feedback in LangSmith.

Now, run the eval with your runner of choice:

vitest run test_correctness.eval.ts

Feedback from the prebuilt evaluator will be automatically logged in LangSmith as a table of results like this in your terminal (if you've set up your reporter):

Terminal results

And you should also see the results in the experiment view in LangSmith:

LangSmith results

Evaluate

Alternatively, you can create a dataset in LangSmith and use your created evaluators with LangSmith's evaluate function:

import { evaluate } from "langsmith/evaluation";
import { createLLMAsJudge, CONCISENESS_PROMPT } from "openevals";

const concisenessEvaluator = createLLMAsJudge({
  prompt: CONCISENESS_PROMPT,
  feedbackKey: "conciseness",
  model: "openai:o3-mini",
});

await evaluate(
  (inputs) => "What color is the sky?",
  {
    data: datasetName,
    evaluators: [concisenessEvaluator],
  }
);