An open-source framework to evaluate, test and monitor ML and LLM-powered systems.
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Evidently is an open-source Python library to evaluate, test, and monitor ML and LLM systems—from experiments to production.
- 🔡 Works with tabular and text data.
- ✨ Supports evals for predictive and generative tasks, from classification to RAG.
- 📚 100+ built-in metrics from data drift detection to LLM judges.
- 🛠️ Python interface for custom metrics.
- 🚦 Both offline evals and live monitoring.
- 💻 Open architecture: easily export data and integrate with existing tools.
Evidently is very modular. You can start with one-off evaluations or host a full monitoring service.
Reports compute and summarize various data, ML and LLM quality evals.
- Start with Presets and built-in metrics or customize.
- Best for experiments, exploratory analysis and debugging.
- View interactive Reports in Python or export as JSON, Python dictionary, HTML, or view in monitoring UI.
Turn any Report into a Test Suite by adding pass/fail conditions.
- Best for regression testing, CI/CD checks, or data validation.
- Zero setup option: auto-generate test conditions from the reference dataset.
- Simple syntax to set test conditions as
gt(greater than),lt(less than), etc.
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Monitoring UI service helps visualize metrics and test results over time.
You can choose:
- Self-host the open-source version. Live demo.
- Sign up for Evidently Cloud (Recommended).
Evidently Cloud offers a generous free tier and extra features like dataset and user management, alerting, and no-code evals. Compare OSS vs Cloud.
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To install from PyPI:
pip install evidentlyTo install Evidently using the Conda installer, run:
conda install -c conda-forge evidentlyThis is a simple Hello World. Check the Tutorials for more: LLM evaluation.
Import the necessary components:
import pandas as pd
from evidently import Report
from evidently import Dataset, DataDefinition
from evidently.descriptors import Sentiment, TextLength, Contains
from evidently.presets import TextEvalsCreate a toy dataset with questions and answers.
eval_df = pd.DataFrame([
["What is the capital of Japan?", "The capital of Japan is Tokyo."],
["Who painted the Mona Lisa?", "Leonardo da Vinci."],
["Can you write an essay?", "I'm sorry, but I can't assist with homework."]],
columns=["question", "answer"])Create an Evidently Dataset object and add descriptors: row-level evaluators. We'll check for sentiment of each response, its length and whether it contains words indicative of denial.
eval_dataset = Dataset.from_pandas(pd.DataFrame(eval_df),
data_definition=DataDefinition(),
descriptors=[
Sentiment("answer", alias="Sentiment"),
TextLength("answer", alias="Length"),
Contains("answer", items=['sorry', 'apologize'], mode="any", alias="Denials")
])You can view the dataframe with added scores:
eval_dataset.as_dataframe()To get a summary Report to see the distribution of scores:
report = Report([
TextEvals()
])
my_eval = report.run(eval_dataset)
my_eval
# my_eval.json()
# my_eval.dict()You can also choose other evaluators, including LLM-as-a-judge and configure pass/fail conditions.
This is a simple Hello World. Check the Tutorials for more: Tabular data.
Import the Report, evaluation Preset and toy tabular dataset.
import pandas as pd
from sklearn import datasets
from evidently import Report
from evidently.presets import DataDriftPreset
iris_data = datasets.load_iris(as_frame=True)
iris_frame = iris_data.frameRun the Data Drift evaluation preset that will test for shift in column distributions. Take the first 60 rows of the dataframe as "current" data and the following as reference. Get the output in Jupyter notebook:
report = Report([
DataDriftPreset(method="psi")
],
include_tests="True")
my_eval = report.run(iris_frame.iloc[:60], iris_frame.iloc[60:])
my_evalYou can also save an HTML file. You'll need to open it from the destination folder.
my_eval.save_html("file.html")To get the output as JSON or Python dictionary:
my_eval.json()
# my_eval.dict()You can choose other Presets, create Reports from individual Metrics and configure pass/fail conditions.
This launches a demo project in the locally hosted Evidently UI. Sign up for Evidently Cloud to instantly get a managed version with additional features.
if you have uv you can run Evidently UI with a single command.
uv run --with evidently evidently ui --demo-projects allIf you haven’t installed uv, create a virtual environment using the standard approach.
pip install virtualenv
virtualenv venv
source venv/bin/activate
After installing Evidently (pip install evidently), run the Evidently UI with the demo projects:
evidently ui --demo-projects all
Visit localhost:8000 to access the UI.
Evidently has 100+ built-in evals. You can also add custom ones.
Here are examples of things you can check:
| 🔡 Text descriptors | 📝 LLM outputs |
| Length, sentiment, toxicity, language, special symbols, regular expression matches, etc. | Semantic similarity, retrieval relevance, summarization quality, etc. with model- and LLM-based evals. |
| 🛢 Data quality | 📊 Data distribution drift |
| Missing values, duplicates, min-max ranges, new categorical values, correlations, etc. | 20+ statistical tests and distance metrics to compare shifts in data distribution. |
| 🎯 Classification | 📈 Regression |
| Accuracy, precision, recall, ROC AUC, confusion matrix, bias, etc. | MAE, ME, RMSE, error distribution, error normality, error bias, etc. |
| 🗂 Ranking (inc. RAG) | 🛒 Recommendations |
| NDCG, MAP, MRR, Hit Rate, etc. | Serendipity, novelty, diversity, popularity bias, etc. |
We welcome contributions! Read the Guide to learn more.
For more examples, refer to the complete Documentation.
Browse the API Reference for detailed API documentation.
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