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AI Evaluation & Observability

Prompttools

Prompttools is an open-source Python developer toolkit for testing prompt and parameter variations across language models, evaluating vector-database retrieval, and inspecting experiments through code, notebooks or a Streamlit playground.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Prompttools — OpenViST project profile in AI Evaluation & Observability.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Prompttools during prompt development or model selection when you want to run structured comparisons in code, notebooks or a playground, or investigate vector-database retrieval behavior.

Who it is for

  • Developers comparing prompt variants or model parameters.
  • Teams building repeatable LLM evaluation workflows in Python or notebooks.
  • Practitioners examining retrieval behavior across supported vector databases.

Documented uses

  • Compare outputs across model and prompt choices.
  • Run and visualize repeatable prompt experiments.
  • Evaluate retrieval accuracy in supported vector databases.
  • Export experiment results as CSV, JSON, LoRA JSON or to MongoDB.
Before you choose

Requirements & limits

The project is open source under the Apache-2.0 license and documents self-hosted use as well as a hosted Streamlit playground. The hosted playground does not support LlamaCpp.

  • The project identifies Python as its primary language and offers code, Jupyter notebook and Streamlit playground interfaces.
  • The integration list distinguishes supported options from those in progress or exploratory; Ollama and Epsilla are in progress, while Milvus and LlamaIndex are exploratory.
  • The README says LLM calls execute from the user's machine directly to providers, and that API keys and inputs and outputs are not stored on a server.
  • The README also describes usage-error reporting to Sentry and an opt-out through the SENTRY_OPT_OUT environment variable.
  • Experiment results can be exported to CSV, JSON, LoRA JSON or MongoDB.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the OpenAI chat experiment notebook

Open the repository's documented OpenAI chat experiment notebook locally.

Before you start

  • Python environment with pip
  • Git
  • Jupyter Notebook
  • Access credentials for the OpenAI model used by the notebook

Steps

  1. Install Prompttools with pip install prompttools.

  2. Clone the Prompttools repository from GitHub.

  3. Change into the repository directory and open examples/notebooks/OpenAIChatExperiment.ipynb with Jupyter Notebook.

Expected outcome

The notebook provides the documented local starting point for an OpenAI chat experiment; the README's example varies model choices and temperature, then runs and visualizes the experiment.

Guide limitations

  • The README does not specify credential configuration steps.
  • The notebook route is an example workflow, not a guarantee that a particular model or API account will be available.
Documentation 1 ↗
Source-based practical guide

Launch the local Streamlit playground

Set up and start Prompttools' playground from the cloned repository.

Before you start

  • Python environment with pip
  • Git
  • Jupyter Notebook, if it is not already installed

Steps

  1. Install notebook if needed, then install Prompttools using pip.

  2. Clone the Prompttools repository.

  3. From the repository directory, run streamlit run prompttools/playground/playground.py.

Expected outcome

The documented command starts the local Streamlit playground for interacting with Prompttools.

Guide limitations

  • The README does not detail playground-specific model credential setup.
  • The README notes that the hosted playground does not support LlamaCpp; that stated limitation applies to the hosted version.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Vector search

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
PrompttoolsCompare outputs across model and prompt choices.CLI, Cloud, Self-hostedThe project is open source under the Apache-2.0 license and documents self-hosted use as well as a hosted Streamlit playground. The hosted playground does not support LlamaCpp.
SmallevalsGenerate synthetic questions from chunks or documents for retrieval evaluation.Linux, macOS, Self-hosted, WindowsSmallevals is presented as a public Python repository and documents installation with pip. The repository information supplied does not identify a license.
MyScale Vector Database BenchmarkMeasure queries per second for vector search.CLI
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
10 October 2026
Original website title
hegelai/prompttools · Official homepage title · retained in its source language
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