Search Results for "python::module" - Page 37
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Showing 2765 open source projects for "python::module"
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Guardrails
Framework for validating and controlling LLM outputs in AI apps
Guardrails is an open source Python framework designed to help developers build more reliable and controlled applications powered by large language models. It provides mechanisms for validating and constraining both the inputs sent to a model and the outputs generated by it, helping reduce risks such as harmful content, prompt injection, or inaccurate responses. Guardrails works by applying configurable guards that intercept and evaluate interactions with the model before results are returned to the end user. ... -
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Code2Prompt
Convert codebases into structured prompts optimized for LLM analysis
...The generated output can be saved to a file, printed to standard output, or copied to the clipboard for immediate use. In addition to the core command line interface, the project also includes a library, Python bindings, and an MCP server. -
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AutoViz
Automatically Visualize any dataset, any size
AutoViz is a Python data visualization library designed to automate exploratory data analysis by generating multiple visualizations with minimal code. The primary goal of the project is to help data scientists and analysts quickly understand patterns, relationships, and anomalies within datasets without manually writing complex plotting code. With a single command, the library can automatically generate dozens of charts and graphs that reveal insights into the structure and quality of the data. ... -
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TensorFlow Quantum
Open-source Python framework for hybrid quantum-classical ml learning
TensorFlow Quantum is an open-source software framework designed for building and training hybrid quantum-classical machine learning models within the TensorFlow ecosystem. The framework enables researchers and developers to represent quantum circuits as data and integrate them directly into machine learning workflows. By combining classical deep learning techniques with quantum algorithms, the platform allows experimentation with quantum machine learning methods that may offer advantages... -
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machine learning tutorials
machine learning tutorials (mainly in Python3)
machine-learning is a continuously updated repository documenting the author’s learning journey through data science and machine learning topics using practical tutorials and experiments. The project presents educational notebooks that combine mathematical explanations with code implementations using Python’s scientific computing ecosystem. Topics covered include classical machine learning algorithms, deep learning models, reinforcement learning, model deployment, and time-series analysis.... -
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Machine learning algorithms
Minimal and clean examples of machine learning algorithms
Machine learning algorithms is an open-source repository that provides minimal and clean implementations of machine learning algorithms written primarily in Python. The project focuses on demonstrating how fundamental machine learning methods work internally by implementing them from scratch rather than relying on high-level libraries. This approach allows learners to study the mathematical and algorithmic details behind widely used models in a transparent and readable way. The repository includes implementations of both supervised and unsupervised learning techniques, along with dimensionality reduction and clustering methods. ... -
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Prompt Poet
Streamlines and simplifies prompt design for both developers
Prompt Poet is an open-source framework designed to simplify the creation, organization, and maintenance of prompts for large language model applications. The project focuses on transforming prompt engineering into a structured design process rather than ad-hoc string manipulation within application code. It allows developers and non-technical users to build prompts using templated configurations based on YAML and Jinja2, which makes prompts easier to compose, reuse, and modify across... -
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Shell-AI
LangChain powered shell command generator and runner CLI
Shell-AI is an open-source command-line interface utility that allows users to generate and execute shell commands using natural language prompts. Instead of requiring users to remember complex command syntax, the tool lets them describe their intent in plain English and automatically suggests commands that accomplish the task. The system is powered by large language models and integrates with frameworks such as LangChain to interpret user requests and translate them into executable shell... -
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SeaGOAT
local-first semantic code search engine
...By combining vector search with tools like ripgrep, SeaGOAT provides a hybrid approach that supports both natural language queries and precise keyword matching in source files. It is built primarily in Python and is intended to work on common operating systems such as Linux, macOS, and Windows. -
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ControlFlow
Take control of your AI agents
ControlFlow is an open-source Python framework developed to help engineers design and orchestrate agentic workflows powered by large language models. The framework provides a structured approach for building AI systems by breaking complex tasks into smaller units called tasks that can be assigned to specialized AI agents. Developers can combine these tasks into flows that define how work is executed, enabling the creation of multi-step reasoning pipelines and collaborative agent systems. ... -
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Pixeltable
Data Infrastructure providing an approach to multimodal AI workloads
Pixeltable is an open-source Python data infrastructure framework designed to support the development of multimodal AI applications. The system provides a declarative interface for managing the entire lifecycle of AI data pipelines, including storage, transformation, indexing, retrieval, and orchestration of datasets. Unlike traditional architectures that require multiple tools such as databases, vector stores, and workflow orchestrators, Pixeltable unifies these functions within a table-based abstraction. ... -
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OmAgent
Build multimodal language agents for fast prototype and production
OmAgent is an open-source Python framework designed to simplify the development of multimodal language agents that can reason, plan, and interact with different types of data sources. The framework provides abstractions and infrastructure for building AI agents that operate on text, images, video, and audio while maintaining a relatively simple interface for developers. -
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DocETL
A system for agentic LLM-powered data processing and ETL
DocETL is an open-source system designed to build and execute data processing pipelines powered by large language models, particularly for analyzing complex collections of documents and unstructured datasets. The platform allows developers and researchers to construct structured workflows that extract, transform, and organize information from sources such as reports, transcripts, legal documents, and other text-heavy data. Instead of relying on single prompts or ad-hoc scripts, DocETL... -
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LLM Workflow Engine
Power CLI and Workflow manager for LLMs (core package)
LLM Workflow Engine is an open-source command-line framework designed to integrate large language models into automated workflows and developer environments. The platform allows users to interact with AI models directly from the terminal, enabling conversational AI access through shell commands and scripts. Instead of focusing solely on chat interactions, the system is built to embed LLM calls into larger automation pipelines where model outputs can drive decision making or trigger... -
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Claude Code Usage Monitor
Real-time Claude Code usage monitor with predictions and warnings
...The project is designed to help users avoid unexpectedly hitting usage caps by continuously tracking token burn rate, message counts, and estimated costs during active sessions. It presents analytics through a visually rich terminal interface built with modern Python tooling, making it easy to interpret usage trends at a glance. The system includes predictive logic that estimates whether a session is likely to exceed limits before completion, allowing proactive adjustments to workflows. Its architecture emphasizes modularity and extensibility, supporting multiple Claude plan configurations and customizable monitoring behavior. ... -
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Agent Lightning
The absolute trainer to light up AI agents
Agent Lightning is an open-source framework developed by Microsoft to train and optimize AI agents using techniques like reinforcement learning (RL), supervised fine-tuning, and automatic prompt optimization, with minimal or zero changes to existing agent code. It’s designed to be compatible with a wide range of agent architectures and frameworks — from LangChain and OpenAI Agent SDKs to AutoGen and custom Python agents — making it broadly applicable across different agent tooling ecosystems. Agent-Lightning introduces a lightweight training pipeline that observes agents’ execution traces, converts them into structured data, and feeds them into training algorithms, enabling users to improve agent behaviors systematically. The project emphasizes minimalist integration, so you can drop this into existing systems without extensive rewrites, focusing instead on iterative performance improvement. -
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WorldGen
Generate Any 3D Scene in Seconds
WorldGen is an AI model and library that can generate full 3D scenes in a matter of seconds from either text prompts or reference images. It is designed to create interactive environments suitable for games, simulations, robotics research, and virtual reality, rather than just static 3D assets. The core idea is that you describe a world in natural language and WorldGen produces a navigable 3D scene that you can freely explore in 360 degrees, with loop closure so that the space remains... -
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Instructor
Structured outputs for llms
Instructor is a tool that enables developers to extract structured data from natural language using Large Language Models (LLMs). Integrating with Python's Pydantic library allows users to define desired output structures through type hints, facilitating schema validation and seamless integration with IDEs. Instructor supports various LLM providers, including OpenAI, Anthropic, Litellm, and Cohere, offering flexibility in implementation. Its customizable nature permits the definition of... -
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YData Synthetic
Synthetic data generators for tabular and time-series data
...This repository contains material related to Generative Adversarial Networks for synthetic data generation, in particular regular tabular data and time-series. It consists a set of different GANs architectures developed using Tensorflow 2.0. Several example Jupyter Notebooks and Python scripts are included, to show how to use the different architectures. YData synthetic has now a UI interface to guide you through the steps and inputs to generate structure tabular data. The streamlit app is available form v1.0.0 onwards. -
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OpenVINO Training Extensions
Trainable models and NN optimization tools
OpenVINO™ Training Extensions provide a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference. When ote_cli is installed in the virtual environment, you can use the ote command line interface to perform various actions for templates related to the chosen task type, such as running, training, evaluating, exporting, etc. ote train trains a model (a particular model template) on a dataset and saves results in two files. ote... -
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tf2onnx
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
tf2onnx converts TensorFlow (tf-1.x or tf-2.x), keras, tensorflow.js and tflite models to ONNX via command line or python API. Note: tensorflow.js support was just added. While we tested it with many tfjs models from tfhub, it should be considered experimental. TensorFlow has many more ops than ONNX and occasionally mapping a model to ONNX creates issues. tf2onnx will use the ONNX version installed on your system and installs the latest ONNX version if none is found. -
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ClearML
Streamline your ML workflow
...It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments and other workflows with ClearML powerful and versatile set of classes and methods. The ClearML Server storing experiment, model, and workflow data, and supports the Web UI experiment manager, and ML-Ops automation for reproducibility and tuning. ... -
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TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model using a single language throughout the lifecycle of model exploration and production. ... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
...We have outsourced a lot of functionality of PyTorch Geometric to other packages, which needs to be additionally installed. These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations. -
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MedicalGPT
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training
MedicalGPT training medical GPT model with ChatGPT training pipeline, implementation of Pretraining, Supervised Finetuning, Reward Modeling and Reinforcement Learning. MedicalGPT trains large medical models, including secondary pre-training, supervised fine-tuning, reward modeling, and reinforcement learning training.
