Open Source Python Artificial Intelligence Software - Page 32
Sort By:
Python Artificial Intelligence Software
View 15744 business solutionsBrowse free open source Python Artificial Intelligence Software and projects below. Use the toggles on the left to filter open source Python Artificial Intelligence Software by OS, license, language, programming language, and project status.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
1
Chatterbox TTS Server
Self-host the powerful Chatterbox TTS model
Chatterbox-TTS-Server is a self-hosted server for running the Chatterbox text-to-speech model through both a web interface and API endpoints. It is designed for users who want local or private speech generation without depending entirely on a hosted voice platform. The project supports predefined voices, voice cloning, and longer text workflows, making it useful for audiobooks, narration, content tools, and assistant-style applications. It also includes OpenAI-compatible API behavior, which helps developers connect it to existing tools that already expect that style of endpoint. The server can run on NVIDIA CUDA, AMD ROCm, or CPU, giving it flexibility across different hardware setups. Its main value is packaging a powerful TTS workflow into a practical service that can be accessed through a browser or integrated into other software. -
2
Ciphey
Decrypt encryptions without knowing the key or cipher
Fully automated decryption/decoding/cracking tool using natural language processing & artificial intelligence, along with some common sense. You don't know, you just know it's possibly encrypted. Ciphey will figure it out for you. Ciphey can solve most things in 3 seconds or less. Ciphey aims to be a tool to automate a lot of decryptions & decodings such as multiple base encodings, classical ciphers, hashes or more advanced cryptography. If you don't know much about cryptography, or you want to quickly check the ciphertext before working on it yourself, Ciphey is for you. The technical part. Ciphey uses a custom-built artificial intelligence module (AuSearch) with a Cipher Detection Interface to approximate what something is encrypted with. And then a custom-built, customizable natural language processing Language Checker Interface, which can detect when the given text becomes plaintext. -
3
Claude Code SDK Python
Python SDK for Claude Agent
claude-code-sdk-python is the Python SDK for Claude Code, Anthropic’s agentic coding system. It provides abstractions to easily query Claude Code (with streaming support) and conduct interactive sessions. The SDK includes core client classes, asynchronous query functions, and support for custom tools and hooks within Claude sessions. It is designed to integrate with local Python workflows and allow developers to embed Claude Code capabilities directly in their applications or scripts. The repo is MIT-licensed and includes documentation and installation instructions (requires Python 3.10+, Node installation of Claude Code). Example usage shows how to stream responses, parse structured message blocks, or create persistent client sessions. -
4
Claude SEO
Universal SEO skill for Claude Code
Claude SEO is an open-source Claude Code skill suite for running structured SEO analysis through AI-assisted workflows. It combines 25 sub-skills and 18 specialist agents across technical SEO, content quality, Schema.org markup, GEO and AEO, local SEO, e-commerce, international SEO, backlinks, semantic clustering, and Google API workflows. The system is designed to produce prioritized action plans instead of generic audit notes. Recommendations include first-principle observations, dependency relationships, falsifiability checks, and leading indicators. It can run full site audits, single-page reviews, schema validation, AI search readiness checks, sitemap workflows, local SEO analysis, and SEO reporting. Claude SEO is useful for agencies, in-house teams, and consultants who want repeatable SEO audits inside Claude Code. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
5
CodiumAI PR-Agent
AI-Powered tool for automated pull request analysis
CodiumAI PR-Agent is an open-source tool aiming to help developers review pull requests faster and more efficiently. It automatically analyzes the pull request and can provide several types of commands. See the Usage Guide for instructions how to run the different tools from CLI, online usage, Or by automatically triggering them when a new PR is opened. You can try GPT-4 powered PR-Agent, on your public GitHub repository, instantly. Just mention @CodiumAI-Agent and add the desired command in any PR comment. The agent will generate a response based on your command. -
6
Core ML Tools
Core ML tools contain supporting tools for Core ML model conversion
Use Core ML Tools (coremltools) to convert machine learning models from third-party libraries to the Core ML format. This Python package contains the supporting tools for converting models from training libraries. Core ML is an Apple framework to integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to fine-tune models, all on the user’s device. Core ML optimizes on-device performance by leveraging the CPU, GPU, and Neural Engine while minimizing its memory footprint and power consumption. Running a model strictly on the user’s device removes any need for a network connection, which helps keep the user’s data private and your app responsive. -
7
DALL-E 2 - Pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch. The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network) To train DALLE-2 is a 3 step process, with the training of CLIP being the most important. To train CLIP, you can either use x-clip package, or join the LAION discord, where a lot of replication efforts are already underway. Then, you will need to train the decoder, which learns to generate images based on the image embedding coming from the trained CLIP. -
8
DB-GPT-Hub
A repository that contains models, datasets, and fine-tuning
DB-GPT-Hub is an open-source repository that provides datasets, models, and training tools designed to improve large language models for database interaction tasks, particularly Text-to-SQL. The project serves as a specialized extension of the broader DB-GPT ecosystem, focusing on the preparation and evaluation of models capable of translating natural language questions into structured database queries. It offers a modular framework that supports data preparation, model fine-tuning, benchmarking, and inference for Text-to-SQL systems. The repository includes datasets and experiment configurations that allow researchers to train models on real database schemas and evaluate them using standardized benchmarks. Its design encourages experimentation with different large language models and fine-tuning techniques, including parameter-efficient training approaches. -
9
Data Version Control
Git-based data version control for machine learning workflows
DVC (Data Version Control) is an open source tool designed to bring version control principles to machine learning and data science workflows. It enables developers and data scientists to track datasets, machine learning models, and experiment results in a way that integrates with existing Git repositories. Instead of storing large datasets directly in Git, DVC keeps lightweight metadata in the repository while storing the actual data in external storage systems. This approach allows teams to manage large files efficiently while maintaining a clear history of changes to data and models. DVC also provides a pipeline system that defines the stages of machine learning workflows, making experiments reproducible and easier to manage. By tracking dependencies between code, data, and parameters, the system ensures that only the necessary stages are re-run when changes occur. DVC also includes experiment tracking capabilities that allow users to compare different training runs. -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
-
10
DeepSeek-OCR 2
Visual Causal Flow
DeepSeek-OCR-2 is the second-generation optical character recognition system developed to improve document understanding by introducing a “visual causal flow” mechanism, enabling the encoder to reorder visual tokens in a way that better reflects semantic structure rather than strict raster scan order. It is designed to handle complex layouts and noisy documents by giving the model causal reasoning capabilities that mimic human visual scanning behavior, enhancing OCR performance on documents with rich spatial structure. The repository provides model code and inference scripts that let researchers and developers run and benchmark the system on both images and PDFs, with support for batch evaluation and optimized pipelines leveraging vLLM and transformers. -
11
DeepSpeed
Deep learning optimization library: makes distributed training easy
DeepSpeed is an easy-to-use deep learning optimization software suite that enables unprecedented scale and speed for Deep Learning Training and Inference. With DeepSpeed you can: 1. Train/Inference dense or sparse models with billions or trillions of parameters 2. Achieve excellent system throughput and efficiently scale to thousands of GPUs 3. Train/Inference on resource constrained GPU systems 4. Achieve unprecedented low latency and high throughput for inference 5. Achieve extreme compression for an unparalleled inference latency and model size reduction with low costs DeepSpeed offers a confluence of system innovations, that has made large scale DL training effective, and efficient, greatly improved ease of use, and redefined the DL training landscape in terms of scale that is possible. These innovations such as ZeRO, 3D-Parallelism, DeepSpeed-MoE, ZeRO-Infinity, etc. fall under the training pillar. -
12
DeepStack
The World's Leading Cross Platform AI Engine for Edge Devices
DeepStack is an AI API engine that serves pre-built models and custom models on multiple edge devices locally or on your private cloud. DeepStack runs completely offline and independent of the cloud. You can also install and run DeepStack on any cloud VM with docker installed to serve as your private, state-of-the-art and real-time AI server. -
13
Diffgram
Training data (data labeling, annotation, workflow) for all data types
From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality. Training Data is the art of supervising machines through data. This includes the activities of annotation, which produces structured data; ready to be consumed by a machine learning model. Annotation is required because raw media is considered to be unstructured and not usable without it. That’s why training data is required for many modern machine learning use cases including computer vision, natural language processing and speech recognition. -
14
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 provides a declarative pipeline framework that breaks complex document analysis tasks into manageable operations that can be optimized and orchestrated automatically. Pipelines are typically defined using a low-code YAML interface, giving users full control over prompts and processing steps while still simplifying workflow creation. -
15
Dream Textures
Stable Diffusion built-in to Blender
Create textures, concept art, background assets, and more with a simple text prompt. Use the 'Seamless' option to create textures that tile perfectly with no visible seam. Texture entire scenes with 'Project Dream Texture' and depth to image. Re-style animations with the Cycles render pass. Run the models on your machine to iterate without slowdowns from a service. Create textures, concept art, and more with text prompts. Learn how to use the various configuration options to get exactly what you're looking for. Texture entire models and scenes with depth to image. Inpaint to fix up images and convert existing textures into seamless ones automatically. Outpaint to increase the size of an image by extending it in any direction. Perform style transfer and create novel animations with Stable Diffusion as a post processing step. Dream Textures has been tested with CUDA and Apple Silicon GPUs. Over 4GB of VRAM is recommended. -
16
Ecco
Explain, analyze, and visualize NLP language models
Ecco is an interpretability tool for transformers that helps visualize and analyze how language models generate text, making model behavior more transparent. -
17
EmoLLM
Pre & Post-training & Dataset & Evaluation & Depoly & RAG
EmoLLM is an open-source family of large language models focused on mental health support and counseling-oriented interactions. The project is designed to help users through mental health conversations and has been fine-tuned from existing instruction-following LLMs rather than built as a base model from scratch. Its repository includes multiple model variants and training configurations spanning several underlying model families, including InternLM, Qwen, DeepSeek, Mixtral, LLaMA, and others, which shows that the initiative is structured as a broad ecosystem rather than a single release. The project also covers more than just model weights, with material for datasets, fine-tuning, evaluation, deployment, demos, RAG, and related subprojects such as its psychological digital assistant work. -
18
Fast3R
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Fast3R is Meta AI’s official CVPR 2025 release for “Towards 3D Reconstruction of 1000+ Images in One Forward Pass.” It represents a next-generation feedforward 3D reconstruction model capable of producing dense point clouds and camera poses for hundreds to thousands of images or video frames in a single inference pass—eliminating the need for slow, iterative structure-from-motion pipelines. Built on PyTorch Lightning and extending concepts from DUSt3R and Spann3r, Fast3R unifies multi-view geometry, depth estimation, and camera registration within a single transformer-based architecture. It outputs high-quality 3D scene representations from unordered or sequential views, scaling to large datasets and varied camera intrinsics. The repository includes pretrained models, Gradio-based demos, and modular APIs for direct integration into research or production workflows. -
19
FastRAG
Efficient Retrieval Augmentation and Generation Framework
fastRAG is a research framework for efficient and optimized retrieval augmented generative pipelines, incorporating state-of-the-art LLMs and Information Retrieval. fastRAG is designed to empower researchers and developers with a comprehensive tool set for advancing retrieval augmented generation. -
20
FiftyOne
The open-source tool for building high-quality datasets
The open-source tool for building high-quality datasets and computer vision models. Nothing hinders the success of machine learning systems more than poor-quality data. And without the right tools, improving a model can be time-consuming and inefficient. FiftyOne supercharges your machine learning workflows by enabling you to visualize datasets and interpret models faster and more effectively. Improving data quality and understanding your model’s failure modes are the most impactful ways to boost the performance of your model. FiftyOne provides the building blocks for optimizing your dataset analysis pipeline. Use it to get hands-on with your data, including visualizing complex labels, evaluating your models, exploring scenarios of interest, identifying failure modes, finding annotation mistakes, and much more! Surveys show that machine learning engineers spend over half of their time wrangling data, but it doesn't have to be that way. -
21
FinRobot
An Open-Source AI Agent Platform for Financial Analysis using LLMs
FinRobot is an open-source AI framework focused on automating financial data workflows by combining data ingestion, feature engineering, model training, and automated decision-making pipelines tailored for quantitative finance applications. It provides developers and quants with structured modules to fetch market data, process time series, generate technical indicators, and construct features appropriate for machine learning models, while also supporting backtesting and evaluation metrics to measure strategy performance. Built with modularity in mind, FinRobot allows users to plug in custom models — from classical algorithms to deep learning architectures — and orchestrate components in pipelines that can run reproducibly across experiments. The framework also tends to include automation layers for deployment, enabling trained models to operate in live or simulated environments with scheduled re-training and risk controls in place. -
22
FlashInfer
FlashInfer: Kernel Library for LLM Serving
FlashInfer is a kernel library designed to enhance the serving of Large Language Models (LLMs) by optimizing inference performance. It provides a high-performance framework that integrates seamlessly with existing systems, aiming to reduce latency and improve efficiency in LLM deployments. FlashInfer supports various hardware architectures and is built to scale with the demands of production environments. -
23
Free-Auto-GPT
Free AutoGPT enables autonomous AI tasks without paid APIs
Free Auto GPT is an open source project that delivers a simplified version of an autonomous AI agent capable of completing tasks independently. It allows users to run an AutoGPT-style system without relying on paid OpenAI APIs, making it more accessible for experimentation and personal use. Free Auto GPT can take a goal, break it into smaller steps, and execute actions in a loop to achieve results with minimal human input. Designed for ease of use, the project focuses on removing cost barriers while still demonstrating how autonomous agents function. It is suitable for developers, learners, and hobbyists who want to explore AI-driven automation without subscription requirements. It provides a lightweight implementation that highlights core AutoGPT concepts such as task decomposition, iteration, and independent execution in a simplified environment. -
24
FullTClash
General proxy performance testing tool based on Clash using Telegram
Back end part useClash project(It can also be called nowmihomo)The relevant code is used as the outing agent. The front end part uses Telegram API as the interactive interface, which needs to be used in conjunction with Telegram, that is, a Telegram robot (bot), FullTClash bot is a Telegram robot (hereinafter referred to as bot) carrying its test tasks. -
25
Future AGI
Open-source platform for evaluating, observing, and improving LLM
Future AGI is an open-source, end-to-end platform for evaluating, observing, protecting, and improving AI agent applications. It is built for teams that need more than basic tracing, combining evaluations, simulations, datasets, guardrails, gateway routing, and optimization in one feedback loop. The platform helps developers detect hallucinations, measure agent quality, monitor production behavior, and use evaluation results to improve prompts or workflows over time. It supports both cloud and self-hosted deployment models, making it useful for teams with different privacy, infrastructure, and compliance needs. Future AGI is especially relevant for agent-heavy products where reliability, regression testing, and safety checks matter before and after release. Its main value is turning AI agent development into a measurable engineering process instead of an informal cycle of prompting, guessing, and manual review.