Open Source Python Artificial Intelligence Software - Page 64
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Python Artificial Intelligence Software
View 17667 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.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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End-to-End Negotiator
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
End-to-End Negotiator is a PyTorch-based research framework developed by Facebook AI Research to train neural agents capable of conducting strategic negotiations in natural language. The project implements the models presented in two key papers: “Deal or No Deal? End-to-End Learning for Negotiation Dialogues” and “Hierarchical Text Generation and Planning for Strategic Dialogue”. It enables agents to plan, reason, and communicate effectively to maximize outcomes in multi-turn negotiations over shared resources. The framework provides code for both supervised learning (training from human dialogue data) and reinforcement learning (via self-play and rollout-based planning). It introduces a hierarchical latent model, where high-level intents are first clustered and then translated into coherent language, improving dialogue diversity and goal consistency. The repository also includes the Negotiate dataset, comprising over 5,800 dialogues across 2,200 unique scenarios. -
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Eng2BN CSV Translator
Translate English to Bangla using CSV file format and range wise.
Eng2BN CSV Translator user-friendly Python tool that enables efficient translation of English text to Bangla within CSV files. The application supports large datasets and allows users to translate specific row ranges, making it ideal for batch processing. -
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Engram
A New Axis of Sparsity for Large Language Models
Engram is a high-performance embedding and similarity search library focused on making retrieval-augmented workflows efficient, scalable, and easy to adopt by developers building search, recommendation, or semantic matching systems. It provides utilities to generate embeddings from text or other structured data, index them using efficient approximate nearest neighbor algorithms, and perform real-time similarity queries even on large corpora. Engineered with speed and memory efficiency in mind, Engram supports batched indexing, incremental updates, and custom distance metrics so developers can tailor search behaviors to their domain’s needs. In addition to raw similarity search, the project includes tools for clustering, ranking, and filtering results, enabling richer user experiences like “related content”, semantic auto-completion, and contextual filtering. -
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Err
err is a plugin based chatbot designed to be easily extensible
err is a plugin based chatbot designed to be easily deployable, extensible and maintainable. It allows you to start scripts interactively from your chatrooms for any reason: random humour, starting a build, monitoring commits, triggering alerts ... It is really easy to add your own feature. Features Backends support: - XMPP : Tested with hipchat, openfire and Jabber but should be compatible with any standard XMPP servers. - CampFire support - Supports MUCs (chatrooms) - Local Graphical Console (for testing/dev) - Local Text Console (for testing/dev) -
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.
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Errbot
Chatbot daemon that connects to your favorite chat services
Errbot is a chatbot, a daemon that connects to your favorite chat service and brings your tools into the conversation. The goal of the project is to make it easy for you to write your own plugins so you can make it do whatever you want, a deployment, retrieving some information online, trigger a tool via an API, troll a co-worker, etc. Errbot is being used in a lot of different contexts, chatops (tools for devops), online gaming chatrooms like EVE, video streaming chatrooms like livecoding.tv, home security, etc. Extending Errbot and adding your own commands can be done by creating a plugin, which is simply a class derived from BotPlugin. The docstrings will be automatically reused by the !help command. We aim to give you all the tools you need to build a customized bot safely, without having to worry about basic functionality. As such, Errbot comes with a wealth of features out of the box. One of the main goals of Errbot is to make it easy to share your plugin with others as well. -
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EvaDB
Database system for building simpler and faster AI-powered application
Over the last decade, AI models have radically changed the world of natural language processing and computer vision. They are accurate on various tasks ranging from question answering to object tracking in videos. To use an AI model, the user needs to program against multiple low-level libraries, like PyTorch, Hugging Face, Open AI, etc. This tedious process often leads to a complex AI app that glues together these libraries to accomplish the given task. This programming complexity prevents people who are experts in other domains from benefiting from these models. Running these deep learning models on large document or video datasets is costly and time-consuming. For example, the state-of-the-art object detection model takes multiple GPU years to process just a week’s videos from a single traffic monitoring camera. Besides the money spent on hardware, these models also increase the time that you spend waiting for the model inference to finish. -
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EvalAI
Evaluating state of the art in AI
EvalAI is an open-source platform for evaluating and comparing machine learning (ML) and artificial intelligence (AI) algorithms at scale. We allow the creation of an arbitrary number of evaluation phases and dataset splits, compatibility using any programming language, and organizing results in both public and private leaderboards. Certain large-scale challenges need special computing capabilities for evaluation. If the challenge needs extra computational power, challenge organizers can easily add their own cluster of worker nodes to process participant submissions while we take care of hosting the challenge, handling user submissions, and maintaining the leaderboard. EvalAI lets participants submit code for their agent in the form of docker images which are evaluated against test environments on the evaluation server. During the evaluation, the worker fetches the image, test environment, and model snapshot and spins up a new container to perform the evaluation. -
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Evals
Evals is a framework for evaluating LLMs and LLM systems
The openai/evals repository is a framework and registry for evaluating large language models and systems built with LLMs. It’s designed to let you define “evals” (evaluation tasks) in a structured way and run them against different models or agents, with the ability to score, compare, and analyze results. The framework supports templated YAML eval definitions, solver-based evaluations, custom metrics, and composition of multi-step evaluations. It includes utilities and APIs to plug in completion functions, manage prompts, wrap retries or error handling, and register new evaluation types. It also maintains a growing registry of standard benchmarks or “evals” that users can reuse (for example, tasks measuring reasoning, factual accuracy, or chain-of-thought capabilities). The design is modular so you can extend or compose new evals, integrate with your own model APIs, and capture rich metadata about each run (prompt, responses, metrics). -
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Evaluate
A library for easily evaluating machine learning models and datasets
Evaluate is a library that makes evaluating and comparing models and reporting their performance easier and more standardized. -
Patching and Endpoint Management for Under $1/EndpointSplashtop AEM gives IT teams automated patching, monitoring, and unattended remote access for Windows and macOS. No long setup, no paying extra for key features.
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Eve is a AI project written in python that takes commands verbally or textually to control the computer and eveyday functions.
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EverMemOS
Long-term memory OS for AI with structured recall and context awarenes
EverMemOS is an open-source memory operating system built to give AI agents long-term, structured memory. It captures conversations, transforms them into organized memory units, and enables agents to recall past interactions with context and meaning. Instead of treating each prompt independently, it builds evolving user profiles, tracks preferences, and connects related events into coherent narratives. Its architecture combines memory storage, indexing, and retrieval with agent-level reasoning, allowing AI systems to make informed decisions based on prior interactions. EverMemOS goes beyond simple retrieval by actively applying stored knowledge to current tasks, improving personalization and consistency. EverMemOS uses a multi-stage memory lifecycle to convert raw dialogue into structured semantic data, supporting long-horizon reasoning and adaptive behavior across sessions. -
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EverOS
One portable memory layer for every AI agent
EverOS is a local-first memory runtime that gives AI agents and applications a portable long-term memory layer. It stores conversations, files, and agent trajectories as readable Markdown rather than hiding them only inside a database. Local SQLite and LanceDB indexes provide fast retrieval while the Markdown files remain the canonical source of truth. Memories can be edited, diffed, versioned with Git, and reused across tools and workflows. The system is designed to preserve user context and agent experience across sessions without forcing dependence on a hosted service. EverOS can serve as shared memory infrastructure for coding assistants, personal agents, and custom applications. Its architecture emphasizes user ownership, portability, and self-evolving reuse of accumulated knowledge. -
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EverydayWechat
Python tool that automates WeChat messages, replies, & group utilities
EverydayWechat is a Python-based automation tool designed to enhance and automate interactions on the WeChat messaging platform. Built using Python 3 and the Itchat library, it connects to the web version of WeChat to perform various automated messaging tasks. It allows users to send scheduled messages to friends or group chats, including daily weather updates, reminders, inspirational quotes, and other personalized content. It also supports intelligent automatic replies to incoming messages by integrating with multiple chatbot services. In addition to personal messaging automation, the project includes a group assistant that can respond to queries and provide useful information within chat groups. These group utilities can retrieve data such as weather conditions, calendar details, garbage classification information, movie box office statistics, delivery tracking updates, and air quality reports. -
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Evidently
Evaluate and monitor ML models from validation to production
Evidently is an open-source Python library for data scientists and ML engineers. It helps evaluate, test, and monitor ML models from validation to production. It works with tabular, text data and embeddings. -
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EvoAgentX
Self-evolving AI agent framework for automated workflows
EvoAgentX is an open source framework for building, evaluating, and continuously improving LLM-based agents and multi-agent workflows. It moves beyond static pipelines by introducing a self-evolving system where agents are automatically generated, tested, and optimised through iterative feedback. Developers can define goals in natural language, while the framework handles workflow creation, execution, and refinement. Its modular architecture supports layered components for agents, workflows, evaluation, and evolution, enabling flexible experimentation and scaling. EvoAgentX integrates optimisation algorithms to refine prompts, tool usage, and workflow structures over time. This allows agents to adapt dynamically instead of relying on fixed logic. It is designed for researchers and developers who want to automate complex agent systems and improve performance through continuous learning cycles, reducing manual orchestration and enabling more efficient development. -
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EvoTorch
Advanced evolutionary computation library built on top of PyTorch
EvoTorch is an evolutionary optimization framework built on top of PyTorch, developed by NNAISENSE. It is designed for large-scale optimization problems, particularly those that require evolutionary algorithms rather than gradient-based methods. -
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ExSTraCS
Extended Supervised Tracking and Classifying System
This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. ExSTraCS was primarily developed to address problems in epidemiological data mining to identify complex patterns relating predictive attributes in noisy datasets to disease phenotypes of interest. ExSTraCS combines a number of recent advancements into a single algorithmic platform. It can flexibly handle (1) discrete or continuous attributes, (2) missing data, (3) balanced or imbalanced datasets, and (4) binary or many classes. A complete users guide for ExSTraCS is included. Coded in Python 2.7. -
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Excors is a natural language interpretation system (chatterbot framework) that uses XML and Python for natural language modules.
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ExoPlanet
GUI based toolkit for running common Machine Learning algorithms.
ExoPlanet provides a graphical interface for the construction, evaluation and application of a Machine Learning model in predictive analysis. With the back-end built using the numpy and scikit-learn libraries, as a toolkit, ExoPlanet couples fast and well tested algorithms, a UI designed over the Qt4 framework, and graphs rendered using Matplotlib to provide the user with a rich interface, rapid analytics and interactive visuals. ExoPlanet is designed to have a minimal learning curve, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. Post analysis, the toolkit further automates building the visual representations for the trained model. -
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Experiential
Experiential is the open source, zero markup gateway for BYOK
Experiential is an open-source AI gateway and model router that unifies access to hosted, self-hosted, and bring-your-own-key language models through an OpenAI-compatible API. It supports more than 1,000 marketplace models while allowing developers to connect their own providers. The platform controls which users and agents can access specific models and how much they can spend. It collects execution traces to evaluate model performance, costs, and response speed. Developers can optimize custom routing strategies using real agent traffic and simulation workflows. It also supports fine-tuning specialized open-source models based on collected interactions. Experiential integrates with coding agents such as Claude Code, Codex, and Cursor and provides configurable telemetry, access controls, and local deployment options. -
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Exposure
Learning infinite-resolution image processing with GAN and RL
Learning infinite-resolution image processing with GAN and RL from unpaired image datasets, using a differentiable photo editing model. ACM Transactions on Graphics (presented at SIGGRAPH 2018) Exposure is originally designed for RAW photos, which assumes 12+ bit color depth and linear "RGB" color space (or whatever we get after demosaicing). jpg and png images typically have only 8-bit color depth (except 16-bit pngs) and the lack of information (dynamic range/activation resolution) may lead to suboptimal results such as posterization. Moreover, jpg and most pngs assume an sRGB color space, which contains a roughly 1/2.2 Gamma correction, making the data distribution different from training images (which are linear). Exposure is just a prototype (proof-of-concept) of our latest research, and there are definitely a lot of engineering efforts required to make it suitable for a real product. -
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Extended Dreambooth How-To Guides
Implementation of Dreambooth
Extended Dreambooth How-To Guides is an implementation and extended toolkit for fine-tuning Stable Diffusion models using the DreamBooth technique, enabling users to train AI image generators to reproduce specific subjects, styles, or identities from a small set of reference images. The project adapts and expands upon earlier DreamBooth research by providing practical scripts, notebooks, and workflows that allow users to train personalized models on local machines, cloud environments, or platforms such as Google Colab. It focuses heavily on usability, offering detailed guides for different setups while still exposing advanced configuration options for experienced users. The system allows users to bind a unique token to a subject, which can later be used in prompts to generate consistent and recognizable outputs across different contexts. It also supports captioning, multi-subject training, and regularization techniques to improve generalization and avoid overfitting. -
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ExtractThinker
ExtractThinker is a Document Intelligence library for LLMs
ExtractThinker is a tool designed to facilitate the extraction and analysis of information from various data sources, aiding in data processing and knowledge discovery. -
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FARM
Fast & easy transfer learning for NLP
FARM makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker. With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning of language models to your task and domain language. AMP optimizers (~35% faster) and parallel preprocessing (16 CPU cores => ~16x faster). Modular design of language models and prediction heads. Switch between heads or combine them for multitask learning. Full Compatibility with HuggingFace Transformers' models and model hub. Smooth upgrading to newer language models. Integration of custom datasets via Processor class. Powerful experiment tracking & execution. -
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FATE
An industrial grade federated learning framework
FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. FATE became open-source in February 2019. FATE TSC was established to lead FATE open-source community, with members from major domestic cloud computing and financial service enterprises. FedAI is a community that helps businesses and organizations build AI models effectively and collaboratively, by using data in accordance with user privacy protection, data security, data confidentiality and government regulations.
