AI Agents & Automation

Chat01.ai, previously known as OpenAI01.net, provides users with free and unlimited access to OpenAI and ChatGPT models, positioning itself as a robust alternative to ChatGPT Pro. The platform offers a selection of models, including GPT-5.5-thinking, GPT-5.4-thinking, GPT-5.3, and GPT-4.5, with advanced 'pro' models like GPT-5.5-pro, GPT-5.4-pro, and GPT-5.2-pro available on paid plans. Users can ask questions and receive answers from these models. The service operates on a credit system, offering daily free credits and permanent credits through paid plans or invites. It aims to assist users with various queries, leveraging advanced AI capabilities.

AtlasPro AI offers a spatial intelligence layer designed for AI agents to understand and operate within the physical world. It serves Fortune 500 companies, government agencies, and AI companies by making the physical world machine-readable. Key features include MCP Automation for GIS workflows, Topology Reasoning using GNNs for network analysis and optimization, and Real-Time Processing of spatial data from various sources. The platform provides full-stack spatial AI, including autonomous spatial AI agents, MCP tools for spatial intelligence integration, and natural language interfaces for querying spatial data on interactive maps. It supports industries like Telecom, Utilities, Supply Chain, Real Estate, Finance, and Defense.

MushroomRL is a comprehensive Python library designed for Reinforcement Learning research and development. Its modular architecture allows seamless integration with popular Python libraries for tensor computation, such as PyTorch and TensorFlow, and supports various RL benchmarks like Gymnasium, PyBullet, and Deepmind Control Suite. The library simplifies the process of conducting RL experiments by providing implementations of both classical RL algorithms (e.g., Q-Learning, SARSA, FQI) and deep RL algorithms (e.g., DQN, DDPG, SAC, TD3, TRPO, PPO). It also offers extensive support for physics simulators like Pybullet and MuJoCo, and advanced simulation platforms such as Habitat and iGibson, enabling realistic and sensory-rich learning environments. Full documentation and tutorials are available to guide users through installation and experiment setup.

AISP is an official open-source library for AI Image Signal Processing and Computational Photography, developed by mv-lab. It serves as a comprehensive resource for researchers and developers working on low-level computer vision and imaging tasks. The library includes implementations for learned Image Signal Processors (ISPs), RAW image restoration, super-resolution, and reconstruction from sRGB. Additionally, it offers functionalities for image enhancement such as denoising and deblurring, and advanced features like bokeh rendering. AISP is actively used in prominent challenges like NTIRE (CVPR) and AIM (ICCV/ECCV), providing base code and solutions for various image processing tasks, including tutorials for generating degraded RAW images and training restoration methods.

ChatLab is an open-source desktop application designed to help users rediscover and analyze their social memories through local, AI-powered analysis. It combines a flexible SQL engine with AI agents to enable exploration of patterns, asking better questions, and extracting insights from chat data, all processed on the user's own machine. The tool supports various chat platforms including WhatsApp, LINE, WeChat, QQ, Discord, Instagram, and Telegram, with plans to add iMessage, Messenger, and KakaoTalk. Key features include efficient processing for large chat histories, privacy by keeping data local, AI agent and function calling workflows for data operations, and rich visual views for trends and patterns. Its architecture prioritizes local-first processing, streaming for efficiency, composable AI intelligence, and a schema-first approach for data consistency.

BemiDB is an open-source alternative to Snowflake and Fivetran, designed to centralize data without complex pipelines. It seamlessly connects to different data sources, syncing data in a compressed columnar format to S3. Its Postgres-compatible analytical query engine allows users to run complex queries up to 2000x faster than regular Postgres, making it ideal for high-speed analytical workloads. BemiDB integrates with existing Postgres tools and services, enabling querying data with BI tools, notebooks, and ORMs. It's packaged in a single Docker image with stateless processes for easy deployment and supports continuous data archiving from databases.

clawPDF is an open-source virtual (network) printer for Windows, offering advanced features typically found in enterprise solutions. It enables users to create documents in various formats such as PDF/A-1b, PDF/A-2b, PDF/A-3b, PDF/X, PDF/Image, OCR, SVG, PNG, JPEG, TIF, and TXT. Users can protect documents with passwords, encrypt them with 256-bit AES, and easily manage metadata. The tool includes a scripting interface for process automation and application integration, and supports shared network printing. Compatible with all major Windows client and server operating systems (x86/x64/ARM64), clawPDF also supports multi-user environments and offers features like command-line support, silent printing, and multiple profiles.

ComfyUI-QwenVL is a custom node designed to seamlessly integrate the powerful Qwen-VL series of vision-language models (LVLMs) from Alibaba Cloud into ComfyUI workflows. This includes support for the latest Qwen3-VL and Qwen2.5-VL models, as well as GGUF backends and text-only Qwen3 support. The tool provides advanced multimodal AI capabilities, facilitating efficient text generation, comprehensive image understanding, and detailed video analysis. Key features include automatic model downloading from Hugging Face, on-the-fly quantization (4-bit, 8-bit, FP16), and a flexible preset and custom prompt system. It offers both standard and advanced nodes for varying levels of user control, along with hardware-aware safeguards for FP8 model compatibility and SageAttention support for GPU-optimized performance.

Agent Scan is a robust security scanner designed for AI agents, Model Context Protocol (MCP) servers, and agent skills. It automatically discovers and inventories installed agent components, including harnesses, MCP servers, and skills, then scans them for common threats such as prompt injections, sensitive data handling, and malware payloads hidden in natural language. The tool supports a wide range of agents like Claude, Cursor, Windsurf, Gemini CLI, and Amazon Q, detecting over 15 distinct security risks. Agent Scan operates in both a CLI scan mode, generating detailed reports, and a background mode for continuous monitoring by security teams. It offers capabilities to scan specific MCP configurations or individual agent skill files, ensuring comprehensive coverage for AI agent security.

DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a cutting-edge reinforcement learning agent that operates entirely within a diffusion world model. This innovative approach allows the agent to learn and play in an autoregressive imagination of environments like Atari and Counter-Strike: Global Offensive. Recognized as a NeurIPS 2024 Spotlight, DIAMOND offers researchers and developers a powerful tool for exploring diffusion-based world models in reinforcement learning. It provides quick installation for pretrained models, options for launching training runs, and extensive configuration management via Hydra. Users can visualize policy play, human interaction, and dataset replays, making it a versatile platform for advanced AI research.

Dictionariez is a highly customizable, open-source browser extension designed to significantly enhance language learning. It allows users to double-click on any word on a webpage to instantly access its definition, translation, and pronunciation. Supporting over 20 languages, including English, Chinese, Japanese, Korean, German, Spanish, French, Italian, Portuguese, and Swedish, it integrates with more than 1000 dictionaries. Beyond quick lookups, Dictionariez provides text-to-speech functionality, translation services, and seamless integration with Anki for vocabulary retention. The extension also features auto-completion, word history, and keyboard shortcuts, making it a comprehensive tool for students and language enthusiasts alike. It is available on Chrome, Firefox, and Edge, with specialized versions like Ordböcker for Swedish learners and SidePal for a lighter side-panel experience.

edm is the official PyTorch implementation of the NeurIPS 2022 paper "Elucidating the Design Space of Diffusion-Based Generative Models." This open-source tool provides a clear framework for understanding and experimenting with diffusion models, separating concrete design choices in sampling and training processes, as well as score network preconditioning. It introduces improvements that lead to state-of-the-art FID scores for CIFAR-10, FFHQ, AFHQv2, and ImageNet, with significantly faster sampling times. The project includes pre-trained models, tools for generating images, calculating Fréchet Inception Distance (FID), and preparing custom datasets. It supports both Linux and Windows, recommending Linux for performance, and requires high-end NVIDIA GPUs for optimal use.

DREAMPlace is an open-source, deep learning toolkit-enabled VLSI placement tool designed for flexibility and efficiency in very large-scale integration (VLSI) design. It supports both CPU and GPU execution, achieving over 30X speedup in global placement and legalization compared to CPU implementations like RePlAce on ISPD 2005 benchmarks with a Nvidia Tesla V100 GPU. The tool integrates ABCDPlace, a GPU-accelerated detailed placer, which provides around 16X speedup on million-size benchmarks over NTUPlace3. Key features include multi-threaded CPU and optional GPU acceleration, net weighting, incremental placement, LEF/DEF support, and Python binding. It also supports timing optimization in global placement, fence regions, and deterministic modes.

gemma.cpp is a lightweight, standalone C++ inference engine specifically designed for Google's Gemma foundation models. It provides a minimalist implementation for Gemma 2-3 and PaliGemma 2 models, prioritizing simplicity and directness over full generality, making it suitable for experimentation and research. The engine supports CPU-only inference, offering features like sampling with TopK and temperature, and a backward pass (VJP) with Adam optimizer for Gemma research. It includes optimizations such as mixed-precision GEMM (fp8, bf16, fp32, fp64 bit), automatic runtime autotuning, and integrated weight compression. The project leverages the Google Highway Library for portable SIMD, ensuring efficient CPU inference. It offers C++ APIs with streaming for single and batched inference, a basic interactive command-line app, and Python bindings. gemma.cpp is designed to be easily embeddable in other projects with minimal dependencies and is highly modifiable, featuring a small core implementation.

Frigate-hass-integration is an open-source project that seamlessly integrates Frigate, an AI-powered Network Video Recorder (NVR), with Home Assistant. This integration enhances smart home surveillance by providing a rich media browser with thumbnails and navigation directly within Home Assistant. Users gain access to various sensor entities, including Camera FPS, Detection FPS, Process FPS, Skipped FPS, and Objects detected, along with binary sensor entities for object motion. It also offers camera entities for live view and object detected snapshots, and switch entities for controlling recording, detection, snapshots, and contrast improvement. Furthermore, the integration provides services for manual events and PTZ control, supporting multiple Frigate instances for comprehensive home security management.

Fregata is a lightweight, super-fast, and large-scale machine learning library designed for Apache Spark. It offers high-level APIs in Scala, enabling developers and data scientists to build and deploy intelligent applications efficiently. A key differentiator is its ability to achieve higher accuracy and significantly faster convergence compared to MLLib, often training Generalized Linear Models in minutes for massive datasets. Fregata utilizes GSA SGD optimization, making it parameter-free by dynamically calculating appropriate learning rates. It supports Spark 1.x and 2.x with Scala 2.10 and 2.11, and includes algorithms like Trillion LR, Trillion SoftMax, and Logistic Regression. Its architecture is designed for seamless integration into existing Spark data processing workflows.

AI Angels offers a platform for users to chat with over 70 AI angel girlfriends, providing romantic, supportive, and 24/7 NSFW AI companion experiences. Key features include persistent memory across conversations, uncensored chat, unlimited messaging, and real-time voice chat. Users can customize their AI girlfriend's personality, interests, appearance, and style. The platform also supports AI girlfriend image generation on demand and roleplay scenarios, aiming for realistic companions with emotional support capabilities. AI Angels differentiates itself with free unlimited messages and no content filters, unlike some alternatives.

Causal Foundry offers Kenkai, an adaptive AI platform designed for real-time personalization, optimization, and scalable decision-making. Built on ClickHouse, Kenkai streams and queries high-resolution data instantly, enabling enterprise-scale interventions. It leverages reinforcement learning and contextual bandits to continuously optimize engagement strategies through experimentation and adaptation. The platform also includes embedded metrics and analytics, allowing users to define governed metrics once and explore them everywhere, integrating live dashboards directly into existing systems without black boxes. Causal Foundry aims to democratize reinforcement learning for organizations worldwide, adapting to individual preferences, environments, and behaviors.

Self-Driving Delivery Agent, also known as DriVLMe, is an open-source project providing the official implementation of the IROS 2024 paper: "Enhancing LLM-based Autonomous Driving Agents with Embodied and Social Experience." This tool is designed for researchers and developers working on autonomous driving systems, particularly those interested in integrating large language models (LLMs) with real-world driving experiences. It offers a framework for setting up a conda environment, preparing LLaVA weights, and training/finetuning models on datasets like bddx and SDN. The project includes scripts for pretraining, finetuning, and evaluating autonomous driving agents, making it a valuable resource for advancing the field of AI-driven autonomous vehicles.

Strix is an open-source AI security tool designed to identify and remediate application vulnerabilities. It employs autonomous AI agents that mimic real hackers, dynamically running code to find and validate vulnerabilities with proof-of-concepts. Built for developers and security teams, Strix offers fast, accurate security testing without the overhead of manual penetration testing or the false positives common with static analysis tools. Key capabilities include a full hacker toolkit, collaborative agent teams, real validation with PoCs, a developer-first CLI with actionable reports, and auto-fix and reporting features to accelerate remediation. It integrates seamlessly with GitHub Actions and CI/CD pipelines, allowing for automatic vulnerability scanning on every pull request.

Verl-Tool is a comprehensive framework designed for training AI agents that can effectively use diverse tools. It offers a unified and easy-to-extend architecture, leveraging verl as a submodule to benefit from ongoing updates. Key features include a complete decoupling of actor rollout and environment interaction, a "tool-as-environment" paradigm where each tool interaction can modify and reload environment states, and native RL framework support for multi-turn interactive loops. The platform also provides a user-friendly evaluation suite, allowing users to launch trained models with OpenAI API alongside a tool server for seamless interaction and output generation. It supports the latest verl (0.6.0) and vllm (0.11.0) versions, ensuring modularity and maintainability.

VLA-Adapter is an open-source implementation offering an effective paradigm for tiny-scale Vision-Language-Action (VLA) models. It provides a robust framework for training and deploying VLA models, particularly for robotic control and real-world system integration. The tool supports various GPU configurations, from extremely limited VRAM (10-12GB) to professional-grade GPUs (80GB+), making it accessible for diverse research and development environments. Key features include support for LIBERO and CALVIN benchmarks, an enhanced Pro version for improved performance, and compatibility with various foundation models and real-world robotic systems like ALOHA and Franka. It also offers detailed guidance on data preparation and training configurations.

agent-device is a command-line interface (CLI) designed for AI agents to control and observe iOS, tvOS, macOS, Android, and AndroidTV devices. It facilitates UI automation by providing structured snapshots of the accessibility tree, allowing agents to understand and interact with mobile UIs efficiently. The tool supports deterministic interactions, session-aware workflows, and replayable flows, making it suitable for repeated automation runs and debugging. Key features include inspecting UI states, collecting logs, network inspection, and performance snapshots. It also integrates with React DevTools for deeper component-level insights, making it a comprehensive solution for agent-driven mobile app testing and automation.

53AI Hub is an open-source AI portal designed to help developers and enterprises quickly build and operate production-grade AI agents, prompts, and tools. It offers seamless integration with popular development platforms such as Coze, Dify, FastGPT, RAGFlow, and 53AI Studio, as well as cloud platforms like Aliyun, Tencent Cloud, and Baidu Cloud. The platform simplifies the creation of AI portals, even for users without extensive technical backgrounds, significantly lowering the barrier to AI implementation. Key features include platform integration, comprehensive application management for AI assets, user operations management, and independent deployment options for both cloud and local environments.