
ComfyUI
The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.
Stars: 89407

ComfyUI is a powerful and modular visual AI engine and application that allows users to design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. It provides a user-friendly environment for creating complex Stable Diffusion workflows without the need for coding. ComfyUI supports various models for image editing, video processing, audio manipulation, 3D modeling, and more. It offers features like smart memory management, support for different GPU types, loading and saving workflows as JSON files, and offline functionality. Users can also use API nodes to access paid models from external providers through the online Comfy API.
README:
ComfyUI lets you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. Available on Windows, Linux, and macOS.
- The easiest way to get started.
- Available on Windows & macOS.
- Get the latest commits and completely portable.
- Available on Windows.
Supports all operating systems and GPU types (NVIDIA, AMD, Intel, Apple Silicon, Ascend).
See what ComfyUI can do with the example workflows.
- Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
- Image Models
- SD1.x, SD2.x (unCLIP)
- SDXL, SDXL Turbo
- Stable Cascade
- SD3 and SD3.5
- Pixart Alpha and Sigma
- AuraFlow
- HunyuanDiT
- Flux
- Lumina Image 2.0
- HiDream
- Qwen Image
- Hunyuan Image 2.1
- Image Editing Models
- Video Models
- Audio Models
- 3D Models
- Asynchronous Queue system
- Many optimizations: Only re-executes the parts of the workflow that changes between executions.
- Smart memory management: can automatically run large models on GPUs with as low as 1GB vram with smart offloading.
- Works even if you don't have a GPU with:
--cpu
(slow) - Can load ckpt and safetensors: All in one checkpoints or standalone diffusion models, VAEs and CLIP models.
- Safe loading of ckpt, pt, pth, etc.. files.
- Embeddings/Textual inversion
- Loras (regular, locon and loha)
- Hypernetworks
- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.
- Saving/Loading workflows as Json files.
- Nodes interface can be used to create complex workflows like one for Hires fix or much more advanced ones.
- Area Composition
- Inpainting with both regular and inpainting models.
- ControlNet and T2I-Adapter
- Upscale Models (ESRGAN, ESRGAN variants, SwinIR, Swin2SR, etc...)
- GLIGEN
- Model Merging
- LCM models and Loras
- Latent previews with TAESD
- Works fully offline: core will never download anything unless you want to.
- Optional API nodes to use paid models from external providers through the online Comfy API.
- Config file to set the search paths for models.
Workflow examples can be found on the Examples page
ComfyUI follows a weekly release cycle targeting Friday but this regularly changes because of model releases or large changes to the codebase. There are three interconnected repositories:
-
- Releases a new stable version (e.g., v0.7.0)
- Serves as the foundation for the desktop release
-
- Builds a new release using the latest stable core version
-
- Weekly frontend updates are merged into the core repository
- Features are frozen for the upcoming core release
- Development continues for the next release cycle
Keybind | Explanation |
---|---|
Ctrl + Enter
|
Queue up current graph for generation |
Ctrl + Shift + Enter
|
Queue up current graph as first for generation |
Ctrl + Alt + Enter
|
Cancel current generation |
Ctrl + Z /Ctrl + Y
|
Undo/Redo |
Ctrl + S
|
Save workflow |
Ctrl + O
|
Load workflow |
Ctrl + A
|
Select all nodes |
Alt + C
|
Collapse/uncollapse selected nodes |
Ctrl + M
|
Mute/unmute selected nodes |
Ctrl + B
|
Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through) |
Delete /Backspace
|
Delete selected nodes |
Ctrl + Backspace
|
Delete the current graph |
Space |
Move the canvas around when held and moving the cursor |
Ctrl /Shift + Click
|
Add clicked node to selection |
Ctrl + C /Ctrl + V
|
Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes) |
Ctrl + C /Ctrl + Shift + V
|
Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
Shift + Drag
|
Move multiple selected nodes at the same time |
Ctrl + D
|
Load default graph |
Alt + +
|
Canvas Zoom in |
Alt + -
|
Canvas Zoom out |
Ctrl + Shift + LMB + Vertical drag |
Canvas Zoom in/out |
P |
Pin/Unpin selected nodes |
Ctrl + G
|
Group selected nodes |
Q |
Toggle visibility of the queue |
H |
Toggle visibility of history |
R |
Refresh graph |
F |
Show/Hide menu |
. |
Fit view to selection (Whole graph when nothing is selected) |
Double-Click LMB | Open node quick search palette |
Shift + Drag |
Move multiple wires at once |
Ctrl + Alt + LMB |
Disconnect all wires from clicked slot |
Ctrl
can also be replaced with Cmd
instead for macOS users
There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the releases page.
Simply download, extract with 7-Zip and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
If you have trouble extracting it, right click the file -> properties -> unblock
See the Config file to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
You can install and start ComfyUI using comfy-cli:
pip install comfy-cli
comfy install
Python 3.13 is very well supported. If you have trouble with some custom node dependencies you can try 3.12
Git clone this repo.
Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
Put your VAE in: models/vae
AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4
This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4
(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip. More information can be found here
- To install PyTorch xpu, use the following command:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/xpu
This is the command to install the Pytorch xpu nightly which might have some performance improvements:
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu
(Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance.
- visit Installation for more information.
Nvidia users should install stable pytorch using this command:
pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu129
This is the command to install pytorch nightly instead which might have performance improvements.
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129
If you get the "Torch not compiled with CUDA enabled" error, uninstall torch with:
pip uninstall torch
And install it again with the command above.
Install the dependencies by opening your terminal inside the ComfyUI folder and:
pip install -r requirements.txt
After this you should have everything installed and can proceed to running ComfyUI.
You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
- Install pytorch nightly. For instructions, read the Accelerated PyTorch training on Mac Apple Developer guide (make sure to install the latest pytorch nightly).
- Follow the ComfyUI manual installation instructions for Windows and Linux.
- Install the ComfyUI dependencies. If you have another Stable Diffusion UI you might be able to reuse the dependencies.
- Launch ComfyUI by running
python main.py
Note: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in ComfyUI manual installation.
This is very badly supported and is not recommended. There are some unofficial builds of pytorch ROCm on windows that exist that will give you a much better experience than this. This readme will be updated once official pytorch ROCm builds for windows come out.
pip install torch-directml
Then you can launch ComfyUI with: python main.py --directml
For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the installation page. Here's a step-by-step guide tailored to your platform and installation method:
- Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary.
- Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform.
- Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the Installation page.
- Finally, adhere to the ComfyUI manual installation guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.
For models compatible with Cambricon Extension for PyTorch (torch_mlu). Here's a step-by-step guide tailored to your platform and installation method:
- Install the Cambricon CNToolkit by adhering to the platform-specific instructions on the Installation
- Next, install the PyTorch(torch_mlu) following the instructions on the Installation
- Launch ComfyUI by running
python main.py
For models compatible with Iluvatar Extension for PyTorch. Here's a step-by-step guide tailored to your platform and installation method:
- Install the Iluvatar Corex Toolkit by adhering to the platform-specific instructions on the Installation
- Launch ComfyUI by running
python main.py
python main.py
Try running it with this command if you have issues:
For 6700, 6600 and maybe other RDNA2 or older: HSA_OVERRIDE_GFX_VERSION=10.3.0 python main.py
For AMD 7600 and maybe other RDNA3 cards: HSA_OVERRIDE_GFX_VERSION=11.0.0 python main.py
You can enable experimental memory efficient attention on recent pytorch in ComfyUI on some AMD GPUs using this command, it should already be enabled by default on RDNA3. If this improves speed for you on latest pytorch on your GPU please report it so that I can enable it by default.
TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL=1 python main.py --use-pytorch-cross-attention
You can also try setting this env variable PYTORCH_TUNABLEOP_ENABLED=1
which might speed things up at the cost of a very slow initial run.
Only parts of the graph that have an output with all the correct inputs will be executed.
Only parts of the graph that change from each execution to the next will be executed, if you submit the same graph twice only the first will be executed. If you change the last part of the graph only the part you changed and the part that depends on it will be executed.
Dragging a generated png on the webpage or loading one will give you the full workflow including seeds that were used to create it.
You can use () to change emphasis of a word or phrase like: (good code:1.2) or (bad code:0.8). The default emphasis for () is 1.1. To use () characters in your actual prompt escape them like \( or \).
You can use {day|night}, for wildcard/dynamic prompts. With this syntax "{wild|card|test}" will be randomly replaced by either "wild", "card" or "test" by the frontend every time you queue the prompt. To use {} characters in your actual prompt escape them like: \{ or \}.
Dynamic prompts also support C-style comments, like // comment
or /* comment */
.
To use a textual inversion concepts/embeddings in a text prompt put them in the models/embeddings directory and use them in the CLIPTextEncode node like this (you can omit the .pt extension):
embedding:embedding_filename.pt
Use --preview-method auto
to enable previews.
The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with TAESD, download the taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth and place them in the models/vae_approx
folder. Once they're installed, restart ComfyUI and launch it with --preview-method taesd
to enable high-quality previews.
Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"
Use --tls-keyfile key.pem --tls-certfile cert.pem
to enable TLS/SSL, the app will now be accessible with https://...
instead of http://...
.
Note: Windows users can use alexisrolland/docker-openssl or one of the 3rd party binary distributions to run the command example above.
If you use a container, note that the volume mount-v
can be a relative path so... -v ".\:/openssl-certs" ...
would create the key & cert files in the current directory of your command prompt or powershell terminal.
Discord: Try the #help or #feedback channels.
Matrix space: #comfyui_space:matrix.org (it's like discord but open source).
See also: https://www.comfy.org/
As of August 15, 2024, we have transitioned to a new frontend, which is now hosted in a separate repository: ComfyUI Frontend. This repository now hosts the compiled JS (from TS/Vue) under the web/
directory.
For any bugs, issues, or feature requests related to the frontend, please use the ComfyUI Frontend repository. This will help us manage and address frontend-specific concerns more efficiently.
The new frontend is now the default for ComfyUI. However, please note:
- The frontend in the main ComfyUI repository is updated fortnightly.
- Daily releases are available in the separate frontend repository.
To use the most up-to-date frontend version:
-
For the latest daily release, launch ComfyUI with this command line argument:
--front-end-version Comfy-Org/ComfyUI_frontend@latest
-
For a specific version, replace
latest
with the desired version number:--front-end-version Comfy-Org/[email protected]
This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes.
If you need to use the legacy frontend for any reason, you can access it using the following command line argument:
--front-end-version Comfy-Org/ComfyUI_legacy_frontend@latest
This will use a snapshot of the legacy frontend preserved in the ComfyUI Legacy Frontend repository.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for ComfyUI
Similar Open Source Tools

ComfyUI
ComfyUI is a powerful and modular visual AI engine and application that allows users to design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. It provides a user-friendly environment for creating complex Stable Diffusion workflows without the need for coding. ComfyUI supports various models for image editing, video processing, audio manipulation, 3D modeling, and more. It offers features like smart memory management, support for different GPU types, loading and saving workflows as JSON files, and offline functionality. Users can also use API nodes to access paid models from external providers through the online Comfy API.

moly
Moly is an AI LLM client written in Rust, showcasing the capabilities of the Makepad UI toolkit and Project Robius, a framework for multi-platform application development in Rust. It is currently in beta, allowing users to build and run Moly on macOS, Linux, and Windows. The tool provides packaging support for different platforms, such as `.app`, `.dmg`, `.deb`, AppImage, pacman, and `.exe` (NSIS). Users can easily set up WasmEdge using `moly-runner` and leverage `cargo` commands to build and run Moly. Additionally, Moly offers pre-built releases for download and supports packaging for distribution on Linux, Windows, and macOS.

sandbox
Sandbox is an open-source cloud-based code editing environment with custom AI code autocompletion and real-time collaboration. It consists of a frontend built with Next.js, TailwindCSS, Shadcn UI, Clerk, Monaco, and Liveblocks, and a backend with Express, Socket.io, Cloudflare Workers, D1 database, R2 storage, Workers AI, and Drizzle ORM. The backend includes microservices for database, storage, and AI functionalities. Users can run the project locally by setting up environment variables and deploying the containers. Contributions are welcome following the commit convention and structure provided in the repository.

chatgpt-cli
ChatGPT CLI provides a powerful command-line interface for seamless interaction with ChatGPT models via OpenAI and Azure. It features streaming capabilities, extensive configuration options, and supports various modes like streaming, query, and interactive mode. Users can manage thread-based context, sliding window history, and provide custom context from any source. The CLI also offers model and thread listing, advanced configuration options, and supports GPT-4, GPT-3.5-turbo, and Perplexity's models. Installation is available via Homebrew or direct download, and users can configure settings through default values, a config.yaml file, or environment variables.

xlang
XLang™ is a cutting-edge language designed for AI and IoT applications, offering exceptional dynamic and high-performance capabilities. It excels in distributed computing and seamless integration with popular languages like C++, Python, and JavaScript. Notably efficient, running 3 to 5 times faster than Python in AI and deep learning contexts. Features optimized tensor computing architecture for constructing neural networks through tensor expressions. Automates tensor data flow graph generation and compilation for specific targets, enhancing GPU performance by 6 to 10 times in CUDA environments.

log10
Log10 is a one-line Python integration to manage your LLM data. It helps you log both closed and open-source LLM calls, compare and identify the best models and prompts, store feedback for fine-tuning, collect performance metrics such as latency and usage, and perform analytics and monitor compliance for LLM powered applications. Log10 offers various integration methods, including a python LLM library wrapper, the Log10 LLM abstraction, and callbacks, to facilitate its use in both existing production environments and new projects. Pick the one that works best for you. Log10 also provides a copilot that can help you with suggestions on how to optimize your prompt, and a feedback feature that allows you to add feedback to your completions. Additionally, Log10 provides prompt provenance, session tracking and call stack functionality to help debug prompt chains. With Log10, you can use your data and feedback from users to fine-tune custom models with RLHF, and build and deploy more reliable, accurate and efficient self-hosted models. Log10 also supports collaboration, allowing you to create flexible groups to share and collaborate over all of the above features.

raycast_api_proxy
The Raycast AI Proxy is a tool that acts as a proxy for the Raycast AI application, allowing users to utilize the application without subscribing. It intercepts and forwards Raycast requests to various AI APIs, then reformats the responses for Raycast. The tool supports multiple AI providers and allows for custom model configurations. Users can generate self-signed certificates, add them to the system keychain, and modify DNS settings to redirect requests to the proxy. The tool is designed to work with providers like OpenAI, Azure OpenAI, Google, and more, enabling tasks such as AI chat completions, translations, and image generation.

steel-browser
Steel is an open-source browser API designed for AI agents and applications, simplifying the process of building live web agents and browser automation tools. It serves as a core building block for a production-ready, containerized browser sandbox with features like stealth capabilities, text-to-markdown session management, UI for session viewing/debugging, and full browser control through popular automation frameworks. Steel allows users to control, run, and manage a production-ready browser environment via a REST API, offering features such as full browser control, session management, proxy support, extension support, debugging tools, anti-detection mechanisms, resource management, and various browser tools. It aims to streamline complex browsing tasks programmatically, enabling users to focus on their AI applications while Steel handles the underlying complexity.

langstream
LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

comfyui
ComfyUI is a highly-configurable, cloud-first AI-Dock container that allows users to run ComfyUI without bundled models or third-party configurations. Users can configure the container using provisioning scripts. The Docker image supports NVIDIA CUDA, AMD ROCm, and CPU platforms, with version tags for different configurations. Additional environment variables and Python environments are provided for customization. ComfyUI service runs on port 8188 and can be managed using supervisorctl. The tool also includes an API wrapper service and pre-configured templates for Vast.ai. The author may receive compensation for services linked in the documentation.

skyvern
Skyvern automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows, replacing brittle or unreliable automation solutions. Traditional approaches to browser automations required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts changed. Instead of only relying on code-defined XPath interactions, Skyvern adds computer vision and LLMs to the mix to parse items in the viewport in real-time, create a plan for interaction and interact with them. This approach gives us a few advantages: 1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code 2. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate 3. Skyvern leverages LLMs to reason through interactions to ensure we can cover complex situations. Examples include: 1. If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16 2. If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!) Want to see examples of Skyvern in action? Jump to #real-world-examples-of- skyvern

code2prompt
Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks. It bridges the gap between codebases and LLMs by converting projects into AI-friendly prompts, enabling users to leverage AI for various software development tasks. The tool offers features like holistic codebase representation, intelligent source tree generation, customizable prompt templates, smart token management, Gitignore integration, flexible file handling, clipboard-ready output, multiple output options, and enhanced code readability.

LEADS
LEADS is a lightweight embedded assisted driving system designed to simplify the development of instrumentation, control, and analysis systems for racing cars. It is written in Python and C/C++ with impressive performance. The system is customizable and provides abstract layers for component rearrangement. It supports hardware components like Raspberry Pi and Arduino, and can adapt to various hardware types. LEADS offers a modular structure with a focus on flexibility and lightweight design. It includes robust safety features, modern GUI design with dark mode support, high performance on different platforms, and powerful ESC systems for traction control and braking. The system also supports real-time data sharing, live video streaming, and AI-enhanced data analysis for driver training. LEADS VeC Remote Analyst enables transparency between the driver and pit crew, allowing real-time data sharing and analysis. The system is designed to be user-friendly, adaptable, and efficient for racing car development.

RoboMatrix
RoboMatrix is a skill-centric hierarchical framework for scalable robot task planning and execution in an open-world environment. It provides a structured approach to robot task execution using a combination of hardware components, environment configuration, installation procedures, and data collection methods. The framework is developed using the ROS2 framework on Ubuntu and supports robots from DJI's RoboMaster series. Users can follow the provided installation guidance to set up RoboMatrix and utilize it for various tasks such as data collection, task execution, and dataset construction. The framework also includes a supervised fine-tuning dataset and aims to optimize communication and release additional components in the future.

Fabric
Fabric is an open-source framework designed to augment humans using AI by organizing prompts by real-world tasks. It addresses the integration problem of AI by creating and organizing prompts for various tasks. Users can create, collect, and organize AI solutions in a single place for use in their favorite tools. Fabric also serves as a command-line interface for those focused on the terminal. It offers a wide range of features and capabilities, including support for multiple AI providers, internationalization, speech-to-text, AI reasoning, model management, web search, text-to-speech, desktop notifications, and more. The project aims to help humans flourish by leveraging AI technology to solve human problems and enhance creativity.

blinkid-ios
BlinkID iOS is a mobile SDK that enables developers to easily integrate ID scanning and data extraction capabilities into their iOS applications. The SDK supports scanning and processing various types of identity documents, such as passports, driver's licenses, and ID cards. It provides accurate and fast data extraction, including personal information and document details. With BlinkID iOS, developers can enhance their apps with secure and reliable ID verification functionality, improving user experience and streamlining identity verification processes.
For similar tasks

ComfyUI
ComfyUI is a powerful and modular visual AI engine and application that allows users to design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. It provides a user-friendly environment for creating complex Stable Diffusion workflows without the need for coding. ComfyUI supports various models for image editing, video processing, audio manipulation, 3D modeling, and more. It offers features like smart memory management, support for different GPU types, loading and saving workflows as JSON files, and offline functionality. Users can also use API nodes to access paid models from external providers through the online Comfy API.
For similar jobs

sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.