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Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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PearAI
The Open Source AI-Powered Code Editor. A fork of VSCode and Continue
Ask questions or generate code with the context of your codebase for accurate results. This also works with choosing specific folders, online docs, terminal content, files, and more. PearAI can directly code in your files, and allow you to see diffs. Try CMD+I (CTRL+I on Windows). Here, we ask PearAI to help us add error handling and comments. Without writing a single line of code, we were able to make a new feature in an unfamiliar codebase: adding a documentation page to the PearAI landing... -
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AnythingLLM
The all-in-one Desktop & Docker AI application with full RAG and AI
...This application allows you to pick and choose which LLM or Vector Database you want to use as well as supporting multi-user management and permissions. AnythingLLM is a full-stack application where you can use commercial off-the-shelf LLMs or popular open-source LLMs and vectorDB solutions to build a private ChatGPT with no compromises that you can run locally as well as host remotely and be able to chat intelligently with any documents you provide it. AnythingLLM divides your documents into objects called workspaces. A Workspace functions a lot like a thread, but with the addition of containerization of your documents. ... -
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LlamaIndex
Central interface to connect your LLM's with external data
...These indices help to abstract away common boilerplate and pain points for in-context learning. Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when the context is too big. Offers you a comprehensive toolset, trading off cost and performance. -
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FastVLM
This repository contains the official implementation of FastVLM
FastVLM is an efficiency-focused vision-language modeling stack that introduces FastViTHD, a hybrid vision encoder engineered to emit fewer visual tokens and slash encoding time, especially for high-resolution images. Instead of elaborate pruning stages, the design trades off resolution and token count through input scaling, simplifying the pipeline while maintaining strong accuracy. Reported results highlight dramatic speedups in time-to-first-token and competitive quality versus contemporary open VLMs, including comparisons across small and larger variants. The repository documents model variants, showcases head-to-head numbers against known baselines, and explains how the encoder integrates with common LLM backbones. ... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
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LobeHub
Workspace to find, build, and collaborate with AI agents
LobeHub is an all-in-one workspace designed to help humans and AI agents collaborate, grow, and evolve together. It treats AI agents as true teammates rather than one-off tools, enabling deeper context, continuity, and productivity. Users can build personalized agent teams that understand their workflows, preferences, and goals over time. LobeHub brings multiple models, tools, and modalities into a single unified environment under the user’s control. With built-in collaboration features, agents can work in parallel, share context, and support complex projects seamlessly. ... -
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GPTel
A no-frills ChatGPT client for Emacs
...Also, it’s async. Interact with ChatGPT from any buffer in Emacs. ChatGPT’s responses are in Markdown or Org markup (configurable). Supports conversations (not just one-off queries) and multiple independent sessions. You can go back and edit your previous prompts, or even ChatGPT’s previous responses when continuing a conversation. These will be fed back to ChatGPT. Run M-x gptel to start or switch to the ChatGPT buffer. It will ask you for the key if you skipped the previous step. Run it with a prefix-arg to start a new session. ... -
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Perplexica
Perplexica is an AI-powered answering engine.
...Powered by SearxNG, it aggregates results from multiple search engines while keeping your identity and queries private. Perplexica offers multiple search modes—Speed, Balanced, and Quality—so you can trade off latency and depth depending on the task. It also enhances the experience with widgets and rich search types like images, videos, and domain-limited queries. With local search history, file uploads, and an easy Docker-based setup, it’s built for everyday research without sacrificing control or privacy. -
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Empirical
Test and evaluate LLMs and model configurations
Empirical is the fastest way to test different LLMs and model configurations, across all the scenarios that matter for your application. -
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RWKV Runner
A RWKV management and startup tool, full automation, only 8MB
...Default configs has enabled custom CUDA kernel acceleration, which is much faster and consumes much less VRAM. If you encounter possible compatibility issues, go to the Configs page and turn off Use Custom CUDA kernel to Accelerate. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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DI-engine
OpenDILab Decision AI Engine
DI-engine is a unified reinforcement learning (RL) platform for reproducible and scalable RL research. It offers modular pipelines for various RL algorithms, with an emphasis on production-level training and evaluation. -
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Open-AutoGLM
An open phone agent model & framework
...It aims to create an “AI phone agent” that can perceive on-screen content, reason about user goals, and execute sequences of taps, swipes, and text input via automated device control interfaces like ADB, enabling hands-off completion of multi-step tasks such as navigating apps, filling forms, and more. Unlike traditional automation scripts that depend on brittle heuristics, Open-AutoGLM uses pretrained large language and vision-language models to interpret visual context and natural language instructions, giving the agent robust adaptability across apps and interfaces. -
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DeepSeek Coder
DeepSeek Coder: Let the Code Write Itself
...This dataset covers project-level code structure (not just line-by-line snippets), using a large context window (e.g. 16K) and a secondary fill-in-the-blank objective to encourage better contextual completions and infilling. Multiple sizes of the model are offered (e.g. 1B, 5.7B, 6.7B, 33B) so users can trade off inference cost vs capability. The repo provides model weights, documentation on training setup, evaluation results on common benchmarks (HumanEval, MultiPL-E, APPS, etc.), and inference tools. -
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CodePilot
A native desktop GUI for Claude Code
...Created with Electron and Next.js, CodePilot delivers a polished experience where users can talk to Claude models, view syntax-highlighted responses, attach files, and inspect project context via a live file tree. It supports session management so chats and project work persist between restarts, letting users pick up where they left off without losing history. Unlike traditional CLI-only workflows, CodePilot brings panels, drag-to-resize layouts, and controls for tool permissions that make it feel like a modern desktop code assistant. It also includes project-aware context so Claude understands the specific codebase you’re working on, helping generate smarter suggestions and clearer explanations. -
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smolagents
Agents write python code to call tools and orchestrate other agents
This library is the simplest framework out there to build powerful agents. We provide our definition in this page, where you’ll also find tips for when to use them or not (spoilers: you’ll often be better off without agents). smolagents is a lightweight framework for building AI agents using large language models (LLMs). It simplifies the development of AI-driven applications by providing tools to create, train, and deploy language model-based agents. -
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MagicMirror²
Modular smart mirror platform with a list of installable modules
MagicMirror² is Open Source, free and maintained by a big group of enthusiasts. Got a nice idea? Send us a pull request and become a part of the big list of contributors. The core of MagicMirror² contains a strong API which allows 3rd party developers to build additional modules. Modules you can use. Modules you can develop. Read our extensive documentation to find out everything you want to know about the MagicMirror² project. The full API description allows you to build your own modules.... -
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Agently 4
Build GenAI application quick and easy
...It abstracts away boilerplate around model API calls, tool usage, prompt management, and workflow state. The project aims at production-grade GenAI application development rather than just one-off scripts — you’ll find examples of news gathering, agentic workflows, control systems, etc. It is licensed under Apache-2.0, allowing commercial use and modification. Because it's built in Python, it integrates easily with existing data pipelines, databases, and agent architectures. -
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MobileLLM
MobileLLM Optimizing Sub-billion Parameter Language Models
...The framework integrates several architectural innovations—SwiGLU activation, deep and thin network design, embedding sharing, and grouped-query attention (GQA)—to achieve a superior trade-off between model size, inference speed, and accuracy. MobileLLM demonstrates remarkable performance, with the 125M and 350M variants outperforming previous state-of-the-art models of the same scale by up to 4.3% on zero-shot commonsense reasoning tasks. -
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OpenAI Swarm
Educational framework exploring multi-agent orchestration
Swarm focuses on making agent coordination and execution lightweight, highly controllable, and easily testable. It accomplishes this through two primitive abstractions; Agents and handoffs. An Agent encompasses instructions and tools, and can at any point choose to hand off a conversation to another Agent. These primitives are powerful enough to express rich dynamics between tools and networks of agents, allowing you to build scalable, real-world solutions while avoiding a steep learning curve. Approaches similar to Swarm are best suited for situations dealing with a large number of independent capabilities and instructions. ... -
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Simple StyleGan2 for Pytorch
Simplest working implementation of Stylegan2
...You can also specify the location where intermediate results and model checkpoints should be stored. You can increase the network capacity (which defaults to 16) to improve generation results, at the cost of more memory. By default, if the training gets cut off, it will automatically resume from the last checkpointed file. Once you have finished training, you can generate images from your latest checkpoint. If a previous checkpoint contained a better generator, (which often happens as generators start degrading towards the end of training), you can load from a previous checkpoint with another flag. ... -
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BotKube
An app that helps you monitor your Kubernetes cluster
...BotKube watches Kubernetes resources and sends a notification to the channel if any event occurs for example a ImagePullBackOff error. You can customize the objects and level of events you want to get from the Kubernetes cluster. You can turn on/off notifications simply by sending a message to @BotKube. BotKube can execute kubectl commands on Kubernetes cluster without giving access to Kubeconfig or underlying infrastructure. With BotKube you can debug your deployment, services or anything about your cluster right from your messaging window. -
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TensorFlow Model Optimization Toolkit
A toolkit to optimize ML models for deployment for Keras & TensorFlow
The TensorFlow Model Optimization Toolkit is a suite of tools for optimizing ML models for deployment and execution. Among many uses, the toolkit supports techniques used to reduce latency and inference costs for cloud and edge devices (e.g. mobile, IoT). Deploy models to edge devices with restrictions on processing, memory, power consumption, network usage, and model storage space. Enable execution on and optimize for existing hardware or new special purpose accelerators. Choose the model... -
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Discord4J
Reactive library to enable quick and easy development of bots
...Discord4J breaks itself into modules to allow advanced users to interact with our API at lower levels to build minimal and fast runtimes or even add their own abstractions. Discord4J can be used to develop any bot, big or small. We offer many tools for developing large-scale bots from custom distribution frameworks, off-heap caching, and its interaction with Reactor allows complete integration. -
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The Agency
A complete AI agency at your fingertips
...Rather than providing generic prompts, the project organizes each agent as a structured expert profile with personality traits, mission, workflow, deliverables, examples, and success metrics so that each one feels more like a reusable operational role than a one-off instruction. The repository is built for people who want role-based AI collaboration, whether that means using the agents directly inside Claude Code, adapting them as references, or converting them for use in other agentic tools such as Cursor, Aider, Windsurf, Gemini CLI, and OpenCode. -
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Argilla
The open-source data curation platform for LLMs
...Argilla is free, open-source, and 100% compatible with major NLP libraries (Hugging Face transformers, spaCy, Stanford Stanza, Flair, etc.). In fact, you can use and combine your preferred libraries without implementing any specific interface. Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. Once a model goes into production, you want to monitor and analyze its predictions, and collect more data to improve your model over time. Argilla is designed to close this gap, enabling you to iterate as much as you need. -
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Protenix
A trainable PyTorch reproduction of AlphaFold 3
...Protenix provides a complete pipeline for turning protein sequences (with optional MSA / sequence alignment) or structural inputs (e.g. PDB/CIF) into full 3D atomic-level structure predictions. It supports both “full” models and lightweight variants such as “Protenix-Mini,” offering a trade-off between speed/compute cost and predictive accuracy — making structure prediction accessible even in resource-constrained environments. The project also includes support for constraints (e.g., specifying residue- or atom-level contact constraints, or pocket constraints) to guide predictions toward biologically or experimentally relevant conformations, which enhances its utility for tasks like modeling complexes, ligands, or antibody–antigen interactions.
