Open Autonomy is a framework for creating agent services that run as a multi-agent-system and offer enhanced functionalities on-chain. It enables executing complex operations like machine-learning algorithms in a decentralized, trust-minimized, transparent, and robust manner.

Deepchecks is a holistic open-source solution for AI & ML validation needs, enabling thorough testing of data and models from research to production. It includes components for testing, CI & testing management, and monitoring. Users can install and use Deepchecks for testing and monitoring their AI models, with customizable checks and suites for tabular, NLP, and computer vision data. The tool provides visual reports, pythonic/json output for processing, and a dynamic UI for collaboration and monitoring. Deepchecks is open source, with premium features available under a commercial license for monitoring components.

The `any-llm` repository provides a unified API to access different LLM (Large Language Model) providers. It offers a simple and developer-friendly interface, leveraging official provider SDKs for compatibility and maintenance. The tool is framework-agnostic, actively maintained, and does not require a proxy or gateway server. It addresses challenges in API standardization and aims to provide a consistent interface for various LLM providers, overcoming limitations of existing solutions like LiteLLM, AISuite, and framework-specific integrations.

DeepChat is a versatile chat tool that supports multiple model cloud services and local model deployment. It offers multi-channel chat concurrency support, platform compatibility, complete Markdown rendering, and easy usability with a comprehensive guide. The tool aims to enhance chat experiences by leveraging various AI models and ensuring efficient conversation management.

PySpur is a graph-based editor designed for LLM (Large Language Models) workflows. It offers modular building blocks, node-level debugging, and performance evaluation. The tool is easy to hack, supports JSON configs for workflow graphs, and is lightweight with minimal dependencies. Users can quickly set up PySpur by cloning the repository, creating a .env file, starting docker services, and accessing the portal. PySpur can also work with local models served using Ollama, with steps provided for configuration. The roadmap includes features like canvas, async/batch execution, support for Ollama, new nodes, pipeline optimization, templates, code compilation, multimodal support, and more.

GPTSwarm is a graph-based framework for LLM-based agents that enables the creation of LLM-based agents from graphs and facilitates the customized and automatic self-organization of agent swarms with self-improvement capabilities. The library includes components for domain-specific operations, graph-related functions, LLM backend selection, memory management, and optimization algorithms to enhance agent performance and swarm efficiency. Users can quickly run predefined swarms or utilize tools like the file analyzer. GPTSwarm supports local LM inference via LM Studio, allowing users to run with a local LLM model. The framework has been accepted by ICML2024 and offers advanced features for experimentation and customization.

verl is a flexible and efficient RL training library for large language models (LLMs). It offers easy extension of diverse RL algorithms, seamless integration with existing LLM infra, flexible device mapping, and integration with popular Hugging Face models. The library provides state-of-the-art throughput, efficient actor model resharding, and supports various RL algorithms like PPO, GRPO, and more. It also supports model-based and function-based rewards for tasks like math and coding, vision-language models, and multi-modal RL. verl is used for tasks like training large language models, reasoning tasks, reinforcement learning with diverse algorithms, and multi-modal RL.

ZenGuard AI enables AI developers to integrate production-level, low-code LLM (Large Language Model) guardrails into their generative AI applications effortlessly. With ZenGuard AI, ensure your application operates within trusted boundaries, is protected from prompt injections, and maintains user privacy without compromising on performance.

Roo Code is an AI-powered development tool that integrates with your code editor to help you generate code from natural language descriptions and specifications, refactor and debug existing code, write and update documentation, answer questions about your codebase, automate repetitive tasks, and utilize MCP servers. It offers different modes such as Code, Architect, Ask, Debug, and Custom Modes to adapt to various tasks and workflows. Roo Code provides tutorial and feature videos, documentation, a YouTube channel, a Discord server, a Reddit community, GitHub issues tracking, and a feature request platform. Users can set up and develop Roo Code locally by cloning the repository, installing dependencies, and running the extension in development mode or by automated/manual VSIX installation. The tool uses changesets for versioning and publishing. Please note that Roo Code, Inc. does not make any representations or warranties regarding the tools provided, and users assume all risks associated with their use.

SkyPilot is a framework for running LLMs, AI, and batch jobs on any cloud, offering maximum cost savings, highest GPU availability, and managed execution. SkyPilot abstracts away cloud infra burdens: - Launch jobs & clusters on any cloud - Easy scale-out: queue and run many jobs, automatically managed - Easy access to object stores (S3, GCS, R2) SkyPilot maximizes GPU availability for your jobs: * Provision in all zones/regions/clouds you have access to (the _Sky_), with automatic failover SkyPilot cuts your cloud costs: * Managed Spot: 3-6x cost savings using spot VMs, with auto-recovery from preemptions * Optimizer: 2x cost savings by auto-picking the cheapest VM/zone/region/cloud * Autostop: hands-free cleanup of idle clusters SkyPilot supports your existing GPU, TPU, and CPU workloads, with no code changes.

AgC is an open-core platform designed for deploying, running, and orchestrating AI agents at scale. It treats agents as first-class compute units, providing a modular, observable, cloud-neutral, and production-ready environment. Open Agentic Compute empowers developers and organizations to run agents like cloud-native workloads without lock-in.

Local Deep Research is a powerful AI-powered research assistant that performs deep, iterative analysis using multiple LLMs and web searches. It can be run locally for privacy or configured to use cloud-based LLMs for enhanced capabilities. The tool offers advanced research capabilities, flexible LLM support, rich output options, privacy-focused operation, enhanced search integration, and academic & scientific integration. It also provides a web interface, command line interface, and supports multiple LLM providers and search engines. Users can configure AI models, search engines, and research parameters for customized research experiences.

Promptfoo is a tool for testing and evaluating LLM output quality. With promptfoo, you can build reliable prompts, models, and RAGs with benchmarks specific to your use-case, speed up evaluations with caching, concurrency, and live reloading, score outputs automatically by defining metrics, use as a CLI, library, or in CI/CD, and use OpenAI, Anthropic, Azure, Google, HuggingFace, open-source models like Llama, or integrate custom API providers for any LLM API.

RepoMaster is an AI agent that leverages GitHub repositories to solve complex real-world tasks. It transforms how coding tasks are solved by automatically finding the right GitHub tools and making them work together seamlessly. Users can describe their tasks, and RepoMaster's AI analysis leads to auto discovery and smart execution, resulting in perfect outcomes. The tool provides a web interface for beginners and a command-line interface for advanced users, along with specialized agents for deep search, general assistance, and repository tasks.

Tingly Box is a tool that helps in deciding which model to call, compressing context, and routing requests efficiently. It offers secure, reliable, and customizable functional extensions. With features like unified API, smart routing, context compression, auto API translation, blazing fast performance, flexible authentication, visual control panel, and client-side usage stats, Tingly Box provides a comprehensive solution for managing AI models and tokens. It supports integration with various IDEs, CLI tools, SDKs, and AI applications, making it versatile and easy to use. The tool also allows seamless integration with OAuth providers like Claude Code, enabling users to utilize existing quotas in OpenAI-compatible tools. Tingly Box aims to simplify AI model management and usage by providing a single endpoint for multiple providers with minimal configuration, promoting seamless integration with SDKs and CLI tools.

OpenHealth is an AI health assistant that helps users manage their health data by leveraging AI and personal health information. It allows users to consolidate health data, parse it smartly, and engage in contextual conversations with GPT-powered AI. The tool supports various data sources like blood test results, health checkup data, personal physical information, family history, and symptoms. OpenHealth aims to empower users to take control of their health by combining data and intelligence for actionable health management.

Open Autonomy is a framework for creating agent services that run as a multi-agent-system and offer enhanced functionalities on-chain. It enables executing complex operations like machine-learning algorithms in a decentralized, trust-minimized, transparent, and robust manner.

LLM-Subtrans is an open source subtitle translator that utilizes LLMs as a translation service. It supports translating subtitles between any language pairs supported by the language model. The application offers multiple subtitle formats support through a pluggable system, including .srt, .ssa/.ass, and .vtt files. Users can choose to use the packaged release for easy usage or install from source for more control over the setup. The tool requires an active internet connection as subtitles are sent to translation service providers' servers for translation.

PaperBanana is an automated academic illustration tool designed for AI scientists. It implements an agentic framework for generating publication-quality academic diagrams and statistical plots from text descriptions. The tool utilizes a two-phase multi-agent pipeline with iterative refinement, Gemini-based VLM planning, and image generation. It offers a CLI, Python API, and MCP server for IDE integration, along with Claude Code skills for generating diagrams, plots, and evaluating diagrams. PaperBanana is not affiliated with or endorsed by the original authors or Google Research, and it may differ from the original system described in the paper.

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.

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.

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.

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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 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 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 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.