kodus-ai

Kodus AI is an open-source AI agent designed to review code like a real teammate, providing personalized, context-aware code reviews to help teams catch bugs, enforce best practices, and maintain a clean codebase. It seamlessly integrates with Git workflows, learns team coding patterns, and offers custom review policies. Kodus supports all programming languages with semantic and AST analysis, enhancing code review accuracy and providing actionable feedback. The tool is available in Cloud and Self-Hosted editions, offering features like self-hosting, unlimited users, custom integrations, and advanced compliance support.

CAMEL is an open-source library designed for the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.

AgentQL is a suite of tools for extracting data and automating workflows on live web sites featuring an AI-powered query language, Python and JavaScript SDKs, a browser-based debugger, and a REST API endpoint. It uses natural language queries to pinpoint data and elements on any web page, including authenticated and dynamically generated content. Users can define structured data output and apply transforms within queries. AgentQL's natural language selectors find elements intuitively based on the content of the web page and work across similar web sites, self-healing as UI changes over time.

This project is a c++ version of the AI reasoning library that supports the reasoning of tensorrt models. It provides accelerated deployment cases of deep learning CV popular models and supports dynamic-batch image processing, inference, decode, and NMS. The project has been updated with various models and provides tutorials for model exports. It also includes a producer-consumer inference model for specific tasks. The project directory includes implementations for model inference applications, backend reasoning classes, post-processing, pre-processing, and target detection and tracking. Speed tests have been conducted on various models, and onnx downloads are available for different models.

AutoDev is an AI-powered coding wizard that supports multiple languages, including Java, Kotlin, JavaScript/TypeScript, Rust, Python, Golang, C/C++/OC, and more. It offers a range of features, including auto development mode, copilot mode, chat with AI, customization options, SDLC support, custom AI agent integration, and language features such as language support, extensions, and a DevIns language for AI agent development. AutoDev is designed to assist developers with tasks such as auto code generation, bug detection, code explanation, exception tracing, commit message generation, code review content generation, smart refactoring, Dockerfile generation, CI/CD config file generation, and custom shell/command generation. It also provides a built-in LLM fine-tune model and supports UnitEval for LLM result evaluation and UnitGen for code-LLM fine-tune data generation.

Groma is a grounded multimodal assistant that excels in region understanding and visual grounding. It can process user-defined region inputs and generate contextually grounded long-form responses. The tool presents a unique paradigm for multimodal large language models, focusing on visual tokenization for localization. Groma achieves state-of-the-art performance in referring expression comprehension benchmarks. The tool provides pretrained model weights and instructions for data preparation, training, inference, and evaluation. Users can customize training by starting from intermediate checkpoints. Groma is designed to handle tasks related to detection pretraining, alignment pretraining, instruction finetuning, instruction following, and more.

DinoPal is an AI voice assistant residing in the Mac menu bar, offering real-time voice and video chat, screen sharing, online search, and multilingual support. It provides various AI assistants with unique strengths and characteristics to meet different conversational needs. Users can easily install DinoPal and access different communication modes, with a call time limit of 30 minutes. User feedback can be shared in the Discord community. DinoPal is powered by Google Gemini & Pipecat.

FFAIVideo is a lightweight node.js project that utilizes popular AI LLM to intelligently generate short videos. It supports multiple AI LLM models such as OpenAI, Moonshot, Azure, g4f, Google Gemini, etc. Users can input text to automatically synthesize exciting video content with subtitles, background music, and customizable settings. The project integrates Microsoft Edge's online text-to-speech service for voice options and uses Pexels website for video resources. Installation of FFmpeg is essential for smooth operation. Inspired by MoneyPrinterTurbo, MoneyPrinter, and MsEdgeTTS, FFAIVideo is designed for front-end developers with minimal dependencies and simple usage.

LLaVA-MORE is a new family of Multimodal Language Models (MLLMs) that integrates recent language models with diverse visual backbones. The repository provides a unified training protocol for fair comparisons across all architectures and releases training code and scripts for distributed training. It aims to enhance Multimodal LLM performance and offers various models for different tasks. Users can explore different visual backbones like SigLIP and methods for managing image resolutions (S2) to improve the connection between images and language. The repository is a starting point for expanding the study of Multimodal LLMs and enhancing new features in the field.

Findto is a decentralized search tool for the Web and AI that puts people in control of algorithms. It aims to provide a better search experience by offering diverse sources, privacy and carbon level information, trends exploration, autosuggest, voice search, and more. Findto encourages a free search experience and promotes a healthier internet by empowering users with democratic choices.

Comet Opik is a repository containing two main services: a frontend and a backend. It provides a Python SDK for easy installation. Users can run the full application locally with minikube, following specific installation prerequisites. The repository structure includes directories for applications like Opik backend, with detailed instructions available in the README files. Users can manage the installation using simple k8s commands and interact with the application via URLs for checking the running application and API documentation. The repository aims to facilitate local development and testing of Opik using Kubernetes technology.

SimAI is the industry's first full-stack, high-precision simulator for AI large-scale training. It provides detailed modeling and simulation of the entire LLM training process, encompassing framework, collective communication, network layers, and more. This comprehensive approach offers end-to-end performance data, enabling researchers to analyze training process details, evaluate time consumption of AI tasks under specific conditions, and assess performance gains from various algorithmic optimizations.

BeeAI Framework is a versatile tool for building production-ready multi-agent systems. It offers flexibility in orchestrating agents, seamless integration with various models and tools, and production-grade controls for scaling. The framework supports Python and TypeScript libraries, enabling users to implement simple to complex multi-agent patterns, connect with AI services, and optimize token usage and resource management.

E2B Cookbook provides example code and guides for building with E2B. E2B is a platform that allows developers to build custom code interpreters in their AI apps. It provides a dedicated SDK for building custom code interpreters, as well as a core SDK that can be used to build on top of E2B. E2B also provides documentation at e2b.dev/docs.

The Portkey Python SDK is a control panel for AI apps that allows seamless integration of Portkey's advanced features with OpenAI methods. It provides features such as AI gateway for unified API signature, interoperability, automated fallbacks & retries, load balancing, semantic caching, virtual keys, request timeouts, observability with logging, requests tracing, custom metadata, feedback collection, and analytics. Users can make requests to OpenAI using Portkey SDK and also use async functionality. The SDK is compatible with OpenAI SDK methods and offers Portkey-specific methods like feedback and prompts. It supports various providers and encourages contributions through Github issues or direct contact via email or Discord.

Telco-AIX is a collaborative experimental workspace dedicated to exploring data-driven decision-making use-cases using open source AI capabilities and open datasets. The repository focuses on projects related to revenue assurance, fraud management, service assurance, latency predictions, 5G network operations, sustainability, energy efficiency, SecOps-AI for networking, AI-powered SmartGrid, IoT perimeter security, anomaly detection, root cause analysis, customer relationship management voice app, Starlink quality of experience predictions, and NoC AI augmentation for OSS.

github-pr-summary is a bot designed to summarize GitHub Pull Requests, helping open source contributors make faster decisions. It automatically summarizes commits and changed files in PRs, triggered by new commits or a magic trigger phrase. Users can deploy their own code review bot in 3 steps: create a bot from their GitHub repo, configure it to review PRs, and connect to GitHub for access to the target repo. The bot runs on flows.network using Rust and WasmEdge Runtimes. It utilizes ChatGPT/4 to review and summarize PR content, posting the result back as a comment on the PR. The bot can be used on multiple repos by creating new flows and importing the source code repo, specifying the target repo using flow config. Users can also change the magic phrase to trigger a review from a PR comment.

A project that utilizes large models to help with Code Review on Gitlab, aimed at improving development efficiency. The project is customized for Gitlab and is developing a Multi-Agent plugin for collaborative review. It integrates various large models for code security issues and stays updated with the latest Code Review trends. The project architecture is designed to be powerful, flexible, and efficient, with easy integration of different models and high customization for developers.

Kodus AI is an open-source AI agent designed to review code like a real teammate, providing personalized, context-aware code reviews to help teams catch bugs, enforce best practices, and maintain a clean codebase. It seamlessly integrates with Git workflows, learns team coding patterns, and offers custom review policies. Kodus supports all programming languages with semantic and AST analysis, enhancing code review accuracy and providing actionable feedback. The tool is available in Cloud and Self-Hosted editions, offering features like self-hosting, unlimited users, custom integrations, and advanced compliance support.

Airflint is a tool designed to enforce best practices for all your Airflow Directed Acyclic Graphs (DAGs). It is currently in the alpha stage and aims to help users adhere to recommended practices when working with Airflow. Users can install Airflint from PyPI and integrate it into their existing Airflow environment to improve DAG quality. The tool provides rules for function-level imports and jinja template syntax usage, among others, to enhance the development process of Airflow DAGs.

Code Review GPT uses Large Language Models to review code in your CI/CD pipeline. It helps streamline the code review process by providing feedback on code that may have issues or areas for improvement. It should pick up on common issues such as exposed secrets, slow or inefficient code, and unreadable code. It can also be run locally in your command line to review staged files. Code Review GPT is in alpha and should be used for fun only. It may provide useful feedback but please check any suggestions thoroughly.

Digma is a Continuous Feedback platform that provides code-level insights related to performance, errors, and usage during development. It empowers developers to own their code all the way to production, improving code quality and preventing critical issues. Digma integrates with OpenTelemetry traces and metrics to generate insights in the IDE, helping developers analyze code scalability, bottlenecks, errors, and usage patterns.

AI Code Reviewer is a GitHub Action that utilizes OpenAI's GPT-4 API to provide intelligent feedback and suggestions on pull requests. It helps enhance code quality and streamline the code review process by offering insightful comments and filtering out specified files. The tool is easy to set up and integrate into GitHub workflows.

Sourcery is an automated code reviewer tool that provides instant feedback on pull requests, helping to speed up the code review process, improve code quality, and accelerate development velocity. It offers high-level feedback, line-by-line suggestions, and aims to mimic the type of code review one would expect from a colleague. Sourcery can also be used as an IDE coding assistant to understand existing code, add unit tests, optimize code, and improve code quality with instant suggestions. It is free for public repos/open source projects and offers a 14-day trial for private repos.

Sourcegraph is a code search and navigation tool that helps developers read, write, and fix code in large, complex codebases. It provides features such as code search across all repositories and branches, code intelligence for navigation and refactoring, and the ability to fix and refactor code across multiple repositories at once.

PR-Agent is a tool that helps to efficiently review and handle pull requests by providing AI feedbacks and suggestions. It supports various commands such as generating PR descriptions, providing code suggestions, answering questions about the PR, and updating the CHANGELOG.md file. PR-Agent can be used via CLI, GitHub Action, GitHub App, Docker, and supports multiple git providers and models. It emphasizes real-life practical usage, with each tool having a single GPT-4 call for quick and affordable responses. The PR Compression strategy enables effective handling of both short and long PRs, while the JSON prompting strategy allows for modular and customizable tools. PR-Agent Pro, the hosted version by CodiumAI, provides additional benefits such as full management, improved privacy, priority support, and extra features.

Code Review GPT uses Large Language Models to review code in your CI/CD pipeline. It helps streamline the code review process by providing feedback on code that may have issues or areas for improvement. It should pick up on common issues such as exposed secrets, slow or inefficient code, and unreadable code. It can also be run locally in your command line to review staged files. Code Review GPT is in alpha and should be used for fun only. It may provide useful feedback but please check any suggestions thoroughly.

Devoxx Genie is a Java-based IntelliJ IDEA plugin that integrates with local and cloud-based LLM providers to aid in reviewing, testing, and explaining project code. It supports features like code highlighting, chat conversations, and adding files/code snippets to context. Users can modify REST endpoints and LLM parameters in settings, including support for cloud-based LLMs. The plugin requires IntelliJ version 2023.3.4 and JDK 17. Building and publishing the plugin is done using Gradle tasks. Users can select an LLM provider, choose code, and use commands like review, explain, or generate unit tests for code analysis.

code2prompt is a command-line tool that converts your codebase into a single LLM prompt with a source tree, prompt templating, and token counting. It automates generating LLM prompts from codebases of any size, customizing prompt generation with Handlebars templates, respecting .gitignore, filtering and excluding files using glob patterns, displaying token count, including Git diff output, copying prompt to clipboard, saving prompt to an output file, excluding files and folders, adding line numbers to source code blocks, and more. It helps streamline the process of creating LLM prompts for code analysis, generation, and other tasks.

AI Code Reviewer is a GitHub Action that utilizes OpenAI's GPT-4 API to provide intelligent feedback and suggestions on pull requests. It helps enhance code quality and streamline the code review process by offering insightful comments and filtering out specified files. The tool is easy to set up and integrate into GitHub workflows.

github-pr-summary is a bot designed to summarize GitHub Pull Requests, helping open source contributors make faster decisions. It automatically summarizes commits and changed files in PRs, triggered by new commits or a magic trigger phrase. Users can deploy their own code review bot in 3 steps: create a bot from their GitHub repo, configure it to review PRs, and connect to GitHub for access to the target repo. The bot runs on flows.network using Rust and WasmEdge Runtimes. It utilizes ChatGPT/4 to review and summarize PR content, posting the result back as a comment on the PR. The bot can be used on multiple repos by creating new flows and importing the source code repo, specifying the target repo using flow config. Users can also change the magic phrase to trigger a review from a PR comment.

Fitten Code AI Programming Assistant for Neovim provides fast completion using AI, asynchronous I/O, and support for various actions like document code, edit code, explain code, find bugs, generate unit test, implement features, optimize code, refactor code, start chat, and more. It offers features like accepting suggestions with Tab, accepting line with Ctrl + Down, accepting word with Ctrl + Right, undoing accepted text, automatic scrolling, and multiple HTTP/REST backends. It can run as a coc.nvim source or nvim-cmp source.