Brokk is a code assistant tool named after the Norse god of the forge. It is designed to understand code semantically, enabling LLMs to work effectively on large codebases. Users can sign up at Brokk.ai, install jbang, and follow instructions to run Brokk. The tool uses Gradle with Scala support and requires JDK 21 or newer for building. Brokk aims to enhance code comprehension and productivity by providing semantic understanding of code.

PrAIvateSearch is a NextJS web application that aims to implement similar features to SearchGPT in an open-source, local, and private way. It allows users to search the web using their own AI model. The application provides a user-friendly interface for interacting with the AI model and accessing search results. PrAIvateSearch is designed to be easy to install and use, with detailed instructions provided in the readme file. The project is in beta stage and welcomes contributions from the community to improve and enhance its functionality. Users are encouraged to support the project through funding to help it grow and continue to be maintained as an open-source tool under the MIT license.

PolyMind is a multimodal, function calling powered LLM webui designed for various tasks such as internet searching, image generation, port scanning, Wolfram Alpha integration, Python interpretation, and semantic search. It offers a plugin system for adding extra functions and supports different models and endpoints. The tool allows users to interact via function calling and provides features like image input, image generation, and text file search. The application's configuration is stored in a `config.json` file with options for backend selection, compatibility mode, IP address settings, API key, and enabled features.

HugeSCM is a cloud-based version control system designed to address R&D repository size issues. It effectively manages large repositories and individual large files by separating data storage and utilizing advanced algorithms and data structures. It aims for optimal performance in handling version control operations of large-scale repositories, making it suitable for single large library R&D, AI model development, and game or driver development.

The `unstructured` library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more. The use cases of `unstructured` revolve around streamlining and optimizing the data processing workflow for LLMs. `unstructured` modular functions and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and efficient in transforming unstructured data into structured outputs.

A Pythonic framework to simplify AI service building. The LeptonAI Python library allows you to build an AI service from Python code with ease. Key features include a Pythonic abstraction Photon, simple abstractions to launch models like those on HuggingFace, prebuilt examples for common models, AI tailored batteries, a client to automatically call your service like native Python functions, and Pythonic configuration specs to be readily shipped in a cloud environment.

Hal9 is a tool that allows users to create and deploy generative applications such as chatbots and APIs quickly. It is open, intuitive, scalable, and powerful, enabling users to use various models and libraries without the need to learn complex app frameworks. With a focus on AI tasks like RAG, fine-tuning, alignment, and training, Hal9 simplifies the development process by skipping engineering tasks like frontend development, backend integration, deployment, and operations.

FastTrackML is an experiment tracking server focused on speed and scalability, fully compatible with MLFlow. It provides a user-friendly interface to track and visualize your machine learning experiments, making it easy to compare different models and identify the best performing ones. FastTrackML is open source and can be easily installed and run with pip or Docker. It is also compatible with the MLFlow Python package, making it easy to integrate with your existing MLFlow workflows.

Redbox is a retrieval augmented generation (RAG) app that uses GenAI to chat with and summarise civil service documents. It increases organisational memory by indexing documents and can summarise reports read months ago, supplement them with current work, and produce a first draft that lets civil servants focus on what they do best. The project uses a microservice architecture with each microservice running in its own container defined by a Dockerfile. Dependencies are managed using Python Poetry. Contributions are welcome, and the project is licensed under the MIT License. Security measures are in place to ensure user data privacy and considerations are being made to make the core-api secure.

ScreenAgent is a project focused on creating an environment for Visual Language Model agents (VLM Agent) to interact with real computer screens. The project includes designing an automatic control process for agents to interact with the environment and complete multi-step tasks. It also involves building the ScreenAgent dataset, which collects screenshots and action sequences for various daily computer tasks. The project provides a controller client code, configuration files, and model training code to enable users to control a desktop with a large model.

CoML (formerly MLCopilot) is an interactive coding assistant for data scientists and machine learning developers, empowered on large language models. It offers an out-of-the-box interactive natural language programming interface for data mining and machine learning tasks, integration with Jupyter lab and Jupyter notebook, and a built-in large knowledge base of machine learning to enhance the ability to solve complex tasks. The tool is designed to assist users in coding tasks related to data analysis and machine learning using natural language commands within Jupyter environments.

Aisuite is a simple, unified interface to multiple Generative AI providers. It allows developers to easily interact with various Language Model (LLM) providers like OpenAI, Anthropic, Azure, Google, AWS, and more through a standardized interface. The library focuses on chat completions and provides a thin wrapper around python client libraries, enabling creators to test responses from different LLM providers without changing their code. Aisuite maximizes stability by using HTTP endpoints or SDKs for making calls to the providers. Users can install the base package or specific provider packages, set up API keys, and utilize the library to generate chat completion responses from different models.

LiveBench is a benchmark tool designed for Language Model Models (LLMs) with a focus on limiting contamination through monthly new questions based on recent datasets, arXiv papers, news articles, and IMDb movie synopses. It provides verifiable, objective ground-truth answers for accurate scoring without an LLM judge. The tool offers 18 diverse tasks across 6 categories and promises to release more challenging tasks over time. LiveBench is built on FastChat's llm_judge module and incorporates code from LiveCodeBench and IFEval.

OllamaKit is a Swift library designed to simplify interactions with the Ollama API. It handles network communication and data processing, offering an efficient interface for Swift applications to communicate with the Ollama API. The library is optimized for use within Ollamac, a macOS app for interacting with Ollama models.

2P-Kt is a Kotlin-based and multi-platform reboot of tuProlog (2P), a multi-paradigm logic programming framework written in Java. It consists of an open ecosystem for Symbolic Artificial Intelligence (AI) with modules supporting logic terms, unification, indexing, resolution of logic queries, probabilistic logic programming, binary decision diagrams, OR-concurrent resolution, DSL for logic programming, parsing modules, serialisation modules, command-line interface, and graphical user interface. The tool is designed to support knowledge representation and automatic reasoning through logic programming in an extensible and flexible way, encouraging extensions towards other symbolic AI systems than Prolog. It is a pure, multi-platform Kotlin project supporting JVM, JS, Android, and Native platforms, with a lightweight library leveraging the Kotlin common library.

Describer is a tool that analyzes codebases using AI to generate architectural overviews, documentation, explanations, bug reports, and more. It scans all files in a directory and uses Google's Gemini AI to provide insights such as markdown architectural overviews, codebase summaries, code pattern analysis, codebase structure documentation, bug identification, and test idea generation. The tool respects .gitignore rules by default but allows users to include/exclude specific files or patterns for analysis.

RepoAgent is an LLM-powered framework designed for repository-level code documentation generation. It automates the process of detecting changes in Git repositories, analyzing code structure through AST, identifying inter-object relationships, replacing Markdown content, and executing multi-threaded operations. The tool aims to assist developers in understanding and maintaining codebases by providing comprehensive documentation, ultimately improving efficiency and saving time.

Brokk is a code assistant tool named after the Norse god of the forge. It is designed to understand code semantically, enabling LLMs to work effectively on large codebases. Users can sign up at Brokk.ai, install jbang, and follow instructions to run Brokk. The tool uses Gradle with Scala support and requires JDK 21 or newer for building. Brokk aims to enhance code comprehension and productivity by providing semantic understanding of code.

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.

RTL-Coder is a tool designed to outperform GPT-3.5 in RTL code generation by providing a fully open-source dataset and a lightweight solution. It targets Verilog code generation and offers an automated flow to generate a large labeled dataset with over 27,000 diverse Verilog design problems and answers. The tool addresses the data availability challenge in IC design-related tasks and can be used for various applications beyond LLMs. The tool includes four RTL code generation models available on the HuggingFace platform, each with specific features and performance characteristics. Additionally, RTL-Coder introduces a new LLM training scheme based on code quality feedback to further enhance model performance and reduce GPU memory consumption.

Awesome LLM for APR is a repository dedicated to exploring the capabilities of Large Language Models (LLMs) in Automated Program Repair (APR). It provides a comprehensive collection of research papers, tools, and resources related to using LLMs for various scenarios such as repairing semantic bugs, security vulnerabilities, syntax errors, programming problems, static warnings, self-debugging, type errors, web UI tests, smart contracts, hardware bugs, performance bugs, API misuses, crash bugs, test case repairs, formal proofs, GitHub issues, code reviews, motion planners, human studies, and patch correctness assessments. The repository serves as a valuable reference for researchers and practitioners interested in leveraging LLMs for automated program repair.

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.