Codellm-devkit (CLDK) is a Python library that serves as a multilingual program analysis framework bridging traditional static analysis tools and Large Language Models (LLMs) specialized for code (CodeLLMs). It simplifies the process of analyzing codebases across multiple programming languages, enabling the extraction of meaningful insights and facilitating LLM-based code analysis. The library provides a unified interface for integrating outputs from various analysis tools and preparing them for effective use by CodeLLMs. Codellm-devkit aims to enable the development and experimentation of robust analysis pipelines that combine traditional program analysis tools and CodeLLMs, reducing friction in multi-language code analysis and ensuring compatibility across different tools and LLM platforms. It is designed to seamlessly integrate with popular analysis tools like WALA, Tree-sitter, LLVM, and CodeQL, acting as a crucial intermediary layer for efficient communication between these tools and CodeLLMs. The project is continuously evolving to include new tools and frameworks, maintaining its versatility for code analysis and LLM integration.

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.

Zshot is a highly customizable framework for performing Zero and Few shot named entity and relationships recognition. It can be used for mentions extraction, wikification, zero and few shot named entity recognition, zero and few shot named relationship recognition, and visualization of zero-shot NER and RE extraction. The framework consists of two main components: the mentions extractor and the linker. There are multiple mentions extractors and linkers available, each serving a specific purpose. Zshot also includes a relations extractor and a knowledge extractor for extracting relations among entities and performing entity classification. The tool requires Python 3.6+ and dependencies like spacy, torch, transformers, evaluate, and datasets for evaluation over datasets like OntoNotes. Optional dependencies include flair and blink for additional functionalities. Zshot provides examples, tutorials, and evaluation methods to assess the performance of the components.

cuVS is a library that contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.

LlamaIndex is a data framework for building LLM applications. It provides tools for ingesting, structuring, and querying data, as well as integrating with LLMs and other tools. LlamaIndex is designed to be easy to use for both beginner and advanced users, and it provides a comprehensive set of features for building LLM applications.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are: * `MLflow Tracking `_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects `_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models `_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry `_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

This repository contains an assistant proposal for the Semantic Kernel, allowing the usage of assistants without relying on OpenAI Assistant APIs. It runs locally planners and plugins for the assistants, providing scenarios like Assistant with Semantic Kernel plugins, Multi-Assistant conversation, and AutoGen conversation. The Semantic Kernel is a lightweight SDK enabling integration of AI Large Language Models with conventional programming languages, offering functions like semantic functions, native functions, and embeddings-based memory. Users can bring their own model for the assistants and host them locally. The repository includes installation instructions, usage examples, and information on creating new conversation threads with the assistant.

Nous is an open-source TypeScript platform for autonomous AI agents and LLM based workflows. It aims to automate processes, support requests, review code, assist with refactorings, and more. The platform supports various integrations, multiple LLMs/services, CLI and web interface, human-in-the-loop interactions, flexible deployment options, observability with OpenTelemetry tracing, and specific agents for code editing, software engineering, and code review. It offers advanced features like reasoning/planning, memory and function call history, hierarchical task decomposition, and control-loop function calling options. Nous is designed to be a flexible platform for the TypeScript community to expand and support different use cases and integrations.

Embodied Agents is a toolkit for integrating large multi-modal models into existing robot stacks with just a few lines of code. It provides consistency, reliability, scalability, and is configurable to any observation and action space. The toolkit is designed to reduce complexities involved in setting up inference endpoints, converting between different model formats, and collecting/storing datasets. It aims to facilitate data collection and sharing among roboticists by providing Python-first abstractions that are modular, extensible, and applicable to a wide range of tasks. The toolkit supports asynchronous and remote thread-safe agent execution for maximal responsiveness and scalability, and is compatible with various APIs like HuggingFace Spaces, Datasets, Gymnasium Spaces, Ollama, and OpenAI. It also offers automatic dataset recording and optional uploads to the HuggingFace hub.

RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with PostgreSQL or SQLite. It offers configurable options for choosing LLM providers, database types, and rerankers. The toolkit is fast and permissive, utilizing lightweight dependencies and hardware acceleration. RAGLite provides features like PDF to Markdown conversion, multi-vector chunk embedding, optimal semantic chunking, hybrid search capabilities, adaptive retrieval, and improved output quality. It is extensible with a built-in Model Context Protocol server, customizable ChatGPT-like frontend, document conversion to Markdown, and evaluation tools. Users can configure RAGLite for various tasks like configuring, inserting documents, running RAG pipelines, computing query adapters, evaluating performance, running MCP servers, and serving frontends.

GOLEM is an open-source AI framework focused on optimization and learning of structured graph-based models using meta-heuristic methods. It emphasizes the potential of meta-heuristics in complex problem spaces where gradient-based methods are not suitable, and the importance of structured models in various problem domains. The framework offers features like structured model optimization, metaheuristic methods, multi-objective optimization, constrained optimization, extensibility, interpretability, and reproducibility. It can be applied to optimization problems represented as directed graphs with defined fitness functions. GOLEM has applications in areas like AutoML, Bayesian network structure search, differential equation discovery, geometric design, and neural architecture search. The project structure includes packages for core functionalities, adapters, graph representation, optimizers, genetic algorithms, utilities, serialization, visualization, examples, and testing. Contributions are welcome, and the project is supported by ITMO University's Research Center Strong Artificial Intelligence in Industry.

LABO is a time series forecasting and analysis framework that integrates pre-trained and fine-tuned LLMs with multi-domain agent-based systems. It allows users to create and tune agents easily for various scenarios, such as stock market trend prediction and web public opinion analysis. LABO requires a specific runtime environment setup, including system requirements, Python environment, dependency installations, and configurations. Users can fine-tune their own models using LABO's Low-Rank Adaptation (LoRA) for computational efficiency and continuous model updates. Additionally, LABO provides a Python library for building model training pipelines and customizing agents for specific tasks.

chromem-go is an embeddable vector database for Go with a Chroma-like interface and zero third-party dependencies. It enables retrieval augmented generation (RAG) and similar embeddings-based features in Go apps without the need for a separate database. The focus is on simplicity and performance for common use cases, allowing querying of documents with minimal memory allocations. The project is in beta and may introduce breaking changes before v1.0.0.

CompressAI-Vision is a tool that helps you develop, test, and evaluate compression models with standardized tests in the context of compression methods optimized for machine tasks algorithms such as Neural-Network (NN)-based detectors. It currently focuses on two types of pipeline: Video compression for remote inference (`compressai-remote-inference`), which corresponds to the MPEG "Video Coding for Machines" (VCM) activity. Split inference (`compressai-split-inference`), which includes an evaluation framework for compressing intermediate features produced in the context of split models. The software supports all the pipelines considered in the related MPEG activity: "Feature Compression for Machines" (FCM).

Quantus is a toolkit designed for the evaluation of neural network explanations. It offers more than 30 metrics in 6 categories for eXplainable Artificial Intelligence (XAI) evaluation. The toolkit supports different data types (image, time-series, tabular, NLP) and models (PyTorch, TensorFlow). It provides built-in support for explanation methods like captum, tf-explain, and zennit. Quantus is under active development and aims to provide a comprehensive set of quantitative evaluation metrics for XAI methods.

Raga LLM Hub is a comprehensive evaluation toolkit for Language and Learning Models (LLMs) with over 100 meticulously designed metrics. It allows developers and organizations to evaluate and compare LLMs effectively, establishing guardrails for LLMs and Retrieval Augmented Generation (RAG) applications. The platform assesses aspects like Relevance & Understanding, Content Quality, Hallucination, Safety & Bias, Context Relevance, Guardrails, and Vulnerability scanning, along with Metric-Based Tests for quantitative analysis. It helps teams identify and fix issues throughout the LLM lifecycle, revolutionizing reliability and trustworthiness.

LLM4EDA is a repository dedicated to showcasing the emerging progress in utilizing Large Language Models for Electronic Design Automation. The repository includes resources, papers, and tools that leverage LLMs to solve problems in EDA. It covers a wide range of applications such as knowledge acquisition, code generation, code analysis, verification, and large circuit models. The goal is to provide a comprehensive understanding of how LLMs can revolutionize the EDA industry by offering innovative solutions and new interaction paradigms.

DeGPT is a tool designed to optimize decompiler output using Large Language Models (LLM). It requires manual installation of specific packages and setting up API key for OpenAI. The tool provides functionality to perform optimization on decompiler output by running specific scripts.

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.

SinkFinder + LLM is a closed-source semi-automatic vulnerability discovery tool that performs static code analysis on jar/war/zip files. It enhances the capability of LLM large models to verify path reachability and assess the trustworthiness score of the path based on the contextual code environment. Users can customize class and jar exclusions, depth of recursive search, and other parameters through command-line arguments. The tool generates rule.json configuration file after each run and requires configuration of the DASHSCOPE_API_KEY for LLM capabilities. The tool provides detailed logs on high-risk paths, LLM results, and other findings. Rules.json file contains sink rules for various vulnerability types with severity levels and corresponding sink methods.

OpenRepoWiki is a tool designed to automatically generate a comprehensive wiki page for any GitHub repository. It simplifies the process of understanding the purpose, functionality, and core components of a repository by analyzing its code structure, identifying key files and functions, and providing explanations. The tool aims to assist individuals who want to learn how to build various projects by providing a summarized overview of the repository's contents. OpenRepoWiki requires certain dependencies such as Google AI Studio or Deepseek API Key, PostgreSQL for storing repository information, Github API Key for accessing repository data, and Amazon S3 for optional usage. Users can configure the tool by setting up environment variables, installing dependencies, building the server, and running the application. It is recommended to consider the token usage and opt for cost-effective options when utilizing the tool.

CodebaseToPrompt is a simple tool that converts a local directory into a structured prompt for Large Language Models (LLMs). It allows users to select specific files for code review, analysis, or documentation by exploring and filtering through the file tree in a browser-based interface. The tool generates a formatted output that can be directly used with AI tools, provides token count estimates, and supports local storage for saving selections. Users can easily copy the selected files in the desired format for further use.

air is an R formatter and language server written in Rust. It is currently in alpha stage, so users should expect breaking changes in both the API and formatting results. The tool draws inspiration from various sources like roslyn, swift, rust-analyzer, prettier, biome, and ruff. It provides formatters and language servers, influenced by design decisions from these tools. Users can install air using standalone installers for macOS, Linux, and Windows, which automatically add air to the PATH. Developers can also install the dev version of the air CLI and VS Code extension for further customization and development.

Code-graph is a tool composed of FalkorDB Graph DB, Code-Graph-Backend, and Code-Graph-Frontend. It allows users to store and query graphs, manage backend logic, and interact with the website. Users can run the components locally by setting up environment variables and installing dependencies. The tool supports analyzing C & Python source files with plans to add support for more languages in the future. It provides a local repository analysis feature and a live demo accessible through a web browser.

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.

Codellm-devkit (CLDK) is a Python library that serves as a multilingual program analysis framework bridging traditional static analysis tools and Large Language Models (LLMs) specialized for code (CodeLLMs). It simplifies the process of analyzing codebases across multiple programming languages, enabling the extraction of meaningful insights and facilitating LLM-based code analysis. The library provides a unified interface for integrating outputs from various analysis tools and preparing them for effective use by CodeLLMs. Codellm-devkit aims to enable the development and experimentation of robust analysis pipelines that combine traditional program analysis tools and CodeLLMs, reducing friction in multi-language code analysis and ensuring compatibility across different tools and LLM platforms. It is designed to seamlessly integrate with popular analysis tools like WALA, Tree-sitter, LLVM, and CodeQL, acting as a crucial intermediary layer for efficient communication between these tools and CodeLLMs. The project is continuously evolving to include new tools and frameworks, maintaining its versatility for code analysis and LLM integration.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.