sdialog

SDialog is an MIT-licensed open-source toolkit for building, simulating, and evaluating LLM-based conversational agents end-to-end. It aims to bridge agent construction, user simulation, dialog generation, and evaluation in a single reproducible workflow, enabling the generation of reliable, controllable dialog systems or data at scale. The toolkit standardizes a Dialog schema, offers persona-driven multi-agent simulation with LLMs, provides composable orchestration for precise control over behavior and flow, includes built-in evaluation metrics, and offers mechanistic interpretability. It allows for easy creation of user-defined components and interoperability across various AI platforms.

ZenML is an extensible, open-source MLOps framework for creating portable, production-ready machine learning pipelines. By decoupling infrastructure from code, ZenML enables developers across your organization to collaborate more effectively as they develop to production.

Semantic Kernel is an SDK that integrates Large Language Models (LLMs) like OpenAI, Azure OpenAI, and Hugging Face with conventional programming languages like C#, Python, and Java. Semantic Kernel achieves this by allowing you to define plugins that can be chained together in just a few lines of code. What makes Semantic Kernel _special_ , however, is its ability to _automatically_ orchestrate plugins with AI. With Semantic Kernel planners, you can ask an LLM to generate a plan that achieves a user's unique goal. Afterwards, Semantic Kernel will execute the plan for the user.

LightAgent is a lightweight, open-source Agentic AI development framework with memory, tools, and a tree of thought. It supports multi-agent collaboration, autonomous learning, tool integration, complex task handling, and multi-model support. It also features a streaming API, tool generator, agent self-learning, adaptive tool mechanism, and more. LightAgent is designed for intelligent customer service, data analysis, automated tools, and educational assistance.

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.

The Unify Python Package provides access to the Unify REST API, allowing users to query Large Language Models (LLMs) from any Python 3.7.1+ application. It includes Synchronous and Asynchronous clients with Streaming responses support. Users can easily use any endpoint with a single key, route to the best endpoint for optimal throughput, cost, or latency, and customize prompts to interact with the models. The package also supports dynamic routing to automatically direct requests to the top-performing provider. Additionally, users can enable streaming responses and interact with the models asynchronously for handling multiple user requests simultaneously.

The LiveKit Agent Framework is designed for building real-time, programmable participants that run on servers. Easily tap into LiveKit WebRTC sessions and process or generate audio, video, and data streams. The framework includes plugins for common workflows, such as voice activity detection and speech-to-text. Agents integrates seamlessly with LiveKit server, offloading job queuing and scheduling responsibilities to it. This eliminates the need for additional queuing infrastructure. Agent code developed on your local machine can scale to support thousands of concurrent sessions when deployed to a server in production.

Swiftide is a fast, streaming indexing and query library tailored for Retrieval Augmented Generation (RAG) in AI applications. It is built in Rust, utilizing parallel, asynchronous streams for blazingly fast performance. With Swiftide, users can easily build AI applications from idea to production in just a few lines of code. The tool addresses frustrations around performance, stability, and ease of use encountered while working with Python-based tooling. It offers features like fast streaming indexing pipeline, experimental query pipeline, integrations with various platforms, loaders, transformers, chunkers, embedders, and more. Swiftide aims to provide a platform for data indexing and querying to advance the development of automated Large Language Model (LLM) applications.

The Bee Agent Framework is an open-source tool for building, deploying, and serving powerful agentic workflows at scale. It provides AI agents, tools for creating workflows in Javascript/Python, a code interpreter, memory optimization strategies, serialization for pausing/resuming workflows, traceability features, production-level control, and upcoming features like model-agnostic support and a chat UI. The framework offers various modules for agents, llms, memory, tools, caching, errors, adapters, logging, serialization, and more, with a roadmap including MLFlow integration, JSON support, structured outputs, chat client, base agent improvements, guardrails, and evaluation.

AgentFly is an extensible framework for building LLM agents with reinforcement learning. It supports multi-turn training by adapting traditional RL methods with token-level masking. It features a decorator-based interface for defining tools and reward functions, enabling seamless extension and ease of use. To support high-throughput training, it implemented asynchronous execution of tool calls and reward computations, and designed a centralized resource management system for scalable environment coordination. A suite of prebuilt tools and environments are provided.

Arbigent (Arbiter-Agent) is an AI agent testing framework designed to make AI agent testing practical for modern applications. It addresses challenges faced by traditional UI testing frameworks and AI agents by breaking down complex tasks into smaller, dependent scenarios. The framework is customizable for various AI providers, operating systems, and form factors, empowering users with extensive customization capabilities. Arbigent offers an intuitive UI for scenario creation and a powerful code interface for seamless test execution. It supports multiple form factors, optimizes UI for AI interaction, and is cost-effective by utilizing models like GPT-4o mini. With a flexible code interface and open-source nature, Arbigent aims to revolutionize AI agent testing in modern applications.

MInference is a tool designed to accelerate pre-filling for long-context Language Models (LLMs) by leveraging dynamic sparse attention. It achieves up to a 10x speedup for pre-filling on an A100 while maintaining accuracy. The tool supports various decoding LLMs, including LLaMA-style models and Phi models, and provides custom kernels for attention computation. MInference is useful for researchers and developers working with large-scale language models who aim to improve efficiency without compromising accuracy.

Multi-Agent Orchestrator is a flexible and powerful framework for managing multiple AI agents and handling complex conversations. It intelligently routes queries to the most suitable agent based on context and content, supports dual language implementation in Python and TypeScript, offers flexible agent responses, context management across agents, extensible architecture for customization, universal deployment options, and pre-built agents and classifiers. It is suitable for various applications, from simple chatbots to sophisticated AI systems, accommodating diverse requirements and scaling efficiently.

Agent Squad is a flexible, lightweight open-source framework for orchestrating multiple AI agents to handle complex conversations. It intelligently routes queries, maintains context across interactions, and offers pre-built components for quick deployment. The system allows easy integration of custom agents and conversation messages storage solutions, making it suitable for various applications from simple chatbots to sophisticated AI systems, scaling efficiently.

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.

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.

Awesome-LLM-Interpretability is a curated list of materials related to LLM (Large Language Models) interpretability, covering tutorials, code libraries, surveys, videos, papers, and blogs. It includes resources on transformer mechanistic interpretability, visualization, interventions, probing, fine-tuning, feature representation, learning dynamics, knowledge editing, hallucination detection, and redundancy analysis. The repository aims to provide a comprehensive overview of tools, techniques, and methods for understanding and interpreting the inner workings of large language models.

This project focuses on implementing Sparse Autoencoders (SAEs) for mechanistic interpretability in Large Language Models (LLMs) like Llama 3.2-3B. The SAEs aim to untangle superimposed representations in LLMs into separate, interpretable features for each neuron activation. The project provides an end-to-end pipeline for capturing training data, training the SAEs, analyzing learned features, and verifying results experimentally. It includes comprehensive logging, visualization, and checkpointing of SAE training, interpretability analysis tools, and a pure PyTorch implementation of Llama 3.1/3.2 chat and text completion. The project is designed for scalability, efficiency, and maintainability.

SDialog is an MIT-licensed open-source toolkit for building, simulating, and evaluating LLM-based conversational agents end-to-end. It aims to bridge agent construction, user simulation, dialog generation, and evaluation in a single reproducible workflow, enabling the generation of reliable, controllable dialog systems or data at scale. The toolkit standardizes a Dialog schema, offers persona-driven multi-agent simulation with LLMs, provides composable orchestration for precise control over behavior and flow, includes built-in evaluation metrics, and offers mechanistic interpretability. It allows for easy creation of user-defined components and interoperability across various AI platforms.

LitServe is a high-throughput serving engine designed for deploying AI models at scale. It generates an API endpoint for models, handles batching, streaming, and autoscaling across CPU/GPUs. LitServe is built for enterprise scale with a focus on minimal, hackable code-base without bloat. It supports various model types like LLMs, vision, time-series, and works with frameworks like PyTorch, JAX, Tensorflow, and more. The tool allows users to focus on model performance rather than serving boilerplate, providing full control and flexibility.

Lidar AI Solution is a highly optimized repository for self-driving 3D lidar, providing solutions for sparse convolution, BEVFusion, CenterPoint, OSD, and Conversion. It includes CUDA and TensorRT implementations for various tasks such as 3D sparse convolution, BEVFusion, CenterPoint, PointPillars, V2XFusion, cuOSD, cuPCL, and YUV to RGB conversion. The repository offers easy-to-use solutions, high accuracy, low memory usage, and quantization options for different tasks related to self-driving technology.

This repository showcases how to deploy generative AI models from Amazon SageMaker JumpStart using the AWS CDK. Generative AI is a type of AI that can create new content and ideas, such as conversations, stories, images, videos, and music. The repository provides a detailed guide on deploying image and text generative AI models, utilizing pre-trained models from SageMaker JumpStart. The web application is built on Streamlit and hosted on Amazon ECS with Fargate. It interacts with the SageMaker model endpoints through Lambda functions and Amazon API Gateway. The repository also includes instructions on setting up the AWS CDK application, deploying the stacks, using the models, and viewing the deployed resources on the AWS Management Console.

cake is a pure Rust implementation of the llama3 LLM distributed inference based on Candle. The project aims to enable running large models on consumer hardware clusters of iOS, macOS, Linux, and Windows devices by sharding transformer blocks. It allows running inferences on models that wouldn't fit in a single device's GPU memory by batching contiguous transformer blocks on the same worker to minimize latency. The tool provides a way to optimize memory and disk space by splitting the model into smaller bundles for workers, ensuring they only have the necessary data. cake supports various OS, architectures, and accelerations, with different statuses for each configuration.

Awesome-Robotics-3D is a curated list of 3D Vision papers related to Robotics domain, focusing on large models like LLMs/VLMs. It includes papers on Policy Learning, Pretraining, VLM and LLM, Representations, and Simulations, Datasets, and Benchmarks. The repository is maintained by Zubair Irshad and welcomes contributions and suggestions for adding papers. It serves as a valuable resource for researchers and practitioners in the field of Robotics and Computer Vision.

TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. It enables a data & learning flywheel for LLMs by unifying inference, observability, optimization, and experimentation. The platform includes a high-performance model gateway, structured schema-based inference, observability, experimentation, and data warehouse for analytics. TensorZero Recipes optimize prompts and models, and the platform supports experimentation features and GitOps orchestration for deployment.

This repository provides an easy-to-use solution for running inference servers on Slurm-managed computing clusters using vLLM. All scripts in this repository run natively on the Vector Institute cluster environment. Users can deploy models as Slurm jobs, check server status and performance metrics, and shut down models. The repository also supports launching custom models with specific configurations. Additionally, users can send inference requests and set up an SSH tunnel to run inference from a local device.

Rhesis is a comprehensive test management platform designed for Gen AI teams, offering tools to create, manage, and execute test cases for generative AI applications. It ensures the robustness, reliability, and compliance of AI systems through features like test set management, automated test generation, edge case discovery, compliance validation, integration capabilities, and performance tracking. The platform is open source, emphasizing community-driven development, transparency, extensible architecture, and democratizing AI safety. It includes components such as backend services, frontend applications, SDK for developers, worker services, chatbot applications, and Polyphemus for uncensored LLM service. Rhesis enables users to address challenges unique to testing generative AI applications, such as non-deterministic outputs, hallucinations, edge cases, ethical concerns, and compliance requirements.