Search Results for "open source" - Page 68
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Showing 16659 open source projects for "open source"
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ADX MCP Server
A Model Context Protocol (MCP) server that enables AI assistants
The Azure Data Explorer MCP Server is a Model Context Protocol (MCP) server that enables AI assistants to query and analyze Azure Data Explorer databases through standardized interfaces. It allows the execution of Kusto Query Language (KQL) queries and exploration of data within Azure Data Explorer clusters. -
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Dataherald
Interact with your SQL database, Natural Language to SQL using LLMs
Dataherald is a platform that allows users to query structured databases using natural language, automatically converting plain English into SQL. It is designed to enable real-time, self-service analytics without needing technical knowledge of databases, making business data easily accessible to non-technical users. Dataherald focuses on speed, accuracy, and scalability for enterprise settings. -
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LazyLLM
Easiest and laziest way for building multi-agent LLMs applications
LazyLLM is an optimized, lightweight LLM server designed for easy and fast deployment of large language models. It is fully compatible with the OpenAI API specification, enabling developers to integrate their own models into applications that normally rely on OpenAI’s endpoints. LazyLLM emphasizes low resource usage and fast inference while supporting multiple models. -
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Liger Kernel
Efficient Triton Kernels for LLM Training
Liger Kernel is a unified kernel developed by LinkedIn to streamline data science and machine learning workflows across different languages and tools. It provides a consistent interface for running code in various languages (such as Python, R, SQL) within a single Jupyter-like environment, enhancing productivity and collaboration for data scientists working in mixed-language projects. -
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Mctx
Monte Carlo tree search in JAX
mctx is a Monte Carlo Tree Search (MCTS) library developed by Google DeepMind for reinforcement learning research. It enables efficient and flexible implementation of MCTS algorithms, including those used in AlphaZero and MuZero. -
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LiteMultiAgent
The Library for LLM-based multi-agent applications
LiteMultiAgent is a lightweight and extensible multi-agent reinforcement learning (MARL) platform designed for rapid experimentation. It allows researchers to design and test coordination, competition, and collaboration scenarios in simulated environments. -
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Zeta
Build high-performance AI models with modular building blocks
zeta is a deep learning library focused on providing cutting-edge AI and neural network models with a strong emphasis on research-grade architectures. It includes state-of-the-art implementations for rapid experimentation and model building. -
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DictDataBase
A python NoSQL dictionary database, with concurrent access and ACID
DictDataBase (DictDB) is a lightweight, Python-based in-memory database that uses dictionaries as its primary data structure. It provides a simple and efficient way to store, retrieve, and manipulate data without requiring an external database server. DictDB is useful for applications needing fast lookups, temporary storage, or embedded database functionalities. -
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SetFit
Efficient few-shot learning with Sentence Transformers
SetFit is an efficient and prompt-free framework for few-shot fine-tuning of Sentence Transformers. It achieves high accuracy with little labeled data - for instance, with only 8 labeled examples per class on the Customer Reviews sentiment dataset, SetFit is competitive with fine-tuning RoBERTa Large on the full training set of 3k examples. -
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MegaParse
File Parser optimised for LLM Ingestion with no loss
MegaParse is a file parser optimized for Large Language Model (LLM) ingestion, ensuring no loss of information. It efficiently parses various document formats, such as PDFs, DOCX, and PPTX, converting them into formats ideal for processing by LLMs. This tool is essential for applications that require accurate and comprehensive data extraction from diverse document types. -
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CloudEvents
CloudEvents Specification
Events are everywhere. However, event producers tend to describe events differently. The lack of a common way of describing events means developers must constantly re-learn how to consume events. This also limits the potential for libraries, tooling and infrastructure to aide the delivery of event data across environments, like SDKs, event routers or tracing systems. The portability and productivity we can achieve from event data is hindered overall. CloudEvents is a specification for... -
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DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that run locally on your machine for evaluation.
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Backtrack Sampler
An easy-to-understand framework for LLM samplers
Backtrack Sampler is a framework designed for experimenting with custom sampling strategies for language models (LLMs), enabling the ability to rewind and revise generated tokens. It allows developers to create and test their own token generation strategies by providing a base structure for manipulating logits and probabilities, making it a flexible tool for those interested in fine-tuning the behavior of LLMs. -
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GPT All Star
AI-powered code generation tool for scratch development of web apps
AI-powered code generation tool for scratch development of web applications with a team collaboration of autonomous AI agents. This is a research project, and its primary value is to explore the possibility of autonomous AI agents. -
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AgentOps
Python SDK for agent monitoring, LLM cost tracking, benchmarking, etc.
Industry-leading developer platform to test and debug AI agents. We built the tools so you don't have to. Visually track events such as LLM calls, tools, and multi-agent interactions. Rewind and replay agent runs with point-in-time precision. Keep a full data trail of logs, errors, and prompt injection attacks from prototype to production. Native integrations with the top agent frameworks. -
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PyKEEN
A Python library for learning and evaluating knowledge graph embedding
PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-modal information). PyKEEN is a Python package for reproducible, facile knowledge graph embeddings. PyKEEN has a function pykeen.env() that magically prints relevant version information about PyTorch, CUDA, and your operating system that can be used for debugging. If you’re in a Jupyter Notebook, it will be pretty-printed as an HTML table. -
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Semantic Router
Superfast AI decision making and processing of multi-modal data
Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow, unreliable LLM generations to make tool-use or safety decisions, we use the magic of semantic vector space — routing our requests using semantic meaning. Combining LLMs with deterministic rules means we can be confident that our AI systems behave as intended. Cramming agent tools into the limited context window is expensive, slow, and fundamentally limited. Semantic Router enables... -
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Elyra
Elyra extends JupyterLab with an AI centric approach
Elyra is a set of AI-centric extensions to JupyterLab Notebooks. The Elyra Getting Started Guide includes more details on these features. A version-specific summary of new features is located on the releases page. -
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TorchRL
A modular, primitive-first, python-first PyTorch library
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. TorchRL provides PyTorch and python-first, low and high-level abstractions for RL that are intended to be efficient, modular, documented, and properly tested. The code is aimed at supporting research in RL. Most of it is written in Python in a highly modular way, such that researchers can easily swap components, transform them, or write new ones with little effort. -
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exo
Run your own AI cluster at home with everyday devices
Run your own AI cluster at home with everyday devices. Maintained by exo labs. Forget expensive NVIDIA GPUs, unify your existing devices into one powerful GPU, iPhone, iPad, Android, Mac, Linux, or pretty much any device. Now the default models, run 8B, 70B, and 405B parameter models on your own devices. -
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Hera
Hera is an Argo Python SDK
Hera is an Argo Python SDK. Hera aims to make the construction and submission of various Argo Project resources easy and accessible to everyone! Hera abstracts away low-level setup details while still maintaining a consistent vocabulary with Argo. -
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parallel-ssh
Asynchronous parallel SSH client library.
parallel-ssh is an asynchronous parallel SSH library designed for large-scale automation. It differentiates itself from alternatives, other libraries and higher-level frameworks like Ansible or Chef in several ways. -
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Metarget
Framework for automatic construction of vulnerable infrastructures
...In the field of cloud-native security, thanks to the complexity of cloud-native systems, this issue is more terrible. There are already some excellent security projects like Vulhub, and VulApps in the open-source community, which pack vulnerable scenes into container images so that researchers could utilize them and deploy scenes quickly. Hence, we develop Metarget and hope to solve the deployment issue above to some extent. Furthermore, we also expect that Metarget could help to construct multilayer vulnerable cloud native scenes automatically. -
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pydna
Clone with Python! Data structures for double stranded DNA
Clone with Python! Data structures for double stranded DNA & simulation of homologous recombination, Gibson assembly, cut & paste cloning. Planning genetic constructs with many parts and assembly steps, such as recombinant metabolic pathways, are often difficult to properly document as is evident from the poor state of documentation in the scientific literature. The pydna python package provide a human-readable formal description of cloning and genetic assembly strategies in Python which... -
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GemGIS
Spatial data processing for geomodeling
GemGIS is a Python-based, open-source geographic information processing library. It is capable of preprocessing spatial data such as vector data (shape files, geojson files, geopackages,…), raster data (tif, png,…), data obtained from online services (WCS, WMS, WFS) or XML/KML files (soon). Preprocessed data can be stored in a dedicated Data Class to be passed to the geomodeling package GemPy in order to accelerate the model-building process.
