Search Results for "model-builder" - Page 42
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Showing 2224 open source projects for "model-builder"
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One API
The LLM API management & distribution system
One API is an open-source platform designed to unify access to multiple AI model providers through a single, consistent API interface. It acts as a gateway that allows developers to manage and route requests to different large language models and AI services without needing to integrate each provider separately. The system supports multiple backends, enabling users to switch between providers or balance usage based on cost, performance, or availability. -
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Monkey Code
Enterprise-grade AI programming assistant designed for R&D collab
Monkey Code is an enterprise-grade AI programming assistant designed to transform how development teams collaborate, build, and manage code across complex environments. It goes beyond traditional AI coding tools by combining intelligent code generation, conversational programming, and automated DevOps-style workflows into a unified platform that integrates directly with Git-based repositories. One of its defining characteristics is its support for private deployment and fully offline... -
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database.build
In-browser Postgres sandbox with AI assistance
...It eliminates the need for traditional database setup by running a lightweight version of PostgreSQL locally using PGlite, enabling developers to experiment, prototype, and analyze data without relying on external servers. Each database instance is paired with a large language model, allowing users to perform tasks such as generating schemas, importing CSV data, building diagrams, and creating reports using natural language interactions. The platform supports persistent storage through IndexedDB, ensuring that database changes remain available across sessions. It also provides tools for generating charts and visualizations, making it useful not only for developers but also for analysts and data-driven workflows. -
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TensorRT LLM
TensorRT LLM provides users with an easy-to-use Python API
TensorRT-LLM is an open-source high-performance inference library specifically designed to optimize and accelerate large language model deployment on NVIDIA GPUs. It provides a Python-based API built on top of PyTorch that allows developers to define, customize, and deploy LLMs efficiently across a variety of hardware configurations, from single GPUs to large multi-node clusters. The library focuses on maximizing throughput and minimizing latency through advanced techniques such as quantization, custom attention kernels, and optimized memory management strategies. ... -
Stop vibe-debugging.AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
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NVIDIA PhysicsNeMo
Open-source deep-learning framework for building and training
NVIDIA PhysicsNeMo is an open-source deep learning framework designed for building artificial intelligence models that incorporate physical laws and scientific knowledge into machine learning workflows. The framework focuses on the emerging field of physics-informed machine learning, where neural networks are used alongside physical equations to model complex scientific systems. PhysicsNeMo provides modular Python components that allow developers to create scalable training and inference pipelines for models that combine data-driven learning with physics-based constraints. It is built on top of the PyTorch ecosystem and integrates with GPU-accelerated computing environments to handle computationally demanding simulations and datasets. ... -
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Puck
Open source visual editor for building React drag-and-drop pages
...Puck integrates directly into existing React environments and works well with frameworks such as Next.js, enabling developers to embed editing capabilities directly inside their applications. It operates with a structured data model that stores content as a JSON payload, allowing the same configuration to power both the editing interface and the production rendering of pages. This approach gives developers full ownership of their content data while avoiding vendor lock-in. -
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ggml
Tensor library for machine learning
ggml is an open-source tensor library designed for efficient machine learning computation with a focus on running models locally and with minimal dependencies. Written primarily in C and C++, the library provides low-level tensor operations and automatic differentiation that allow developers to implement machine learning algorithms and neural networks efficiently. The project emphasizes portability and performance, enabling machine learning inference across a wide range of hardware... -
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LLM Vision
Visual intelligence for your home.
LLM Vision is an open-source integration for Home Assistant that adds multimodal large language model capabilities to smart home environments. The project enables Home Assistant to analyze images, video files, and live camera feeds using vision-capable AI models. Instead of relying only on traditional object detection pipelines, it allows users to send prompts about visual content and receive contextual descriptions or answers about what is happening in camera footage. -
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Neuron AI
The PHP Agentic Framework to build production-ready AI driven apps
...The framework provides an Agent class that can be extended to inherit core capabilities like memory, tools, function calling, and retrieval-augmented generation. Its design is modular, so developers can swap model providers with minimal changes to their application code, which makes it practical for teams that need flexibility across vendors. The project also supports structured output, monitoring, MCP connectivity, and workflow patterns that include human-in-the-loop intervention when automated flows need review or correction. -
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SageAttention
NeurIPS2025 Spotlight] Quantized Attention
...Since attention operations are often the most computationally expensive component of modern AI models, SageAttention introduces quantization techniques that significantly reduce computational overhead while preserving model accuracy. The system achieves this by using low-precision numerical formats such as INT4, FP8, or INT8 to represent key matrices within the attention computation. These optimizations allow models to perform matrix operations faster and consume less memory during inference. SageAttention is designed to function as a plug-and-play replacement for standard attention implementations, enabling developers to accelerate existing models without modifying their architecture. -
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MLX Engine
LM Studio Apple MLX engine
...Its design focuses on high-performance on-device inference, leveraging Apple’s MLX stack to accelerate computation on M-series chips. The project introduces modular VisionAddOn components that allow image embeddings to be integrated seamlessly into language model workflows. It is bundled with newer versions of LM Studio but can also be used independently for experimentation and development. Overall, mlx-engine serves as a specialized high-efficiency runtime for local AI workloads on macOS systems. -
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MCP Apps
Official repo for spec & SDK of MCP Apps protocol
MCP Apps is an extension ecosystem for a context-driven AI application protocol that lets developers build modular, interoperable apps that work together in shared model environments. The project defines standards, interfaces, and reusable components so different application modules can communicate context, state, and user intent reliably while preserving privacy and user control. Designed to work with large language models and agent systems, Ext-Apps lets developers create plugins that extend core behaviors — such as search integration, data retrieval, workflow automation, or domain-specific tools — without rewriting the host system. ... -
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Agentic Data Scientist
An end-to-end Data Scientist
Agentic Data Scientist is an experimental AI-driven research framework that orchestrates data science workflows through autonomous agents that can reason, plan, and execute complex analytics tasks. Unlike traditional scripted pipelines, this project lets AI agents break down high-level research goals into sub-tasks such as data acquisition, cleaning, modeling, evaluation, and reporting, with minimal human direction. Each agent is designed to independently call functions, interact with data... -
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SurfSense
Connect any LLM to your internal knowledge sources
SurfSense is an open-source AI research and knowledge assistant platform that connects any large language model to internal knowledge sources so teams and individuals can explore, query, and collaborate on insights in real time. Built as an alternative to proprietary tools like NotebookLM, Perplexity, and Glean, SurfSense allows integrations with a wide range of external data sources including Slack, Notion, Google Drive, GitHub, YouTube, and many enterprise systems, making it possible to interact with documents, chat logs, and structured data using natural language. ... -
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FlowGram
Extensible workflow development framework
FlowGram is an open-source, node-based workflow development framework and toolkit aimed at helping developers build custom AI-workflow platforms or automation systems through a visual, drag-and-drop interface. Instead of shipping as a ready-made product, it provides the building blocks — a canvas for wiring together nodes, a form engine for configuring node parameters, a variable-scope and type-inference engine, and a set of “materials” (pre-built node types such as code execution,... -
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Anthropic SDK TypeScript
Access to Anthropic's safety-first language model APIs
anthropic-sdk-typescript is the TypeScript / JavaScript client library for the Anthropic REST API, enabling backend or Node.js usage of models like Claude. It wraps API endpoints for creating messages, streaming responses, and managing parameters in a type-safe TS environment. The library is designed for server-side use, interfacing with REST, and is stable for integration in web services or backend agents. Example usage shows how to instantiate the Anthropic client, call... -
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HumanEval
Code for the paper "Evaluating Large Language Models Trained on Code"
...It consists of hand-written programming problems with unit tests, designed to assess functional correctness rather than superficial metrics like text similarity. Each task includes a natural language prompt and a function signature, requiring the model to generate an implementation that passes all provided tests. The benchmark has become a standard for evaluating code generation models, including those in the Codex and GPT families. Researchers can use the dataset to run reproducible comparisons across models and track improvements in functional code synthesis. By focusing on correctness through execution, human-eval provides a rigorous and practical way to evaluate programming capabilities in AI systems. -
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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. Whether your application is implemented via RAG or fine-tuning, LangChain, or LlamaIndex, DeepEval has you covered. ...
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UMAP
Uniform Manifold Approximation and Projection
Uniform Manifold Approximation and Projection (UMAP) is a dimension reduction technique that can be used for visualization similarly to t-SNE, but also for general non-linear dimension reduction. It is possible to model the manifold with a fuzzy topological structure. The embedding is found by searching for a low-dimensional projection of the data that has the closest possible equivalent fuzzy topological structure. First of all UMAP is fast. It can handle large datasets and high dimensional data without too much difficulty, scaling beyond what most t-SNE packages can manage. ... -
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BetaML.jl
Beta Machine Learning Toolkit
The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, Python, R and any other language with a Julia binding. All models are implemented entirely in Julia and are hosted in the repository itself (i.e. they are not wrapper to third-party models). If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its implementation. Thanks to its JIT compiler, Julia is indeed in the sweet spot where we can easily write models in a high-level language and still have them running efficiently. -
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React Native RAG
Private, local RAGs. Supercharge LLMs with your own knowledge base
...The library is modular, so developers can choose pieces such as LLM, embeddings, vector store, and text splitter components. It is also extensible, allowing teams to implement their own interfaces when they need custom model or storage behavior. React Native RAG supports multiple integration styles, including a simple hook, a class-based API, and direct component usage. Its main value is giving React Native apps a privacy-friendly way to combine local knowledge retrieval with on-device AI workflows. -
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MLOps Zoomcamp
Free MLOps course from DataTalks.Club
...The course is designed to teach data scientists and engineers how to move machine learning models from experimentation environments into scalable production services. The repository provides lessons, code examples, and assignments that cover the entire MLOps lifecycle, including model training, experiment tracking, deployment, monitoring, and infrastructure management. Students learn to use widely adopted tools such as MLflow, orchestration frameworks, and cloud platforms to manage machine learning pipelines. The curriculum emphasizes hands-on projects so learners gain practical experience building automated ML pipelines and maintaining deployed models. -
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Sim
Build, deploy, and orchestrate AI agents
...The project focuses on low-code and no-code accessibility while still supporting advanced customization through TypeScript and modern web tooling. Its architecture supports integrations with major model providers and common agent patterns such as retrieval-augmented generation and tool calling. Sim emphasizes rapid prototyping and production readiness, allowing users to design agent pipelines and scale them as needs grow. Overall, it functions as a full-stack orchestration framework for organizations building sophisticated agentic automation systems. -
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ClaudeBar
A macOS menu bar application that monitors AI coding assistant usage
...Rather than constantly running CLI commands or navigating web dashboards, users can glance at their quota statistics for services like Claude, Codex, Gemini, GitHub Copilot, and Antigravity directly from the menu bar. The application provides real-time tracking of session, weekly, and model-specific usage percentages, using visual indicators such as color-coded progress bars to communicate when quotas are healthy, nearing limits, or depleted. It includes options to enable or disable monitoring for individual providers, supports multiple visual themes (including dark mode and a festive theme), and refreshes data at configurable intervals so users always have up-to-date information. -
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Moondream
Tiny vision language model
Moondream is a creative code project and visual experimentation repository that explores generative graphics, aesthetic patterns, and interactive art through code. The project typically showcases procedural visualizations, algorithmic designs, and artistic experiments that push the boundaries of what can be expressed with programming languages and rendering frameworks. While the exact nature can vary by commit or branch, Moondream’s work often blends geometry, color theory, and motion to...
