Search Results for "install from git"
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Showing 10 open source projects for "install from git"
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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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1
lumen
Beautiful git diff viewer, generate commits with AI
...The platform supports multiple AI providers, allowing developers to connect to models from services such as OpenAI, Claude, Groq, or locally hosted inference engines. It also includes interactive exploration features that allow users to search through commits and understand the history of changes in a repository. Because it runs entirely from the command line, the tool integrates seamlessly into existing Git workflows without requiring graphical interfaces or additional IDE plugins. -
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Superset LLM
Run an army of Claude Code, Codex, etc. on your machine
...The tool enables developers to run many autonomous coding agents in parallel without the typical overhead of manually managing multiple terminals, repositories, or branches. Each agent task is isolated in its own Git worktree, ensuring that code changes from different agents do not interfere with each other while allowing developers to track their progress independently. The platform includes built-in monitoring capabilities so users can observe the activity of each agent, receive notifications when tasks are completed, and quickly review changes produced by automated coding workflows. ... -
3
oh my PI
AI Coding agent for the terminal
Oh-My-Pi is an open-source AI agent toolkit focused on creating intelligent coding assistants that operate directly from the terminal environment. The project provides a command-line coding agent capable of analyzing repositories, generating commits, editing code, and interacting with development tools through an integrated tool system. Instead of functioning as a simple prompt-based assistant, the system includes an agent architecture that can inspect Git repositories, analyze changes, and perform development actions with fine-grained control. ... -
4
LLM CLI
Access large language models from the command-line
A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine. -
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
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5
SD.Next
All-in-one WebUI for AI generative image and video creation
...The project emphasizes broad model support and includes mechanisms for discovering, downloading, and configuring models through integrated tooling, lowering the setup burden for experimentation. It also provides documentation and an ecosystem of guides that help users move from basic generation to more advanced usage patterns, including API-based automation. SD.Next is built to run across common desktop platforms and focuses on practicality: install, generate, iterate, and automate with minimal friction. -
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Mirascope
LLM abstractions that aren't obstructions
Mirascope is a powerful, flexible, and user-friendly library that simplifies the process of working with LLMs through a unified interface that works across various supported providers, including OpenAI, Anthropic, Mistral, Gemini, Groq, Cohere, LiteLLM, Azure AI, Vertex AI, and Bedrock. Whether you're generating text, extracting structured information, or developing complex AI-driven agent systems, Mirascope provides the tools you need to streamline your development process and create... -
7
handy-ollama
Implement CPU from scratch and play with large model deployments
handy-ollama is an open-source educational project designed to help developers and AI enthusiasts learn how to deploy and run large language models locally using the Ollama platform. The repository serves as a structured tutorial that explains how to install, configure, and use Ollama to run modern language models on personal hardware without requiring advanced infrastructure. A key focus of the project is enabling users to run large models even without GPUs by leveraging optimized CPU-based... -
8
text-generation-webui-colab
A colab gradio web UI for running Large Language Models
...By leveraging Google Colab, the repository enables users to run open-source models such as LLaMA-based systems and other instruction-tuned models using accessible GPU resources. The notebooks automatically install dependencies, download models, and launch the graphical interface that allows users to interact with language models directly from the browser. This approach makes it easier for researchers, hobbyists, and developers to test different LLMs, fine-tuned variants, and quantized models with minimal setup. -
9
Emb-GAM
An interpretable and efficient predictor using pre-trained models
Deep learning models have achieved impressive prediction performance but often sacrifice interpretability, a critical consideration in high-stakes domains such as healthcare or policymaking. In contrast, generalized additive models (GAMs) can maintain interpretability but often suffer from poor prediction performance due to their inability to effectively capture feature interactions. In this work, we aim to bridge this gap by using pre-trained neural language models to extract embeddings for... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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10
DomE
Implements a reference architecture for creating information systems
DomE Experiment is an implementation of a reference architecture for creating information systems from the automated evolution of the domain model. The architecture comprises elements that guarantee user access through automatically generated interfaces for various devices, integration with external information sources, data and operations security, automatic generation of analytical information, and automatic control of business processes. All these features are generated from the domain...
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