Search Results for "devops"
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Showing 10 open source projects for "devops"
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Auth0 for AI Agents now in GAConnect your AI agents to apps and data more securely, give users control over the actions AI agents can perform and the data they can access, and enable human confirmation for critical agent actions.
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Grafana: The open and composable observability platformGrafana is the open source analytics & monitoring solution for every database.
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MCP Server Azure DevOps
An MCP server for Azure DevOps
The Azure DevOps MCP Server is an MCP server implementation that allows AI assistants to interact with Azure DevOps APIs through a standardized protocol. It facilitates access and management of projects, work items, repositories, and more. -
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BISHENG
BISHENG is an open LLM devops platform for next generation apps
BISHENG is an open LLM application DevOps platform, focusing on enterprise scenarios. It has been used by a large number of industry-leading organizations and Fortune 500 companies. "Bi Sheng" was the inventor of movable type printing, which played a vital role in promoting the transmission of human knowledge. We hope that BISHENG can also provide strong support for the widespread implementation of intelligent applications. -
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Errbot
Chatbot daemon that connects to your favorite chat services
...The goal of the project is to make it easy for you to write your own plugins so you can make it do whatever you want, a deployment, retrieving some information online, trigger a tool via an API, troll a co-worker, etc. Errbot is being used in a lot of different contexts, chatops (tools for devops), online gaming chatrooms like EVE, video streaming chatrooms like livecoding.tv, home security, etc. Extending Errbot and adding your own commands can be done by creating a plugin, which is simply a class derived from BotPlugin. The docstrings will be automatically reused by the !help command. We aim to give you all the tools you need to build a customized bot safely, without having to worry about basic functionality. ... -
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Kalavai
Turn everyday devices into your own AI cluster
...Aggregate resources from multiple machines and say goodbye to CUDA out-of-memory errors. Deploy your favorite open-source LLM, fine-tune it with your own data, or simply run your distributed work, zero-DevOps. Simple. Private. Yours. -
Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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MLRun
Machine Learning automation and tracking
...MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements. In MLRun the assets, metadata, and services (data, functions, jobs, artifacts, models, secrets, etc.) are organized into projects. Projects can be imported/exported as a whole, mapped to git repositories or IDE projects (in PyCharm, VSCode, etc.), which enables versioning, collaboration, and CI/CD. ... -
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Phidata
Build multi-modal Agents with memory, knowledge, tools and reasoning
...Phidata also allows developers to bring their own cloud infrastructure, offering flexibility for custom setups. The platform provides robust support for enterprises, including security features, agent guardrails, and automated DevOps for smoother deployment processes. -
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AutoMLOps
Build MLOps Pipelines in Minutes
AutoMLOps is a service that generates, provisions, and deploys CI/CD integrated MLOps pipelines, bridging the gap between Data Science and DevOps. AutoMLOps provides a repeatable process that dramatically reduces the time required to build MLOps pipelines. The service generates a containerized MLOps codebase, provides infrastructure-as-code to provision and maintain the underlying MLOps infra, and provides deployment functionalities to trigger and run MLOps pipelines. AutoMLOps gives flexibility over the tools and technologies used in the MLOps pipelines, allowing users to choose from a wide range of options for artifact repositories, build tools, provisioning tools, orchestration frameworks, and source code repositories. ... -
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ThinkReview
AI-powered code reviews for GitLab & Azure DevOps. Zero setup. Powered
I Copilot for Gitlab and Azure DevOps Merge requests (MRs) and Pull Requests (PRs) in your browser. The extension Summarises merge requests , provided suggestions and find potential security issues , provide smart questions to dive deeper and chat with your pull requests The extension works out of the box without any complex setup - Supports custom Self hosted Gitlab as well as gitlab.com and gitlab enterprise Enhance your GitLab MRs and Azure Devops PRsworkflows with automatic patch diff access and AI-powered code reviews. ... -
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DevOpsGPT
Multi agent system for AI-driven software development
Welcome to the AI Driven Software Development Automation Solution, abbreviated as DevOpsGPT. We combine LLM (Large Language Model) with DevOps tools to convert natural language requirements into working software. This innovative feature greatly improves development efficiency, shortens development cycles, and reduces communication costs, resulting in higher-quality software delivery. The automated software development process significantly reduces delivery time, accelerating software deployment and iterations. ... -
Rent Manager SoftwareRent Manager is award-winning property management software built for residential, commercial, and short-term-stay portfolios of any size. The program’s fully customizable features include a double-entry accounting system, maintenance management/scheduling, marketing integration, mobile applications, more than 450 insightful reports, and an API that integrates with the best PropTech providers on the market.
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Kite
Primary Kite repo, private bits replaced with XXXXXXX
The main Kite repo (originally kiteco/kiteco) was intended for private use. It has been lightly adapted for publication here by replacing private information with XXXXXXX. As a result, many components here may not work out of the box. We used a variety of infrastructure, on a mix of cloud platforms, depending on what was most economical, though it was mostly on AWS. You should be able to develop, build, and test Kite entirely on your local machine. However, we do have cloud instances & VMs...
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