Search Results for "users" - Page 9
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Showing 301 open source projects for "users"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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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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Cloudflare OS
Agent workspace built on Cloudflare Workers for creating documents
Cloudflare OS is an open-source AI productivity environment originally developed for internal use across Cloudflare. It combines an agent chat interface, company-specific knowledge, sandboxed application creation, and a capability-based security framework. Users can ask agents to prepare documents, build dashboards, create collaborative tools, or work with connected services. Generated applications are called Gadgets and run as private, isolated instances that can be modified and securely shared. Gatekeepers control external access, manage authorization, log actions, restrict resources, and queue side effects for later human approval. ... -
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Agent Stuff
These are commands I use with agents, mostly Claude
...Its Pi extensions add workflow tools such as session control, file browsing, long-running goals, notifications, enhanced edits, task lists, and prompt-mode switching. Many items are tuned for the author’s own environment, so users should expect to adjust paths and defaults. It is best treated as a practical personal agent toolkit and reference for building reusable AI coding workflows. -
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AutoBE
AI Vibe Coding Agent of TS backend server
...It uses an agentic workflow supported by compiler-friendly checks and test generation, which helps reduce the risk of incomplete AI output. AutoBE can be explored through a local playground where users chat with agents and manage sessions. Its main value is giving developers and non-programmers a structured way to generate backend systems from requirements while still producing documentation and tests. -
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GSD Pi
Development system that enables agents to work for long periods
...It is built for developers who want an AI-assisted workflow that can handle structured tasks rather than only single chat prompts. The project emphasizes spec-driven development, context engineering, and longer autonomous work sessions. It helps users break ideas into plans, manage execution steps, verify results, and keep track of progress across a project. Because it runs as a command-line tool, it fits naturally into developer environments and repository-based workflows. Its main value is turning AI coding assistance into a more organized project system with planning, task management, and implementation support. -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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Alook
The collaboration layer for your AI workforce
Alook is an open-source, self-hosted platform for running local AI coding agents as a coordinated workforce. It lets users give agents defined roles, email addresses, task boards, calendars, and shared workflows so they can collaborate more like a real team. The platform connects local agents to external communication channels while keeping the actual agents and codebase running on the user’s own machine. It is designed for developers, solo founders, and teams that want multiple AI agents to handle development, operations, research, and routine work with clearer structure. ... -
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Team9
Team9 is a collaborative workspace for AI agents
...It includes orchestration mechanisms that allow agents to operate continuously through scheduled tasks, event-driven triggers, and long-running processes. The platform also integrates messaging gateways and communication channels, enabling agents to interact with users and systems in real time. Its design reflects a shift toward treating AI agents as operational units within organizations rather than isolated tools. -
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Deep Research Web UI
AI-powered research assistant that performs iterative, deep research
...It operates as a front-end system for deep research agents that iteratively refine queries, retrieve information from multiple sources, and synthesize structured outputs into coherent reports. The platform emphasizes long-horizon reasoning, allowing users to explore topics in depth rather than receiving shallow, single-response answers. Built with modern web technologies such as Vue and TypeScript, it provides a responsive interface for managing research sessions, tracking intermediate steps, and reviewing collected data. The system supports integration with advanced models like DeepSeek R1, enabling more sophisticated reasoning and contextual understanding across multiple sources. -
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koishi-plugin-novelai
Koishi plugin for NovelAI image generation with advanced controls
A Koishi-based plugin that enables image generation through NovelAI, designed for chat-driven workflows and flexible prompt control. It supports multiple configuration options, including model switching, sampler selection, and adjustable image sizes, giving users control over output quality and style. It includes advanced prompt syntax to refine results and allows automatic translation of Chinese keywords to improve usability across languages. A customizable banned word list helps filter unwanted content, while timed message recall can automatically delete generated outputs after a set period. ... -
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Cognita
Open source RAG framework for building scalable modular AI apps
...Cognita provides reusable components such as parsers, data loaders, embedders, retrievers, and query controllers, allowing teams to customize each stage of the RAG pipeline independently. It includes both a backend service and a frontend interface, enabling users to upload documents, experiment with configurations, and perform question-answering tasks interactively. Cognita supports incremental indexing, meaning it processes only new or updated data to reduce computational overhead and improve efficiency. -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Kiln
Open source platform for managing, testing, and deploying AI apps
...It provides a unified environment for managing prompts, datasets, and evaluation workflows, allowing teams to iterate on AI behavior in a controlled and measurable way. Kiln emphasizes reproducibility, enabling users to track changes to prompts and models while comparing outputs across different configurations. Kiln also supports systematic testing of AI systems by defining evaluation criteria and running experiments to assess performance over time. Its workflow-oriented approach helps teams move from experimentation to production by organizing assets and results in a consistent format. ... -
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PySpur
Visual tool for building, testing, and deploying AI agent workflows
PySpur is a visual development environment designed to help AI engineers build, test, and iterate on agent-based workflows more efficiently. It provides a structured playground where users can define test cases, construct agents either through Python code or a graphical interface, and continuously refine their behavior. It addresses common challenges in AI agent development such as prompt tuning difficulties and lack of visibility into workflow execution. By offering a visual representation of workflows, PySpur makes it easier to debug interactions between components and identify failures in complex pipelines. ... -
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PeterCat
A conversational Q&A agent configuration system
...The system allows developers to build AI-powered bots that understand project documentation, GitHub issues, and other repository content in order to answer questions from users or contributors. By simply providing the repository name or URL, the platform can automatically collect relevant project information and construct a knowledge base that the agent uses to respond to inquiries. The framework also provides a configuration system that allows developers to customize how the conversational agent behaves and how it integrates with external services. ... -
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GPT Crawler
Crawl a site to generate knowledge files to create your own custom GPT
...Developers can integrate it into automated pipelines to keep knowledge sources fresh and synchronized with live websites. The overall architecture emphasizes extensibility, allowing users to customize crawling depth, parsing rules, and output handling. -
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Cloudflare Agents
Build and deploy AI Agents on Cloudflare
...Developers can orchestrate complex behaviors using workflows and durable objects, making it suitable for production-grade autonomous systems. Overall, Cloudflare Agents aims to streamline the development of scalable AI automation that operates close to users for improved performance. -
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Refly
The first open-source agent skills builder
Refly is an AI-native workflow platform that democratizes automated workflow and skills creation for both technical and non-technical users by offering a visual, natural-language-driven interface. Instead of requiring code, Refly lets creators define tasks and business logic through simple “vibes,” which are compiled into structured, reusable agent skills that can be executed on engines like Claude Code, Cursor, or other supported runtimes. With a focus on making automation accessible, it provides a visual canvas and low-code components that feel similar to drag-and-drop builders but backed by powerful AI orchestration, memory handling, and integrations with external services. ... -
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QMD
mini cli search engine for your docs, knowledge bases, etc.
...Designed to keep all search activity local, it combines classic full-text search techniques with modern semantic features such as vector similarity and hybrid ranking so that queries return not just literal matches but conceptually relevant results. Users can organize content into named collections, embed documents for semantic retrieval, and then perform keyword searches, semantic searches, or hybrid natural-language queries to quickly surface the most useful information across all indexed sources. Because the entire system runs on the user’s machine, privacy is preserved and there’s no risk of exposing sensitive content to outside providers. -
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ElatoAI
Realtime AI Voice Agents with SoTA Multimodal AI models on Arduino ESP
ElatoAI is a real-time AI voice agent platform built around IoT hardware (ESP32) that enables continuous speech-to-speech conversations using state-of-the-art multimodal voice models with minimal latency and global performance via edge computing. The system integrates voice synthesis and recognition by connecting an ESP32 device through secure WebSockets to edge server functions written in Deno, allowing users to speak naturally with AI agents hosted through cloud APIs including OpenAI’s Realtime API, Gemini’s Live API, xAI’s Grok Voice Agent API, and others. It includes a web client (built with Next.js) for managing devices, controlling volume, and viewing conversation transcripts, while the hardware runs optimized firmware to deliver responses in near real time — even supporting >15-minute uninterrupted conversations. -
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agentation
The visual feedback tool for agents
Agentation is a visual annotation and feedback tool designed to make interacting with AI coding agents more intuitive and precise by letting developers visually click on frontend elements in a browser and annotate them with context before sending structured feedback to an agent. Instead of describing UI elements in text — like “the blue button in the sidebar” — users click directly on elements to automatically capture selectors, positions, and contextual metadata that can be consumed by AI agents to locate exact code references. This approach dramatically improves clarity and reduces ambiguity when working with AI tools that generate or modify UI code, making the handoff between human design intent and AI execution much clearer. ... -
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SuperDesign
AI Product Design Agent
...With a single prompt, SuperDesign can produce full UI mockups, reusable components, and wireframes, dramatically speeding up the early stages of product creation and iteration. Beyond simple visual generation, it lets users fork and refine designs, supporting a dynamic workflow where ideas evolve quickly within the context of your project. This tool is designed to support modularity and consistency, helping teams maintain a coherent design system without manually crafting every element. Because it is open source, anyone can inspect, extend, and customize the agent to fit specific workflows. -
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Dafthunk
A workflow execution platform built on top of the fantastic Cloudflare
...It’s designed around durable, multi-step execution so workflows can keep going (and recover) even when individual steps fail, time out, or need retries. The platform is closely aligned with Cloudflare’s ecosystem, using edge-native building blocks for execution, orchestration, and storage so workflows can run near users with low latency. It aims to combine the approachability of a visual editor with the practical needs of real automation: state persistence, execution history, reusable nodes, and integrations with external systems. A key appeal is that you can go from idea to running automation quickly in a hosted-like experience while still keeping the project open source and infrastructure-aware. -
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Transformer Debugger
Tool for exploring and debugging transformer model behaviors
...It combines automated interpretability methods with sparse autoencoders, enabling researchers to analyze how specific neurons, attention heads, and latent features contribute to a model’s outputs. TDB allows users to intervene directly in the forward pass of a model and observe how such interventions change predictions, making it possible to answer questions like why a token was selected or why an attention head focused on a certain input. It automatically identifies and explains the most influential components, highlights activation patterns, and maps relationships across circuits within the model. ... -
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litlyx
Analytics for developers, setup Analytics in 30 seconds
...The brand emphasizes sustainability by reducing single-use plastics and promoting long-term use of their products. Their cleaning kit is compact, portable, and designed to be effective for everyday use, ensuring that users can maintain clear vision without the hassle of disposable wipes or sprays. -
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agentmemory
#1 Persistent memory for AI coding agents
agentmemory is a persistent memory server for AI coding agents that captures project context, past decisions, bugs, preferences, and implementation details so users do not have to re-explain the same information in every session. It works across tools such as Claude Code, Cursor, Gemini CLI, Codex CLI, OpenCode, Cline, and any client that supports MCP or REST APIs. The system automatically records what agents do, compresses that activity into searchable memory, and injects relevant context when a new coding session begins. ... -
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Gemma 4 Browser Assistant
On-device AI agent Chrome extension powered by Transformers.js
...It uses Transformers.js and Gemma models to run inference locally in the browser, eliminating the need for external servers and preserving user privacy. The extension includes a side panel interface that allows users to interact with the AI while browsing, enabling tasks such as summarizing pages and answering questions. It can access and analyze page content, browsing history, and tab state to provide contextual assistance. The architecture follows modern browser extension standards, with separate components for background processing, content scripts, and UI rendering. ... -
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Gradient Bang
Gradient Bang is an online multiplayer universe
Gradient Bang is an experimental open-source project developed within the Pipecat ecosystem that reimagines AI interaction as a persistent, multiplayer simulation where users and large language models coexist inside a shared virtual environment. Rather than functioning as a traditional application or API, it is conceptualized as an “online multiplayer universe” in which participants can explore, trade, battle, and collaborate while interacting with AI agents as active entities within the system. The project serves both as a prototype and a conceptual playground for testing how conversational AI systems behave when embedded into dynamic, game-like environments rather than static chat interfaces. ...