Showing 6 open source projects for "terminals"

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  • 1
    Omnara

    Omnara

    Talk to Your AI Agents from Anywhere

    Omnara is an open-source agent control platform that empowers developers to turn autonomous AI tools (e.g., Claude Code, Cursor, GitHub Copilot) into collaborative teammates by offering real-time dashboards, push notifications, and remote guidance across terminals, web, and mobile. Omnara transforms your AI agents (Claude Code, Codex CLI, n8n, and more) from silent workers into communicative teammates. Get real-time visibility into what your agents are doing, and respond to their questions instantly from a single dashboard on web and mobile. The primary way to use CLI coding agents (Claude Code, Codex CLI) with Omnara.
    Downloads: 10 This Week
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  • 2
    Omnigent

    Omnigent

    A meta-harness for all your AI agents

    ...It works with Claude Code, Codex, Cursor, Pi, and custom YAML-defined agents, so users can swap or combine agent runtimes without rebuilding their workflows. Sessions can move across terminal, browser, desktop, and mobile interfaces while keeping messages, files, terminals, and subagents in sync. The platform supports collaboration, shared live sessions, co-driving, conversation forking, and remote access from deployed servers. It also includes policy controls for approvals, tool access, spending limits, sandboxing, and server-wide governance. Omnigent is useful for teams and power users who want multi-agent supervision, safer execution, and flexible access from different devices.
    Downloads: 0 This Week
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  • 3
    D4RL

    D4RL

    Collection of reference environments, offline reinforcement learning

    D4RL (Datasets for Deep Data-Driven Reinforcement Learning) is a benchmark suite focused on offline reinforcement learning — i.e., learning policies from fixed datasets rather than via online interaction with the environment. It contains standardized environments, tasks and datasets (observations, actions, rewards, terminals) aimed at enabling reproducible research in offline RL. Researchers can load a dataset for a given task (e.g., maze navigation, manipulation) and apply their algorithm without the need to collect fresh transitions, which accelerates experimentation and comparison. The API is based on Gymnasium (via gym.make) and each environment also exposes a method get_dataset() that returns the offline data to learn from. ...
    Downloads: 0 This Week
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  • 4
    YTS Terminal Manager

    YTS Terminal Manager

    Terminal Manager: Your All-in-One Remote Management Solution

    YTS Terminal Manager is an all-in-one terminal and remote-session manager for Windows 10 and 11. It handles SSH, Telnet, serial (RS-232/RS-485), SFTP, FTP, SCP and RDP connections as tabs in a single window, so the console cable, the jump host, the file transfer and the device web UI all live in one place and share the same favorites, logging and shortcuts. A built-in AI assistant reads your live terminal, understands the device you are connected to, and can diagnose problems, explain...
    Downloads: 5 This Week
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  • 5
    Bot on Anything

    Bot on Anything

    Large model-based chatbot builder that can quickly integrate AI models

    ...At its heart, the project abstracts away the glue logic between AI model APIs and disparate application “channels,” enabling the same bot logic to run in Slack, Telegram, Gmail, enterprise tools, web UIs, or command-line terminals. Configuration is handled simply through a central JSON file where you define which model and which application channel you want to glue together, so developers can create sophisticated AI assistants without rewriting integration code from scratch. The architecture emphasizes reusability and extensibility, allowing the addition of new model backends or new channels with relative ease. ...
    Downloads: 0 This Week
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  • 6
    Deep Daze

    Deep Daze

    Simple command line tool for text to image generation

    Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and...
    Downloads: 0 This Week
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