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    Enables AI assistants to understand lab topologies, follow operational principles, and interact with real network devices through observable terminals while keeping private access information hidden.
    7
    GPL 3.0
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    Enables AI agents to drive genuine interactive Windows console sessions through ConPTY, so they can launch PowerShell, pwsh or CMD, send keystrokes, answer prompts, watch rendered screens and block until output matches. Multiple persistent shells can run in parallel, supporting live input, incremental reads, interrupts and window resizing.
    10
    MIT
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    MCP server for managing interactive processes, enabling AI agents to start, interact with, and terminate long-running programs like SSH sessions, REPLs, and installers via read/write operations.
    8
    8
    MIT
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    Enables AI agents to open persistent interactive terminal sessions with network devices over SSH, Telnet, TCP console, or serial, write raw commands including nested ssh/telnet transitions, and read full output without requiring a particular prompt format. It supports multiple concurrent sessions and keeps credentials/secrets out of MCP arguments or audit logs.
    7
    MIT
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    Enables LLMs to create and manage persistent, interactive shell sessions with full terminal emulation and PTY support. It allows for sequential command execution and supports interactive programs like vim or htop through specialized streaming and snapshot output modes.
    5
    MIT
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    MCP server that enables AI agents to run fully interactive SSH sessions (via tmux) and execute commands like a human operator, with persistent sessions and multiple concurrent connections.
    6
    MIT
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    Provides AI agents with fully interactive terminal sessions, including TUI support, keyboard control, and screen capture across Windows, Linux, and Mac.
    MIT
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    Aggregates network operations tools such as retrieving local network configurations and scanning ports, enabling IT professionals to perform these tasks via natural language.
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    Enables AI-assisted development tools like Cursor, Cline, and Windsurf to request user confirmation before acting, running shell commands and displaying their output inside a Qt feedback window that returns user notes and logs to the model. This human-in-the-loop loop lets the assistant consolidate many speculative tool calls into a single feedback-aware request, saving API usage and improving accuracy.
    MIT
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    Enables interaction with lightning addresses and common lightning tools via your LLM, providing Lightning Network functionality through natural language.
    3
    12 npm
    1
    MIT
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    A passive MCP server that exposes a toolbox of executable tools (shell, network, HTTP, AI search, SSH, S3 file operations) to autonomous agents via Streamable HTTP, with strong security features including Docker sandboxing and WAF.
    2
    Apache 2.0
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    Enables coding agents to perform file, search, patch, git, process, test, package, network, and system operations through 60 typed MCP tools with structured inputs/outputs, structured errors, and a full event journal, replacing terminal use with a typed machine API.
    MIT