Mono Dash vs Agent Lab

Mono Dash is an open-source, third-party mobile management client for 1Panel. Connect to your own 1Panel servers and manage daily operations from your phone with a focused, native interface. Key features: - Monitor CPU, memory, disk, network traffic and host details - Manage websites, domains, HTTPS, logs and configuration - Browse, upload, download, edit, move, compress and delete server files - Work with databases, containers, images, compose projects and app updates - View system, login, operation, SSH and task logs - Open terminal sessions and manage SSH-related settings - Manage backups, snapshots, cron jobs, firewall rules, processes and runtimes Open source: Mono Dash is developed as an open-source project. You can review the source code, follow development, report issues and contribute through the project repository. Free and in-app purchase: The free version lets you add and manage one 1Panel server. Mono Dash Unlimited is an optional one-time in-app purchase that unlocks unlimited server management and future premium features where available. Purchases are processed by the App Store and can be restored in the app. Privacy and data: Mono Dash stores server connection settings on your device and connects directly to the 1Panel servers you configure. Purchase status may be processed through Apple and RevenueCat for entitlement management. Mono Dash does not operate a backend for collecting your 1Panel server data. Important: Mono Dash is an independent third-party client. It is not an official 1Panel product and is not affiliated with, endorsed by or sponsored by 1Panel. Use it only with servers you own, administer or are authorized to access. Source Code: https://github.com/bin64/Mono-Dash Privacy Policy: https://github.com/bin64/Mono-Dash/blob/main/PRIVACY.md Terms of Use: https://github.com/bin64/Mono-Dash/blob/main/TERMS.md

Agent Lab is a learning and experimentation playground for AI agents, built for AI learners, agent developers, and product managers. It doesn't just run agents — it makes every step of an agent's execution visible, so you can finally understand how agents actually work. Unlike an ordinary AI chat app, Agent Lab is about learning, experimenting, debugging, and visualization. On every run you can see the prompts, tool calls, retrieval, tokens, and latency — nothing is hidden. Get started fast · On-device local models: download a small Qwen model on first use (Wi-Fi recommended), then run it fully offline — no API key and no second machine required. Cost is always 0, and tokens and latency are shown for real. · Download on demand: grab other open-source models your device can actually run, with capability gating based on your chip and memory. · Cloud models: works with Chat Completions-compatible APIs (DeepSeek, Moonshot, OpenRouter, and more) and Anthropic Claude — just enter your key, base URL, and model. Three built-in demos · Customer Support Agent — demonstrates tool calling and workflow with a built-in mock order system, no real backend needed. · RAG Knowledge Base — comes with curated reference docs (LangGraph, MCP, LLM APIs, Python) and lets you upload your own PDF / Markdown / TXT, then inspect the retrieved chunks, similarity scores, and final answer. · Research Agent — shows how an agent plans on its own: Planner → Search → Read → Summarize → Answer. Unified visual debugging Every demo offers three views: · Timeline — the full step-by-step sequence of LLM and tool calls · Graph — the workflow graph, highlighting the active node as it runs · Console — expand any step to see System / User / Assistant prompts, the complete LLM request and response JSON, tool requests and responses, tokens, estimated cost, latency, and errors Security and privacy · API keys are stored encrypted on-device via the system Keychain — local only, never uploaded to any server. · Uploaded knowledge-base files and the vector indexes built from them stay on your device and can be deleted anytime. · On-device inference runs fully offline; your data never leaves the device. No sign-up, no login — open it and go. Make every step an agent takes visible.