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magentic ui

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Basic Information

Magentic-UI is a research prototype user interface and multi-agent system designed to automate web tasks while keeping a human in control. It enables agents to browse websites, perform interactions such as form filling and deep navigation, generate and execute code, and analyze or modify uploaded files. The project provides a session-based UI where users create tasks, watch progress in a browser view, review and approve step-by-step plans, and interact with agents in real time. It is built on an underlying multi-agent framework and is intended both as a usable tool for task automation and as a platform for studying human-agent interaction. The repository includes installation options via PyPI and Docker, a command line interface, and configuration hooks for custom model clients and external MCP servers.

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7081
App URL
https://github.com/microsoft/magentic-ui
Github Repository
https://github.com/microsoft/magentic-ui/blob/main/README.md

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App Details

Features
The README highlights several core capabilities: Co-Planning for collaborative plan creation and approval, Co-Tasking to interrupt or guide execution, and Action Guards that require explicit user approval for sensitive actions. It supports file uploads for analysis, a plan gallery for plan learning and retrieval, and parallel task execution across sessions with status indicators. The UI exposes a split view with a plan editor and a live browser for agent actions, plus a top progress bar. The system is extensible with custom MCP agents and MCP server integrations. Model clients are configurable to use different LLM providers and local servers. The project is implemented using an AutoGen-based agent architecture and ships as a PyPI package and Docker images.
Use Cases
Magentic-UI helps users and researchers automate complex web workflows that require contextual navigation, tool use and occasional human approval. It is useful for tasks such as filling forms, extracting or transforming online data, configuring multi-step web interactions, and producing code-driven outputs like charts from online sources. The human-centered controls make it safer to delegate sensitive operations by asking for approvals and enabling users to steer execution mid-task. For researchers and developers it provides an extensible platform to prototype multi-agent teams, add custom MCP servers, configure model clients, and reproduce evaluation experiments. The packaged installation paths and CLI make it practical to run locally for experimentation, demonstration, and iterative development.

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