Juggler
Juggler, released on July 13, 2026, by Jules Storer and the Juggler AI team, is a next-generation, GUI-based coding assistant that allows AI agents to control the entire process of analyzing and editing code by structuring it in a visual file explorer format. It operates as a headless server developed in Go and a lightweight native desktop application based on the Wails architecture, helping developers fully control the AI's code modification workflow. It's like navigating multi-layered folders in the Finder environment on macOS.
Juggler, released on July 13, 2026, by Jules Storer and the Juggler AI team, is a next-generation, GUI-based coding assistant that allows AI agents to visually structure and control the entire process of analyzing and editing code in a file explorer format. Developed in Go, it operates as a headless server and a lightweight native desktop application based on the Wails architecture, helping developers fully control the AI's code modification workflow. Similar to deeply navigating multi-layered folders in macOS's Finder environment, Juggler visualizes the AI's thought process and tool call history in an intuitive Miller-column view, providing users with an optimal workbench where they can easily track and immediately intervene.
In traditional simple text terminal threads or unidirectional chatbot environments, it was difficult to understand which source files the AI agent was exploring and modifying in a hidden background, and it was almost impossible to cleanly revert to a previous state when a tool execution failed. Juggler perfectly solves the limitations of existing linear chats by managing all session data using Yjs, a conflict-free replicated data type. Users can create independent branching threads at intermediate points in the AI's execution path, easily experiment with various modification suggestions, and perfectly perform undo and redo operations through backtracking. Furthermore, by adopting a JavaScript plugin ecosystem, it provides the flexibility to modularly customize and extend the LLM's internal exploration loop control, slash commands, and analysis context items.
Bioinformatics researchers or computational biologists can use Juggler effectively when building custom scripts for large-scale omics data processing pipelines or machine learning models for protein 3D coordinate transformations. For example, when a researcher is coding an RNA sequencing normalization module using complex NumPy and SciPy operation packages, Juggler can be used to instantly monitor which function definitions and matrix operation syntax the AI agent is modifying, and to immediately interrupt and stop the process when incorrect code generation is detected. Even if incorrect parameters are introduced into hundreds of lines of code in an existing pipeline, code quality can be maximized by simply backtracking to a specific timeline and branching out a new logic branch. Furthermore, it is easy to implement multi-client collaboration, where a headless server is running in a remote computing cluster (workstation multi-node) environment, and multiple colleagues can simultaneously connect to a single session via a web browser to debug a shared coding context.
๐ป System Requirements
0 (Can operate using CPU only; however, when integrating with local LLMs such as Ollama, GPU VRAM of 8GB or higher is recommended)
Approximately 100MB for the package excluding models (Desktop app size is approximately 40MB)
โก Installation
4-1. Quick Start
On macOS, download the .dmg file from the official Releases page or website and execute it. If a Gatekeeper warning appears upon first launch, hold down the Control key while clicking to select Open.
To run the server standalone (headless) or for remote connections, execute as follows:
juggler
4-2. Detailed Installation
To build directly from source code, you must have the Go development environment and Node.js installed.
git clone https://github.com/juggler-ai/juggler.git cd juggler git submodule update --init --recursive make build
๐งฌ Bio Use Cases
Optimization of Omics Data Preprocessing Scripts
When developing a sequencing data filtering pipeline based on Biopython and Pandas, Juggler and the Claude 3.5 Sonnet model were integrated to generate code for parallel processing using 8 CPU threads. Agent tool call tracking and undo functionality were used to resolve data loading bottlenecks and improve preprocessing speed by 250%.
Branching of Protein Structure Analysis Pipeline
While writing a script to extract protein 3D coordinates using the PyMOL API and Biopython, Yjs CRDT session splitting was used to create parallel branches for automating two different geometric filtering parameters (distance threshold of 3.5ร vs. 4.0ร ). The results were compared in real-time on the GUI, and the final script was selected.
Collaborative Debugging via Headless Server
A Juggler headless server was launched (integrated with Ollama/Gemini API) on a Linux-based HPC cluster (multi-node workstation) to open a remote session. LAN sharing was enabled, allowing three researchers to simultaneously connect via browser and collaboratively debug, in real-time, a custom loss function error in a machine learning-based cancer cell image classification model (PyTorch), improving the model's training accuracy from 89% to 94%.
FAQ
What is Juggler?
Juggler, released on July 13, 2026, by Jules Storer and the Juggler AI team, is a next-generation, GUI-based coding assistant that allows AI agents to visually structure and control the entire process of analyzing and editing code in a file explorer format. Developed in Go, it operates as a headless server and a lightweight native desktop application based on the Wails architecture, helping developers fully control the AI's code modification workflow. Similar to deeply navigating multi-layered folders in macOS's Finder environment, Juggler visualizes the AI's thought process and tool call history in an intuitive Miller-column view, providing users with an optimal workbench where they can easily track and immediately intervene. In traditional simple text terminal threads or unidirectional chatbot environments, it was difficult to understand which source files the AI agent was exploring and modifying in a hidden background, and it was almost impossible to cleanly revert to a previous state when a tool execution failed. Juggler perfectly solves the limitations of existing linear chats by managing all session data using Yjs, a conflict-free replicated data type. Users can create independent branching threads at intermediate points in the AI's execution path, easily experiment with various modification suggestions, and perfectly perform undo and redo operations through backtracking. Furthermore, by adopting a JavaScript plugin ecosystem, it provides the flexibility to modularly customize and extend the LLM's internal exploration loop control, slash commands, and analysis context items. Bioinformatics researchers or computational biologists can use Juggler effectively when building custom scripts for large-scale omics data processing pipelines or machine learning models for protein 3D coordinate transformations. For example, when a researcher is coding an RNA sequencing normalization module using complex NumPy and SciPy operation packages, Juggler can be used to instantly monitor which function definitions and matrix operation syntax the AI agent is modifying, and to immediately interrupt and stop the process when incorrect code generation is detected. Even if incorrect parameters are introduced into hundreds of lines of code in an existing pipeline, code quality can be maximized by simply backtracking to a specific timeline and branching out a new logic branch. Furthermore, it is easy to implement multi-client collaboration, where a headless server is running in a remote computing cluster (workstation multi-node) environment, and multiple colleagues can simultaneously connect to a single session via a web browser to debug a shared coding context.
When should I use Juggler?
Juggler, released on July 13, 2026, by Jules Storer and the Juggler AI team, is a next-generation, GUI-based coding assistant that allows AI agents to control the entire process of analyzing and editing code by structuring it in a visual file explorer format. It operates as a headless server developed in Go and a lightweight native desktop application based on the Wails architecture, helping developers fully control the AI's code modification workflow. It's like navigating multi-layered folders in the Finder environment on macOS.
What is a biomedical use case for Juggler?
Optimization of Omics Data Preprocessing Scripts: When developing a sequencing data filtering pipeline based on Biopython and Pandas, Juggler and the Claude 3.5 Sonnet model were integrated to generate code for parallel processing using 8 CPU threads. Agent tool call tracking and undo functionality were used to resolve data loading bottlenecks and improve preprocessing speed by 250%.
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