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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.

💻 System Requirements

🧠RAM

0 (Can operate using CPU only; however, when integrating with local LLMs such as Ollama, GPU VRAM of 8GB or higher is recommended)

💾Storage

Approximately 100MB for the package excluding models (Desktop app size is approximately 40MB)

⚡ 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

🧬 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 3.5Å vs. 4.0Å). 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%.

📄 Official Docs🐙 GitHub

📝 Update Notes

  1. vv0.7.710/22/2026

    이번 업데이트에서는 run_code 도구가 도입되어 생물정보학 스크립트를 더욱 정교하게 실행하고 결과물을 확인할 수 있는 '코드 모드' 기능이 강화되었습니다. 특히 이미지 처리 기능이 개선되어 현미경 사진이나 실험 데이터의 시각적 분석을 AI가 더 정확하게 수행할 수 있게 된 점이 주목할 만합니다. 또한, 분석된 대화 내용을 HTML이나 마크다운으로 간편하게 내보낼 수 있어 실험 로그를 동료들과 공유하고 기록하는 작업이 훨씬 효율적으로 변할 것입니다.

  2. vv0.7.610/10/2026

    이번 업데이트에서는 도구 호출을 직접 제어할 수 있는 'Tool hooks' 기능이 추가되어, 민감한 실험 데이터를 다루는 과정에서 보안과 데이터 무결성을 더욱 정밀하게 관리할 수 있어요. 특히 query_code 실행 중 중단이 가능해지고 오류 발생 시에도 로그를 확인할 수 있게 되어, 복잡한 생물정보학(Bioinformatics) 스크립트의 디버깅 작업이 훨씬 수월해졌습니다. 또한 워크스페이스 중심의 파일 완성 기능 개선으로 대규모 유전체 데이터나 실험 결과 파일을 더욱 효율적으로 탐색하고 관리할 수 있게 되었답니다.

  3. vv0.7.410/4/2026

    이번 업데이트에서는 TIFF, BMP 등 다양한 이미지 형식과 비디오, 오디오 파일 지원이 강화되어 현미경 이미지나 실험 영상 분석이 훨씬 수월해졌어요. 10MB 이상의 대용량 바이너리 파일도 안정적으로 열 수 있고 이미지 업로드 오류도 해결되어, 대규모 생물학적 데이터 처리가 더욱 정확해졌습니다. 또한, 모델의 '사고 단계(thinking levels)' 설정과 토큰 제한 정보가 명확해져 복잡한 유전체 분석이나 단백질 구조 예측을 위한 정교한 프롬프트 설계가 가능해졌어요.

  4. vv0.7.29/29/2026

    Juggler v0.7.2는 초기 설정 가이드를 강화하여 Claude나 ChatGPT 같은 AI 모델을 연구 환경에 더 쉽고 빠르게 연결할 수 있도록 도와줘요. 앱 내에서 프로젝트 폴더를 즉시 생성하거나 드래그 앤 드롭으로 열 수 있는 기능이 추가되어, 방대한 실험 데이터와 분석 프로젝트를 더욱 체계적으로 관리할 수 있습니다. 또한 UI 탐색 기능과 최근 프로젝트 목록 확인 기능이 개선되어, 복잡한 바이오 데이터 분석 워크플로우를 더욱 직관적이고 효율적으로 제어할 수 있게 되었어요.

  5. vv0.7.09/24/2026

    이번 업데이트에서는 분석 환경을 용도별로 분리해 관리할 수 있는 '워크스페이스' 기능이 새롭게 도입되었습니다. 특히 GPT-6 Sol 및 Luna 모델의 도입으로 수백만 토큰에 달하는 거대한 컨텍스트 창을 활용할 수 있게 되어, 긴 유전체 서열이나 방대한 단백질 구조 데이터를 분석할 때 매우 유용합니다. 또한 모델의 실제 컨텍스트 크기를 정확히 인식하고 오류 메시지를 상세화하여, 복잡한 바이오인포매틱스 파이프라인의 디버깅과 대규모 데이터 처리의 신뢰도를 한층 높였습니다.

  6. vv0.6.49/15/2026

    macOS 멀티 모니터 환경에서 창이 메인 화면으로 돌아가던 문제가 해결되어, 여러 화면을 동시에 사용하는 연구 작업이 더욱 안정적으로 변했습니다. 특히 실험 데이터나 구조 정보를 담은 SVG 및 HTML 블록이 끊김 없이 끝까지 렌더링되므로, 시각적 자료를 확인하는 과정이 훨씬 매끄러워졌습니다. 또한 대화 기록 정리 시 불필요한 데이터를 삭제해 디스크 용량을 효율적으로 확보할 수 있어, 대용량 데이터를 다루는 연구원님들의 저장 공간 관리가 더욱 편리해졌습니다.

  7. vv0.6.09/6/2026

    이번 업데이트에서는 새로운 '핀보드' 기능이 도입되어 실험 계획, 할 일, 관련 파일들을 한곳에 모아 체계적으로 관리할 수 있어요. 특히 토큰 관리와 자동 압축 기능이 정교해져서, 대규모 유전체 데이터나 복잡한 단백질 구조 분석 시 비용과 효율성을 동시에 잡을 수 있습니다. 또한 AI 모델 제어와 작업 중지 기능이 더욱 세밀해져서, 복잡한 생물학적 데이터 처리 파이프라인을 더욱 안정적으로 운영할 수 있게 되었답니다.

  8. vv0.4.27/18/2026

    이번 업데이트에서는 llama.cpp와 Copilot 등 더 다양한 AI 모델 제공자가 추가되어, 연구 목적에 따라 최적화된 모델을 선택해 생물학적 데이터를 분석하기 좋아졌어요. MCP와 확장 프로그램 로딩 관련 버그가 수정되어 복잡한 바이오인포매틱스 워크플로우를 더욱 안정적으로 운영할 수 있습니다. 또한 프로젝트 폴더 변경 시 발생하던 오류가 해결되어, 대용량 유전체 데이터나 실험 결과 폴더를 관리할 때의 혼선을 줄일 수 있어요.

  9. vv0.4.17/17/2026
    • Added a Skills marketplace to discover and install Agent Skills from GitHub registries
    • Made lots of fixes/improvements to skill handling and the skill-related items in a conversation
    • Bin now shows its on-disk size on the Bin button and Empty-Bin action
    • Write approvals now enforce project-path containment on all platforms
    • Writes outside the project folder always prompt, with a clear warning
    • Pre-approval write validation no longer creates directories or files
    • Windows absolute paths n
  10. vv0.4.07/16/2026

    모델별로 사고 수준(thinking-level)을 직접 조절할 수 있는 기능이 추가되어, 복잡한 생물학적 추론이 필요한 작업에서 모델의 성능을 정밀하게 제어할 수 있습니다. 새로운 '에이전트 스킬' 기능은 필요한 도구를 즉시 불러올 수 있게 해주어, 실험 데이터 분석이나 문헌 조사와 같은 연구 워크플로우를 더욱 효율적으로 자동화할 수 있습니다. 마크다운 파일 미리보기 개선과 설정 저장 오류 수정 등 사용 편의성이 향상되어, 연구 기록을 더욱 쾌적하고 안정적으로 관리할 수 있습니다.

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