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Head to head

Juggler vs zot

Juggler is a free, open-source, GUI-first coding-agent workbench (every session is an editable, visually navigable branching tree, not a scrolling transcript), while Zot is a free, terminal-only single Go binary built for scripting and remote automation, with 30+ model providers, a Telegram bridge and background subagent swarms, so the real choice is visual, inspectable session control versus a minimal, scriptable harness for automation and CI.

Juggler and Zot both rethink the same problem (a single-user coding-agent harness you run against your own LLM) from opposite ends. Juggler, built by JUCE and Tracktion creator Julian Storer, is resolutely a GUI app: it runs as a native desktop client or a headless server reachable from a browser, syncing one live session across any number of clients via Yjs. Every conversation is an editable document rather than a log: any point can fork into a recursive sub-thread, and the whole tree is navigable and inspectable in a Miller-column view, alongside every tool call, approval and raw context message. It drives roughly six providers (Claude, OpenAI, Gemini, Ollama, OpenRouter, DeepSeek) through a swappable JavaScript plugin system. Zot, built by independent developer Patrice Eckhart, is the opposite bet: a single static Go binary with no Node runtime, Docker or account required, running as a terminal TUI, a scriptable print/JSON-streaming mode, or a headless daemon. It supports over 30 model providers (including local Ollama models) and adds a Telegram bridge that mirrors a session to your phone bidirectionally, plus "Swarm" background subagents that run in parallel on the same repository.

The branching feature both tools advertise is not quite the same thing. Zot's `/session fork` and `/session tree` genuinely branch a new session from any past message and render every session in a directory as a parent/child tree (confirmed live against zot.sh 2026-07-24), but that tree is a textual, terminal-rendered structure, not a navigable visual document. Juggler's entire product is that visual layer: branches are edited, dragged and inspected directly in a GUI, which is the exact gap an HN reviewer flagged when praising "the session tree in particular" as a genuinely differentiated take. So the honest framing is visual, editable tree navigation (Juggler) versus text-mode fork/tree plus remote and parallel-automation features a GUI app doesn't attempt (Zot).

Licensing and risk cut in opposite directions too. Juggler's core app ships AGPLv3 (a strong copyleft license, a real constraint versus the permissive MIT/Apache terms most rival open-source coding agents use), while its extension SDK is Apache-2.0 and the whole thing is free with no signup. Zot is MIT-licensed throughout, the more permissive choice, also free with no account required. But Zot carries a disclosed risk Juggler does not: its own README and HN thread confirm it reuses Anthropic's and OpenAI's first-party CLI OAuth client IDs so a Claude Pro/Max or ChatGPT Plus/Pro subscription can authenticate from a third-party tool. The README itself warns this "may be against their terms of service and may be revoked at any time" (the API-key path avoids the risk entirely). Juggler carries the more ordinary early-stage risks instead: the creator calls it alpha software, it has no ACP (Agent Client Protocol) support yet, and headless/server mode on Linux needs the `libwebkitgtk-6.0` system library as an extra deployment step.

How Juggler and zot compare

Scroll across to compare every side. Feature names stay in view.

FeatureJugglerVisit Juggler ↗zotVisit zot ↗
DescriptionOpen-source desktop workbench for AI coding agents, with inspectable tool calls and branching threads.Single-binary terminal coding agent harness in Go: five built-in tools, most providers, extensions in any language.
TagsAutonomousClaudeFree tierGPTMulti-agentOpen modelsOpen sourceSelf-hostedAPIAutonomousClaudeFree tierGPTMulti-agentOpen modelsOpen sourceSelf-hosted
Pricing modelfreefree
Model / LLMModel-agnostic (Claude Code, OpenAI/Codex, GitHub Copilot, Gemini, Mistral, Z.ai, Ollama, OpenRouter, DeepSeek, OpenCode Zen)30+ providers (Anthropic, OpenAI, Codex, Gemini, Vertex, Kimi, Moonshot, DeepSeek, Copilot, Bedrock, Azure, OpenRouter, Groq, Cerebras, xAI, Together, Hugging Face, Mistral, Z.AI, Xiaomi, Fireworks, Vercel AI Gateway, OpenCode, Cloudflare AI, Ollama, llama.cpp)
Bring your own modelYes-
InterfaceVisualCLI
DeploymentSelf-hostSelf-host
Debugs and fixesYesPartly
Writes tests-Partly
Reviews code-Partly (zot-review extension, costs extra)
Whole-codebase work-Partly
In a browserPartlyNo
API accessPartlyYes
MCP serverPartlyPartly (zot-mcp extension, costs extra)
A company can buy itNo-
Limitations
  • Genuinely early-stage: the creator still calls it alpha software, the codebase is on a 0.5.x line, and a launch-day tester hit an LLM-connection error on the very first run with DeepSeek. (1 source)
  • No Agent Client Protocol (ACP) support yet: a real blocker for developers who want to port existing agent-client plugins (Pi, Zed) rather than start over. (1 source)
  • The main application is AGPLv3, a strong copyleft license. That is a materially different commitment than the permissive (MIT/Apache) licenses most rival open-source coding agents use. (1 source)
  • Headless/server mode on Linux needs GTK4 and WebKitGTK development packages at build time, and the server's engine runs in a hidden webview, which is a real dependency-management step flagged for server deployments. (1 source)
  • The public LAN mode is password-free: anyone who can reach the address can drive the agent, so it must only be enabled on networks you trust. (1 source)
  • Zot's own README discloses that subscription login for Anthropic, OpenAI, Kimi, xAI and GitHub Copilot reuses the OAuth client IDs published in each provider's first-party CLI. The README warns "reusing them from a third-party tool may be against their terms of service and may be revoked at any time." The API-key flow avoids this risk entirely. (2 sources)
  • The project describes itself as "lightweight and in beta forever," and the README discloses it is written "vibe-slopped." An honest signal, but a real reason to test carefully before trusting it with sensitive work. (2 sources)
  • A third-party HN commenter asked directly why they should pick Zot "over pi, opencode, or other existing options" and got no clear answer in-thread beyond the branching-session UI. Buyers already invested in a rival harness may not find enough reason to switch. (1 source)

Who each one is for

Juggler

Developers who want hands-on visibility into what an AI coding agent is doing to their codebase and accept early-stage, self-hosted, open-source software.

From Free

zot

Developers who want a fast, free, Go-native coding-agent harness with real session-branching control and are comfortable running fast-moving, self-described "beta forever" software from an independent maintainer.

From Free

Verdict: Juggler or zot?

Want to see and steer a coding-agent session visually: drag, inspect and branch a conversation as a document, across a synced desktop-and-browser client → Juggler. Want a disposable, scriptable binary for terminal use, CI, or remote/parallel automation across 30+ providers, with a Telegram bridge to drive it from your phone → Zot. Care about license terms for redistribution → Zot's MIT is more permissive than Juggler's AGPLv3 core. Signed into Claude Pro/Max or ChatGPT Plus/Pro and want to avoid any ToS gray area → use Zot's API-key auth path, not its CLI-OAuth-reuse path, or use Juggler instead (no such reuse). Either way, both are free, open-source, and genuinely early-stage. Test before trusting either with sensitive or production work.

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Frequently asked questions

Are Juggler and Zot free?
Yes, both are free and open-source with no signup required. Juggler's core app is AGPLv3-licensed (its extension SDK is Apache-2.0); the creator has floated a possible future paid "pro" tier but none exists yet. Zot is MIT-licensed throughout with no pricing mentioned anywhere.
Which one has a visual interface?
Only Juggler. It is a native desktop app (or a headless server reachable from a browser), and its core feature is a visually navigable, editable branching-session tree in a Miller-column view. Zot is terminal-only (a TUI, a scriptable print/JSON mode, or a headless daemon) and while its `/session tree` command does render sessions as a parent/child tree, it is a text representation, not a GUI.
Which supports more LLM providers?
Zot, by a wide margin: 30+ providers including Anthropic, OpenAI/Codex, Gemini, Kimi, DeepSeek, GitHub Copilot, Bedrock, Azure OpenAI, OpenRouter and local Ollama models. Juggler drives roughly six (Claude Code via CLI/SDK, OpenAI, Gemini, Ollama, OpenRouter, DeepSeek) through a swappable JavaScript plugin system, with the list built to expand.
Is there any legal or ToS risk to using either tool?
Zot carries a disclosed one: its own README and its Show HN thread confirm it reuses the OAuth client IDs published in Anthropic's Claude Code CLI and OpenAI's Codex CLI so a Claude Pro/Max or ChatGPT Plus/Pro subscription can authenticate from a third-party tool. The README itself warns this "may be against their terms of service and may be revoked at any time." Using Zot's API-key flow instead avoids the risk. Juggler has no equivalent disclosed risk.
Which is more mature or production-ready?
Neither is production-hardened yet. Juggler's own creator calls it alpha software, it lacks ACP (Agent Client Protocol) support, and Linux headless deployments need the `libwebkitgtk-6.0` system library installed separately. Zot describes itself as "lightweight and in beta forever," and its author has said on Hacker News the terminal UI is "literally 98% vibe coded", an honest disclosure, but a real signal to test carefully before trusting either tool with sensitive or production work.

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At a glance

Juggler is a free, open-source, GUI-first coding-agent workbench (every session is an editable, visually navigable branching tree, not a scrolling transcript), while Zot is a free, terminal-only single Go binary built for scripting and remote automation, with 30+ model providers, a Telegram bridge and background subagent swarms, so the real choice is visual, inspectable session control versus a minimal, scriptable harness for automation and CI.