Freu vs Juggler: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Freu and Juggler — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Freu
Freu AI (freu-ai)
Ahead-of-time web automation that records browser sessions and compiles them into reusable deterministic skill commands to reduce agent token use.
Key features
- Ahead-of-Time Compilation: Records a browser session once (via Chrome extension and CDP) and compiles it into a reusable JSON-based DSL skill that agents can execute deterministically.
- Token Usage Reduction: Offloads repeated visual and reasoning steps to compiled programs, reducing LLM/agent token consumption (repo claims up to ~90% savings) and lowering recurring inference costs.
- Chrome Extension + CDP Runner: Captures user interaction and driving Chrome DevTools Protocol commands for precise, reproducible playback and capture of complex UI flows.
- Skill DSL & Artifacts: Emits human- and agent-readable artifacts (SKILL.md and <Cmd>.json steps) that document the workflow, provide structured muscle memory, and enable auditing and reuse.
- Local HTTP Bridge: Runs a Python HTTP service (default 127.0.0.1:8787) to serve skills to agents and orchestrate learn/run cycles programmatically.
- Deterministic Execution: Converts volatile DOM parsing and visual reasoning into stable, deterministic commands so agents can skip expensive visual interpretation.
- Extensibility Toward Desktop: Roadmap includes an OS-level Computer Use Agent (CUA) and vision-based desktop automation to extend AOT pipeline beyond browsers.
- Record browser sessions via a Chrome extension and CDP command runner
- Compile recorded sessions into reusable, deterministic JSON skill files (DSL)
- Local Python HTTP bridge (freu-cli) that communicates with the Chrome extension (default 127.0.0.1:8787)
- Outputs SKILL.md and <Cmd>.json structured steps suitable for agent consumption
- Reduces LLM token usage by delegating repeated deterministic actions to compiled skills
- Logging and intermediate artifact generation during learn/run workflows
- CLI-based workflow: learn (capture + LLM) and run (execute compiled skill)
- Planned extension to OS-level vision-based desktop automation (Computer Use Agent)
Best for
- Automating repetitive web workflows (form submission, navigation, multi-step interactions) by recording once and replaying as a compiled skill.
- Reducing LLM/agent operating costs by converting expensive, repeated web reasoning into deterministic skills loaded into the agent context.
- Building stable enterprise automation where auditability and reproducibility are required—SKILL.md and JSON steps provide documented, inspectable workflows.
- Integrating precompiled skills into agent pipelines via the local HTTP bridge to coordinate when and how skills are executed programmatically.
- Creating reusable automation libraries across teams: capture a complex sales/CRM flow once and share the compiled skill for consistent execution.
- Translating human-performed browser sessions into structured automation artifacts for testing, monitoring, and regression checks.
- Stabilize and accelerate complex enterprise web workflows by precompiling repetitive tasks
- Reduce costs for LLM-enabled agents by offloading deterministic UI interactions to compiled skills
- Build reusable skill libraries for agent-driven automation and RPA-like tasks
- Automated end-to-end test recording and deterministic playback for web applications
- Integrate precompiled web skills into agent context windows to avoid DOM re-parsing
Juggler
Julian Storer
A native desktop workbench for AI coding agents with branching conversation trees, inspectable tool calls and editable context.
Key features
- Branching Conversation Trees: Fork the session at any point, recursively, so competing approaches and tangents run side by side without polluting the main context.
- Miller Column Navigation: A Finder-style column layout lays out tool calls, item properties and nested sub-threads for long reading and editing sessions.
- Transaction Inspector: Open any model transaction to see the assembled system prompt, messages, tool definitions, output, token use, timing and stop reason.
- The Context Surgeon: Fold history into a new thread, move or copy items between branches, expand a branch back into its parent, and undo structural changes.
- Local or Remote Sessions: Run the desktop app locally or the headless binary on the machine holding the code, then attach from the app, a browser or a phone.
- Durable Sessions: Sessions are stored on disk as live-synced Yjs documents, so quits, relaunches and dropped connections do not lose the conversation.
- Automatic Context Sizing: Juggler measures the full request before each call, reserves room for the answer and compacts older history before limits become an error.
- Inspectable MCP Tools: Follow an MCP handoff end to end - schema offered, arguments generated, approval, result and errors - with server status, logs and per-tool filtering.
- JavaScript Extension SDK: Context items, LLM loop strategies, slash commands, viewers and Pinboard tabs are extensions you can fork or replace, under a permissive Apache-2.0 SDK.
Best for
- Exploring Competing Fixes: Branch a thread into two sub-threads to try different approaches to the same bug and compare results before committing.
- Auditing Agent Behavior: Inspect exactly what the model received and returned when an agent makes a surprising edit to the codebase.
- Remote Development: Run the server on a dev box or GPU machine where the repository lives and drive the same live session from a laptop or browser.
- Long Refactors: Keep a multi-hour session alive across quits and reconnects, with the agent paused awaiting approval for its next step.
- Provider Comparison: Drive Claude Code, Codex, Copilot, Gemini and local Ollama models through one interface to compare behavior on the same task.
- Custom Tooling: Write JavaScript extensions that add slash commands, file viewers or new LLM loop strategies to the workbench.