BearDrive vs Juggler: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of BearDrive and Juggler — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

BearDrive

Runbear

Free

Open-source shared folder that syncs the files your team's AI agents create, with versions, authorship, and shareable links — no server required.

Filesystem-native sharing: The shared surface is your local filesystem, so any AI agent that writes files can participate without a special SDK.
Second-scale sync: Files sync to teammates within seconds of being written, so an agent's output is immediately available to another teammate's agent.
Automatic versioning: Every synced change becomes a version that is kept, attributed to a user or agent, and restorable.
Shareable web view: Every file gets a link you can drop into Slack; teammates open a web view of the file with no install required.
Team history and authorship: See who — or which agent — produced each file and when, across the whole team.
Open source, self-hostable: AGPL-3.0 with the server, teams, history, and links code publicly available on GitHub.

Agent-produced research sharing: An analyst's research agent drops long-form HTML reports into a shared folder; a teammate's agent reads them the next minute.
Design/data handoff: A design or data agent writes assets or CSVs at a stable path so downstream agents pick them up without brittle Slack uploads.
Cross-agent workflows: One agent's output is another agent's input — BearDrive keeps the artifacts and their versions instead of scattering copies.
Team memory for AI work: Teams keep a browsable history of everything their agents have produced, restorable and attributable.
Self-hosted deployments: Regulated teams host BearDrive on their own infra to keep AI-produced files inside their perimeter.

Julian Storer

Free

A native desktop workbench for AI coding agents with branching conversation trees, inspectable tool calls and editable context.

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.

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.