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

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

Juggler

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.

Base Compute

Free

A macOS app that runs chat, coding and meeting AI entirely on your own Mac, with no cloud, no account and no per-token cost.

BaseRT Chip-Tuned Engine: Base Compute's inference runtime compiles for your specific Apple silicon on first launch, claiming up to 5.4x more tokens per second than engines other local apps ship.
Privacy Mode: Every request runs on the Mac and no data leaves the device — memories are stored locally, and facts marked sensitive are pinned to the machine permanently.
In-Folder Agentic Coding: Point Local at a project and it reads, edits and runs code in place without ever uploading the codebase.
On-Device Meeting Transcription: Meetings are transcribed locally with speakers labelled, so recordings and transcripts never reach a third-party service.
Memory You Can Edit: A short, fully visible list of lasting facts about you that you can review, edit or delete, rather than an opaque profile.
Memory-Aware Model Recommendations: Local reads your chip and RAM (8-16 GB, 24 GB, or 32-128 GB tiers) and suggests which open models will actually run well.
Office Mode: Serve the largest model from your fastest machine — Mac Studio, AMD Strix Halo, NVIDIA DGX Spark or an on-prem server — and reach it from Local on every laptop in the office.
Cost and Speed Analytics: A dashboard showing tokens processed, per-model throughput, and the equivalent cloud API cost you avoided.

Confidential Document Review: Drop a contract or PDF into chat and get a summary without the file ever touching a cloud provider.
Regulated-Industry Coding: Work agentically inside a proprietary codebase at a firm whose policy forbids uploading source to external AI services.
Private Meeting Notes: Transcribe internal calls with speaker attribution while keeping the audio on the laptop.
Zero-Marginal-Cost Experimentation: Run heavy prompt iteration without watching a per-token meter, since inference happens on hardware you already own.
Small-Office AI Server: Host a large open model on the one powerful Mac in the office and let the whole team query it from their own laptops.
Offline Fieldwork: Keep a full chat and coding assistant available on a flight or at a site with no reliable connectivity.
Hybrid Frontier Access: Keep everyday work local and connect your own OpenAI or Anthropic key only for the rare job that needs a frontier model.