Juggler vs Tables.so: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Juggler and Tables.so — 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.

Tables

Freemium

AI prospecting platform that searches 300M+ contacts, enriches leads with verified emails and phone numbers, and researches every prospect.

AI Search: Describe your ideal customer in plain language and get a scored, qualified lead list in minutes instead of hand-building filters.
Contact Database: Search over 300 million contacts and companies across 30+ criteria including title, seniority, technology stack, and geography.
Verified Contact Data: Reveal verified work emails, mobile numbers, and direct dials, with credits charged only when data is actually found.
Custom AI Research Columns: Add scores, dropdowns, and yes/no fields answered by AI, each with its reasoning and source citations.
Claude MCP Server: Run agentic prospecting workflows inside Claude, including reading local lead lists and enriching them with live data.
Chrome Extension: Reveal emails and phone numbers on any LinkedIn profile and push contacts straight to your CRM.
CRM Sync: Export whole lists or cherry-pick individual leads into your CRM and keep records in sync as they change.
ICP Scoring: Every prospect is scored for fit against your ideal customer profile so reps focus on the highest-value accounts.

An outbound SDR team builds a targeted prospect list for a new segment without hours of manual scraping.
A founder-led sales motion needs verified mobile numbers and emails for decision makers at specific company types.
A RevOps lead enriches an existing CRM export with missing contact details and firmographic data.
A marketer researches which prospects use a given technology — Shopify, WooCommerce, Magento — before running a campaign.
A seller preparing for a call pulls AI-researched context on a prospect's business, hiring, and priorities.
An agent-driven workflow in Claude reads a local CSV of leads and enriches each row automatically via MCP.