Juggler vs Kaily: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Juggler and Kaily — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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.
Kaily
Kaily (formerly Copilot.live)
An AI teammate for helpdesk, website chat, voice calls and collaborative document Q&A that automates support and team workflows.
Key features
- Multi-Channel Support: Combines website chatbots, helpdesk automation and AI-driven voice calls to handle customer interactions, qualify leads, and escalate to humans when needed.
- Team-Shared Document Chat: Allows multiple team members to participate in a single PDF or document conversation concurrently, enabling group Q&A and collaborative review in real time.
- Multi-Source Ingestion: Consolidates information from PDFs, Google Docs, Notion pages, and website links to produce answers that span multiple sources.
- Citations & Traceability: Every generated answer includes exact page and section citations (e.g., page numbers and chapter pointers) so users can verify source material quickly.
- Integrations & Embeds: Connects with Slack, Chrome, Google Drive and supports embeddable website chat to fit into existing workflows and touchpoints.
- Workflow Automation: Automates repetitive support and sales tasks, issue resolution steps, and can be configured to trigger downstream actions based on conversations.
- Enterprise Customization: Offers team and enterprise-focused options with customizable pricing, onboarding, security settings, and integrations to meet organizational requirements.
- Team-shared document chats allowing multiple users to participate simultaneously in a single PDF or document conversation
- Supports ingestion of PDFs, Google Docs, Notion pages, and website links (multi-source data integration)
- Per-answer citations with exact page numbers and sections for traceability and verification
- Integrations: Slack connector, Chrome extension, Google Drive integration (embeds into existing workflows)
- Web-based interface optimized for collaborative workflows and document-centric Q&A
- Configurable for team/enterprise usage — pricing and plans customized by team size
- Can be embedded or used as an AI chatbot (used as a portfolio chatbot example)
- Focused on enterprise/team scenarios such as contract review, HR policy lookups, and research report analysis
Best for
- Contract review for legal teams: Multiple lawyers collaboratively query PDFs, get pinpointed answers with page citations, and discuss findings in a shared document chat.
- HR policy lookup: HR staff search across handbooks and policy documents in one place to answer employee questions and cite exact sections.
- Research and reports analysis: Research teams aggregate PDFs, Google Docs and web sources to extract insights, annotate pages, and hold shared Q&A sessions.
- Customer support automation: Deploy website chatbot and AI voice calls to answer common customer questions, create tickets, and escalate complex issues to agents.
- Sales qualification on websites: Use embedded chat to engage visitors, automate qualification flows, and route leads to sales reps with context and transcripts.
- Knowledge base consolidation: Unify scattered documentation (Docs, Notion, websites) into a searchable, source-cited assistant for internal teams.
- Legal teams collaboratively reviewing and querying contract PDFs with page/section citations
- HR teams searching and discussing internal policy manuals across documents
- Research teams analyzing reports and consolidating answers from multiple document sources
- Customer support or operations teams automating resolution workflows and knowledge lookup
- Embedding an AI chatbot on websites/portfolios to provide contextual information about content