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

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

Contextberg

Freemium

Surfaces your active work as persistent agent memory and serves it to agents via the Model Context Protocol (MCP).

Work-to-Memory Conversion: Extracts contextual signals from a user's workspace (files, tabs, app state and activity) and converts them into structured memory artifacts usable by agents.
MCP Serving: Exposes collected memory via the Model Context Protocol (MCP) so any MCP-compatible agent or tool can query and consume context in a standard way.
Long-Term Persistence: Stores and indexes historical context across sessions to provide agents with continuity and long-term state for multi-step or recurring tasks.
Interoperability with Agent Tooling: Designed to plug into developer workflows and agent infrastructures, enabling multiple agents and platforms to reuse the same context artifacts.
Context Enrichment: Organizes and surfaces relevant snippets of work history so agents receive concise, actionable context rather than raw logs or bulk files.
Serve work history and artifacts as agent-readable memory via the Model Context Protocol (MCP).
Index and persist long-term context so agents can access historical state across sessions.
Provide a standardized memory endpoint for agent frameworks and tooling to query context.
Integrate with developer workflows and tooling to capture relevant context from work artifacts.
Reduce context-switching by making workspace context available to multiple agents and tools.

Persistent Coding Assistants: Provide code-focused agents with project history, design decisions, and prior edits so suggestions and refactorings consider long-term context.
Customer Support Augmentation: Supply support agents with the user’s prior interactions, documents, and troubleshooting steps to enable faster, context-aware responses.
Personal Productivity Agents: Let personal assistants recall past tasks, notes, and project context to manage follow-ups, scheduling, and multi-session workflows.
Team Knowledge Access: Serve a shared, queryable memory layer to team agents so newcomers and tools can access project context and rationale without manual handoffs.
Agent Handoffs and Orchestration: Allow multiple specialized agents to request the same memory artifacts via MCP when coordinating complex, multi-step automations.
Enable agents to complete multi-step tasks using a user's historical project context.
Provide persistent memory for coding assistants so they retain project-specific state between sessions.
Bridge work artifacts (files, commits, notes) into a standardized memory layer for orchestration.
Improve agent decision-making by serving relevant long-term context during automated workflows.

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.