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

A side-by-side comparison of Contextberg and Doop — 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.

Kevin Goedecke

Free

Open-source infinite design canvas where humans and AI agents design together live, with agents joining through a built-in MCP server.

Agent-Native MCP Canvas: Agents connect over an HTTP MCP endpoint with a single command and one browser OAuth approval, then edit the canvas as you, attributed and accountable, with no API keys handed over.
Streaming Frames: Every section an agent writes renders on the canvas the moment it lands, so you watch the design arrive rather than waiting on a spinner.
Comments as Tasks: A note left anywhere on the canvas becomes a task the right agent picks up, works on, and replies to with a screenshot, turning feedback directly into the backlog.
Agent Self-Review: A built-in headless renderer gives agents screenshots of their own frames so they judge fit, spacing and contrast like a senior designer and correct issues before handoff.
Shared Canvas Memory: Tasks, decisions and comments live on the canvas rather than in one agent's context, so any agent that joins later plugs into the same state and continues.
Learned Taste Profile: Casual feedback such as 'rounder corners' or 'keep it to the blue' is distilled into a persistent taste profile applied to every new frame and inherited by every agent.
Live Export URLs: Each frame is a URL that can be embedded in a doc, a post or an og:image and re-renders whenever the design changes, so shared assets never go stale.
Reference and URL Import: Paste screenshots to have agents distill palette, type and mood into a written brief, or paste a public URL to land an editable snapshot of your existing page on the canvas for side-by-side variants.

Agent-Assisted Landing Pages: Steering Claude Code or Codex through hero, pricing and footer frames on one canvas and watching each render live.
Design Review Loops: Leaving contrast or spacing notes on a frame and letting an agent apply the fix and return a screenshot without a synchronous handoff.
Redesign Comparison: Importing an existing public page as an editable snapshot so agent-generated variants sit next to the original instead of replacing it blind.
Team Design Sessions: Multiple people and multiple agents working the same canvas, each seeing what the others' agents are doing in real time.
Style Consistency: Building a canvas taste profile once so every subsequent frame and every new agent inherits the same corner radius, palette and type decisions.
Always-Fresh Shared Assets: Embedding live frame URLs in documentation or social posts so the shared image updates automatically when the design changes.