Slashspace

Slashspace is an infinite AI canvas for research and deep work that lets users branch conversations, compare multiple models, and wire in various sources.

Slashspace is an infinite canvas platform built for researchers, academics, and polymaths who perform non-linear work. It replaces rigid chat threads with a visual space where users can branch off any answer to chase tangents without losing the original thread. Users can wire PDFs, YouTube videos, web pages, and notes into their chat to provide full source context. The platform enables users to compare different models side by side, including Claude, ChatGPT, Gemini, and local models. It also features an Orchestrator mode that plans tasks and utilizes subagents to handle complex workflows, and integrates with external tools like Notion and GitHub via Composio and MCP.

Slashspace at a glance

Best for: Researcher, Student
Runs on: macOS, Windows, Linux
Pricing: 3-day free trial; $89/year; $249 lifetime. Hosted models use wallet credits; personal API and local models use own subscriptions.
Released: April 2025
Category: AI research tool
Website: slashspace.ai

Key features

Infinite canvas for non-linear research
Branching chat threads
Multi-model side-by-side comparison
Context wiring for documents and web sources
Orchestrator mode for complex agent tasks
Support for local AI models
Integration with Notion, GitHub, and Gmail

Use it for

Conducting deep market or competitor research
Managing complex academic study projects
Connecting disparate concepts from different fields
Building and testing code with multi-agent workflows

Alternatives to Slashspace

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OpenResearch — Run parallel research workflows locally or on remote compute.
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HyperResearch — Generates verified reports with citations checked against full source texts and a persistent vault.
Plot Workspace — Keep research, feedback, ICP, and positioning on one movable board.
Lacuna by Tiptree Systems — Traces code imports and comments directly to the foundational research papers.
Auto Deep Researcher — Automates experiment operations to reduce manual babysitting of training loops.
Jevon Tane — It provides a rigorous, pre-registered framework for AI agents to conduct science together.
Local Deep Research — Fully local and encrypted execution with proper citations.
Keryx — Compensates verified creators directly in USDC for cited sources

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