Kanwas
VerifiedAn open-source brain for your team. For you, your agent, your coworker and their agent.
Tool overview
Overview
Kanwas is a shared context board for teams and agents. It provides one place to create, edit, share and compound product context, replacing the need to juggle Claude chats, local folders, Obsidian, VS Code, Git, and docs. The product is described as a team's context brain and builds shared context by learning about the user, the business, and decisions. It combines a canvas with shared context so teams and agents can work over the same context, with evidence, ideas, and trade-offs transparent to everyone. Kanwas generates structured, execution-ready deliverables for every stage of implementation, and its knowledge base compounds so every decision and outcome makes the next thinking and deliverable better than the last.
Features
include a canvas for real work that brings code, docs, tasks, embeds, and iframes into one place; a context graph that compounds; an agent that follows user instructions, rules, workflows, and skills; support for any model such as Claude, GPT, and Gemini; a terminal-grade agent without forcing everyone into terminal; Git-backed .md files with version history; real-time collaboration with permissions; 1,000+ connections and a CLI tool; and no lock-in with a transparent filesystem.
Screenshots
Features
- Productivity workflow
- Team operations
- Data or task automation
Tags
Use Cases
Teams use Kanwas to create, edit, share and compound product context in one place.
Teams use Kanwas to work with teammates and agents over the same shared context.
Teams use Kanwas to generate structured, execution-ready deliverables for implementation stages.
Teams use Kanwas to turn sessions into living shared context.
Teams use Kanwas to build a shared knowledge base over time from boards, notes, tasks, and decisions.
Teams use Kanwas to work live with teammates, share boards, and control access with permissions.
Teams use Kanwas to pull in context from tools they already use.
Teams use Kanwas to run the model stack that fits their team.
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