Self-Evolving Team Knowledge Workspace for Human-Agent Collaboration
Teams using AI agents waste hours re-explaining product context across Claude chats, Notion pages, GitHub issues, and scattered docs. Kanwas launched as an open-source canvas-based workspace where team knowledge compounds through human editing and agent interaction. Launch feedback from solo founders and product teams confirmed the core pain: keeping context consistent and fresh across multiple AI agents is a full-time job that no existing tool solves well.
Problem Statement
When a team uses multiple AI agents for coding, marketing, and product work, each agent session starts fresh and needs the product vision, architecture decisions, user research, and current priorities re-explained. Teams currently copy-paste context into Claude system prompts, maintain CLAUDE.md files, or use Notion docs—but none of these update automatically, and none are designed for both human readability and agent consumption. The result is context drift, where agents operate on stale assumptions and humans waste time synchronizing.
The Idea
An open-source collaborative canvas workspace that is the single source of truth for team knowledge, accessible to both humans and AI agents, with a built-in gardener that keeps context fresh and relevant.
Why Now
The explosion of AI coding agents (Claude Code, Cursor, Codex) and product agents means teams now run 3-10 agents simultaneously, each needing the same product context. Claude chats, Notion pages, and GitHub issues fragment this knowledge across tools. The demand for persistent, agent-readable context emerged in Q1-Q2 2026 as agent workflows became daily practice rather than experiments.
Target User
Solo founders and small product teams (2-10 people) who use multiple AI agents daily for coding, marketing, and product development.
Target Market
AI agent context management and team knowledge infrastructure for agent-augmented product teams.
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- Score rationale across 11 dimensions
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