Cadenya
Freemium
Developers building agentic workflows
AI coding agents are only as useful as their context — and when a codebase spans dozens of modules, the agent is flying blind through a flat file tree. mind-expander turns that file tree into a source-backed graph on an infinite canvas, giving both you and the agent a shared map of modules, types, calls, and dependencies.
The agent drives the canvas: it can run `npx mind-expander` in the background, load skill integrations, and build guided tours through architecture. You see the same graph the agent is reasoning about, which means review decisions and refactor plans are grounded in actual dependency structure — not the agent's approximation of it. That shared view is the differentiator. The ceiling arrives with language support: Rust and TypeScript are covered, the docs describe more language frontends as planned. Teams whose core services are in Go, Python, or Java will hit that wall on day one.
Bottom line: Pick this when your AI-assisted refactor lives in a Rust or TypeScript codebase and you need both you and the agent working from the same dependency graph — but plan a different approach when your stack is outside those two languages.
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Freemium
Developers building agentic workflows
Free · Open source
Long-term continuity in multi-session coding projects
AI agents planning a refactor can describe a module graph accurately in simple cases and confidently wrong in complex ones — because they reconstruct structure from text, not from the actual source. mind-expander closes that gap. It parses a codebase into an infinite-canvas graph: modules, types, call relationships, dependency edges, and the specific nodes touched by a commit or PR. Agents connect through a skill integration, invoke the tool in the background, and can steer the visualization directly. You and the agent share the same source-backed workspace, not separate mental models.
The differentiating capability is agent-directed guided tours. An agent can walk through codebase architecture interactively — highlighting the path from an entry point through its dependency chain, or isolating the blast radius of a proposed change. This is not a static diagram the agent describes; the vendor describes agents actively creating and steering these tours, which means onboarding a developer or scoping a PR review becomes a collaborative session with the agent rather than a briefing followed by manual verification.
The tool fits best on teams running AI-assisted architecture work where the codebase is Rust or TypeScript — pre-refactor scoping, PR review across system boundaries, and developer onboarding where a new team member needs to understand module ownership fast. It breaks immediately for teams whose primary services are in Go, Python, Java, or any language outside the two supported frontends. The project is Apache-2.0 licensed, fully open-source, self-hosted via a local server, and carries no paid tier — so the ceiling is entirely about capability, not cost.
Distribution is through npm (`npx mind-expander`) alongside a Rust core built with Cargo. The repository includes a `.claude/` directory with hooks and a dedicated `skill/` directory, indicating Claude-specific agent integration is a first-class workflow rather than an afterthought. An `AGENTS.md` file documents agent interaction protocols directly in the repo.
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