# An AI Workspace for Developers

> Bring your code, architecture, project context, and AI coding agents onto one visual workspace. Map systems on an infinite canvas, connect your project sources, and work with AI agents that can reason from the context you've chosen.

## The developer context problem

Code, architecture, documentation, issues, team decisions, and AI chat usually live in different places. The codebase tells you what exists. Architecture diagrams explain relationships. Documentation captures intent. Conversations contain decisions. AI coding agents need enough of that context to work effectively — and pasting fragments into a blank prompt does not recreate the map.

```text
Code
   +
Architecture
   +
Documentation
   +
Issues
   +
Team decisions
   +
AI chat
        ↓
Often scattered across tools
```

## From codebase to visual context

Drawsy AI gives developers a visual layer around the tools they already use: repository context from GitHub, architecture and plans on the canvas, project knowledge from docs and conversations, and a bridge to local coding tools through Companion.

![Developer architecture and project context mapped on a Drawsy AI canvas](https://app.drawsyai.com/assets/use-systems.png)

```text
GitHub
   ↓
Repository context

Canvas
   ↓
Architecture + plans

Notion / Slack / Drive
   ↓
Project knowledge

Companion
   ↓
Coding tools

        ↓

   Drawsy AI workspace
        ↓
    AI / agents
```

## The developer workflow

A practical loop on Drawsy AI looks like this:

```text
Map
 ↓
Connect
 ↓
Contextualize
 ↓
Ask / Agent
 ↓
Build
```

- Map the system — architecture, services, dependencies, and flows on the canvas
- Connect the project — bring in relevant sources such as GitHub, Notion, or Slack
- Add context — keep decisions, requirements, notes, and diagrams together
- Work with AI — ask questions or run agents against the context you chose
- Build — take the result into your editor, CLI, or review workflow

## What developers can use it for

Real development jobs, not generic feature buckets:

- Understand an existing codebase — map architecture and connect the repository
- Plan a feature — combine requirements, architecture, and implementation considerations
- Design a system — explore services, APIs, and dependencies visually
- Prepare an AI coding task — give the agent relevant architecture and project context
- Investigate a technical problem — put evidence, code references, and hypotheses on the canvas
- Document decisions — keep the reasoning behind an implementation next to the resulting architecture

## AI coding agents + Drawsy AI

AI coding agents can execute multi-step development tasks, but the quality of their work depends partly on the context available for the task. Drawsy AI provides a visual project layer around that workflow, connecting architecture, project information, and the coding environment.

Learn what [AI coding agents](/ai-coding-agents) are and how they differ from assistants — then come back here for the developer workflow.

## Bring the canvas into your development workflow

Drawsy Companion connects the Drawsy canvas with local project folders and tools such as Codex CLI and OpenCode CLI. Your project files stay on your machine; the canvas holds the spatial map of what you are building.

Explore [Drawsy Companion](/companion) for installers and setup. Companion is a local bridge — whether prompts reach an external model still depends on the coding agent and provider you configure.

## Why a visual workspace?

Architecture and software systems contain relationships that are difficult to understand from isolated files. A visual workspace gives developers a place to represent those relationships while keeping the underlying project context nearby.

Learn about [visual thinking](/visual-thinking) if you want the broader reasoning model — this page is about applying that model to day-to-day development on Drawsy AI.

## What Drawsy AI isn't

Drawsy AI isn't intended to replace your code editor, GitHub repository, documentation system, or existing development tools. It provides a visual workspace around them — so architecture, project context, and AI agents can share the same board as you build.

## Related

For the category definition, see [What is an AI workspace?](/ai-workspace).

For how sources become shared project context, see [connected context](/connected-context).

For designing what AI sees, see [context engineering](/context-engineering).

For architecture-focused workflows — including existing systems, AWS maps, and architecture → agent → build — see [AI system architecture tool](/use-cases/system-architecture).

Start free: https://drawsyai.com

## FAQ

### What can developers use Drawsy AI for?

Mapping architecture, planning features, preparing AI coding tasks, investigating technical problems, and keeping decisions next to the systems they describe — with connected project sources on one canvas.

### Can I connect a GitHub repository?

Yes. GitHub is a core connector for developer workflows. You authorize access and link relevant repositories to the workspace so AI and humans can work from that project context.

### Can developers use AI coding agents with Drawsy AI?

Yes. Drawsy AI provides a visual project layer around agent workflows, and Drawsy Companion bridges the canvas with local tools such as Codex CLI and OpenCode CLI.

### Does Drawsy AI replace my IDE?

No. Drawsy AI is a visual workspace around your existing tools — editor, GitHub, docs, and coding agents — not a replacement for them.

### Can I use Drawsy AI without signing in?

Yes. You can open the canvas at https://drawsyai.com in guest mode without an account. Sign in when you need to save, share, or use account features.

### What coding tools work with Drawsy Companion?

Drawsy Companion currently documents bridges for Codex CLI and OpenCode CLI, plus local project folders. See the Companion page for the latest supported setup.

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HTML: https://app.drawsyai.com/use-cases/developers
llms.txt: https://app.drawsyai.com/llms.txt