🎉 500 stars! Thank you to everyone who starred, tried, reported issues, and contributed. DreamCoder is only five months old, and it has grown step by step because of you. Next, we'll keep pace with the best practices of frontier coding agents and try everything that can make the experience better, along with brand-new features. Our goal will always be to reach for the stars.

🌱 Contributions welcome! Browse good first issues and help wanted, and read the contributing guide before starting.

DreamCoder runs its desktop app and session service locally, bringing model configuration, coding sessions, the terminal, and tool activity into one workspace.

• Choose a model provider: Configure Anthropic or OpenAI-compatible endpoints. Presets include DeepSeek, Qwen, Kimi, Zhipu GLM, LM Studio, and Ollama; custom endpoints are also supported. Model availability depends on your provider and configuration.
• Follow the work: Review sessions, file changes, terminal activity, and tool calls on your desktop.
• Continue on your phone: Enable H5 Access to reach desktop sessions from a phone browser on the same LAN. Access across networks requires a reverse proxy you configure yourself.
• Learn how a coding agent works: A seven-part source code walkthrough follows one code change through the execution loop, tools, permissions, sessions, and desktop integration. See the Coding Agent Implementation Guide.

Provider settings and API keys are stored in local files. When you use a cloud model, requests are sent to your chosen provider. See the privacy notice.

• Smoother session management: Visual history, a sidebar with several ways to organize sessions, and a side panel per session for file changes and terminals.
• Terminal built into the workflow: Built-in PTY (PowerShell/Bash/Zsh) with xterm.js.
• Settings you can manage visually: Configure providers, API keys, and preferences without editing JSON files.

• Presets and custom endpoints: Set the API key, endpoint, and model mapping for your chosen provider.
• Connection check: Test the availability and latency of configured endpoints in Settings.

• Computer Use: operates desktop apps and browsers in the background through UI elements, powered by cua-driver (MIT). DreamCoder downloads it when you turn Computer Use on; no Python needed. Verified on macOS; Windows needs community testing, Linux is a preview.
• Transparent tool execution: File edits and terminal commands are surfaced clearly, so it's easy to understand and review what the agent is doing.
• MCP extensibility: Expand context and tooling through the Model Context Protocol.

• H5 Access: Enable access in Settings, manage the token, and use the QR code to connect a phone on the same LAN.
• Access scope: Keep the desktop app running. Cross-network access requires your own reverse proxy; the deployment guide is still in progress.

• Visual configuration: Manage MCP servers in the UI instead of editing JSON by hand.

To learn how a coding agent works under the hood, read the Coding Agent Implementation Guide (also available in Chinese). The seven-part series follows one small code change through the execution loop, code tools, permissions, sessions and context, streaming and failure recovery, and desktop integration, with source links for each point.

The maintainer now develops primarily on macOS (Apple Silicon), running from source (bun run tauri dev); it ships as a .dmg from v0.5.0. Windows x64 was the main platform before and has been tested extensively, but the maintainer no longer develops on it day to day, so feedback on how new changes behave on Windows is especially welcome. Linux has #[cfg(target_os = "linux")] branches in the codebase, but it is not part of the maintainer's day-to-day workflow, so Linux support is still best-effort. For the validated Ubuntu browser workflow, exFAT installation workaround, and local Ollama results, see the Linux validation report. For startup, shutdown, and termination by PID, see the browser usage guide. If you're running DreamCoder on Windows or Linux, issues and PRs are especially valuable; for the current Linux memory investigation, see #25. Check GitHub Releases for the actual assets available with each release.

• Phase 1: Desktop GUI implementation + Multi-Provider System + Project Workspace
• Phase 2: CLI Backend Integration + Computer Use + MCP + Skills + Agent Teams
• Phase 2.5: Performance — bundle splitting, polling throttle, terminal LRU, sessionStore refactor
• Phase 3: H5 LAN Access (access desktop sessions from a phone/browser after enabling it)
• Phase 4: Release Automation + Auto-update
• Phase 5: Remote Agent Backend (run the agent on a remote host over SSH / WebSocket)

See ROADMAP

• Bun >= 1.0
• Rust (required to build the desktop app)
• Node.js >= 18 (still needed by parts of the dependency chain)

Installers for macOS (Apple Silicon) and Windows x64 are on Releases. They are not code-signed yet; the release notes explain how to open them the first time. The steps below run the development version from source. This is a Bun monorepo, with separate dependencies at the repo root and in desktop/. Run all the steps below to avoid startup failures in tauri dev, especially around the sidecar binary, the frontend build output, or missing Tauri CLI pieces.

# 0. Clone the repo
git clone https://github.com/GoDiao/dreamcoder.git
cd dreamcoder

# 1. Install root workspace dependencies (sidecar runtime: Anthropic SDK, AWS SDK, ink, etc.)
bun install

# 2. Install desktop dependencies (Tauri CLI + React frontend)
cd desktop && bun install

# 3. Build the sidecar binary
bun run build:sidecars

# 4. Build the frontend once (creates desktop/dist; without it tauri dev fails with resource path `../dist` doesn't exist)
bun run build

# 5. Launch the desktop app in dev mode
bun run tauri dev

Linux users: you will also need system packages such as WebKitGTK, libappindicator, and librsvg. See the Tauri prerequisites guide for details. If you can contribute distro-specific setup commands, a PR would be greatly appreciated.

1. Open DreamCoder and go to Settings -> Providers.
2. Add your API key (for example Anthropic, OpenAI, or DeepSeek).
3. Choose a default model and start coding.

On first launch, DreamCoder detects your system locale: Chinese systems (zh-*) get the Chinese UI, everything else defaults to English. You can switch languages anytime in Settings, and your manual choice is persisted and always takes precedence afterwards.

DreamCoder is maintained by one person in their spare time. Companies and individuals are welcome to sponsor its development. See the sponsorship policy for tiers and rules, and email diaoshengjia@gmail.com to get in touch.

Issues and pull requests are welcome. If you'd like to improve DreamCoder, start with the Contributing Guide to get familiar with the workflow and collaboration style.

For release history and notable updates, see CHANGELOG.md.

MIT © 2024-2026 GoDiao & DreamCoder Contributors