SuperCoder Desktop
A local desktop coding agent for planning, editing, and reviewing work in your own codebase.
What does this agent do, and when should you use it?
SuperCoder is a local-first, open-source desktop coding agent that uses credentials supplied by the developer. Its Tauri 2 and React desktop application is a thin adapter over a pure-Rust agent core with Ask, Plan, and Coding modes, subagents, skills, tool approval, and prompt caching. The app supports in-place edits, checkpoints and rewind, diff review, an interactive terminal, and a file explorer for a selected local folder. An optional Go Context Engine indexes repositories with tree-sitter, vector retrieval, a call graph, and BM25, backed by Qdrant and FalkorDB. The current product is built from source; the legacy 2024 pipeline in v1/ is frozen and is neither maintained nor built.
After the user configures a provider's base_url, api_key, and model in Settings, they create a session, choose a folder, and select Ask, Plan, or Coding mode. The desktop app delegates to the Rust agent core, whose documented harness includes a loop, tools, modes, subagents, skills, tool approval, and prompt caching; the app can make in-place edits, show diff review, provide checkpoints and rewind, and expose an interactive terminal and file explorer. When Settings → Context engine is enabled, a locally run docker compose service indexes the repository through tree-sitter into vector, call-graph, and BM25 retrieval structures. The agent queries that service through codebase_search and codebase_graph. Model requests go directly from the machine to the configured OpenAI chat-completions or Anthropic Messages API.
- A developer who wants to edit a local project in place, inspect diffs, and return to earlier checkpoints.
- An engineer who wants to outline a change in Plan mode before carrying it out in Coding mode.
- A contributor working in a large repository who needs call-graph, vector, and lexical retrieval to find relevant code.
- A developer with OpenAI chat-completions or Anthropic Messages credentials who wants to configure their own provider endpoint and model.
- A desktop-focused programmer who wants file browsing and an interactive terminal alongside an agent session.
What are this agent's strengths and limitations?
- Requests go directly from the developer's machine to the configured model provider, without an intermediary vendor backend.
- The pure-Rust agent core is separated from the Tauri 2 and React desktop adapter.
- Its optional Context Engine combines tree-sitter, vector search, call-graph retrieval, and BM25 for repository navigation.
- The local workflow includes in-place editing, diff review, checkpoints and rewind, a terminal, and a file explorer.
- Prebuilt binaries are not yet available, so adoption currently requires building from source.
- Users must supply a provider base_url, api_key, and model; model requests still go to the provider they choose.
- The optional Context Engine adds Docker Compose deployment and requires SUPERCODER_OPENAI_API_KEY.
- The legacy v1/ pipeline is frozen, unmaintained, and not built.
How do you install or deploy this agent?
Install Rust stable, the Tauri 2 system prerequisites for your OS, Node.js 20+, and npm. Then run:
cd apps/desktop
npm install
npm run tauri:dev
To build a release bundle, run npm run tauri:build. On first launch, add an LLM provider in Settings with base_url, api_key, and model. The optional Context Engine also requires Docker with Compose:
cd services/context-engine
cp .env.example .env
docker compose up -d --build
Set SUPERCODER_OPENAI_API_KEY in .env, then enable Settings → Context engine in the app.
How do you use this agent?
Launch the desktop app and configure an LLM provider in Settings. Create a session, select the folder to work in, and choose Ask, Plan, or Coding mode. For repository-scale structural retrieval, start the Context Engine and enable Settings → Context engine; the agent can then use codebase_search and codebase_graph. Use checkpoints and rewind during changes, and inspect results with diff review.