GCWing/BitFun
RustBitFun is a desktop-grade Agent runtimeand a ready-to-use suite of desktop Agent applications.with built-in Code Agent 、 Cowork Agent、Computer Use. It has memory, personality, and the ability to evolve over time
cargo add bitfunEnglish 中文
Local AI Workbench Built Around the Code Agent
BitFun is a local AI workbench built around a Code Agent designed for long-horizon tasks, engineering execution, and token economy.
It can understand complex context, call tools, wait for results, correct deviations, and keep long-horizon tasks moving until they reach a deliverable state. Coding, research, office work, documents, desktop operations, and extensible workflows all happen in the same local desktop environment.
Core goal: move AI from iterative Agent Loop execution into a productivity system that can autonomously complete long-horizon work.
Agent Core Metrics
The data below evaluates BitFun's core Agent capabilities. All measurements use Deepseek-V4-Pro and are grouped into completion results, token economy, and other experience metrics.
The current numbers are BitFun's initial evaluation results, with each case run once. Benchmarks can fluctuate with task sampling, model versions, runtime environment, and single-run variance, so these scores are meant as an initial sanity signal that the current Agent is already reasonably capable, not as a fixed ranking claim or final ceiling. We will keep optimizing and release full benchmark details later.
1. Completion Results
BitFun leads Open Code and Claude Code on both SWE-Bench-Pro and SWE-Bench-Verified. SWE-Bench-Pro focuses on complex software engineering, while SWE-Bench-Verified focuses on human-verified GitHub issue fixes.
Benchmark references: SWE-Bench-Pro / SWE-Bench-Verified
2. Token Economy
Agent economy needs to be evaluated across end-to-end token consumption, execution time, and KV Cache reuse. The current snapshot first covers KV Cache behavior from the same SWE-Bench-Pro round: BitFun's average KV Cache hit rate was 98.67%. The follow-up full benchmark report will add the broader cost and latency metrics.
3. Other Experience Metrics
Beyond cost, Agent experience also depends on how quickly it can retrieve context in very large engineering projects. For tens-of-millions-line repositories such as Chromium, BitFun uses flashgrep to reduce search time by up to about 94.6%, with an average speedup of about 36.1x.
Two Core Scenarios, One Extensible Agent Desktop
You can hand two kinds of complex work to BitFun: shipping code in real repositories and turning source material into office deliverables. When a task needs the browser, desktop apps, the terminal, or a remote environment, it can enter the real workspace; when your workflow needs more, you can extend it with custom Agents, MCP, Skills, and Mini Apps.
Core Scenarios
| Scenario | Delivery goal | Typical capabilities |
|---|---|---|
| Coding | Move from a real repository to a mergeable result. | Agentic, Plan, Debug, testing, Git, Deep Review, long-horizon tasks, and benchmarks. |
| Office Work | Move from source material to deliverable documents. | Research, PPT, DOCX, XLSX, PDF, summarization, writing, meeting notes, and reports. |
Shared Capabilities
- Desktop execution layer: Computer Use, browser operation, desktop apps, the filesystem, terminals, remote workspaces, and Mini Apps let the Agent enter real work environments.
- Customization layer: MCP, Skills, custom Agents, Mini Apps, and source-level extension let BitFun keep growing around your tools, roles, and interfaces.
Ready Out of the Box
Download directly
Go to Releases to download the latest desktop installer. After installation, configure your model and start using BitFun.
Run from source
Prerequisites:
- Node.js 22.12+ (LTS recommended)
- pnpm 10.15.0 via Corepack
- Rust toolchain
- Tauri prerequisites
pnpm install
pnpm run desktop:dev
For more development details, see CONTRIBUTING.md.
Customize Your BitFun
BitFun's extension paths progress continuously from light to deep customization:
| Tier | Path | Best for |
|---|---|---|
| L1 | Custom Agent | Defining roles, flows, constraints, and tool bundles. |
| L2 | MCP / Skills | Connecting external tools, professional capabilities, and workflows. |
| L3 | Mini App | Generating dedicated interfaces, forms, panels, or visualizations for tasks. |
| L4 | Source-level customization | Changing tools, adapters, UI, Runtime, or product shape. |
You can use BitFun's Code Agent to extend BitFun itself.
Contributing
Stars, Issues, and PRs are welcome. We especially care about:
- Code Agent, Deep Review, debugging, and long-task execution capabilities
- Cowork, research, document, and desktop workflows
- MCP, Skills, Mini App, LSP plugins, and new domain Agents
- Runtime stability, performance, context efficiency, and verifiability
Please submit PRs directly to the main branch. For more details, see CONTRIBUTING.md.
Disclaimer
- This project is spare-time exploration and research into next-generation human-machine collaboration, not a commercial profit-making project.
- This project is 97%+ built through Vibe Coding. Code feedback is welcome, and AI-assisted refactoring and optimization are encouraged.
- This project depends on and references many open-source projects. Thanks to all open-source authors. If your rights are affected, please contact us for remediation.