BitFun
A desktop AI agent that turns every task into an app you can open.
- Source repo
- GCWing/OpenBitFun
- Stars
- ★ 2.9k
- Last updated
- today
- License
- MIT
- Primary language
- Rust
- FA score
- 40/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platform
- Cost
- Free software; you pay for model usage
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- Developers handling complex coding tasks in real repositories, using modes like Agentic, Plan, Debug, and Deep Review to move from code to merge-ready results.
- Not a fit if
- Teams unwilling to deploy their own relay server for multi-device sync
- Users with legacy 0.2.x data who won't run the migrator
- Source review
- 40/100 · Major gaps 1 safety controls not found
What does this agent do, and when should you use it?
BitFun is a desktop AI agent combining a high-performance Rust runtime with a polished desktop application. It functions as a Code Agent for real Git repositories, handling planning, editing, testing, and committing, while also supporting office tasks like research, writing, presentations, and reports. Its signature Agentic Mini Apps give each task its own interface—charts, boards, forms, panels—with the conversation bound to that interface's live state. BitFun offers self-hosted multi-device control through a relay you deploy, ensuring zero-knowledge privacy. With four tiers of customization (Custom Agents, MCP/Skills/Hooks, Mini Apps, source-level changes), it's highly adaptable. Performance features include a 98.67% average KV cache hit rate and flashgrep for fast searches in massive codebases. Cross-platform support spans Windows, macOS, and Linux, and it is model-agnostic.
BitFun performs end-to-end tasks within a desktop app: it reads local files and Git repositories, executes terminal commands, controls browsers and desktop applications, and accesses remote workspaces. Its Rust-based runtime handles agent execution, while flashgrep provides fast, cross-turn code search. For coding, it plans, edits, tests, and commits code. For office work, it researches, summarizes, writes, and generates presentations. Mini Apps generate task-specific UIs and bind conversations to their live state. It supports MCP, Skills, and Codex-compatible Hooks for extension, and offers self-hosted login and device sync via a relay. Installation is via downloaded installers or from source with pnpm run desktop:dev.
- Developers handling complex coding tasks in real repositories, using modes like Agentic, Plan, Debug, and Deep Review to move from code to merge-ready results.
- Knowledge workers transforming research materials into structured documents, reports, presentations, and meeting notes.
- Users who need cross-device workflows, syncing sessions and settings across devices via a self-hosted relay and controlling one device from another.
- Power users who want to customize agent behavior through Custom Agents, MCP, Skills, Hooks, or Mini Apps.
- Maintainers of large codebases using flashgrep to search Chromium-scale projects efficiently.
- Teams conscious of API costs, leveraging high KV cache hit rates to reduce expenses.
How do you install or deploy this agent?
Download the latest installer for macOS, Windows, or Linux from GitHub Releases. Alternatively, run from source: install Node.js 22.12+, pnpm 10.15.0 (via Corepack), the Rust toolchain, and Tauri prerequisites, then run pnpm install and pnpm run desktop:dev.
How do you use this agent?
On first launch, click Open on the Welcome tab and select a project folder. Then navigate to Settings > Models > Create First Configuration, choose a provider, enter API key, select models, and save. Return to the Session tab, type a concrete task, and press Enter or click Send.
What are this agent's strengths and limitations?
- Agentic Mini Apps provide task-specific interfaces instead of a one-size-fits-all chat box.
- Self-hosted zero-knowledge relay for multi-device control without vendor cloud dependency.
- Proprietary flashgrep enables fast search in massive codebases.
- High KV cache hit rate (98.67%) reduces API costs.
- Model-agnostic and cross-platform (Windows, macOS, Linux).
- Early-stage project; initial benchmark results are single-run and may fluctuate.
- Requires user configuration of model providers and API keys.
- Source-based setup involves multiple dependencies (Node.js, pnpm, Rust, Tauri).
- Self-hosted relay deployment requires additional server infrastructure.
- Documentation does not detail enterprise-grade security certifications.
How does this agent compare with similar options?
Compared to other agent frameworks like OpenClaw, BitFun emphasizes its desktop app with Mini Apps and the privacy benefits of self-hosted relay. It also highlights performance metrics like KV cache hit rate and flashgrep.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| BitFun This agent | 40 · Major gaps | Desktop appFree + model costs | ★ 2.9k | today | Rust | — |
| Devo Desktop Coding Agent | 58 · Major gaps | Desktop appFree + model costs | ★ 338 | 3d ago | Rust | OpenAI API · Claude API |
| Codeg Multi-Agent Coding Workspace | 45 · Major gaps | Desktop appFree + model costs | ★ 4k | today | Rust | Codex · Claude Code · OpenAI API · Claude API |
| Open Interpreter | 45 · Major gaps | CLIFree + model costs | ★ 69k | 8d ago | Rust | Codex |
How does FollowAgents rate this agent?
Why each dimension lost points
Evidence shows: README mentions self-hosted relay, zero-knowledge, local key derivation; SECURITY.md has security policy and vulnerability reporting; Cargo.toml includes encryption dependencies (AES-GCM, Argon2id). However, no explicit least-privilege design, user confirmation mechanisms, data flow transparency, sensitive data handling details, dependency security audit, external effects control, or rollback mechanisms were found. Deductions: insufficient evidence or absence in these areas.
Evidence shows: Cargo.toml has workspace dependency management, CI includes tests and build checks, README has performance metrics. However, dependency availability not verified (e.g., git dependencies), failure messages not documented. Deductions: insufficient evidence for dependency availability and failure messages.
Evidence shows: README clearly identifies target users (developers, office workers) and scenarios (coding, office work), has customization tiers (L1-L4), supports multiple platforms. However, capability boundaries not explicit, trigger precision (e.g., command triggers) not described, environment fit partially mentioned. Deductions: insufficient evidence for capability boundaries and trigger precision.
Evidence shows: README has clear structure, installation instructions, license (MIT), version number (0.2.16). However, missing changelog, known limitations, naming stability, and maintenance responsibility not explicit. Deductions: insufficient evidence in these areas.
Evidence shows: README claims output usability (e.g., generating documents, code), has performance metrics (KV cache hit rate, flashgrep speedup), but no cost-benefit analysis. Deductions: insufficient evidence for output usability and cost-benefit.
Evidence shows: README has benchmark data (SWE-Bench-Pro), but no reproduction method or raw data, facts and inferences not clearly separated. Deductions: insufficient evidence for verifiability.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- Publisher identity unverified; exercise caution regarding supply chain risks.
- Dependencies include git dependencies (tauri-runtime etc.); ensure supply chain security.
- Performance metrics in README (e.g., 98.67% KV cache hit rate) are single-run results and may fluctuate; do not treat as fixed promises.
- No explicit least-privilege or user confirmation mechanisms found; be cautious when granting permissions.
Recent releases
Latest entries from the project's GitHub Releases. Notes are quoted from upstream as written, not rewritten or tested by FollowAgents.
-
v1.0.6 OpenBitFun 1.0.6
Release notes excerpt
下载 / Downloads 桌面端 / Desktop - Windows x64: [安装包](https://github.com/GCWing/OpenBitFun/releases/download/v1.0.6/OpenBitFun_1.0.6_windows-x86_64-installer.exe) - macOS: [Apple Silicon](https://github.com/GCWing/OpenBitFun/releases/download/v1.0.6/OpenBitFun_1.0.6_aarch64.dmg) | [Intel](https://github.com/GCWing/OpenBitFun/releases/download/v1.0.6/OpenBitFun_1.0.6_x64.dmg) …View on GitHub ↗ -
data-migrator-v0.1.2 OpenBitFun Data Migrator data-migrator-v0.1.2
Release notes excerpt
BitFun → OpenBitFun Data Migrator 将旧版 BitFun 的配置、会话、工作区及其他受支持数据迁移到 OpenBitFun。迁移器保留旧版数据，不会自动删除 BitFun 的数据目录。 Migrate settings, sessions, workspaces, and other supported data from BitFun to OpenBitFun. The migrator preserves the original BitFun data and does not automatically delete its data directories. 最新版本与使用指南：[中文](https://github.com/GCWing/OpenBitFun/blob/main/src/apps/data-migrator/README.zh-CN.md) · Latest release and guide: [English](https://github.com/GCWing/OpenBitFun/blob/main/src/apps/data-migrator/README.md) v0.1.2 中文 - 修复助理工作区与会话迁移后的关联问题，解决部分会话无法继续对话的问题。 …View on GitHub ↗ -
v1.0.5 OpenBitFun 1.0.5
Release notes excerpt
下载 / Downloads 桌面端 / Desktop - Windows x64: [安装包](https://github.com/GCWing/OpenBitFun/releases/download/v1.0.5/OpenBitFun_1.0.5_windows-x86_64-installer.exe) - macOS: [Apple Silicon](https://github.com/GCWing/OpenBitFun/releases/download/v1.0.5/OpenBitFun_1.0.5_aarch64.dmg) | [Intel](https://github.com/GCWing/OpenBitFun/releases/download/v1.0.5/OpenBitFun_1.0.5_x64.dmg) …View on GitHub ↗