MCPHub LabRegistryGCWing/BitFun
GCWing

GCWing/BitFun

Built by GCWing 541 stars

What is GCWing/BitFun?

BitFun is a next-generation AI assistant with built-in Code Agent and Cowork Agent. It has memory, personality, and the ability to evolve over time. You can remotely control the desktop through mobile

How to use GCWing/BitFun?

1. Install a compatible MCP client (like Claude Desktop). 2. Open your configuration settings. 3. Add GCWing/BitFun using the following command: npx @modelcontextprotocol/gcwing-bitfun 4. Restart the client and verify the new tools are active.
🛡️ Scoped (Restricted)
npx @modelcontextprotocol/gcwing-bitfun --scope restricted
🔓 Unrestricted Access
npx @modelcontextprotocol/gcwing-bitfun

Key Features

Native MCP Protocol Support
Real-time Tool Activation & Execution
Verified High-performance Implementation
Secure Resource & Context Handling

Optimized Use Cases

Extending AI models with custom local capabilities
Automating system workflows via natural language
Connecting external data sources to LLM context windows

GCWing/BitFun FAQ

Q

Is GCWing/BitFun safe?

Yes, GCWing/BitFun follows the standardized Model Context Protocol security patterns and only executes tools with explicit user-granted permissions.

Q

Is GCWing/BitFun up to date?

GCWing/BitFun is currently active in the registry with 541 stars on GitHub, indicating its reliability and community support.

Q

Are there any limits for GCWing/BitFun?

Usage limits depend on the specific implementation of the MCP server and your system resources. Refer to the official documentation below for technical details.

Official Documentation

View on GitHub

English 中文

<div align="center">

BitFun

</div> <div align="center">

Trendshift

GitHub release Website License: MIT Platform

</div>

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.

readme_hero


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.

Agent benchmark scores

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.

KV Cache hit rate distribution

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.

flashgrep search speed


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

ScenarioDelivery goalTypical capabilities
CodingMove from a real repository to a mergeable result.Agentic, Plan, Debug, testing, Git, Deep Review, long-horizon tasks, and benchmarks.
Office WorkMove 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.

first_screen_screenshot


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:

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:

TierPathBest for
L1Custom AgentDefining roles, flows, constraints, and tool bundles.
L2MCP / SkillsConnecting external tools, professional capabilities, and workflows.
L3Mini AppGenerating dedicated interfaces, forms, panels, or visualizations for tasks.
L4Source-level customizationChanging 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:

  1. Code Agent, Deep Review, debugging, and long-task execution capabilities
  2. Cowork, research, document, and desktop workflows
  3. MCP, Skills, Mini App, LSP plugins, and new domain Agents
  4. Runtime stability, performance, context efficiency, and verifiability

Please submit PRs directly to the main branch. For more details, see CONTRIBUTING.md.


Disclaimer

  1. This project is spare-time exploration and research into next-generation human-machine collaboration, not a commercial profit-making project.
  2. This project is 97%+ built through Vibe Coding. Code feedback is welcome, and AI-assisted refactoring and optimization are encouraged.
  3. 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.

Global Ranking

8.5
Trust ScoreMCPHub Index

Based on codebase health & activity.

Manual Config

{ "mcpServers": { "gcwing-bitfun": { "command": "npx", "args": ["gcwing-bitfun"] } } }