RayCodes_ClaudeCodeArchitecture - GitLite

KO | EN

gitlite — search

#tensorflow #redis #electron #ai-agent #kotlin #http #openai #animation #large-language-models #framework

RayCodes_ClaudeCodeArchitecture

(★ 10)

No description available.

File Explorer

ZIP Download

clean_calculator.py
messy_calculator.py
orchestrator.py
README.md
requirements.txt

# Use via CDN

jsDelivr

jsDelivr serves any public GitHub repository as a CDN with zero setup. Pick a version and a file to get a ready-to-paste link and snippet.

Version

File

Link

Example

Command Glossary

Commands referenced in this DOCs, explained below.

🔍

`ollama`

View Details ▼

A large language model runner.
For a list of available models, see <https://ollama.com/library>.

ollama serve

Start the daemon required to run other commands:

ollama run {{model}}

Run a model and chat with it (will automatically download the model if it's not downloaded):

ollama run {{model}} --think=false "{{prompt}}"

Run a model with a single prompt and thinking turned off:

🔍

`rich`

View Details ▼

A toolbox for fancy output in the terminal.

rich {{path/to/file.py}}

Display a file with syntax highlighting:

rich {{path/to/file.py}} --line-numbers --guides

Add line numbers, and indentation guides:

rich {{path/to/file.py}} --theme {{monokai}}

Apply a theme:

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

A local, zero-cost, multi-agent Python MVP demonstrating SaaS orchestration running entirely locally via Ollama using the `qwen3.5:4b` model.

## Project Description
Local-Claude-Brain demonstrates a fully automated, local multi-agent architecture (Planner -> Executor). This project breaks down a Python refactoring task into granular steps: a Planner Agent formulates a step-by-step refactoring approach, and an Executor Agent implements those exact steps to transform bad code into high-quality code. This architecture operates autonomously without incurring any third-party API costs.

## Tech Stack
* **Language:** Python 3.x
* **AI Provider:** Ollama (Local)
* **LLM:** Qwen3.5:4b (`qwen3.5:4b`)
* **Libraries:** `ollama`, `rich`

## Prerequisites
1. **Install Ollama:** Download and install Ollama from [ollama.com](https://ollama.com/).
2. **Pull the Model:** Open your terminal and pull the Qwen3.5 model. Keep the Ollama app running in the background.
   ```bash
   ollama run qwen3.5:4b
   ```

## Installation Steps
1. **Clone the repository:**
   ```bash
   git clone https://github.com/yourusername/Local-Claude-Brain.git
   cd Local-Claude-Brain
   ```

2. **Install dependencies:**
   ```bash
   pip install -r requirements.txt
   ```

## Usage / Run Project
Execute the orchestrator to see the multi-agent loop in action:

```bash
python orchestrator.py
```

**What to expect:**
1. You will see the **Planner Agent** output its step-by-step refactoring plan to the console in yellow.
2. The **Executor Agent** will generate the refactored text under the hood based on that exact plan.
3. A success message in green will appear, and the final polished Python script will be saved down to `clean_calculator.py`.

## Usecase Examples
This base multi-agent foundation can be scaled for high-value B2B use cases:
1. **Legacy Code Refactoring:** Automatically ingest legacy monoliths piece-by-piece, plan modernization steps, and rewrite into clean microservices, prioritizing documentation and type safety.
2. **Automated QA Validation:** A multi-agent loop where an Executor writes code and a distinct QA Agent immediately writes and runs unit tests, passing bugs/failures back to the Executor in a self-healing loop.
3. **Local Data Sanitization:** Process sensitive local PII/PHI data where one agent evaluates data privacy risks and redacts sensitive entities before passing the cleaned data to downstream applications.

## Future Features
1. **Reviewer Agent Loop:** Add a "Reviewer" agent that critiques the Executor's output and loops execution until arbitrary quality thresholds are met prior to outputting.
2. **Local File-System Tool Calls:** Empower agents with function calling to autonomously read, list, search, and write across an entire codebase footprint rather than just single files.
3. **Vision Model Support:** Integrate models like LlaVa or Qwen-VL to consume physical architectural diagrams, UI mockups, or bug screenshots, translating them directly into agentic logic schemas.