c4agent 0.1

A multi-provider Python code execution and dependency management agent

🚀 CodeAgent

Automate code generation, execution, and debugging for your projects using LLM-powered agents.
Supports multiple providers (Proplexity, Gemini, and more), multimodal input, and dependency management.

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📦 Installation

Install from PyPI:

pip install c4agent

Or install from source:

git clone https://github.com/yourusername/CodeAgent.git
cd CodeAgent
pip install -r requirements.txt

⚡ Quick Start

Initialize Agent

from Agent.CodeAgent import CodeAgent        # v1
from Agent.CodeAgentV2 import CodeAgent      # v2
from Agent.CodeAgentV3 import CodeAgent      # v3

# Example: Initialize with Proplexity API
agent = CodeAgent("<apikey>")

🧑‍💻 Versions

CodeAgent V1

• Generates & runs Python projects
• Provider: "proplexity"

CodeAgent V2

• Generates & runs Python projects
• Providers: "proplexity", "gemini"
• Dependency Manager included

CodeAgent V3

• Generates & runs Python projects
• Supports multiple providers
• Dependency Manager
• Multimodal input (Text + Images)

✨ Usage

🔹 1. Generate Code from Prompt

agent.generate(
    "Explain About Artificial Intelligence"
).json()

🔹 2. Automate Flow - Example Project

prompt = """
You are an AI Agent. You will code like an AI research scientist.

Code For SmolAgents

Instructions:
1. Agent should answer tech-related questions
2. Execution not supported
3. Give only Python code
4. Python only support
5. Should include docstrings

User: Build a multimodal embedding model (Image + Text) using contrastive learning.

Dataset Link and Description:
- Kaggle credentials are already set up
- Dataset: fashion-product-images-small

Load dataset:
```python
!mkdir -p /root/.kaggle
!cp kaggle.json /root/.kaggle
!chmod 600 /root/.kaggle/kaggle.json
!kaggle datasets download paramaggarwal/fashion-product-images-small

Dataset load using Python:

import pandas as pd
df = pd.read_csv("/content/myntradataset/styles.csv", on_bad_lines="skip")
df.head()

Example dataset output:

   id    gender    masterCategory    subCategory    articleType    baseColour    season    year    usage    productDisplayName
0  15970  Men      Apparel         Topwear        Shirts        Navy Blue    Fall      2011.0  Casual   Turtle Check Men Navy Blue Shirt
1  39386  Men      Apparel         Bottomwear     Jeans         Blue         Summer   2012.0  Casual   Peter England Men Party Blue Jeans
2  59263  Women    Accessories     Watches        Watches       Silver       Winter   2016.0  Casual   Titan Women Silver Watch
3  21379  Men      Apparel         Bottomwear     Track Pants   Black        Fall      2011.0  Casual   Manchester United Men Solid Black Track Pants
4  53759  Men      Apparel         Topwear        Tshirts       Grey         Summer   2012.0  Casual   Puma Men Grey T-shirt

Model Requirements:

• Use HuggingFace pretrained BERT and ViT models
• Train using contrastive learning
• Use Torch and optionally LangChain
• Save best model & logs
• Include evaluation, testing, and CUDA support
• Progress bar using tqdm
• Provide full final code """

Run the agent

agent(prompt)

### 🔹 3. V3 Multimodal Example

```python
agent = CodeAgent(
    gemini_apikey="<apikey>",
    provider="gemini"
)

result = agent({
    "text": "Write a Python script to save a plot in ./plot.png",
    "images": ["/content/Loss.png", "/content/Accuracy.png"]
})

print(result)

📂 Outputs are stored in local folders.

📑 Example Output

When running prompts, CodeAgent will:

• ✅ Generate full Python code
• ✅ Manage dependencies
• ✅ Save outputs & logs locally
• ✅ Handle debugging & execution automatically

🔧 Requirements

• Python 3.8+
• Dependencies (auto-installed with pip install c4agent)

📌 Roadmap

• Support Proplexity provider
• Add Gemini provider
• Dependency manager
• Multimodal input (text + images)
• Add more providers (OpenAI, Claude, etc.)
• CLI support
• Web UI for interactive coding

🤝 Contributing

Contributions are welcome!

1. Fork the repo
2. Create your feature branch (git checkout -b feature/awesome-feature)
3. Commit changes (git commit -m 'Add awesome feature')
4. Push to branch (git push origin feature/awesome-feature)
5. Open a Pull Request

📜 License

MIT License © 2025

🌟 Support

If you like this project, please ⭐ the repo to support development!