exomodel 0.1.1b0

ExoModel AI: Object-Oriented Agentic AI

🌌 ExoModel AI

Object-Oriented Agentic AI

ExoModel AI is a high-level Python framework designed to bridge the gap between structured objects and Large Language Models (LLMs). It allows you to transform static data models into "living" entities capable of autonomous updates, self-analysis, and context-aware interactions using RAG (Retrieval-Augmented Generation).

Built on top of LangChain and Pydantic, ExoModel provides a seamless "Object-to-Prompt" interface for building AI-native applications.

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🚀 Key Features

• Smart CRUD: Create, Read, and Update your data models directly through natural language prompts.
• Built-in RAG Support: Attach URLs, PDFs, or Text files to your models to ground AI responses in specific business rules.
• ExoAgent Orchestration: A specialized agent (ExoAgent) that manages tool routing and persona switching (Generalist, Specialist, Hybrid).
• Structured Output: Guarantees that LLM responses strictly follow your Pydantic schemas.
• Fluent List Management: Handle collections of entities with ExoModelList, enabling bulk generation and CSV exports.
• UI-Ready: Built-in to_ui() methods optimized for Telegram, WhatsApp bots, and CLI displays.

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📦 Installation

ExoModel is currently in Beta (0.1.0). You can install it using pip:

pip install exomodel

*Ensure you have your environment variables configured:

• LLM Key (e.g., GOOGLE_API_KEY or OPENAI_API_KEY).*
• MY_LLM_MODEL (e.g., gemini-2.5-flash or gpt-4o).*
• MY_EMBEDDING_MODEL (e.g., gemini-embedding-001 or text-embedding-3-small).*

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🛠 Basic Usage

1. Define your Model

Inherit from ExoModel to give your data structures "AI powers."

from exo_model import ExoModel
from pydantic import Field

class Proposal(ExoModel):
    client: str = Field(description="Legal name of the company")
    business_challenge: str = Field(description="The main problem to solve")
    solution: str = Field(description="AI Strategy, AI Program Execution, or AI Labs")
    pricing: int = Field(default=0)

    @classmethod
    def get_rag_sources(cls):
        return ["docs/proposal_rules.md"]

2. Interact via Prompts

You can initialize an object directly with a prompt or update it later.

# Create and populate
proposal = Proposal(prompt="Create a proposal for Tesla regarding a new AI Predictive Maintenance roadmap.")

# Analyze based on RAG rules
analysis = proposal.run_analysis()
print(analysis)

# Update a specific field
proposal.update_field("pricing", "Based on the roadmap complexity, set a fair price.")

3. Manage Lists

Use ExoModelList to handle multiple entities.

from exomodel_list import ExoModelList

tasks = ExoModelList(item_class=Proposal)
tasks.create_list("Generate 3 different AI proposals for a retail company.")

print(tasks.to_ui())
print(tasks.to_csv())

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📂 Project Structure

src/exomodel/
├── exoagent.py          # The core LLM & RAG handler
├── exomodel.py          # Base ExoModel class (Pydantic + AI)
├── exomodel_list.py      # Generic list management for ExoModels
├── prompt/               # ExoModel prompt templates
└── docs/                 # Documentation

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🤝 Contributing

We welcome contributions! Please feel free to submit a Pull Request or open an Issue.

1. Fork the Project.
2. Create your Feature Branch (git checkout -b feature/AmazingFeature).
3. Commit your Changes (git commit -m 'Add some AmazingFeature').
4. Push to the Branch (git push origin feature/AmazingFeature).
5. Open a Pull Request.

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📄 License

Distributed under the Apache License 2.0. See LICENSE for more information.