exomodel 1.0.0

ExoModel AI: Object-Oriented Agentic AI

🌌 ExoModel AI

The Object-Oriented Framework for Agentic AI

📖 Official Documentation: https://exomodel.ai
📦 GitHub Repository: https://github.com/exomodel-ai/exomodel

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ExoModel brings AI capabilities directly into your data models. Instead of building prompt pipelines around your objects, your objects become the agents — they populate themselves from natural language, consult their own documents via RAG, and validate their state against business rules. Built on top of LangChain and Pydantic.

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⚡ Why ExoModel?

While traditional agent frameworks focus on chat, ExoModel focuses on the Business Entity. It brings type safety and structure to the chaotic world of LLMs.

Traditional AI Apps	With ExoModel
Passive data models	Models that populate and update themselves from natural language
Fragile manual prompting	Type-safe field mapping — the schema is the prompt
Disconnected RAG pipelines	Documents attached directly to the class, consulted at runtime
Complex JSON parsing	Guaranteed schema-validated outputs via Pydantic

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🔥 Core Features

• 🧠 Smart CRUD — Create and update class instances using natural language. ExoModel understands intent and maps it to specific schema fields.
• 📚 Native RAG Grounding — Attach PDFs, URLs, or text files directly to your class. The object uses this "brain" to validate its own state against business rules.
• 🤖 ExoAgent Orchestration — A centralized engine that manages tool routing and persona switching (generalist, specialist, hybrid, orchestrator) to optimize accuracy and cost.
• 🔌 API-First Design — Transform messy human input into the strict JSON schemas required by your existing APIs and services.
• ⚙️ Agentic Tools with @llm_function — Decorate any method with @llm_function to expose it as an agentic tool. The agent discovers and calls it autonomously — no manual tool registration required.
• 📊 Fluent List Management — Handle collections of entities with ExoModelList, enabling bulk LLM generation and CSV/UI exports in a single call.

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📦 Installation

ExoModel 1.0.0 is LLM-agnostic. Install only the provider package you need:

pip install exomodel[google]      # Gemini (default)
pip install exomodel[anthropic]   # Claude
pip install exomodel[openai]      # OpenAI / Azure OpenAI
pip install exomodel[cohere]      # Cohere
pip install exomodel[all]         # all providers

Then create a .env file at the root of your project:

MY_LLM_MODEL=google_genai:gemini-2.5-flash-lite
MY_EMB_MODEL=google_genai:gemini-embedding-001   # optional — auto-detected from provider
GOOGLE_API_KEY=your-key-here

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🚀 Quick Start

1. Create a Knowledge Base

Create a file named proposal_rules.md. This grounds your AI objects in real-world business logic.

# Proposal Rules
- We only accept projects above $10,000.
- Every proposal must include a 10% safety margin in the pricing.
- We do not work with companies in the tobacco industry.

2. Define and Run your Entity

Inherit from ExoModel to give your data structures autonomous reasoning powers.

from exomodel import ExoModel

class Proposal(ExoModel):
    client: str = ""
    budget: float = 0.0

    @classmethod
    def get_rag_sources(cls):
        # The object now 'knows' your specific business rules
        return ["proposal_rules.md"]

# Populate the object from raw natural language
p = Proposal.create("Draft a 50k proposal for Tesla")

# Display a formatted summary
print(p.to_ui())

# The object analyzes itself against proposal_rules.md
print(p.run_analysis())

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🛠 Architecture

ExoModel is built to be modular, scalable, and provider-agnostic.

Class	Role
ExoModel	The intelligent data foundation — schema-driven, AI-powered, RAG-aware. Subclass this to define your entities.
ExoAgent	The reasoning engine that routes tool calls, manages LLM context, and processes RAG sources. Used internally by ExoModel; also available for direct use.
ExoModelList[T]	Typed collection for bulk generation, updating, and export of ExoModel instances in a single LLM call.
@llm_function	Decorator that turns any method into an agentic tool, discoverable and callable by ExoAgent at runtime via master_prompt.

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🎯 Use Cases

• 🤝 Consultative Apps — Build AI advisors that guide users through complex processes (insurance claims, financial planning) by populating structured models in real time.
• 🔌 Agentic Middleware — Bridge human language and rigid backends. Ensure every LLM output fits your API's exact specifications before it hits the wire.
• 📊 Sales & CRM Automation — Draft professional proposals, calculate pricing based on business rules, and update lead status autonomously.
• 🕵️ Smart Auditing & Compliance — Create objects that read their own source contracts to populate audit fields and flag inconsistencies without manual oversight.
• 📈 Intelligent Dashboarding — Transform raw logs or transcripts into lists of structured objects (ExoModelList), ready for data visualization.

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🔧 Logging

ExoModel uses Python's standard logging module. No output is shown by default.

import logging

# Show warnings and errors only (recommended for production):
logging.getLogger("exomodel").setLevel(logging.WARNING)

# Show all internal traces (useful for debugging):
logging.basicConfig(level=logging.DEBUG)
logging.getLogger("exomodel").setLevel(logging.DEBUG)

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🤝 Contributing

We welcome contributions! ExoModel is built by developers for developers.

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.