🦖 Dinnovos Agent

Agile AI Agents with Multi-LLM Support

Dinnovos Agent is a lightweight Python framework for building AI agents that can seamlessly switch between different Large Language Models (OpenAI, Anthropic, Google).

Features

• 🔄 Multi-LLM Support: OpenAI (GPT), Anthropic (Claude), Google (Gemini)
• 🎯 Simple API: Intuitive interface for building conversational agents
• 💾 Context Memory: Automatic conversation history management
• 🔌 Extensible: Easy to add new LLM providers
• 🪶 Lightweight: Minimal dependencies, maximum flexibility

Installation

Basic Installation

pip install dinnovos-agent

With specific LLM support

# For OpenAI only
pip install dinnovos-agent[openai]

# For Anthropic only
pip install dinnovos-agent[anthropic]

# For Google only
pip install dinnovos-agent[google]

# For all LLMs
pip install dinnovos-agent[all]

For development

pip install dinnovos-agent[dev]

Quick Start

from dinnovos import Agent, OpenAILLM

# Create an LLM interface
llm = OpenAILLM(api_key="your-api-key", model="gpt-4")

# Create an Agent
agent = Agent(
    llm=llm,
    system_prompt="You are a helpful assistant."
)

# Chat with your agent
response = agent.chat("Hello! What can you do?")
print(response)

Examples

Using Different LLMs

from dinnovos import Agent, OpenAILLM, AnthropicLLM, GoogleLLM

# OpenAI
openai_llm = OpenAILLM(api_key="sk-...", model="gpt-4")
agent_gpt = Agent(llm=openai_llm)

# Anthropic Claude
anthropic_llm = AnthropicLLM(api_key="sk-ant-...", model="claude-sonnet-4-5-20250929")
agent_claude = Agent(llm=anthropic_llm)

# Google Gemini
google_llm = GoogleLLM(api_key="...", model="gemini-1.5-pro")
agent_gemini = Agent(llm=google_llm)

Custom System Prompt

agent = Agent(
    llm=llm,
    system_prompt="You are an expert Python programmer.",
    max_history=20  # Keep last 20 messages
)

response = agent.chat("Explain decorators in Python")

Managing Conversation

# Get conversation history
history = agent.get_history()

# Reset conversation
agent.reset()

# Change system prompt
agent.set_system_prompt("You are now a math tutor.")

API Reference

Agent Class

Agent(llm: BaseLLM, system_prompt: str = None, max_history: int = 10)

Methods:

• chat(message: str, temperature: float = 0.7) -> str: Send a message and get response
• reset(): Clear conversation history
• get_history() -> List[Dict]: Get conversation history
• set_system_prompt(prompt: str): Change system prompt and reset

LLM Interfaces

OpenAILLM(api_key: str, model: str = "gpt-4")
AnthropicLLM(api_key: str, model: str = "claude-sonnet-4-5-20250929")
GoogleLLM(api_key: str, model: str = "gemini-1.5-pro")

Requirements

• Python 3.8+
• Optional: openai, anthropic, google-generativeai (based on which LLMs you use)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

MIT License - see LICENSE file for details

Links

• Documentation
• Issue Tracker
• Source Code

Support

If you encounter any issues or have questions, please file an issue on GitHub. '''