🤖 AgentAxis

A production-ready, extensible AI Agent Framework for Python. Built with Clean Architecture, it supports multi-provider LLM orchestration (via LiteLLM), memory management, and tool usage.

🚀 Features

• Multi-Provider Support: Switch between OpenAI, Anthropic, Gemini, and local models seamlessly using LiteLLM.
• HTTP Endpoint Support: Connect to any custom LLM endpoint with Bearer token authentication.
• System Prompts: Configure agent behavior with built-in system prompt support.
• Intelligent Routing: Architecture designed for dynamic model selection.
• Tooling System: Register Python functions as tools with automatic JSON schema generation + MCP Support.
• Memory Management: Pluggable memory backends (In-Memory provided, Extensible to Redis/Postgres).
• Vector Store / RAG: Built-in adapter for ChromaDB.
• Clean Architecture: Strictly decoupled core logic from infrastructure.

📦 Installation

pip install agentaxis

⚡ Quick Start

1. Basic Agent with System Prompt

from agentaxis import AgentService, AgentEntity, InMemoryMemory, AIEngine
from agentaxis.adapters.tools import ToolRegistry
import asyncio

async def main():
    # 1. Setup AI Engine with system prompt
    llm = AIEngine(
        model="gpt-4o", 
        api_key="sk-...",
        system_prompt="You are a helpful assistant that speaks like a pirate."
    )
    memory = InMemoryMemory()
    registry = ToolRegistry()
    
    # 2. Register Tools
    @registry.register
    def get_time(timezone: str) -> str:
        """Returns current time in timezone."""
        return "12:00 PM"

    # 3. Create Agent
    entity = AgentEntity(id="bot-1", name="Helper", role_description="Assistant")
    agent = AgentService(agent_entity=entity, llm=llm, memory=memory, tool_registry=registry)

    # 4. Chat
    response = await agent.chat("What time is it in NY?")
    print(response)

if __name__ == "__main__":
    asyncio.run(main())

2. Using Custom HTTP Endpoint

from agentaxis import HTTPEngine

# Connect to any LLM endpoint
llm = HTTPEngine(
    url="https://your-llm-endpoint.com/v1/chat/completions",
    auth_token="your-bearer-token",
    model="custom-model",
    system_prompt="You are an expert coding assistant."
)

3. Switching LLM Providers

# OpenAI
llm = AIEngine(model="gpt-4", api_key="sk-...")

# Anthropic
llm = AIEngine(model="claude-3-opus-20240229", api_key="sk-ant-...")

# Google Gemini
llm = AIEngine(model="gemini/gemini-pro", api_key="...")

# Local models
llm = AIEngine(model="ollama/llama2", api_base="http://localhost:11434")

4. Connecting to MCP Servers via HTTP

from agentaxis import AIEngine, AgentService, AgentEntity, InMemoryMemory
from agentaxis.adapters.tools import HTTPMCPAdapter, ToolRegistry

async def main():
    # Connect to HTTP MCP server
    mcp = HTTPMCPAdapter(
        base_url="http://localhost:3000",
        auth_token="your-token"  # Optional
    )
    
    await mcp.connect()
    tools = await mcp.list_tools()
    
    # Register MCP tools
    registry = ToolRegistry()
    for tool in tools:
        registry.register_tool(tool)
    
    # Create agent with MCP tools
    llm = AIEngine(model="gpt-4", api_key="sk-...")
    entity = AgentEntity(id="bot", name="MCP Agent", role_description="Assistant")
    agent = AgentService(entity, llm, InMemoryMemory(), registry)
    
    response = await agent.chat("Use the MCP tools to help me")
    print(response)

🆕 What's New in v0.3.0

• HTTP MCP Adapter - Connect to MCP servers via HTTP endpoints
• Web Search - Optional web search using DuckDuckGo (default: OFF, no API key needed)
  • Enable with enable_web_search=True parameter

What's New in v0.2.0

• Renamed LiteLLMAdapter to AIEngine - More intuitive naming
• Added HTTPEngine - Support for generic HTTP LLM endpoints
• System Prompt Support - Configure agent personality via system_prompt parameter
• Google Cloud Support - Added litellm[google] for Vertex AI compatibility

🏗️ Architecture

The framework follows Hexagonal Architecture:

• Core/Domain: Pure business entities (Agent, Message).
• Core/Ports: Interfaces for external dependencies (BaseLLM, MemoryPort).
• Adapters: Concrete implementations (AIEngine, HTTPEngine, ChromaAdapter).

🛠️ Development

1. Clone the repository.
2. Install dependencies: pip install -e .[dev]
3. Run tests: pytest

🤝 Contribution

Contributions are welcome! Please follow the Clean Architecture patterns.

📄 License

MIT License - see LICENSE file for details.