AgentNexus Documentation
Introduction
Overview

Overview

What is AgentNexus?

AgentNexus is a Python library designed to streamline the development of AI and LLM (Language Model) agents. It provides a comprehensive framework that handles many of the complex aspects of agent development, including UI generation, API management, event handling, and workflow orchestration.

At its core, AgentNexus enables developers to build sophisticated AI agents that can:

  • Present interactive user interfaces
  • Process user inputs through structured workflows
  • Maintain context across interactions
  • Execute actions based on user requests
  • Generate consistent API manifests for discovery and integration

Problem Space

Building AI agents traditionally involves a significant amount of boilerplate code and complex state management. Developers often face challenges such as:

  • UI Integration: Creating responsive, interactive interfaces for agent interactions
  • State Management: Maintaining context across multi-step processes
  • API Consistency: Ensuring uniform API behavior across different agents
  • Event Handling: Managing user interactions and system events properly
  • Discovery: Making agents and their capabilities discoverable by other systems

AgentNexus addresses these challenges by providing a unified framework with built-in solutions for each aspect of agent development.

Core Philosophy

The design of AgentNexus is guided by several key principles:

  1. Declarative Development: Prefer declarative patterns over imperative code
  2. Component-Based UI: Build interfaces from reusable, self-contained components
  3. Workflow-Driven: Structure complex interactions as well-defined workflows
  4. Context Awareness: Maintain state and context throughout interactions
  5. Decentralized Discovery: Enable agents to be discovered and used across different systems

Use Cases

AgentNexus is suitable for a wide range of AI agent applications, including:

  • Conversational Assistants: Build chatbots and conversational interfaces
  • Code Assistants: Create AI-powered coding helpers with code analysis and generation
  • Data Analysis Agents: Develop interactive data analysis workflows
  • Content Generation: Build agents for creating and editing content
  • Domain-Specific Assistants: Create specialized assistants for travel, finance, healthcare, etc.

Example: A Code Analysis Agent 

from agentnexus.base_types import AgentConfig, Capability, ActionType
from agentnexus.action_manager import agent_action

# Define agent configuration
code_agent = AgentConfig(
    name="Code Assistant",
    version="1.0.0",
    description="AI-powered code analysis agent",
    capabilities=[
        Capability(
            skill_path=["Development", "Code", "Analysis"],
            metadata={
                "languages": ["Python", "JavaScript"],
                "features": ["Quality", "Formatting", "Performance"]
            }
        )
    ]
)

# Create an agent action
@agent_action(
    agent_config=code_agent,
    action_type=ActionType.GENERATE,
    name="Analyze Code",
    description="Analyze code for quality and performance"
)
async def analyze_code(input_data):
    """Analyze code and provide feedback."""
    code = input_data.code
    language = input_data.language
    
    # Analysis logic would go here
    
    return {
        "quality_score": 85,
        "performance_score": 78,
        "suggestions": [
            "Add type hints to improve code readability",
            "Consider using a more efficient algorithm for sorting"
        ]
    }

Where AgentNexus Fits

AgentNexus occupies a unique space in the AI development ecosystem:

  • It’s more structured than raw LLM APIs, providing patterns and components
  • It’s more flexible than end-to-end agent platforms, allowing customization
  • It’s more UI-focused than most agent frameworks, with built-in interactive components
  • It’s more workflow-oriented than simple chatbot frameworks, enabling complex multi-step processes

Next Steps

Key Features