SPADE-LLM : SPADE with Large Language Models

Extension for SPADE that integrates Large Language Models into multi-agent systems.

Features

• Multi-Provider Support: OpenAI, Ollama, LM Studio, vLLM
• Tool System: Function calling with async execution
• Memory System: Dual memory architecture for agent learning and conversation continuity
• Context Management: Multi-conversation support with automatic cleanup
• Message Routing: Conditional routing based on LLM responses
• Guardrails System: Content filtering and safety controls for input/output
• MCP Integration: Model Context Protocol server support
• Production Ready: Comprehensive error handling and logging

Architecture

graph LR
    A[LLMAgent] --> C[ContextManager]
    A --> D[LLMProvider]
    A --> E[LLMTool]
    A --> G[Guardrails]
    A --> M[Memory]
    D --> F[OpenAI/Ollama/etc]
    G --> H[Input/Output Filtering]
    E --> I[Human-in-the-Loop]
    E --> J[MCP]
    E --> P[CustomTool/LangchainTool]
    J --> K[STDIO]
    J --> L[HTTP Streaming]
    M --> N[Agent-based]
    M --> O[Agent-thread]

Quick Start

import spade
from spade_llm import LLMAgent, LLMProvider

async def main():
    provider = LLMProvider.create_openai(
        api_key="your-api-key",
        model="gpt-4o-mini"
    )

    agent = LLMAgent(
        jid="assistant@example.com",
        password="password",
        provider=provider,
        system_prompt="You are a helpful assistant"
    )

    await agent.start()

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

Documentation Structure

Getting Started

• Installation - Setup and requirements
• Quick Start - Basic usage examples

Core Guides

• Architecture - SPADE_LLM general structure
• Providers - LLM provider configuration
• Tools System - Function calling capabilities
• Memory System - Agent learning and conversation continuity
• Context Management - Context control and message management
• Conversations - Conversation lifecycle and management
• Guardrails - Content filtering and safety controls
• Message Routing - Conditional message routing

Reference

• API Reference - Complete API documentation
• Examples - Working code examples

Examples

Explore the examples directory for complete working examples:

• multi_provider_chat_example.py - Chat with different LLM providers
• ollama_with_tools_example.py - Local models with tool calling
• guardrails_example.py - Content filtering and safety controls
• langchain_tools_example.py - LangChain tool integration
• valencia_multiagent_trip_planner.py - Multi-agent workflow