kaire-2222/slm-agents-publish-subscribe-components

Pub/Sub Multi-Agent System

A dynamic multi-agent orchestration system using publish/subscribe architecture with small language models.

Overview

This system allows you to create and orchestrate multiple AI agents that communicate through a message bus using the publish/subscribe pattern. Each agent:

• —Subscribes to specific topics
• —Processes messages when triggered
• —Publishes results to new topics
• —Uses configurable language models

Features

• —🔄 Dynamic Agent Creation: Add/remove agents on the fly
• —📡 Pub/Sub Architecture: Event-driven agent orchestration
• —🤖 Multiple Models: Support for general, medical, coding, and NER models
• —🎯 Topic-Based Routing: Agents communicate through named topics
• —📋 Real-Time Logging: Watch the message flow through the bus
• —⚙️ Customizable Prompts: Each agent has its own prompt template
• —🏷️ Named Entity Recognition: Dedicated NER agents with formatted output display
• —💾 Save/Load with Results: Save configurations including execution results and logs
• —📊 Subscribable Data Sources: Data sources can subscribe to topics and update dynamically during execution

How It Works

1. Agent Configuration

Each agent has:

• —Title: A descriptive name
• —Prompt: Template for processing (can use {schema}, {input}, and {question} placeholders)
• —Model: Which LLM to use
• —Subscribe Topic: Which topic triggers this agent
• —Publish Topic: Where to publish results (use "FINAL" for end results)

2. Message Bus

• —Agents subscribe to topics during initialization
• —System publishes "START" message to begin
• —Agents process messages and publish results
• —Chain continues until no more subscribers

3. Execution Flow

User enters question → Published to START topic
     ↓
Agent1 (subscribes to START) → publishes to TOPIC_A
     ↓
Agent2 (subscribes to TOPIC_A) → publishes to TOPIC_B
     ↓
Agent3 (subscribes to TOPIC_B) → publishes to FINAL
     ↓
Final result displayed to user

Example Use Case: NL to SQL Pipeline

Agent 1: Schema Analyzer

• —Subscribe: START
• —Publish: SCHEMA_ANALYZED
• —Prompt: Analyze database schema and identify relevant tables

Agent 2: SQL Generator

• —Subscribe: SCHEMA_ANALYZED
• —Publish: SQL_GENERATED
• —Prompt: Generate SQL query based on analyzed schema

Agent 3: SQL Validator

• —Subscribe: SQL_GENERATED
• —Publish: SQL_VALIDATED
• —Prompt: Validate and optimize the SQL query

Example Use Case: Medical Analysis (with BioMistral)

Agent 1: Symptom Analyzer

• —Subscribe: START
• —Publish: SYMPTOMS_ANALYZED
• —Model: biomistral:7b-chat
• —Prompt: Analyze medical symptoms from input

Agent 2: Diagnosis Suggester

• —Subscribe: SYMPTOMS_ANALYZED
• —Publish: DIAGNOSIS_SUGGESTED
• —Model: biomistral:7b-chat
• —Prompt: Suggest possible diagnoses based on symptoms

Supported Models

This deployment includes five specialized models:

• —phi4-mini: General-purpose model (3.8B parameters) - Great for text analysis, SQL generation, summarization, reasoning, and general tasks
• —MedAIBase/MedGemma1.5:4b: Medical/healthcare model (4B parameters) - Specialized for clinical reasoning, medical documentation, and healthcare-related tasks
• —deepseek-coder:1.3b: Code generation model (1.3B parameters) - Optimized for programming, code analysis, debugging, and technical documentation
• —samrawal/bert-base-uncased_clinical-ner: Clinical NER model - Extracts medical entities (diseases, symptoms, medications) from clinical text
• —OpenMed/OpenMed-NER-AnatomyDetect-BioPatient-108M: Anatomy NER model (108M parameters) - Detects anatomical entities and patient-related information

These models provide specialized capabilities for medical, coding, NER, and general-purpose tasks.

Architecture

Web UI (React) → FastAPI Server → Message Bus
                                    ↓
                              Agent 1 (LLM)
                                    ↓
                              Agent 2 (LLM)
                                    ↓
                              Agent 3 (LLM)

Technical Stack

• —Backend: FastAPI with streaming responses
• —Frontend: React with Tailwind CSS
• —LLM Runtime: Ollama
• —Orchestration: Custom pub/sub message bus

Educational Value

Perfect for learning:

• —Event-driven architectures
• —Multi-agent systems design
• —Pub/Sub messaging patterns
• —Asynchronous agent orchestration
• —Dynamic workflow composition