🤝 Multi-Agent Systems

Master the art of building collaborative AI systems where multiple agents work together

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🎯 Agent Fundamentals

📚 What are Multi-Agent Systems?
Multi-agent systems consist of multiple intelligent agents that interact and collaborate to solve complex problems that would be difficult for a single agent to handle.
Example: A customer service system where one agent handles inquiries, another processes orders, and a third manages complaints - all working together seamlessly.
Learn to design agent architectures with specialized roles, communication protocols, and coordination mechanisms for efficient task distribution and problem-solving.
# Basic multi-agent setup class Agent: def __init__(self, name, role): self.name = name self.role = role self.messages = [] def communicate(self, target, message): target.receive_message(self, message) def receive_message(self, sender, message): self.messages.append({ 'from': sender.name, 'message': message }) # Create specialized agents researcher = Agent("Researcher", "Information gathering") analyzer = Agent("Analyzer", "Data processing") writer = Agent("Writer", "Content generation")
Implement sophisticated multi-agent architectures with dynamic role assignment, emergent behaviors, and self-organizing capabilities for complex autonomous systems.
# Advanced agent orchestration with LangGraph from langgraph.graph import StateGraph, END from typing import TypedDict, List, Annotated import operator class AgentState(TypedDict): messages: Annotated[List[str], operator.add] current_agent: str task_queue: List[str] results: dict class MultiAgentOrchestrator: def __init__(self): self.graph = StateGraph(AgentState) self.agents = {} self._build_workflow() def _build_workflow(self): # Add nodes for each agent type self.graph.add_node("coordinator", self.coordinator_agent) self.graph.add_node("specialist", self.specialist_agent) self.graph.add_node("validator", self.validator_agent) # Define edges with conditional routing self.graph.add_conditional_edges( "coordinator", self.route_task, { "specialist": "specialist", "validator": "validator", "end": END } ) def route_task(self, state): # Dynamic routing based on task type if state["task_queue"]: task = state["task_queue"][0] if "validate" in task: return "validator" else: return "specialist" return "end"
🔧 Agent Communication
Agents communicate through messages, sharing information and coordinating their actions to achieve common goals.
Key Concepts:
  • Message passing between agents
  • Shared memory or blackboard systems
  • Event-driven communication
  • Request-response patterns
Implement structured communication protocols with message queues, publish-subscribe patterns, and agent negotiation mechanisms.
# Message broker for agent communication import asyncio from dataclasses import dataclass from typing import Dict, List, Callable @dataclass class Message: sender: str receiver: str content: dict msg_type: str timestamp: float class MessageBroker: def __init__(self): self.agents: Dict[str, Callable] = {} self.message_queue = asyncio.Queue() self.topics: Dict[str, List[str]] = {} def register_agent(self, agent_id: str, handler: Callable): self.agents[agent_id] = handler def subscribe(self, agent_id: str, topic: str): if topic not in self.topics: self.topics[topic] = [] self.topics[topic].append(agent_id) async def publish(self, topic: str, message: Message): subscribers = self.topics.get(topic, []) for subscriber in subscribers: if subscriber in self.agents: await self.agents[subscriber](message)
Build sophisticated communication infrastructures with consensus protocols, distributed state management, and Byzantine fault tolerance.

Advanced Communication Patterns

  • Gossip protocols for distributed information sharing
  • Consensus algorithms (Raft, Paxos)
  • Vector clocks for event ordering
  • CRDT for conflict-free replicated data
  • Byzantine fault-tolerant messaging
🎯 Coordination Strategies
Agents coordinate their actions through various strategies to avoid conflicts and achieve optimal collective behavior.

Centralized

One coordinator agent manages all others

Distributed

Agents coordinate peer-to-peer

Hierarchical

Agents organized in management layers

Design coordination mechanisms including task allocation algorithms, resource sharing protocols, and conflict resolution strategies.
# Task allocation coordinator class TaskCoordinator: def __init__(self): self.agents = {} self.task_queue = [] self.agent_capabilities = {} def register_agent(self, agent_id, capabilities): self.agents[agent_id] = { 'status': 'idle', 'capabilities': capabilities, 'current_task': None } def allocate_task(self, task): # Find best agent for task best_agent = None best_score = 0 for agent_id, info in self.agents.items(): if info['status'] == 'idle': score = self._calculate_fitness(task, info['capabilities']) if score > best_score: best_agent = agent_id best_score = score if best_agent: self.assign_task(best_agent, task) else: self.task_queue.append(task)
Implement game-theoretic coordination, market-based mechanisms, and swarm intelligence for optimal multi-agent collaboration.

Advanced Coordination

  • Auction-based task allocation
  • Contract net protocol
  • Stigmergic coordination
  • Formation control algorithms
  • Multi-agent reinforcement learning

🧠 Architecture Patterns

🏗️ Blackboard Architecture
A shared workspace where agents can read and write information, enabling indirect communication and collaboration.
Example: Collaborative Problem Solving
Multiple expert agents contribute partial solutions to a blackboard, gradually building a complete solution together.
Implement blackboard systems with control mechanisms, scheduling policies, and conflict resolution for concurrent agent access.
# Blackboard implementation class Blackboard: def __init__(self): self.data = {} self.agents = [] self.control = BlackboardControl() def write(self, key, value, agent_id): if self.control.can_write(agent_id, key): self.data[key] = value self.notify_agents(key, value) def read(self, key): return self.data.get(key) def subscribe(self, agent, patterns): self.agents.append({ 'agent': agent, 'patterns': patterns })
Design distributed blackboard systems with partitioning, replication, and consistency protocols for scalable multi-agent systems.

Distributed Blackboard Features

  • Partitioned namespaces
  • Eventual consistency models
  • Conflict-free replicated data types
  • Hierarchical blackboards
🎭 Actor Model
Each agent is an independent actor that processes messages asynchronously, maintaining its own state and behavior.
Example: Email Processing System
Different actors handle parsing, spam filtering, categorization, and routing - each working independently but coordinating through messages.
Build actor systems with supervision trees, fault tolerance, and location transparency for robust distributed agents.
# Actor-based agent system import ray @ray.remote class AgentActor: def __init__(self, agent_id): self.agent_id = agent_id self.state = {} self.inbox = [] def receive(self, message): self.inbox.append(message) return self.process_message(message) def process_message(self, message): # Pattern matching on message type if message['type'] == 'query': return self.handle_query(message) elif message['type'] == 'command': return self.handle_command(message) # Create distributed actors ray.init() agents = [AgentActor.remote(i) for i in range(5)]
Implement actor model patterns including event sourcing, CQRS, and saga orchestration for complex distributed workflows.

Advanced Actor Patterns

  • Akka-style supervision strategies
  • Event sourcing with actors
  • Saga pattern for distributed transactions
  • Actor persistence and recovery
🌐 Microservices Pattern
Each agent operates as an independent microservice with its own API, database, and deployment lifecycle.
Example: E-commerce Platform
Separate services for inventory, ordering, payment, and shipping - each managed by specialized agents.
Design microservice-based agents with service discovery, load balancing, and circuit breakers for resilient systems.
# Microservice agent with FastAPI from fastapi import FastAPI, HTTPException from pydantic import BaseModel import httpx class AgentService: def __init__(self, service_name): self.app = FastAPI() self.service_name = service_name self.registry = ServiceRegistry() self._setup_routes() def _setup_routes(self): @self.app.post("/process") async def process_task(task: Task): # Process and potentially delegate if self.needs_delegation(task): service = self.registry.find_service(task.type) async with httpx.AsyncClient() as client: response = await client.post( f"{service.url}/process", json=task.dict() ) return response.json() return self.handle_task(task)
Build service mesh architectures with sidecars, distributed tracing, and advanced traffic management for agent services.

Service Mesh Features

  • Istio/Linkerd integration
  • Distributed tracing with Jaeger
  • Traffic shaping and canary deployments
  • mTLS for secure agent communication

🛠️ Implementation Techniques

🔄
Workflow Orchestration
Create workflows where agents execute tasks in sequence or parallel based on dependencies and conditions.
# Simple workflow with agents class Workflow: def __init__(self): self.agents = [] self.steps = [] def add_step(self, agent, task): self.steps.append({ 'agent': agent, 'task': task }) def execute(self): results = [] for step in self.steps: result = step['agent'].process(step['task']) results.append(result) return results
Build complex workflows with conditional branching, parallel execution, and error handling using orchestration frameworks.
# Advanced workflow with Prefect from prefect import flow, task from prefect.task_runners import ConcurrentTaskRunner @task def research_agent(query): # Research task implementation return f"Research results for {query}" @task def analysis_agent(data): # Analysis task implementation return f"Analysis of {data}" @task def synthesis_agent(analyses): # Synthesis task implementation return f"Synthesis of {len(analyses)} analyses" @flow(task_runner=ConcurrentTaskRunner()) def multi_agent_workflow(queries): # Parallel research research_results = research_agent.map(queries) # Parallel analysis analyses = analysis_agent.map(research_results) # Synthesis of all results final_report = synthesis_agent(analyses) return final_report
Implement dynamic workflow generation, workflow versioning, and distributed workflow execution across agent clusters.

Enterprise Workflow Features

  • BPMN workflow modeling
  • Temporal workflow engine integration
  • Workflow versioning and migration
  • Distributed saga orchestration
  • Compensating transactions
🧩
Agent Frameworks
Use existing frameworks like AutoGen, CrewAI, or LangGraph to quickly build multi-agent systems.
# CrewAI example from crewai import Agent, Task, Crew # Define agents researcher = Agent( role='Research Analyst', goal='Find and analyze information', backstory='Expert at finding relevant data' ) writer = Agent( role='Content Writer', goal='Create engaging content', backstory='Skilled technical writer' ) # Create crew crew = Crew( agents=[researcher, writer], tasks=[research_task, writing_task] ) result = crew.kickoff()
Customize frameworks with custom tools, memory systems, and agent behaviors for domain-specific applications.
# AutoGen with custom agents import autogen config_list = [{ "model": "gpt-4", "api_key": "your-api-key" }] # Custom agent with tools class SpecializedAgent(autogen.AssistantAgent): def __init__(self, name, tools, **kwargs): super().__init__(name, **kwargs) self.tools = tools self.register_function( function_map={tool.name: tool.func for tool in tools} ) def process_with_tools(self, message): # Custom processing logic tool_results = [] for tool in self.tools: if tool.matches(message): result = tool.execute(message) tool_results.append(result) return self.generate_reply(tool_results) # Create specialized agents data_agent = SpecializedAgent( "DataAnalyst", tools=[SQLTool(), PandasTool()], llm_config={"config_list": config_list} )
Build custom frameworks with plugin architectures, extensible agent types, and integration with enterprise systems.

Framework Development

  • Plugin architecture design
  • Custom agent lifecycle management
  • Framework performance optimization
  • Enterprise integration adapters
  • Framework testing strategies
💾
Shared Memory Systems
Implement shared memory systems where agents can store and retrieve information for collective learning.
# Simple shared memory class SharedMemory: def __init__(self): self.short_term = {} self.long_term = {} def store(self, key, value, duration='short'): if duration == 'short': self.short_term[key] = value else: self.long_term[key] = value def retrieve(self, key): return (self.short_term.get(key) or self.long_term.get(key))
Build sophisticated memory systems with vector databases, semantic search, and memory consolidation mechanisms.
# Vector memory system import chromadb from sentence_transformers import SentenceTransformer class VectorMemorySystem: def __init__(self): self.client = chromadb.Client() self.collection = self.client.create_collection("agent_memory") self.encoder = SentenceTransformer('all-MiniLM-L6-v2') def store_memory(self, content, metadata): embedding = self.encoder.encode(content).tolist() self.collection.add( embeddings=[embedding], documents=[content], metadatas=[metadata], ids=[metadata['id']] ) def search_memories(self, query, n_results=5): query_embedding = self.encoder.encode(query).tolist() results = self.collection.query( query_embeddings=[query_embedding], n_results=n_results ) return results def consolidate_memories(self): # Implement memory consolidation # e.g., clustering similar memories pass
Implement distributed memory systems with sharding, replication, and advanced retrieval algorithms for large-scale deployments.

Enterprise Memory Features

  • Distributed vector databases
  • Memory sharding strategies
  • Hierarchical memory organization
  • Memory garbage collection
  • Privacy-preserving memory sharing