Single ReactAgent → Multi-Agent Orchestra
├── Tools as Extensions → Agents as Specialized Services
├── Memory per Session → Shared Knowledge Graph
├── Local Processing → Distributed Agent Network
└── Sequential Tasks → Parallel Agent Coordination
ReactAgent (Your Current Core)
├── Memory System ✅
├── Tool Manager ✅
├── Planner/Executor ✅
└── LLM Integration ✅
# Enhanced Agent Types
class AgentCapability(Enum):
RESEARCH = "research" # Web search, knowledge retrieval
ANALYSIS = "analysis" # Data processing, pattern recognition
CODING = "coding" # Code generation, debugging
PLANNING = "planning" # Task decomposition, scheduling
COORDINATION = "coordination" # Multi-agent orchestration
MEMORY = "memory" # Knowledge management
VALIDATION = "validation" # Quality assurance, fact checking
EXECUTION = "execution" # Action taking, API calls
LEARNING = "learning" # Model training, adaptation
COMMUNICATION = "communication" # Inter-agent messaging
class SpecializedAgent:
def __init__(self, agent_id: str, capabilities: List[AgentCapability]):
self.agent_id = agent_id
self.capabilities = capabilities
self.react_core = ReactAgent() # Your existing core
self.specialized_tools = self._load_specialized_tools()
def _load_specialized_tools(self) -> List[BaseTool]:
"""Load tools specific to this agent's capabilities"""
tools = []
if AgentCapability.RESEARCH in self.capabilities:
tools.extend([WebSearchTool(), WikipediaTool(), ResearchTool()])
if AgentCapability.CODING in self.capabilities:
tools.extend([CppExecutorTool(), PythonExecutorTool(), CodeAnalysisTool()])
# ... more capability-based tool loading
return tools# Message Protocol
@dataclass
class AgentMessage:
sender_id: str
recipient_id: str
message_type: MessageType
content: Dict[str, Any]
priority: int = 1
requires_response: bool = False
correlation_id: Optional[str] = None
timestamp: float = field(default_factory=time.time)
class MessageType(Enum):
TASK_REQUEST = "task_request"
TASK_RESPONSE = "task_response"
INFORMATION_SHARE = "info_share"
COORDINATION = "coordination"
STATUS_UPDATE = "status_update"
ERROR_REPORT = "error_report"
# Communication Hub
class AgentCommunicationHub:
def __init__(self):
self.agents: Dict[str, SpecializedAgent] = {}
self.message_router = MessageRouter()
self.task_orchestrator = TaskOrchestrator()
async def route_message(self, message: AgentMessage) -> bool:
"""Route messages between agents"""
return await self.message_router.route(message)
async def broadcast_task(self, task: Task) -> List[AgentMessage]:
"""Broadcast task to capable agents"""
return await self.task_orchestrator.distribute_task(task)# Shared Knowledge Graph
class SharedKnowledgeGraph:
def __init__(self):
self.knowledge_store = Neo4jKnowledgeStore() # Or your preferred graph DB
self.concept_embeddings = VectorMemory()
self.agent_experiences = {}
async def share_learning(self, agent_id: str, episode: Episode):
"""Share learning across agents"""
# Extract concepts and relationships
concepts = await self._extract_concepts(episode)
relationships = await self._extract_relationships(episode)
# Update shared knowledge
await self.knowledge_store.add_concepts(concepts)
await self.knowledge_store.add_relationships(relationships)
# Update agent experience mapping
self.agent_experiences[agent_id] = episode
# Enhanced Memory Store for Multi-Agent
class MultiAgentMemoryStore(MemoryStore):
def __init__(self):
super().__init__()
self.shared_knowledge = SharedKnowledgeGraph()
self.agent_private_memory = {}
async def store_cross_agent_experience(self, experience: CrossAgentExperience):
"""Store experiences that involve multiple agents"""
await self.shared_knowledge.share_learning(experience.primary_agent, experience.episode)
# Store in episodic memory with multi-agent metadata
await self.store_memory(
MemoryType.EPISODIC,
experience.to_dict(),
metadata={"multi_agent": True, "participants": experience.participants}
)class AgentAsToolIntegration:
"""Integration where specialized agents appear as sophisticated tools"""
def __init__(self, primary_agent: ReactAgent):
self.primary_agent = primary_agent
self.specialized_agents = {
"researcher": ResearcherAgent(),
"analyst": AnalystAgent(),
"coder": CoderAgent()
}
async def enhance_tool_manager(self):
"""Convert specialized agents into tools"""
for role, agent in self.specialized_agents.items():
agent_tool = AgentTool(agent, role)
self.primary_agent.tool_manager.register_tool(agent_tool)
# Usage in your existing ReactAgent
class EnhancedReactAgent(ReactAgent):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.agent_integration = AgentAsToolIntegration(self)
asyncio.create_task(self.agent_integration.enhance_tool_manager())class P2PAgentNetwork:
"""Peer-to-peer network where agents collaborate as equals"""
def __init__(self):
self.agents: Dict[str, SpecializedAgent] = {}
self.communication_hub = AgentCommunicationHub()
self.task_marketplace = TaskMarketplace()
async def register_agent(self, agent: SpecializedAgent):
"""Register agent in the network"""
self.agents[agent.agent_id] = agent
await self.communication_hub.add_agent(agent)
await self.task_marketplace.register_capabilities(agent.agent_id, agent.capabilities)
async def solve_collaboratively(self, complex_task: ComplexTask) -> Solution:
"""Solve task through agent collaboration"""
# 1. Decompose task
subtasks = await self.decompose_task(complex_task)
# 2. Find capable agents for each subtask
agent_assignments = await self.task_marketplace.find_agents(subtasks)
# 3. Coordinate execution
results = await self.coordinate_execution(agent_assignments)
# 4. Synthesize final solution
return await self.synthesize_solution(results)class HierarchicalAgentSystem:
"""Hierarchical system with coordinator and specialist agents"""
def __init__(self):
self.coordinator = CoordinatorAgent()
self.specialists = {
"research_team": [ResearcherAgent() for _ in range(3)],
"analysis_team": [AnalystAgent() for _ in range(2)],
"execution_team": [ExecutorAgent() for _ in range(2)]
}
self.task_delegator = TaskDelegator()
async def process_request(self, user_request: UserRequest) -> Response:
"""Process user request through hierarchical delegation"""
# Coordinator analyzes and plans
execution_plan = await self.coordinator.create_execution_plan(user_request)
# Delegate to specialist teams
team_results = {}
for team_name, tasks in execution_plan.team_assignments.items():
team_agents = self.specialists[team_name]
team_results[team_name] = await self.delegate_to_team(team_agents, tasks)
# Coordinator synthesizes results
final_response = await self.coordinator.synthesize_results(team_results)
return final_response# Extend your current ReactAgent
class MultiCapableReactAgent(ReactAgent):
def __init__(self, capabilities: List[AgentCapability], *args, **kwargs):
super().__init__(*args, **kwargs)
self.capabilities = capabilities
self.specialized_tools = self._load_capability_tools()
self.peer_communication = PeerCommunication(self.agent_id)
async def handle_peer_request(self, request: AgentMessage) -> AgentMessage:
"""Handle requests from other agents"""
if self._can_handle(request.content):
result = await self.run(request.content["query"])
return AgentMessage(
sender_id=self.agent_id,
recipient_id=request.sender_id,
message_type=MessageType.TASK_RESPONSE,
content={"result": result}
)
else:
return self._create_referral_message(request)# Message routing and agent discovery
class AgentRegistry:
def __init__(self):
self.agents = {}
self.capability_index = {}
async def register_agent(self, agent: MultiCapableReactAgent):
self.agents[agent.agent_id] = agent
for capability in agent.capabilities:
if capability not in self.capability_index:
self.capability_index[capability] = []
self.capability_index[capability].append(agent.agent_id)
async def find_agents_by_capability(self, capability: AgentCapability) -> List[str]:
return self.capability_index.get(capability, [])# Multi-agent task orchestration
class MultiAgentTaskOrchestrator:
def __init__(self, agent_registry: AgentRegistry):
self.agent_registry = agent_registry
self.active_collaborations = {}
async def execute_collaborative_task(self, task: ComplexTask) -> CollaborationResult:
# Task decomposition
subtasks = await self.decompose_task(task)
# Agent selection and assignment
assignments = await self.assign_agents_to_subtasks(subtasks)
# Parallel execution with coordination
collaboration_id = str(uuid.uuid4())
self.active_collaborations[collaboration_id] = CollaborationSession(assignments)
results = await self.coordinate_parallel_execution(collaboration_id)
# Result synthesis
final_result = await self.synthesize_results(results)
# Cleanup
del self.active_collaborations[collaboration_id]
return final_result# Implement shared learning across agents
class SharedLearningSystem:
def __init__(self):
self.knowledge_graph = SharedKnowledgeGraph()
self.learning_coordinator = LearningCoordinator()
async def propagate_learning(self, learning_event: LearningEvent):
"""Propagate learning from one agent to relevant others"""
# Extract learnable patterns
patterns = await self.extract_patterns(learning_event)
# Find agents that would benefit
relevant_agents = await self.find_relevant_agents(patterns)
# Propagate learning
for agent_id in relevant_agents:
await self.transfer_knowledge(agent_id, patterns)# Using Redis/RabbitMQ for agent communication
class AgentMessageQueue:
def __init__(self, broker_url: str):
self.broker = aioredis.from_url(broker_url)
self.subscriptions = {}
async def publish(self, topic: str, message: AgentMessage):
await self.broker.publish(topic, message.to_json())
async def subscribe(self, agent_id: str, callback: Callable):
await self.broker.subscribe(f"agent.{agent_id}", callback)class AgentLoadBalancer:
def __init__(self):
self.agent_pool = {}
self.task_queue = asyncio.Queue()
self.metrics_collector = MetricsCollector()
async def distribute_load(self, task: Task) -> str:
"""Distribute task to least loaded capable agent"""
capable_agents = await self.find_capable_agents(task.required_capabilities)
# Select least loaded agent
selected_agent = min(capable_agents, key=lambda a: self.get_agent_load(a))
await self.assign_task(selected_agent, task)
return selected_agent# Dockerfile for individual agent services
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
# Each agent runs as separate service
CMD ["python", "-m", "agent_service", "--agent-type", "${AGENT_TYPE}"]# agent-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: researcher-agents
spec:
replicas: 3
selector:
matchLabels:
app: researcher-agent
template:
metadata:
labels:
app: researcher-agent
spec:
containers:
- name: researcher-agent
image: your-registry/react-agent:latest
env:
- name: AGENT_TYPE
value: "researcher"
- name: AGENT_CAPABILITIES
value: "research,web_search,knowledge_retrieval"class MultiAgentMonitoring:
def __init__(self):
self.metrics = PrometheusMetrics()
self.tracer = JaegerTracer()
self.logger = StructuredLogger()
async def track_collaboration(self, collaboration_id: str, agents: List[str]):
"""Track multi-agent collaboration performance"""
with self.tracer.start_span("multi_agent_collaboration") as span:
span.set_attribute("collaboration_id", collaboration_id)
span.set_attribute("participating_agents", len(agents))
# Track metrics
self.metrics.increment("collaborations_started")
# Log collaboration start
self.logger.info("Collaboration started", {
"collaboration_id": collaboration_id,
"agents": agents
})# Your existing ReactAgent becomes the foundation
class BackwardCompatibleMultiAgent(ReactAgent):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.multi_agent_mode = kwargs.get('multi_agent_mode', False)
if self.multi_agent_mode:
self.communication_hub = AgentCommunicationHub()
self.peer_agents = {}
async def run(self, query: str, session_id: str = None, **kwargs) -> Dict[str, Any]:
"""Enhanced run method with multi-agent capability"""
if self.multi_agent_mode and await self._should_collaborate(query):
return await self._collaborative_run(query, session_id, **kwargs)
else:
# Use existing single-agent logic
return await super().run(query, session_id, **kwargs) This architecture gives you:
- Gradual Migration: Your existing ReactAgent becomes the core of each specialized agent
- Tool Integration: Agents can be tools AND independent services
- Flexible Deployment: Single process OR distributed microservices
- Shared Learning: Knowledge flows between agents
- Scalability: Add agents dynamically based on workload
Would you like me to elaborate on any specific part of this architecture or help you implement the first phase?