HIP-9: Agent SDK - Multi-Agent Orchestration Framework
Abstract
This proposal defines the Agent SDK specification, Hanzo's multi-agent systems framework with orchestration, network routing, shared state management, and MCP support. The SDK enables building, deploying, and managing autonomous AI agents that can collaborate to solve complex tasks.
Repository: github.com/hanzoai/agent
PyPI: hanzoai-agent
NPM: @hanzoai/agent
Motivation
Current agent frameworks lack:
- True Multi-Agent Coordination: Agents work in isolation
- Network Effects: No agent discovery or marketplace
- State Management: Poor shared memory systems
- Tool Integration: Limited external tool support
- Observability: Insufficient debugging and monitoring
The Agent SDK provides a complete framework for building collaborative agent systems.
Specification
Agent Architecture
class HanzoAgent:
"""
Base agent class with full capabilities
"""
def __init__(self, config):
self.id = generate_agent_id()
self.model = config.model # LLM backend
self.tools = config.tools # Available tools
self.memory = AgentMemory() # Long-term memory
self.state = AgentState() # Current state
self.network = AgentNetwork() # P2P networking
async def think(self, observation):
"""Reasoning and planning"""
thought = await self.model.reason(
observation=observation,
memory=self.memory.recall(),
tools=self.tools.available()
)
return thought
async def act(self, thought):
"""Execute actions based on thought"""
if thought.requires_tool:
result = await self.tools.execute(thought.tool_call)
elif thought.requires_collaboration:
result = await self.network.request_help(thought.task)
else:
result = await self.model.generate(thought.prompt)
return result
async def observe(self, environment):
"""Perceive environment changes"""
return await environment.get_observation(self.id)
async def run(self):
"""Main agent loop"""
while not self.state.is_complete:
observation = await self.observe(self.environment)
thought = await self.think(observation)
action = await self.act(thought)
self.state.update(action)
self.memory.store(observation, thought, action)
Multi-Agent Orchestration
class AgentOrchestrator:
"""
Orchestrates multiple agents for complex tasks
"""
def __init__(self):
self.agents = {}
self.task_queue = PriorityQueue()
self.shared_state = SharedState()
def spawn_agent(self, role, config):
"""Create specialized agent"""
agent = HanzoAgent(config)
agent.role = role
self.agents[agent.id] = agent
return agent
async def delegate_task(self, task):
"""Intelligent task delegation"""
# Analyze task requirements
requirements = self.analyze_task(task)
# Find or create suitable agents
if requirements.needs_research:
researcher = self.spawn_agent("researcher", ResearchConfig())
if requirements.needs_coding:
coder = self.spawn_agent("coder", CodingConfig())
if requirements.needs_review:
reviewer = self.spawn_agent("reviewer", ReviewConfig())
# Create execution plan
plan = self.create_plan(task, self.agents)
# Execute plan with coordination
return await self.execute_plan(plan)
Agent Roles and Specializations
Built-in Roles:
Researcher:
model: gpt-4
tools: [search, browse, summarize]
skills: [information_gathering, fact_checking]
Coder:
model: claude-3-opus
tools: [code_interpreter, debugger, linter]
skills: [code_generation, refactoring, testing]
Analyst:
model: gemini-pro
tools: [calculator, data_viz, statistics]
skills: [data_analysis, visualization, reporting]
Designer:
model: dall-e-3
tools: [image_gen, edit, style_transfer]
skills: [visual_design, ui_ux, branding]
Manager:
model: gpt-4
tools: [task_tracker, calendar, communication]
skills: [planning, delegation, coordination]
Agent Network Protocol
class AgentNetwork:
"""
P2P network for agent communication
"""
def __init__(self):
self.peers = {}
self.discovery = ServiceDiscovery()
self.router = MessageRouter()
async def discover_agents(self, capability):
"""Find agents with specific capabilities"""
return await self.discovery.find(
capability=capability,
max_distance=3, # Network hops
timeout=5000
)
async def send_message(self, recipient_id, message):
"""Direct agent-to-agent communication"""
route = await self.router.find_route(recipient_id)
return await self.send_via_route(route, message)
async def broadcast(self, message, scope="local"):
"""Broadcast to multiple agents"""
if scope == "local":
recipients = self.peers.values()
elif scope == "global":
recipients = await self.discovery.all_agents()
tasks = [self.send_message(r.id, message) for r in recipients]
return await asyncio.gather(*tasks)
Shared State Management
class SharedState:
"""
Distributed state management for agents
"""
def __init__(self):
self.store = {}
self.locks = {}
self.subscriptions = defaultdict(list)
async def get(self, key):
"""Read from shared state"""
return self.store.get(key)
async def set(self, key, value, agent_id):
"""Write to shared state with conflict resolution"""
async with self.lock(key):
old_value = self.store.get(key)
# Conflict resolution
if old_value and old_value.version > value.version:
value = self.merge(old_value, value)
self.store[key] = value
# Notify subscribers
await self.notify_subscribers(key, value, agent_id)
async def subscribe(self, key, callback):
"""Subscribe to state changes"""
self.subscriptions[key].append(callback)
Memory Systems
class AgentMemory:
"""
Hierarchical memory system for agents
"""
def __init__(self):
self.working_memory = WorkingMemory(capacity=7)
self.episodic_memory = EpisodicMemory()
self.semantic_memory = SemanticMemory()
self.procedural_memory = ProceduralMemory()
async def store(self, observation, thought, action):
"""Store experience in appropriate memory"""
episode = Episode(observation, thought, action)
# Working memory (immediate)
self.working_memory.add(episode)
# Episodic memory (experiences)
if episode.is_significant():
await self.episodic_memory.store(episode)
# Semantic memory (facts)
facts = self.extract_facts(episode)
await self.semantic_memory.update(facts)
# Procedural memory (skills)
if episode.demonstrates_skill():
await self.procedural_memory.learn(episode)
async def recall(self, query=None):
"""Retrieve relevant memories"""
if query:
return await self.semantic_memory.search(query)
else:
return self.working_memory.get_all()
Tool Integration (MCP)
class MCPToolAdapter:
"""
Model Context Protocol tool integration
"""
def __init__(self):
self.mcp_client = MCPClient()
self.tool_registry = {}
async def register_tool(self, tool_spec):
"""Register MCP-compatible tool"""
tool = MCPTool(tool_spec)
self.tool_registry[tool.name] = tool
return tool
async def execute(self, tool_name, params):
"""Execute tool via MCP"""
tool = self.tool_registry[tool_name]
# Validate parameters
validated = tool.validate_params(params)
# Execute via MCP
result = await self.mcp_client.execute(
tool=tool,
params=validated
)
return result
Observability
class AgentObserver:
"""
Monitoring and debugging for agents
"""
def __init__(self):
self.traces = []
self.metrics = MetricsCollector()
self.logs = LogCollector()
def trace_thought(self, agent_id, thought):
"""Trace agent reasoning"""
self.traces.append({
"timestamp": time.time(),
"agent_id": agent_id,
"type": "thought",
"content": thought,
"tokens_used": thought.token_count
})
def record_metric(self, name, value, tags=None):
"""Record performance metrics"""
self.metrics.record(name, value, tags)
async def export_traces(self):
"""Export traces for analysis"""
return {
"traces": self.traces,
"metrics": await self.metrics.aggregate(),
"logs": self.logs.get_recent()
}
Deployment Patterns
Standalone Agent
# Single agent for specific task
agent = HanzoAgent(
config=AgentConfig(
model="gpt-4",
tools=["search", "calculator"],
memory_size="1GB"
)
)
result = await agent.execute_task(
"Research quantum computing applications"
)
Agent Swarm
# Multiple collaborative agents
orchestrator = AgentOrchestrator()
# Spawn specialized agents
orchestrator.spawn_agent("researcher", ResearchConfig())
orchestrator.spawn_agent("writer", WriterConfig())
orchestrator.spawn_agent("editor", EditorConfig())
# Execute complex task
result = await orchestrator.delegate_task(
"Write a comprehensive report on AI safety"
)
Agent Network
# Distributed agent network
network = AgentNetwork(
discovery_service="hanzo://discovery.hanzo.ai",
network_id="mainnet"
)
# Join network
agent = HanzoAgent(config)
await network.register(agent)
# Find and collaborate
experts = await network.discover_agents(
capability="medical_diagnosis"
)
SDK Examples
Python SDK
from hanzoai.agent import Agent, Orchestrator
# Simple agent
agent = Agent(
name="assistant",
model="gpt-4",
instructions="You are a helpful assistant"
)
response = await agent.run("Help me plan a trip to Japan")
# Multi-agent system
orchestrator = Orchestrator()
orchestrator.add_agent("researcher", research_agent)
orchestrator.add_agent("planner", planning_agent)
itinerary = await orchestrator.execute(
task="Plan a 2-week Japan trip",
agents=["researcher", "planner"]
)
TypeScript SDK
import { Agent, Orchestrator } from '@hanzoai/agent';
// Create agent
const agent = new Agent({
name: 'assistant',
model: 'gpt-4',
instructions: 'You are a helpful assistant'
});
const response = await agent.run('Help me plan a trip');
// Multi-agent
const orchestrator = new Orchestrator();
orchestrator.addAgent('researcher', researchAgent);
orchestrator.addAgent('planner', plannerAgent);
const result = await orchestrator.execute({
task: 'Plan a trip',
agents: ['researcher', 'planner']
});
Implementation Roadmap
Phase 1: Core SDK (Q1 2025)
- Basic agent class
- Simple orchestration
- Memory systems
- Python/TypeScript SDKs
Phase 2: Networking (Q2 2025)
- P2P agent network
- Service discovery
- Message routing
- Shared state
Phase 3: Advanced Features (Q3 2025)
- MCP tool integration
- Advanced orchestration
- Swarm intelligence
- Observability
Phase 4: Scale (Q4 2025)
- Agent marketplace
- Distributed execution
- Edge deployment
- Enterprise features
Security Considerations
Agent Security
- Sandboxed execution
- Resource limits
- Permission system
- Audit trails
Network Security
- Encrypted communication
- Agent authentication
- Byzantine fault tolerance
- Rate limiting
References
Copyright
Copyright and related rights waived via CC0.