? WSaiOS Kernel v0.2 — Async Runtime Kernel
? WSaiOS Kernel v0.3 — Agent Runtime Kernel
? 本质升级
v0.3 = DAG + Async + Agent Node + Tool System + Memory Loop
一句话：
? 从“执行任务” → “运行一群会思考的Agent”
? v0.2 → v0.3 核心变化
| 层级 | v0.2 | v0.3 |
|---|---|---|
| 执行单元 | Node | Agent |
| 状态 | 无状态 | 有状态 |
| 执行模型 | DAG | Agent Graph |
| 能力 | 并发执行 | 思考 + 工具调用 |
| 核心 | Scheduler | Agent Loop |
? v0.3 工程结构
wsaios_kernel_v0.3/
│
├── kernel/
│ ├── task.py
│ ├── agent.py # ? 新增核心
│ ├── graph.py
│ ├── builder.py
│ ├── router.py
│ ├── executor.py
│ ├── memory.py
│ ├── kernel.py
│
├── runtime/
│ ├── scheduler.py
│ ├── worker.py
│
├── tools/
│ ├── tool_registry.py # ? 工具系统
│ ├── search_tool.py
│ ├── calc_tool.py
│
└── main.py? kernel/agent.py（?核心）
class Agent:
def __init__(self, agent_id, role, memory, tools):
self.id = agent_id
self.role = role
self.memory = memory
self.tools = tools
self.state = {}
def think(self, input_text):
if "分析" in input_text:
return "use_tool:search"
if "计算" in input_text:
return "use_tool:calc"
return "final_answer"
def act(self, action):
if action.startswith("use_tool:"):
tool_name = action.split(":")[1]
tool = self.tools.get(tool_name)
result = tool.run("sample input")
self.memory.save(self.id, result)
return result
return f"[Agent {self.id}] final output"? kernel/memory.py（升级为 Agent Memory）
class Memory:
def __init__(self):
self.store = {}
self.agent_state = {}
def save(self, k, v):
self.store[k] = v
def update_agent(self, agent_id, state):
self.agent_state[agent_id] = state? tools/tool_registry.py
class ToolRegistry:
def __init__(self):
self.tools = {}
def register(self, name, tool):
self.tools[name] = tool
def get(self, name):
return self.tools.get(name)? tools/search_tool.py
class SearchTool:
def run(self, query):
return f"[SearchTool] result for: {query}"? tools/calc_tool.py
class CalcTool:
def run(self, expr):
return f"[CalcTool] computed: {expr} = 42"? kernel/executor.py（Agent执行器?）
class Executor:
def __init__(self, router, memory):
self.router = router
self.memory = memory
def run_agent(self, agent, task_input):
action = agent.think(task_input)
result = agent.act(action)
return result? kernel/router.py（升级 Agent Router）
class Router:
def select_agent(self, task):
if "搜索" in task:
return "research_agent"
if "计算" in task:
return "math_agent"
return "general_agent"? kernel/graph.py（Agent Graph）
class AgentNode:
def __init__(self, agent):
self.agent = agent
self.result = None
self.depends = []? kernel/kernel.py（v0.3核心）
from kernel.agent import Agent
from kernel.memory import Memory
from tools.tool_registry import ToolRegistry
from tools.search_tool import SearchTool
from tools.calc_tool import CalcTool
from kernel.executor import Executor
class WSaiOSKernel:
def __init__(self):
self.memory = Memory()
self.tools = ToolRegistry()
self.tools.register("search", SearchTool())
self.tools.register("calc", CalcTool())
self.executor = Executor(None, self.memory)
def run(self, input_text):
agent = Agent(
"agent_1",
"general",
self.memory,
self.tools
)
result = self.executor.run_agent(agent, input_text)
self.memory.save("final", result)
return self.memory.store? main.py（v0.3运行）
from kernel.kernel import WSaiOSKernel
def main():
print("\n? WSaiOS Kernel v0.3 Agent Runtime Starting...\n")
kernel = WSaiOSKernel()
result = kernel.run("帮我分析并计算一个问题")
print("\n? RESULT:\n")
for k, v in result.items():
print(k, "=>", v)
if __name__ == "__main__":
main()? 运行效果
? WSaiOS Kernel v0.3 Agent Runtime Starting...
? RESULT:
agent_1 => [SearchTool] result for: sample input
final => [SearchTool] result for: sample input? v0.3 本质（关键升级?）
? 1. Node → Agent
系统单位从：
执行节点
变成
有思考能力的 Agent
? 2. 从“执行” → “思考 + 行动”
Agent包含：
- think()
- act()
- tool use
- memory write
? 3. 系统进入“认知循环”
input → think → tool → act → memory → output? v0.3 一句话定义
? WSaiOS Kernel v0.3 = 一个具备Agent思考循环与工具调用能力的AI Runtime Kernel
? v0 → v0.3 进化本质
v0.1 = DAG执行器
v0.2 = Async Runtime Kernel
v0.3 = Agent Runtime Kernel（认知系统）? 下一步（真正AI OS分水岭?）
如果继续：
? v0.4：AI OS Kernel
- system call
- long-term memory
- event loop
- multi-agent coordination
- OS-like runtime abstraction
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