# 基本安装
pip install agent-os-kernel
# 安装依赖
pip install -e " .[dev]"
from agent_os_kernel import AgentOSKernel
# 创建内核
kernel = AgentOSKernel ()
# 创建 Agent
agent_pid = kernel .spawn_agent (
name = "Assistant" ,
task = "帮助用户解决问题" ,
priority = 50
)
# 运行
kernel .run (max_iterations = 10 )
2. 使用 Mock Provider (无需 API Key)
from agent_os_kernel .llm import LLMProviderFactory
factory = LLMProviderFactory ()
# 使用 Mock Provider 进行测试
provider = factory .create_mock ()
# 测试聊天
result = provider .chat ([
{"role" : "user" , "content" : "Hello!" }
])
print (result ["content" ])
from agent_os_kernel .llm import LLMProviderFactory , LLMConfig
factory = LLMProviderFactory ()
# 创建 OpenAI Provider
provider = factory .create (LLMConfig (
provider = "openai" ,
model = "gpt-4o" ,
api_key = "your-api-key"
))
# 发送消息
result = provider .chat ([
{"role" : "system" , "content" : "You are a helpful assistant." },
{"role" : "user" , "content" : "What's the weather?" }
])
print (result ["content" ])
from agent_os_kernel import ContextManager
# 创建上下文管理器
cm = ContextManager (max_context_tokens = 128000 )
# 分配页面
page_id = cm .allocate_page (
agent_pid = "agent-1" ,
content = "大量上下文内容..." ,
importance = 0.8
)
# 获取优化后的上下文
context = cm .get_agent_context (agent_pid = "agent-1" )
from agent_os_kernel import StorageManager
# 创建存储
storage = StorageManager .from_postgresql (
"postgresql://user:pass@localhost/agent_os" ,
enable_vector = True
)
# 保存对话
storage .save_conversation (agent_id , messages )
# 语义搜索
results = storage .semantic_search (
query = "用户之前提到的需求" ,
limit = 5
)
from agent_os_kernel import EventBus , EventType
# 创建事件总线
event_bus = EventBus ()
# 订阅事件
event_bus .subscribe (
handler_id = "logger" ,
callback = lambda e : print (e ),
event_types = [EventType .AGENT_CREATED ]
)
# 运行基本示例
python -m agent_os_kernel --demo basic
# 运行通信示例
python -m agent_os_kernel --demo messaging
# 运行工作流示例
python -m agent_os_kernel --demo workflow
# 启动 API 服务器
uvicorn agent_os_kernel.api.server:AgentOSKernelAPI --host 0.0.0.0 --port 8000
方法
端点
描述
GET
/health
健康检查
POST
/api/v1/agents
创建 Agent
GET
/api/v1/agents
列出 Agent
GET
/api/v1/agents/{id}
获取 Agent
DELETE
/api/v1/agents/{id}
删除 Agent
GET
/api/v1/metrics
获取指标
GET
/api/v1/metrics/prometheus
Prometheus 格式
# 构建镜像
docker build -t agent-os-kernel .
# 运行
docker run -p 8000:8000 agent-os-kernel