pip install inferencesh

from inferencesh import inference

client = inference(api_key="inf_your_key")

# 运行AI应用
result = client.run({
    "app": "infsh/flux-schnell",
    "input": {"prompt": "A sunset over mountains"}
})
print(result["output"])

# 标准安装
pip install inferencesh

# 带异步支持的安装
pip install inferencesh[async]

import os
from inferencesh import inference

# 直接传入API密钥
client = inference(api_key="inf_your_key")

# 从环境变量获取（推荐方式）
client = inference(api_key=os.environ["INFERENCE_API_KEY"])

result = client.run({
    "app": "infsh/flux-schnell",
    "input": {"prompt": "A cat astronaut"}
})

print(result["status"])  # "completed"
print(result["output"])  # 输出数据

task = client.run({
    "app": "google/veo-3-1-fast",
    "input": {"prompt": "Drone flying over mountains"}
}, wait=False)

print(f"任务ID: {task['id']}")
# 后续可通过 client.get_task(task['id']) 检查状态

for update in client.run({
    "app": "google/veo-3-1-fast",
    "input": {"prompt": "Ocean waves at sunset"}
}, stream=True):
    print(f"状态: {update['status']}")
    if update.get("logs"):
        print(update["logs"][-1])

result = client.run({
    "app": "image-processor",
    "input": {
        "image": "/path/to/image.png"  # 自动上传
    }
})

from inferencesh import UploadFileOptions

# 基础上传
file = client.upload_file("/path/to/image.png")

# 带选项的上传
file = client.upload_file(
    "/path/to/image.png",
    UploadFileOptions(
        filename="custom_name.png",
        content_type="image/png",
        public=True
    )
)

result = client.run({
    "app": "image-processor",
    "input": {"image": file["uri"]}
})

# 启动新会话
result = client.run({
    "app": "my-app",
    "input": {"action": "init"},
    "session": "new",
    "session_timeout": 300  # 5分钟
})
session_id = result["session_id"]

# 在同一会话中继续执行
result = client.run({
    "app": "my-app",
    "input": {"action": "process"},
    "session": session_id
})

agent = client.agent("my-team/support-agent@latest")

# 发送消息
response = agent.send_message("Hello!")
print(response.text)

# 多轮对话
response = agent.send_message("Tell me more")

# 重置对话
agent.reset()

# 获取聊天历史
chat = agent.get_chat()

from inferencesh import tool, string, number, app_tool

# 定义工具
calculator = (
    tool("calculate")
    .describe("执行计算操作")
    .param("expression", string("数学表达式"))
    .build()
)

image_gen = (
    app_tool("generate_image", "infsh/flux-schnell@latest")
    .describe("生成图片")
    .param("prompt", string("图片描述"))
    .build()
)

# 创建Agent
agent = client.agent({
    "core_app": {"ref": "infsh/claude-sonnet-4@latest"},
    "system_prompt": "You are a helpful assistant.",
    "tools": [calculator, image_gen],
    "temperature": 0.7,
    "max_tokens": 4096
})

response = agent.send_message("What is 25 * 4?")

from inferencesh import (
    string, number, integer, boolean,
    enum_of, array, obj, optional
)

name = string("用户姓名")
age = integer("年龄（岁）")
score = number("分数 0-1")
active = boolean("是否活跃")
priority = enum_of(["low", "medium", "high"], "优先级")
tags = array(string("标签"), "标签列表")
address = obj({
    "street": string("街道"),
    "city": string("城市"),
    "zip": optional(string("邮政编码"))
}, "地址")

greet = (
    tool("greet")
    .display("问候用户")
    .describe("按姓名问候用户")
    .param("name", string("要问候的姓名"))
    .require_approval()
    .build()
)

generate = (
    app_tool("generate_image", "infsh/flux-schnell@latest")
    .describe("根据文本生成图片")
    .param("prompt", string("图片描述"))
    .setup({"model": "schnell"})
    .input({"steps": 20})
    .require_approval()
    .build()
)

from inferencesh import agent_tool

researcher = (
    agent_tool("research", "my-org/researcher@v1")
    .describe("研究指定主题")
    .param("topic", string("研究主题"))
    .build()
)

from inferencesh import webhook_tool

notify = (
    webhook_tool("slack", "https://hooks.slack.com/...")
    .describe("发送Slack通知")
    .secret("SLACK_SECRET")
    .param("channel", string("频道"))
    .param("message", string("消息内容"))
    .build()
)

from inferencesh import internal_tools

config = (
    internal_tools()
    .plan()
    .memory()
    .web_search(True)
    .code_execution(True)
    .image_generation({
        "enabled": True,
        "app_ref": "infsh/flux@latest"
    })
    .build()
)

agent = client.agent({
    "core_app": {"ref": "infsh/claude-sonnet-4@latest"},
    "internal_tools": config
})

def handle_message(msg):
    if msg.get("content"):
        print(msg["content"], end="", flush=True)

def handle_tool(call):
    print(f"\n[工具: {call.name}]")
    result = execute_tool(call.name, call.args)
    agent.submit_tool_result(call.id, result)

response = agent.send_message(
    "Explain quantum computing",
    on_message=handle_message,
    on_tool_call=handle_tool
)

# 从文件路径读取
with open("image.png", "rb") as f:
    response = agent.send_message(
        "What's in this image?",
        files=[f.read()]
    )

# 从base64格式读取
response = agent.send_message(
    "Analyze this",
    files=["data:image/png;base64,iVBORw0KGgo..."]
)

agent = client.agent({
    "core_app": {"ref": "infsh/claude-sonnet-4@latest"},
    "skills": [
        {
            "name": "code-review",
            "description": "代码评审指南",
            "content": "# 代码评审\n\n1. 检查安全性\n2. 检查性能..."
        },
        {
            "name": "api-docs",
            "description": "API文档",
            "url": "https://example.com/skills/api-docs.md"
        }
    ]
})

from inferencesh import async_inference
import asyncio

async def main():
    client = async_inference(api_key="inf_...")

    # 异步执行应用
    result = await client.run({
        "app": "infsh/flux-schnell",
        "input": {"prompt": "A galaxy"}
    })

    # 异步Agent
    agent = client.agent("my-org/assistant@latest")
    response = await agent.send_message("Hello!")

    # 异步流式传输
    async for msg in agent.stream_messages():
        print(msg)

asyncio.run(main())

from inferencesh import RequirementsNotMetException

try:
    result = client.run({"app": "my-app", "input": {...}})
except RequirementsNotMetException as e:
    print(f"缺少依赖项:")
    for err in e.errors:
        print(f"  - {err['type']}: {err['key']}")
except RuntimeError as e:
    print(f"错误: {e}")

def handle_tool(call):
    if call.requires_approval:
        # 展示给用户，获取确认
        approved = prompt_user(f"是否允许执行{call.name}?")
        if approved:
            result = execute_tool(call.name, call.args)
            agent.submit_tool_result(call.id, result)
        else:
            agent.submit_tool_result(call.id, {"error": "用户已拒绝"})

response = agent.send_message(
    "Delete all temp files",
    on_tool_call=handle_tool
)

# JavaScript SDK
npx skills add inference-sh/skills@javascript-sdk

# 完整平台Skill（通过CLI访问所有150+应用）
npx skills add inference-sh/skills@inference-sh

# LLM模型
npx skills add inference-sh/skills@llm-models

# 图片生成
npx skills add inference-sh/skills@ai-image-generation