사용 방법
- 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
- 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
- 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/101-skills/superpowers --skill python-sdk시작 전 확인
- 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
- 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서
원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.
Install the belt CLI skill: npx skills add belt-sh/cliPython SDK
Build AI applications with the inference.sh Python SDK.
Quick Start
bash
pip install inferenceshpython
from inferencesh import inference
client = inference(api_key="inf_your_key")
# Run an AI app
result = client.run({
"app": "infsh/flux-1-dev",
"input": {"prompt": "A sunset over mountains"}
})
print(result["output"])Installation
bash
# Standard installation
pip install inferencesh
# With async support
pip install inferencesh[async]Requirements: Python 3.8+
Authentication
python
import os
from inferencesh import inference
# Direct API key
client = inference(api_key="inf_your_key")
# From environment variable (recommended)
client = inference(api_key=os.environ["INFERENCE_API_KEY"])Get your API key: Settings → API Keys → Create API Key
Running Apps
Basic Execution
python
result = client.run({
"app": "infsh/flux-1-dev",
"input": {"prompt": "A cat astronaut"}
})
print(result["status"]) # "completed"
print(result["output"]) # Output dataFire and Forget
python
task = client.run({
"app": "google/veo-3-1-fast",
"input": {"prompt": "Drone flying over mountains"}
}, wait=False)
print(f"Task ID: {task['id']}")
# Check later with client.get_task(task['id'])Streaming Progress
python
for update in client.run({
"app": "google/veo-3-1-fast",
"input": {"prompt": "Ocean waves at sunset"}
}, stream=True):
print(f"Status: {update['status']}")
if update.get("logs"):
print(update["logs"][-1])Run Parameters
| Parameter | Type | Description |
|---|---|---|
app | string | App ID (namespace/name@version) |
input | dict | Input matching app schema |
setup | dict | Hidden setup configuration |
infra | string | 'cloud' or 'private' |
session | string | Session ID for stateful execution |
session_timeout | int | Idle timeout (1-3600 seconds) |
File Handling
Automatic Upload
python
result = client.run({
"app": "image-processor",
"input": {
"image": "/path/to/image.png" # Auto-uploaded
}
})Manual Upload
python
from inferencesh import UploadFileOptions
# Basic upload
file = client.upload_file("/path/to/image.png")
# With options
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"]}
})Sessions (Stateful Execution)
Keep workers warm across multiple calls:
python
# Start new session
result = client.run({
"app": "my-app",
"input": {"action": "init"},
"session": "new",
"session_timeout": 300 # 5 minutes
})
session_id = result["session_id"]
# Continue in same session
result = client.run({
"app": "my-app",
"input": {"action": "process"},
"session": session_id
})Agent SDK
Template Agents
Use pre-built agents from your workspace:
python
agent = client.agent("my-team/support-agent@latest")
# Send message
response = agent.send_message("Hello!")
print(response.text)
# Multi-turn conversation
response = agent.send_message("Tell me more")
# Reset conversation
agent.reset()
# Get chat history
chat = agent.get_chat()Ad-hoc Agents
Create custom agents programmatically:
python
from inferencesh import tool, string, number, app_tool
# Define tools
calculator = (
tool("calculate")
.describe("Perform a calculation")
.param("expression", string("Math expression"))
.build()
)
image_gen = (
app_tool("generate_image", "infsh/flux-1-dev@latest")
.describe("Generate an image")
.param("prompt", string("Image description"))
.build()
)
# Create 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?")Available Core Apps
| Model | App Reference |
|---|---|
| Claude Sonnet 4 | infsh/claude-sonnet-4@latest |
| Claude 3.5 Haiku | infsh/claude-haiku-35@latest |
| GPT-4o | infsh/gpt-4o@latest |
| GPT-4o Mini | infsh/gpt-4o-mini@latest |
Tool Builder API
Parameter Types
python
from inferencesh import (
string, number, integer, boolean,
enum_of, array, obj, optional
)
name = string("User's name")
age = integer("Age in years")
score = number("Score 0-1")
active = boolean("Is active")
priority = enum_of(["low", "medium", "high"], "Priority")
tags = array(string("Tag"), "List of tags")
address = obj({
"street": string("Street"),
"city": string("City"),
"zip": optional(string("ZIP"))
}, "Address")Client Tools (Run in Your Code)
python
greet = (
tool("greet")
.display("Greet User")
.describe("Greets a user by name")
.param("name", string("Name to greet"))
.require_approval()
.build()
)App Tools (Call AI Apps)
python
generate = (
app_tool("generate_image", "infsh/flux-1-dev@latest")
.describe("Generate an image from text")
.param("prompt", string("Image description"))
.setup({"model": "schnell"})
.input({"steps": 20})
.require_approval()
.build()
)Agent Tools (Delegate to Sub-agents)
python
from inferencesh import agent_tool
researcher = (
agent_tool("research", "my-org/researcher@v1")
.describe("Research a topic")
.param("topic", string("Topic to research"))
.build()
)Webhook Tools (Call External APIs)
python
from inferencesh import webhook_tool
notify = (
webhook_tool("slack", "https://hooks.slack.com/...")
.describe("Send Slack notification")
.secret("SLACK_SECRET")
.param("channel", string("Channel"))
.param("message", string("Message"))
.build()
)Internal Tools (Built-in Capabilities)
python
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
})Streaming Agent Responses
python
def handle_message(msg):
if msg.get("content"):
print(msg["content"], end="", flush=True)
def handle_tool(call):
print(f"\n[Tool: {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
)File Attachments
python
# From file path
with open("image.png", "rb") as f:
response = agent.send_message(
"What's in this image?",
files=[f.read()]
)
# From base64
response = agent.send_message(
"Analyze this",
files=["data:image/png;base64,iVBORw0KGgo..."]
)Skills (Reusable Context)
python
agent = client.agent({
"core_app": {"ref": "infsh/claude-sonnet-4@latest"},
"skills": [
{
"name": "code-review",
"description": "Code review guidelines",
"content": "# Code Review\n\n1. Check security\n2. Check performance..."
},
{
"name": "api-docs",
"description": "API documentation",
"url": "https://example.com/skills/api-docs.md"
}
]
})Async Support
python
from inferencesh import async_inference
import asyncio
async def main():
client = async_inference(api_key="inf_...")
# Async app execution
result = await client.run({
"app": "infsh/flux-1-dev",
"input": {"prompt": "A galaxy"}
})
# Async agent
agent = client.agent("my-org/assistant@latest")
response = await agent.send_message("Hello!")
# Async streaming
async for msg in agent.stream_messages():
print(msg)
asyncio.run(main())Error Handling
python
from inferencesh import RequirementsNotMetException
try:
result = client.run({"app": "my-app", "input": {...}})
except RequirementsNotMetException as e:
print(f"Missing requirements:")
for err in e.errors:
print(f" - {err['type']}: {err['key']}")
except RuntimeError as e:
print(f"Error: {e}")Human Approval Workflows
python
def handle_tool(call):
if call.requires_approval:
# Show to user, get confirmation
approved = prompt_user(f"Allow {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": "Denied by user"})
response = agent.send_message(
"Delete all temp files",
on_tool_call=handle_tool
)Reference Files
- Agent Patterns - Multi-agent, RAG, human-in-the-loop patterns
- Tool Builder - Complete tool builder API reference
- Streaming - Real-time progress updates and SSE handling
- File Handling - Upload, download, and manage files
- Sessions - Stateful execution with warm workers
- Async Patterns - Parallel processing and async/await
Related Skills
bash
# JavaScript SDK
npx skills add inference-sh/skills@javascript-sdk
# Full platform skill (all apps via CLI)
npx skills add inference-sh/skills@infsh-cli
# LLM models
npx skills add inference-sh/skills@llm-models
# Image generation
npx skills add inference-sh/skills@ai-image-generationDocumentation
- Python SDK Reference - Full API documentation
- Agent SDK Overview - Building agents
- Tool Builder Reference - Creating tools
- Authentication - API key setup
- Streaming - Real-time updates
- File Uploads - File handling
