Python Sdk

Python SDK

When to Use

Use this skill as needed to perform the specified automation task.

Build AI applications with the inference.sh Python SDK.

Quick Start

pip install inferencesh

from inferencesh import inference

client = inference(api_key="inf_your_key")

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

Installation

# Standard installation
pip install inferencesh

# With async support
pip install inferencesh[async]

Requirements: Python 3.8+

Authentication

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

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

print(result["status"])  # "completed"
print(result["output"])  # Output data

Fire and Forget

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

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

File Handling

Automatic Upload

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

Manual Upload

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:

# 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:

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:

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-schnell@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

Tool Builder API

Parameter Types

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)

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)

generate = (
    app_tool("generate_image", "infsh/flux-schnell@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)

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)

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)

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

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",
   
   
)

File Attachments

# 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)

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

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-schnell",
        "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

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

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",
   
)

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

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

# Full platform skill (all 150+ apps via CLI)
npx skills add inference-sh/skills@inference-sh

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

# Image generation
npx skills add inference-sh/skills@ai-image-generation

Documentation

• 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