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Sheshiyer
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python-sdk

Build AI workflows with the inference.sh Python SDK.

4|1|Updated Feb 10, 2026
Search Tags:#integration#python#ai#sdk#agents#async#inference-sh
One-click install
npx skills add https://github.com/Sheshiyer/brandmint-oracle-aleph --skill python-sdk-sheshiyer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-sdk
Source: https://github.com/Sheshiyer/brandmint-oracle-aleph/tree/main/skills/external/inference-sh/upstream/ab546d072f1e/sdk/python-sdk
Command: npx skills add https://github.com/Sheshiyer/brandmint-oracle-aleph --skill python-sdk-sheshiyer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Python SDK enables developers to quickly build AI apps and agents on inference.sh, handling app execution, agent orchestration, and integration with 150+ models.

Core Features & Use Cases

  • Create synchronous or asynchronous AI workflows with streaming, file uploads, and multi-step tool interactions.
  • Build reusable agents and integrate with a modular tool builder API to compose capabilities and workflows.
  • Enable rapid prototyping and production-ready deployments for workflows like RAG pipelines, automation, and AI app orchestration.

Quick Start

Install the inferencesh Python SDK and instantiate a client to run an app with sample input.

Frequently Asked Questions about python-sdk

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build AI agents and apps with Python using the inference.sh SDK?▼

To build AI agents and apps with Python, install the inference.sh SDK, instantiate a client, and execute workflows using synchronous or asynchronous code. It supports streaming, file uploads, and multi-step tool interactions for rapid prototyping and production deployment.

Do I need a specific Python version to build asynchronous AI workflows with inference.sh?▼

Yes, Python 3.8+ is required to build asynchronous AI workflows with the inference.sh SDK. This environment supports synchronous and asynchronous execution, streaming responses, file uploads, and modular tool builder API integration for complex agent orchestration.

Can I integrate RAG pipelines and external tools using the inference.sh Python SDK?▼

Yes, you can integrate RAG pipelines and external tools using the inference.sh Python SDK. It provides a modular tool builder API with frontmatter metadata for discovery, enabling developers to compose multi-step tool interactions and orchestrate complex AI workflows.

What is the best way to orchestrate multi-step AI agent workflows in Python?▼

The best way to orchestrate multi-step AI agent workflows in Python is using the inference.sh SDK's modular tool builder API. It enables composition of reusable agents, supports synchronous and asynchronous execution, and handles streaming alongside file uploads for complex automation pipelines.

Does the inference.sh Python SDK support streaming and file uploads for AI app development?▼

Yes, the inference.sh Python SDK supports streaming and file uploads for AI app development. Developers can build synchronous or asynchronous AI workflows that handle multi-step tool interactions, enabling rapid prototyping and production-ready deployments for RAG pipelines and automation.

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