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cloudbase-agent-python

Build FastAPI-based AI agent backends with streaming and OpenAI-compatible APIs.

2|1|Updated Apr 3, 2026
Search Tags:#tool integration#langgraph#agent deployment#sse streaming#ai agent backend#openai compatible api#crewAI
One-click install
npx skills add https://github.com/lxb031018/qintu --skill cloudbase-agent-python-lxb031018
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: cloudbase-agent-python
Source: https://github.com/lxb031018/qintu/tree/main/rules/cloudbase-agent/py
Command: npx skills add https://github.com/lxb031018/qintu --skill cloudbase-agent-python-lxb031018

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

CloudBase Agent Python SDK helps teams avoid building and maintaining custom backend plumbing for AI agents by providing a standardized way to run agent workflows, stream results, and expose an OpenAI-compatible API.

Core Features & Use Cases

  • Build agent backends with multi-framework adapters: Create AI agent servers using LangGraph, CrewAI, or custom implementations via a shared adapter interface.
  • AG-UI protocol streaming + OpenAI-compatible endpoints: Serve real-time event streams (SSE) to AG-UI clients and also support /chat/completions requests for broader integration.
  • Tools, memory, middleware, and observability: Add tool execution, persistent conversation/memory options, JWT-based user context, and tracing/metrics for production readiness.
  • Deploy with a guarded process: Follow a blocking 4-step pipeline (Python 3.10, atomic env/ build, verification, and deployment with manageAgent) to reduce runtime failures.

Quick Start

Ask an AI assistant to generate your agent server using the LangGraph adapter, then deploy it following the four-step blocking pipeline in agent-deployment.md.

Frequently Asked Questions about cloudbase-agent-python

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

FAQPage Schema
How do I deploy a LangGraph or CrewAI agent backend with streaming?▼

To deploy an AI agent backend with streaming, use a FastAPI-based AgentServiceApp with adapters for LangGraph or CrewAI, then follow the atomic env/ build and manageAgent pipeline to ensure reliable runtime execution.

Can I expose an OpenAI-compatible API endpoint for my custom agent?▼

Yes, you can expose an OpenAI-compatible API for custom agents by implementing core adapters that serve /chat/completions requests, allowing broader integration with standard chat clients.

How does SSE streaming work with AG-UI protocol clients?▼

SSE streaming works by producing AG-UI protocol events from your FastAPI-based AgentServiceApp, serving real-time event streams directly to AG-UI clients during chatbot and tool-using applications.

What do I need to build production-ready AI agent servers with authentication?▼

Building production-ready AI agent servers with authentication requires adding JWT-based user context middleware, persistent memory options, and tracing metrics for observability alongside the core streaming adapters.

Does the agent deployment pipeline support Python 3.10 environments?▼

Yes, the deployment pipeline supports Python 3.10 environments by enforcing a blocking 4-step process: Python 3.10 setup, atomic env/ build, verification, and deployment via manageAgent to reduce runtime failures.

Why should I use a standardized SDK for AI agent backends instead of custom plumbing?▼

Using a standardized SDK for AI agent backends avoids building and maintaining custom backend plumbing, providing a unified adapter interface to run workflows, stream results, and expose endpoints across multiple frameworks.

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