# Cloudbase Agent Python

Build production-ready AI agent backends using the CloudBase Agent Python SDK, create...

- Repository: https://github.com/TencentCloudBase/CloudBase-AI-Toolkit (1153 stars)
- Category: Design
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill cloudbase-agent-python -g -a claude-code -y`
Codex: `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill cloudbase-agent-python -g -a codex -y`
Cursor: `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill cloudbase-agent-python -g -a cursor -y`
Gemini CLI: `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill cloudbase-agent-python -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add TencentCloudBase/CloudBase-AI-Toolkit`

## Description

Build production-ready AI agent backends using the CloudBase Agent Python SDK, create agents with LangGraph/CrewAI/LlamaIndex, serve them via FastAPI with AG-UI protocol streaming + OpenAI-compatible endpoints, add tools (bash, filesystem, MCP, code execution), memory (in-memory, TDAI, MySQL, MongoDB), observability (OpenTelemetry/Langfuse), and middleware (auth, logging). Use this skill when the user wants to create an AI agent server, build a chatbot backend, set up human-in-the-loop workflows, integrate MCP tools, add agent observability, or deploy an agent API, even if they don't explicitly mention 'CloudBase Agent.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# CloudBase Agent Python SDK

Build production-ready AI agent backends with multi-framework support, streaming
protocol, rich tools, persistent memory, and full observability.

> **Note:** This skill is for **Python** projects only.

## When to use this skill

Use this skill for **AI agent development** when you need to:

- Deploy AI agents as HTTP services with AG-UI protocol support
- Build agent backends using LangGraph, CrewAI, or LlamaIndex frameworks
- Create custom agent adapters implementing the AbstractAgent interface
- Understand AG-UI protocol events and message streaming
- Build production-ready agent servers with FastAPI

**Do NOT use for:**
- Simple AI model calling without agent capabilities (use `ai-model-*` skills)
- CloudBase cloud functions (use `cloud-functions` skill)
- CloudRun backend services without agent features (use `cloudrun-development` skill)
- TypeScript/JavaScript agent projects (use `cloudbase-agent` skill, refer to the `ts/` sub-directory)

## How to use this skill (for a coding agent)

1. **Choose the right adapter**
   - Use LangGraph adapter for stateful, graph-based workflows
   - Use CrewAI adapter for multi-agent collaboration patterns
   - Build custom adapter for specialized agent logic

2. **Write agent code**, follow the adapter-specific doc from the Routing table

3. **Deploy the agent server**, follow the **blocking deployment pipeline** in [agent-deployment](agent-deployment.md)

## Routing (Execution Order)

> ⚠️ **Deployment is a BLOCKING 4-step pipeline.** Steps marked ✅ BLOCKING
> must be completed AND verified before proceeding to the next step.
> Do NOT call `manageAgent` until all blocking steps pass.

The rest: https://github.com/TencentCloudBase/CloudBase-AI-Toolkit/blob/HEAD/config/.claude/skills/cloudbase-agent/py/SKILL.md