cloudbase-agent-python
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 explici
npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill py --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 adap
- When to use this skill
- How to use this skill (for a coding agent)
- Routing (Execution Order)
- Adapter Selection (Step 0)
- Additional References (read on demand, NOT required for deployment)
- Quick Start (Framework-Agnostic)
- Architecture
- Installation
- Reference Documents
- Key Imports Quick Reference
- Project Structure Convention
- Environment Variables
- Key Design Decisions
Option A: LangGraph-based agent pip install cloudbase-agent-langgraph Option B: CrewAI-based agent pip install cloudbase-agent-crewai Option C: Custom / minimal pip install cloudbase-agent-server Core + Server + LangGraph (most common) Individual packages pip install cloudbase-agent-core # Core framework pip install cloudbase-agent-server # FastAPI server
What does the cloudbase-agent-python skill do?
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 explici
How do I install it?
Run `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill py --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From TencentCloudBase/CloudBase-AI-Toolkit, a repository with 1,066 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.