cloudbase-agent-python

name	cloudbase-agent-python
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.'
alwaysApply	true

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

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.

Step	Task	Document	Blocking?
0	Choose adapter & write agent code	See "Adapter Selection" below	—
1	Ensure Python 3.10	agent-deployment § Step 1	✅ BLOCKING
2	Build env/ (one-shot)	agent-deployment § Step 2	✅ BLOCKING
3	Verify env/ integrity	agent-deployment § Step 3	✅ BLOCKING
4	Deploy with manageAgent	agent-deployment § Step 4	—

Adapter Selection (Step 0)

Framework	Read	Install
LangGraph (stateful graphs)	adapter-langgraph	cloudbase-agent-langgraph
CrewAI (multi-agent crews)	adapter-development	cloudbase-agent-crewai
Coze platform	adapter-coze	cloudbase-agent-coze
Custom / raw FastAPI	server-quickstart + adapter-development	cloudbase-agent-server

Additional References (read on demand, NOT required for deployment)

Task	Read
Server setup, middleware, multi-agent, CORS	server-quickstart
Authentication and user context	authentication

Quick Start (Framework-Agnostic)

Prerequisites: Python >= 3.10 is required.

1. Install dependencies (pick ONE adapter):

# 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

2. Create server entry point:

# server.py — this pattern works with ANY adapter
import os
from dotenv import load_dotenv
load_dotenv()

from cloudbase_agent.server import AgentServiceApp, AgentCreatorResult

# Import your agent (framework-specific, see adapter docs)
# from agents.chat.agent import create_my_agent

def create_agent() -> AgentCreatorResult:
    agent = create_my_agent()  # Your agent factory
    return {"agent": agent}

app = AgentServiceApp()
app.set_cors_config(allow_origins=["*"])

if __name__ == "__main__":
    port = int(os.environ.get("SCF_RUNTIME_PORT", "9000"))
    app.run(create_agent, port=port, host="0.0.0.0")

3. Deploy to CloudBase:

Follow the 4-step deployment pipeline in agent-deployment.

---

Architecture

Client (React / MiniProgram / curl)
   │  HTTP POST + SSE streaming
   ▼
┌─────────────────────────────────────────────┐
│  AgentServiceApp (FastAPI)                   │
│  ├─ /send-message      ← AG-UI SSE         │
│  ├─ /chat/completions  ← OpenAI-compat      │
│  └─ Middleware chain (onion model)           │
├─────────────────────────────────────────────┤
│  Agent Layer                                 │
│  ├─ LangGraphAgent  ├─ CrewAIAgent          │
│  ├─ LlamaIndexAgent ├─ CozeAgent/DifyAgent  │
│  └─ BaseAgent (extend for custom)           │
├──────────────────┬──────────────────────────┤
│  Tools           │  Storage                  │
│  Bash/FS/Code/MCP│  Memory + LongTermMemory  │
├─────────────────────────────────────────────┤
│  Observability (OpenTelemetry + Langfuse)    │
└─────────────────────────────────────────────┘

Installation

CloudBase Agent Python SDK is published to PyPI as separate packages. Note: PyPI package names use hyphens (cloudbase-agent-*), and Python imports use the same namespace (cloudbase_agent.*).

# Core + Server + LangGraph (most common)
pip install cloudbase-agent-langgraph

# Individual packages
pip install cloudbase-agent-core        # Core framework
pip install cloudbase-agent-server      # FastAPI server
pip install cloudbase-agent-langgraph   # LangGraph integration
pip install cloudbase-agent-tools       # Tool system
pip install cloudbase-agent-storage     # Memory/Storage
pip install cloudbase-agent-observability  # OpenTelemetry/Langfuse
pip install cloudbase-agent-coze        # Coze platform
pip install cloudbase-agent-crewai      # CrewAI integration

Import Note: All packages share the cloudbase_agent namespace:

# After installing cloudbase-agent-langgraph, import from cloudbase_agent
from cloudbase_agent.langgraph import LangGraphAgent
from cloudbase_agent.server import AgentServiceApp
from cloudbase_agent.tools import create_bash_tool

Reference Documents

Based on what the user needs, read the corresponding reference document. Only read the relevant reference — don't load all of them.

User Need	Reference	What It Covers
Deploying agent to CloudBase	Read agent-deployment	manageAgent MCP tool (MUST USE), 4-step blocking pipeline, Python 3.10, env/ build, verification
Server setup, deployment, middleware, multi-agent, CORS	Read references/server.md	AgentServiceApp 3 deployment methods, middleware (generator/yield/onion model), multi-agent server, Agent Creator pattern, health checks
LangGraph agent, callbacks, tool proxy, HITL, checkpoints	Read adapter-langgraph	LangGraphAgent constructor, AgentCallback protocol, ToolProxy, human-in-the-loop with interrupt(), TDAICheckpointSaver, client-defined tools
Tools: bash, filesystem, code execution, MCP, custom tools	Read references/tools.md	create_bash_tool, 8 file tools, code executors, MCPToolkit/CloudBaseMCPServer, @tool decorator, BaseTool, framework adapters
Memory, persistence, short/long-term, MySQL, MongoDB	Read references/storage.md	InMemoryMemory, TDAIMemory, MySQLMemory, MongoDBMemory, TDAILongTermMemory, Mem0LongTermMemory, LangGraph checkpoint
Tracing, monitoring, Langfuse, OpenTelemetry	Read references/observability.md	ConsoleTraceConfig, OTLPTraceConfig, setup_observability, env vars, manual observation spans
Common patterns, JWT auth, MCP integration, production	Read references/recipes.md	JWT middleware, MCP + LangGraph, production deployment, adding tools to agents, client-defined tools

Key Imports Quick Reference

# Server
from cloudbase_agent.server import AgentServiceApp, AgentCreatorResult
from cloudbase_agent.server import create_send_message_adapter, create_openai_adapter
from cloudbase_agent.server import RunAgentInput, OpenAIChatCompletionRequest

# Agents
from cloudbase_agent.langgraph import LangGraphAgent
from cloudbase_agent.crewai import CrewAIAgent

# Tools
from cloudbase_agent.tools import create_bash_tool, create_read_tool, create_write_tool
from cloudbase_agent.tools import MCPToolkit, CloudBaseMCPServer, CloudBaseTool
from cloudbase_agent.tools import tool, BaseTool  # custom tools

# Storage
from cloudbase_agent.storage import InMemoryMemory, TDAIMemory
from cloudbase_agent.storage import TDAILongTermMemory, Mem0LongTermMemory
from cloudbase_agent.langgraph import TDAICheckpointSaver, TDAIStore

# Observability
from cloudbase_agent.observability import ConsoleTraceConfig, OTLPTraceConfig, setup_observability

# Schemas
from cloudbase_agent.schemas import Message, MessageRole, StreamEvent, EventType

Project Structure Convention

my-agent-project/
├── agents/
│   ├── agentic_chat/agent.py      # build_workflow() → agent instance
│   ├── human_in_the_loop/agent.py
│   └── __init__.py
├── server.py                       # Main entry: AgentServiceApp().run(...)
├── scf_bootstrap                   # CloudBase startup script (required for deployment)
├── .env                            # OPENAI_API_KEY, etc.
└── requirements.txt

Environment Variables

Variable	Purpose
OPENAI_API_KEY	OpenAI API key
AUTO_TRACES_STDOUT	Enable console tracing (true)
LANGFUSE_PUBLIC_KEY / LANGFUSE_SECRET_KEY	Langfuse keys
TDAI_ENDPOINT / TDAI_API_KEY	TDAI memory/checkpoint endpoint
SCF_RUNTIME_PORT	CloudBase runtime port (set automatically during deployment)

Key Design Decisions

1. Agent Creator Pattern: Every request creates a fresh agent via factory function. Supports cleanup callbacks for resource release.
2. Dual Protocol: Every agent supports both AG-UI native (SSE + rich events) and OpenAI-compatible (/chat/completions).
3. Middleware = Generator: Use yield — pre-yield = pre-processing, post-yield = post-processing (onion model).
4. Namespace Package: cloudbase_agent spans multiple PyPI packages (cloudbase-agent-core, cloudbase-agent-server, cloudbase-agent-langgraph, etc.). PyPI names use hyphens, but all imports use from cloudbase_agent.xxx import ....
5. Observability Auto-Integration: Install cloudbase-agent-observability and tracing works automatically — zero config needed.
6. Deploy with manageAgent: Always use the manageAgent MCP tool for CloudBase deployment. Follow the 4-step blocking pipeline in agent-deployment.