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 explicitly mention 'CloudBase Agent.'

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Repository: TencentCloudBase/CloudBase-AI-Toolkit
Last source activity: June 18, 2026 at 16:21
Detected SKILL.md language: English
Stars: 1,093
Forks: 138

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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	—

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`

# 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

# 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")

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)    │
└─────────────────────────────────────────────┘

# 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

# 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

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

# 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

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

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)