Using the Claude Agent SDK for Non-Coding Workflows

I'm Melvin aka DonvitoCodes on social media, an AI practitioner and software developer. With my growing experience in AI development and implementation, I am eager to share my knowledge about AI.

The Setup

Here’s the core script:

import asyncio from claude_agent_sdk import query, ClaudeAgentOptions, AgentDefinition from claude_agent_sdk.types import McpHttpServerConfig import os async def main(): firecrawl_api_key = os.environ['FIRECRAWL_API_KEY'] firecrawl_mcp = McpHttpServerConfig( type="http", url="https://mcp.firecrawl.dev/v2/mcp", headers={"Authorization": f"Bearer {firecrawl_api_key}"} ) translator_agent = AgentDefinition( description="Translate the content from any language to any other language.", prompt="You are an expert language translator.", tools=["Read", "Edit", "Bash", "Grep"], model="sonnet" ) options = ClaudeAgentOptions( model="glm-4.6", system_prompt="You are an expert news researcher.", permission_mode='bypassPermissions', cwd="/Users/melvin/PycharmProjects/ClaudeCodeSDK/output", mcp_servers={"firecrawl_mcp": firecrawl_mcp}, agents={"translator-agent": translator_agent} ) async for message in query( prompt=( "What are the latest news topics in AI? " "Write the results to a markdown file with URLs as references. " "Then use the translator-agent to translate the content to Korean " "and save it to a separate markdown file." ), options=options ): print(message) asyncio.run(main())

Concept 2: Sub-Agents for Specialized Tasks

After gathering the news, I wanted a translated version.

Instead of hardcoding translation logic, I created a sub-agent — the translator-agent — specifically for that purpose.

The main agent then delegated the translation task to the sub-agent.

ai_news_en.md – English summary

ai_news_ko.md – Korean translation

Why This Matters

The Claude Agent SDK already supports:

Tools (Read, Edit, Bash, etc.)

MCPs (external capability servers)

Skills

Sub-agents

These are the same components other AI agent frameworks build from scratch — but here, it’s all native to Claude’s ecosystem.

With what Claude AI has built, developers and researchers can rapidly compose workflows that go beyond chat — from document generation to automated pipelines.

I used the GLM 4.6 model in this example, but of course, it works perfectly with Claude models like Haiku and Sonnet.

A Great Alternative to AI Agent Frameworks

Frameworks like CrewAI and LangChain are excellent for building complex agent systems — but sometimes, simplicity wins.

The Claude Agent SDK gives you the same building blocks — tools, sub-agents, and external connectors — in a lightweight package that integrates naturally with Claude’s ecosystem.

Using the Claude Agent SDK for Non-Coding Workflows

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The Setup

Concept 1: Using MCPs for Data Retrieval

Concept 2: Sub-Agents for Specialized Tasks

Why This Matters

A Great Alternative to AI Agent Frameworks

Command Palette