LangGraph
Content Generation Agent with LangGraph
Build an AI content generation agent that researches topics, writes blog posts and social media content with consistent brand voice — using LangGraph.
contentblogsocial mediawriting
Working Code
from langchain_openai import ChatOpenAIfrom langchain_core.tools import toolfrom langgraph.prebuilt import create_react_agent
@tooldef web_search(query: str) -> str: """Research a topic before writing content.""" from tavily import TavilyClient client = TavilyClient() results = client.search(query, max_results=3) return "\n\n".join(r["content"] for r in results["results"])
@tooldef save_content(filename: str, content: str) -> str: """Save generated content to a file.""" Path(f"output/{filename}").write_text(content) return f"Saved to output/{filename}"
agent = create_react_agent( ChatOpenAI(model="gpt-4o"), tools=[web_search, save_content], prompt="You are a content writer. Research the topic first, then write engaging content. Save the final output using save_content.",)
result = await agent.ainvoke({ "messages": [("user", "Write a blog post about the benefits of AI agents in customer service")]})print(result["messages"][-1].content)Step by Step
1
Install dependencies
Install LangGraph and the required tools for this use case.
2
Define your tools
Create the domain-specific tool functions your agent will use to interact with external services.
3
Create the agent and run
Initialize the LangGraph agent with your tools, set the system prompt, and execute a query.
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