LangChain
Content Generation Agent with LangChain
Build an AI content generation agent that researches topics, writes blog posts and social media content with consistent brand voice — using LangChain.
contentblogsocial mediawriting
Working Code
from langchain_openai import ChatOpenAIfrom langchain_core.tools import tool
@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}"
model = ChatOpenAI(model="gpt-4o")model_with_tools = model.bind_tools([web_search, save_content])response = model_with_tools.invoke([ ("system", "You are a content writer. Research the topic first, then write engaging content. Save the final output using save_content."), ("user", "Write a blog post about the benefits of AI agents in customer service"),])Step by Step
1
Install dependencies
Install LangChain 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 LangChain agent with your tools, set the system prompt, and execute a query.
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