How do I automate SEO audits with a Python AI agent?

Steps

1. Install langchain-strale and a chat-model package.
2. Create the toolkit with your Strale API key.
3. Bind seo-audit to your model.
4. Ask it to audit a URL and run the tool call the model returns.

The solution

# pip install langchain-strale langchain-openai
from langchain_strale import StraleToolkit
from langchain_openai import ChatOpenAI

toolkit = StraleToolkit(api_key="sk_live_YOUR_KEY")
tools = [t for t in toolkit.get_tools() if t.name == "seo-audit"]

llm = ChatOpenAI(model="gpt-4o").bind_tools(tools)
result = llm.invoke("Run an SEO audit on https://example.com")

# The model picks the tool and its arguments; your code runs the call.
for call in result.tool_calls:
    tool = next(t for t in tools if t.name == call["name"])
    print(tool.invoke(call["args"]))

How it works

The answer has an overall_score, a checks object with the raw data for each signal, a flat issues list and prioritized_fixes, each with a high, medium or low priority. That gives your agent a ranked list of improvements instead of a pass or fail.

The tool's results include a language-model step, so wording can vary between runs. Its performance signal comes from the HTTP response, not from a full browser timing run: use Page Speed Test when you need Core Web Vitals. Backlinks and domain authority are not assessed.

bind_tools only lets the model propose a call. To have the model read the result and answer in words, put the same tools in one of LangChain's agent loops.

Tools in this guide