How to integrate Semrush MCP with Autogen

This guide walks you through connecting Semrush to AutoGen using the Composio tool router. By the end, you'll have a working Semrush agent that can show top anchor texts for example.com, compare backlink profiles for three domains, get keyword overview for 'organic coffee' through natural language commands. This guide will help you understand how to give your AutoGen agent real control over a Semrush account through Composio's Semrush MCP server. Before we dive in, let's take a quick look at the key ideas and tools involved.

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Introduction

This guide walks you through connecting Semrush to AutoGen using the Composio tool router. By the end, you'll have a working Semrush agent that can show top anchor texts for example.com, compare backlink profiles for three domains, get keyword overview for 'organic coffee' through natural language commands.

This guide will help you understand how to give your AutoGen agent real control over a Semrush account through Composio's Semrush MCP server.

Before we dive in, let's take a quick look at the key ideas and tools involved.

Also integrate Semrush with

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TL;DR

Here's what you'll learn:
  • Get and set up your OpenAI and Composio API keys
  • Install the required dependencies for Autogen and Composio
  • Initialize Composio and create a Tool Router session for Semrush
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Semrush tools
  • Run a live chat loop where you ask the agent to perform Semrush operations

What is AutoGen?

Autogen is a framework for building multi-agent conversational AI systems from Microsoft. It enables you to create agents that can collaborate, use tools, and maintain complex workflows.

Key features include:

  • Multi-Agent Systems: Build collaborative agent workflows
  • MCP Workbench: Native support for Model Context Protocol tools
  • Streaming HTTP: Connect to external services through streamable HTTP
  • AssistantAgent: Pre-built agent class for tool-using assistants

What is the Semrush MCP server, and what's possible with it?

The Semrush MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Semrush account. It provides structured and secure access to your SEO, keyword, and advertising analytics, so your agent can perform actions like keyword research, competitor analysis, backlink audits, and ad copy retrieval automatically on your behalf.

  • Comprehensive keyword research and reporting: Let your agent fetch broad match keywords, generate batch keyword overviews, and analyze key SEO metrics like search volume and difficulty in real time.
  • Competitor and backlink analysis: Ask your agent to pull backlink profiles, perform batch comparisons of domains, and summarize backlink authority and link types for competitive intelligence.
  • Ad campaign and copy insights: Have the agent retrieve unique Google Ads copies for any domain, helping you benchmark and optimize your own ad strategies based on real competitor data.
  • Content and category profiling: Enable your agent to analyze and categorize domains or URLs, surfacing topic strengths and audience focus areas for smarter content planning.
  • Anchor text and authority monitoring: Direct your agent to report on anchor text distributions and authority score profiles, giving you actionable insights for improving link-building efforts.

What is the Composio tool router, and how does it fit here?

What is Composio SDK?

Composio's Composio SDK helps agents find the right tools for a task at runtime. You can plug in multiple toolkits (like Gmail, HubSpot, and GitHub), and the agent will identify the relevant app and action to complete multi-step workflows. This can reduce token usage and improve the reliability of tool calls. Read more here: Getting started with Composio SDK

The tool router generates a secure MCP URL that your agents can access to perform actions.

How the Composio SDK works

The Composio SDK follows a three-phase workflow:

  1. Discovery: Searches for tools matching your task and returns relevant toolkits with their details.
  2. Authentication: Checks for active connections. If missing, creates an auth config and returns a connection URL via Auth Link.
  3. Execution: Executes the action using the authenticated connection.

Step-by-step Guide

Step by step08 STEPS
1

Prerequisites

You will need:

  • A Composio API key
  • An OpenAI API key (used by Autogen's OpenAIChatCompletionClient)
  • A Semrush account you can connect to Composio
  • Some basic familiarity with Autogen and Python async
2

Getting API Keys for OpenAI and Composio

OpenAI API Key
  • Go to the OpenAI dashboard and create an API key. You'll need credits to use the models, or you can connect to another model provider.
  • Keep the API key safe.
Composio API Key
  • Log in to the Composio dashboard.
  • Navigate to your API settings and generate a new API key.
  • Store this key securely as you'll need it for authentication.
3

Install dependencies

bash
pip install composio python-dotenv
pip install autogen-agentchat autogen-ext-openai autogen-ext-tools

Install Composio, Autogen extensions, and dotenv.

What's happening:

  • composio connects your agent to Semrush via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

4

Set up environment variables

bash
COMPOSIO_API_KEY=your-composio-api-key
OPENAI_API_KEY=your-openai-api-key
USER_ID=your-user-identifier@example.com

Create a .env file in your project folder.

What's happening:

  • COMPOSIO_API_KEY is required to talk to Composio
  • OPENAI_API_KEY is used by Autogen's OpenAI client
  • USER_ID is how Composio identifies which user's Semrush connections to use
5

Import dependencies and create Tool Router session

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Semrush session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["semrush"]
    )
    url = session.mcp.url
What's happening:
  • load_dotenv() reads your .env file
  • Composio(api_key=...) initializes the SDK
  • create(...) creates a Tool Router session that exposes Semrush tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to
6

Configure MCP parameters for Autogen

python
# Configure MCP server parameters for Streamable HTTP
server_params = StreamableHttpServerParams(
    url=url,
    timeout=30.0,
    sse_read_timeout=300.0,
    terminate_on_close=True,
    headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
)

Autogen expects parameters describing how to talk to the MCP server. That is what StreamableHttpServerParams is for.

What's happening:

  • url points to the Tool Router MCP endpoint from Composio
  • timeout is the HTTP timeout for requests
  • sse_read_timeout controls how long to wait when streaming responses
  • terminate_on_close=True cleans up the MCP server process when the workbench is closed
7

Create the model client and agent

python
# Create model client
model_client = OpenAIChatCompletionClient(
    model="gpt-5",
    api_key=os.getenv("OPENAI_API_KEY")
)

# Use McpWorkbench as context manager
async with McpWorkbench(server_params) as workbench:
    # Create Semrush assistant agent with MCP tools
    agent = AssistantAgent(
        name="semrush_assistant",
        description="An AI assistant that helps with Semrush operations.",
        model_client=model_client,
        workbench=workbench,
        model_client_stream=True,
        max_tool_iterations=10
    )

What's happening:

  • OpenAIChatCompletionClient wraps the OpenAI model for Autogen
  • McpWorkbench connects the agent to the MCP tools
  • AssistantAgent is configured with the Semrush tools from the workbench
8

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Semrush related question or task to the agent.\n")

# Conversation loop
while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ["exit", "quit", "bye"]:
        print("\nGoodbye!")
        break

    if not user_input:
        continue

    print("\nAgent is thinking...\n")

    # Run the agent with streaming
    try:
        response_text = ""
        async for message in agent.run_stream(task=user_input):
            if hasattr(message, "content") and message.content:
                response_text = message.content

        # Print the final response
        if response_text:
            print(f"Agent: {response_text}\n")
        else:
            print("Agent: I encountered an issue processing your request.\n")

    except Exception as e:
        print(f"Agent: Sorry, I encountered an error: {str(e)}\n")
What's happening:
  • The script prompts you in a loop with You:
  • Autogen passes your input to the model, which decides which Semrush tools to call via MCP
  • agent.run_stream(...) yields streaming messages as the agent thinks and calls tools
  • Typing exit, quit, or bye ends the loop

Complete Code

Here's the complete code to get you started with Semrush and AutoGen:

python
import asyncio
import os
from dotenv import load_dotenv
from composio import Composio

from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient
from autogen_ext.tools.mcp import McpWorkbench, StreamableHttpServerParams

load_dotenv()

async def main():
    # Initialize Composio and create a Semrush session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["semrush"]
    )
    url = session.mcp.url

    # Configure MCP server parameters for Streamable HTTP
    server_params = StreamableHttpServerParams(
        url=url,
        timeout=30.0,
        sse_read_timeout=300.0,
        terminate_on_close=True,
        headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")}
    )

    # Create model client
    model_client = OpenAIChatCompletionClient(
        model="gpt-5",
        api_key=os.getenv("OPENAI_API_KEY")
    )

    # Use McpWorkbench as context manager
    async with McpWorkbench(server_params) as workbench:
        # Create Semrush assistant agent with MCP tools
        agent = AssistantAgent(
            name="semrush_assistant",
            description="An AI assistant that helps with Semrush operations.",
            model_client=model_client,
            workbench=workbench,
            model_client_stream=True,
            max_tool_iterations=10
        )

        print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
        print("Ask any Semrush related question or task to the agent.\n")

        # Conversation loop
        while True:
            user_input = input("You: ").strip()

            if user_input.lower() in ['exit', 'quit', 'bye']:
                print("\nGoodbye!")
                break

            if not user_input:
                continue

            print("\nAgent is thinking...\n")

            # Run the agent with streaming
            try:
                response_text = ""
                async for message in agent.run_stream(task=user_input):
                    if hasattr(message, 'content') and message.content:
                        response_text = message.content

                # Print the final response
                if response_text:
                    print(f"Agent: {response_text}\n")
                else:
                    print("Agent: I encountered an issue processing your request.\n")

            except Exception as e:
                print(f"Agent: Sorry, I encountered an error: {str(e)}\n")

if __name__ == "__main__":
    asyncio.run(main())

Conclusion

You now have an Autogen assistant wired into Semrush through Composio's Tool Router and MCP. From here you can:
  • Add more toolkits to the toolkits list, for example notion or hubspot
  • Refine the agent description to point it at specific workflows
  • Wrap this script behind a UI, Slack bot, or internal tool
Once the pattern is clear for Semrush, you can reuse the same structure for other MCP-enabled apps with minimal code changes.
TOOLS

Supported Tools

Every Semrush action and event your agent gets out of the box.

Check Semrush account units balance

Tool to fetch the remaining Semrush Standard API units for the authenticated account.

Get ad copies

Retrieves unique ad copies Semrush has observed for a specified domain from a regional database, detailing ads seen in Google's paid search results.

Get anchor texts

Use this action to get a CSV report of anchor texts for backlinks pointing to a specified, publicly accessible domain, root domain, or URL.

Get authority score profile

Retrieves the Authority Score (AS) profile for a specified target, showing the count of referring domains that link to the target for each AS value from 0 to 100.

Get backlinks

Fetches backlinks for a specified domain or URL as a semicolon-delimited CSV string (parse with `sep=';'`); allows customization of columns, sorting, and filtering.

Backlinks overview

Provides a semicolon-delimited (sep=';') CSV summary of backlinks, including Authority Score and link type breakdowns, for a specified and publicly accessible domain, root domain, or URL.

Batch comparison

Compares backlink profiles for multiple specified targets (domains, subdomains, or URLs) to analyze and compare link-building efforts.

Batch keyword overview

Fetches a keyword overview report from a Semrush regional database for up to 100 keywords, providing metrics like search volume, CPC, and keyword difficulty.

Broad match keyword

Fetches broad match keywords for a given phrase.

Get categories

Retrieves categories and their 0-1 confidence ratings for a specified domain, subdomain, or URL, with results sorted by rating.

Get categories profile

Retrieves a profile of content categories from referring domains for a specified target, analyzing its first 10,000 referring domains and sorting results by domain count.

Get competitor data

Retrieves a CSV-formatted report of competitors for a specified target (root domain, domain, or URL) based on shared backlinks or referring domains.

Get competitors in organic search

Use to get a domain's organic search competitors from Semrush as a semicolon-separated string; `display_date` requires 'YYYYMM15' format if used.

Get competitors in paid search

Retrieves a list of a domain's competitors in paid search results from a specified regional database.

Get domain ad history

Retrieves a domain's 12-month advertising history from Semrush (keywords bid on, ad positions, ad copy) for PPC strategy and competitor analysis; most effective when the domain has ad history in the selected database.

Get domain organic pages

Fetches a report on a domain's unique organic pages ranking in Google's top 100 search results, with options for specifying database, date, columns, sorting, and filtering.

Get domain organic search keywords

Retrieves organic search keywords for a domain from a specified Semrush regional database; `display_positions` must be set if `display_daily=1` for daily updates.

Get domain organic subdomains

Retrieves a report on subdomains of a given domain that rank in Google's top 100 organic search results for a specified regional database.

Get domain paid search keywords

Fetches keywords driving paid search traffic to a specified, existing domain using a supported Semrush regional database.

Get PLA search keywords for a domain

Retrieves Product Listing Ad (PLA) search keywords for a specified domain from a Semrush regional database.

Compare domains

Analyzes keyword rankings by comparing up to five domains to find common, unique, or gap keywords, using specified organic/paid types and comparison logic in the `domains` string.

Get historical data

Retrieves monthly historical backlink and referring domain data for a specified root domain, returned as a time series string with newest records first.

Get indexed pages

Retrieves a list of indexed pages from Semrush for a specified `target` (root domain, domain, or URL) and `target_type`, ensuring `target` is publicly accessible, Semrush-analyzable, and correctly matches `target_type`.

Get keyword difficulty

Determines the Keyword Difficulty (KD) score (0-100, higher means greater difficulty) for a given phrase in a specific Semrush regional database to assess its SEO competitiveness.

Keyword overview all databases

Fetches a keyword overview from Semrush for a specified phrase, including metrics like search volume, CPC, and competition.

Get keyword overview for one database

Fetches a keyword summary for a specified phrase from a chosen regional database.

Get keywords ads history

Fetches a historical report (last 12 months) of domains advertising on a specified keyword in Google Ads, optionally for a specific month ('YYYYMM15') or the most recent period, returning raw CSV-like data.

Get organic results

Retrieves up to 100,000 domains and URLs from Google's top 100 organic search results for a keyword and region, returning a raw string; use `display_date` in 'YYYYMM15' format (day must be '15') for historical data.

Get paid search results

Fetches domains ranking in Google's paid search results (AdWords) for a specified keyword and regional database.

Phrase questions

Fetches question-format keywords semantically related to a given query phrase for a specified regional database, aiding in understanding user search intent and discovering content ideas.

Get PLA competitors

Retrieves domains competing with a specified domain in Google's Product Listing Ads (PLA) from a given Semrush regional database.

Get PLA copies

Fetches Product Listing Ad (PLA) copies that Semrush observed for a domain in Google's paid search results.

Get referring domains

Retrieves a semicolon-delimited text report listing domains that link to a target, with options to filter by type (not value).

Get referring domains by country

Generates a CSV report detailing the geographic distribution of referring domains (by country, determined via IP address) for a specified, publicly accessible target.

Referring i ps

Fetches IP addresses that are sources of backlinks for a specified target domain, root domain, or URL.

Find related keywords

Call this to find related keywords (including synonyms and variations) for a target phrase in a specific regional database; `display_date` (if used for historical data) must be 'YYYYMM15' for a past month.

Get TLD distribution

Fetches a report on the Top-Level Domain (TLD) distribution of referring domains for a specified target, useful for analyzing geographic or categorical backlink diversity.

FRAMEWORKS

How to build Semrush MCP Agent with another framework

ChatGPT logo

ChatGPT

Use Semrush MCP with ChatGPT

OpenAI Agents SDK logo

OpenAI Agents SDK

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Claude Agent SDK logo

Claude Agent SDK

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Claude Code logo

Claude Code

Use Semrush MCP with Claude Code

Claude Cowork logo

Claude Cowork

Use Semrush MCP with Claude Cowork

Codex logo

Codex

Use Semrush MCP with Codex

Cursor logo

Cursor

Use Semrush MCP with Cursor

OpenClaw logo

OpenClaw

Use Semrush MCP with OpenClaw

Hermes logo

Hermes

Use Semrush MCP with Hermes

CLI logo

CLI

Use Semrush MCP with CLI

Google ADK logo

Google ADK

Use Semrush MCP with Google ADK

LangChain logo

LangChain

Use Semrush MCP with LangChain

Vercel AI SDK logo

Vercel AI SDK

Use Semrush MCP with Vercel AI SDK

Mastra AI logo

Mastra AI

Use Semrush MCP with Mastra AI

LlamaIndex logo

LlamaIndex

Use Semrush MCP with LlamaIndex

CrewAI logo

CrewAI

Use Semrush MCP with CrewAI

OpenCode logo

OpenCode

Use Semrush MCP with OpenCode

VS Code logo

VS Code

Use Semrush MCP with VS Code

FAQ

Frequently asked questions

With a standalone Semrush MCP server, the agents and LLMs can only access a fixed set of Semrush tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Semrush and many other apps based on the task at hand, all through a single MCP endpoint.

Yes, you can. Autogen fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Semrush tools.

Yes, absolutely. You can configure which Semrush scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Semrush data and credentials are handled as safely as possible.

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Karan skipped his own birthday party to fix our critical issue. It was 10pm and he diverted his Waymo to help us instead. This really sets the bar — it shows the commitment you need when users rely on your software.

Harsha Gaddipati
Harsha Gaddipati
Co-founder, Slashy

A lot of students tell us that the moment their connected tools start talking to each other inside Opennote feels almost magical. The agent just knows them, and it's immensely helped keep new users on the platform.

Abhi Arya
Abhi Arya
Co-founder, Opennote

We chose Composio over Pipedream because it delivered depth where it mattered — niche tools and tricky edge cases other platforms simply ignored. That gave us confidence to scale without compromising.

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Nirman Dave
CEO, Zams

As a solo builder, shipping fast is life or death. The only way I can outcompete incumbents is by outmanoeuvring them. Getting bogged down managing agent auth would have been a death sentence.

Ryan Yu
Ryan Yu
Founder, Extra Thursday

Before Composio, adding tool integrations was slow and resource-intensive. Each one could take weeks of engineering time, and maintaining them meant constantly keeping up with API changes.

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Founder & CEO, SwarmZero

With hands-on help from their founder, we integrated Gmail and Google Drive in just 30 minutes. This level of personal support and commitment is exactly what startups should strive for.

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Co-Founder, Ingram Technologies