How to integrate Ashby MCP with Autogen

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Introduction

This guide walks you through connecting Ashby to AutoGen using the Composio tool router. By the end, you'll have a working Ashby agent that can list all candidates for open roles, post a new job opening for engineering, summarize candidates in interview stage, export recent hiring activity to csv through natural language commands.

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

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

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 Ashby
  • Wire that MCP URL into Autogen using McpWorkbench and StreamableHttpServerParams
  • Configure an Autogen AssistantAgent that can call Ashby tools
  • Run a live chat loop where you ask the agent to perform Ashby 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 Ashby MCP server, and what's possible with it?

The Ashby MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Ashby account. It provides structured and secure access to your recruiting data, so your agent can perform actions like managing job postings, tracking candidate progress, scheduling interviews, and generating hiring reports on your behalf.

  • Automated job posting management: Easily create, update, or close job listings across your organization with direct agent assistance.
  • Candidate pipeline tracking: Have your agent fetch, organize, and update candidate progress through every stage of the hiring process.
  • Interview scheduling and coordination: Let your agent schedule interviews, send calendar invites, and manage interviewer assignments to streamline the process.
  • Data-driven hiring analytics: Generate reports and insights about your hiring funnel, candidate sources, and time-to-hire with a simple agent request.
  • Centralized communication with applicants: Enable your agent to send status updates, feedback, or reminders to candidates, keeping everyone in the loop automatically.

Supported Tools & Triggers

Tools
Add Candidate TagAdd a tag to a candidate.
Change Application SourceChange the source attribution of an application.
Change Application StageMove an application to a different interview stage.
Create ApplicationCreate a new application for a candidate to a specific job.
Create CandidateCreate a new candidate in the system.
Create Candidate TagCreate a new candidate tag.
Create DepartmentCreate a new department.
Create JobCreate a new job opening.
Get API Key InfoRetrieve information about the current API key, including associated organization, user details, and permissions.
Get Application InfoRetrieve detailed information about a specific application by its ID.
Get Candidate InfoRetrieve detailed information about a specific candidate by their ID.
Get Department InfoRetrieve detailed information about a specific department by its ID.
Get Interview InfoRetrieve detailed information about a specific interview type by its ID.
Get Job InfoRetrieve detailed information about a specific job by its ID.
Get Job Posting InfoRetrieve detailed information about a specific job posting by its ID.
Get Location InfoRetrieve detailed information about a specific location by its ID.
Get Opening InfoRetrieve detailed information about a specific opening (job requisition) by its ID.
Get User InfoRetrieve detailed information about a specific user by their ID.
List Application FeedbackRetrieve all feedback submissions for an application.
List Application HistoryRetrieve the complete history of stage transitions for an application.
List ApplicationsRetrieve a list of applications.
List ApprovalsRetrieve a list of approvals (offer approvals, job approvals, etc.
List Archive ReasonsRetrieve a list of all archive reasons.
List Candidate NotesRetrieve all notes for a specific candidate.
List Candidate ProjectsRetrieve all projects associated with a candidate.
List CandidatesRetrieve a list of candidates.
List Candidate TagsRetrieve a list of all candidate tags.
List Close ReasonsRetrieve a list of all close reasons for jobs and openings.
List Communication TemplatesRetrieve a list of all communication templates.
List Custom FieldsRetrieve a list of all custom field definitions.
List DepartmentsRetrieve a list of all departments in the organization.
List Feedback Form DefinitionsRetrieve a list of all feedback form definitions.
List Interviewer PoolsRetrieve a list of all interviewer pools.
List Interview PlansRetrieve a list of interview plans.
List InterviewsRetrieve a list of interviews.
List Interview SchedulesRetrieve a list of interview schedules.
List Interview Stage GroupsRetrieve a list of interview stage groups.
List Job BoardsRetrieve a list of job boards.
List Job PostingsRetrieve a list of job postings.
List JobsRetrieve a list of jobs.
List Job TemplatesRetrieve a list of job templates.
List LocationsRetrieve a list of all locations.
List OffersRetrieve a list of job offers.
List OpeningsRetrieve a list of openings (job requisitions).
List ProjectsRetrieve a list of all projects.
List SourcesRetrieve a list of all candidate sources.
List Source Tracking LinksRetrieve a list of all source tracking links.
List Survey Form DefinitionsRetrieve a list of all survey form definitions.
List UsersRetrieve a list of all users in the organization.
Search CandidatesSearch for candidates by email or name.
Search JobsSearch for jobs by title.
Search ProjectsSearch for projects by title.
Search UsersSearch for users by email or name.
Set Job StatusSet the status of a job (Open, Closed, Draft).
Update ApplicationUpdate custom fields or other properties of an application.
Update CandidateUpdate candidate information such as name, position, company, or school.
Update DepartmentUpdate department information such as name.
Update JobUpdate job details such as title and other properties.
Update Job PostingUpdate job posting details such as title or listing status.

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

What is Tool Router?

Composio's Tool Router 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 Tool Router

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

How the Tool Router works

The Tool Router 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

Prerequisites

You will need:

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

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.

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 Ashby via MCP
  • autogen-agentchat provides the AssistantAgent class
  • autogen-ext-openai provides the OpenAI model client
  • autogen-ext-tools provides MCP workbench support

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 Ashby connections to use

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 Ashby session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["ashby"]
    )
    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 Ashby tools
  • session.mcp.url is the MCP endpoint that Autogen will connect to

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

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 Ashby assistant agent with MCP tools
    agent = AssistantAgent(
        name="ashby_assistant",
        description="An AI assistant that helps with Ashby 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 Ashby tools from the workbench

Run the interactive chat loop

python
print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
print("Ask any Ashby 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 Ashby 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 Ashby 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 Ashby session
    composio = Composio(api_key=os.getenv("COMPOSIO_API_KEY"))
    session = composio.create(
        user_id=os.getenv("USER_ID"),
        toolkits=["ashby"]
    )
    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 Ashby assistant agent with MCP tools
        agent = AssistantAgent(
            name="ashby_assistant",
            description="An AI assistant that helps with Ashby 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 Ashby 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 Ashby 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 Ashby, you can reuse the same structure for other MCP-enabled apps with minimal code changes.

How to build Ashby MCP Agent with another framework

OpenAI Agents SDK

OpenAI Agents SDK

Use Ashby MCP with OpenAI Agents SDK

Claude Agent SDK

Claude Agent SDK

Use Ashby MCP with Claude Agent SDK

Google ADK

Google ADK

Use Ashby MCP with Google ADK

LangChain

LangChain

Use Ashby MCP with LangChain

Vercel AI SDK

Vercel AI SDK

Use Ashby MCP with Vercel AI SDK

Mastra AI

Mastra AI

Use Ashby MCP with Mastra AI

LlamaIndex

LlamaIndex

Use Ashby MCP with LlamaIndex

CrewAI

CrewAI

Use Ashby MCP with CrewAI

FAQ

What are the differences in Tool Router MCP and Ashby MCP?

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

Can I use Tool Router MCP with Autogen?

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 Ashby tools.

Can I manage the permissions and scopes for Ashby while using Tool Router?

Yes, absolutely. You can configure which Ashby 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.

How safe is my data with Composio Tool Router?

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 Ashby data and credentials are handled as safely as possible.

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