How to integrate Ashby MCP with CrewAI

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Introduction

This guide walks you through connecting Ashby to CrewAI 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 CrewAI 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 a Composio API key and configure your Ashby connection
  • Set up CrewAI with an MCP enabled agent
  • Create a Tool Router session or standalone MCP server for Ashby
  • Build a conversational loop where your agent can execute Ashby operations

What is CrewAI?

CrewAI is a powerful framework for building multi-agent AI systems. It provides primitives for defining agents with specific roles, creating tasks, and orchestrating workflows through crews.

Key features include:

  • Agent Roles: Define specialized agents with specific goals and backstories
  • Task Management: Create tasks with clear descriptions and expected outputs
  • Crew Orchestration: Combine agents and tasks into collaborative workflows
  • MCP Integration: Connect to external tools through Model Context Protocol

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

Before starting, make sure you have:
  • Python 3.9 or higher
  • A Composio account and API key
  • A Ashby connection authorized in Composio
  • An OpenAI API key for the CrewAI LLM
  • Basic familiarity with Python

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 crewai crewai-tools python-dotenv
What's happening:
  • composio connects your agent to Ashby via MCP
  • crewai provides Agent, Task, Crew, and LLM primitives
  • crewai-tools includes MCP helpers
  • python-dotenv loads environment variables from .env

Set up environment variables

bash
COMPOSIO_API_KEY=your_composio_api_key_here
USER_ID=your_user_id_here
OPENAI_API_KEY=your_openai_api_key_here

Create a .env file in your project root.

What's happening:

  • COMPOSIO_API_KEY authenticates with Composio
  • USER_ID scopes the session to your account
  • OPENAI_API_KEY lets CrewAI use your chosen OpenAI model

Import dependencies

python
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional import if you plan to adapt tools
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()
What's happening:
  • CrewAI classes define agents and tasks, and run the workflow
  • MCPServerHTTP connects the agent to an MCP endpoint
  • Composio will give you a short lived Ashby MCP URL

Create a Composio Tool Router session for Ashby

python
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:
  • You create a Ashby only session through Composio
  • Composio returns an MCP HTTP URL that exposes Ashby tools

Configure the LLM

python
llm = LLM(
    model="gpt-5-mini",
    api_key=os.getenv("OPENAI_API_KEY"),
)
What's happening:
  • CrewAI will call this LLM for planning and responses
  • You can swap in a different model if needed

Attach the MCP server and create the agent

python
toolkit_agent = Agent(
    role="Ashby Assistant",
    goal="Help users interact with Ashby through natural language commands",
    backstory=(
        "You are an expert assistant with access to Ashby tools. "
        "You can perform various Ashby operations on behalf of the user."
    ),
    mcps=[
        MCPServerHTTP(
            url=url,
            streamable=True,
            cache_tools_list=True,
            headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
        ),
    ],
    llm=llm,
    verbose=True,
    max_iter=10,
)
What's happening:
  • MCPServerHTTP connects the agent to the Ashby MCP endpoint
  • cache_tools_list saves a tools catalog for faster subsequent runs
  • verbose helps you see what the agent is doing

Add a REPL loop with Task and Crew

python
print("Chat started! Type 'exit' or 'quit' to end.\n")
print("Try asking the agent to perform Ashby operations.\n")

conversation_context = ""

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

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

    if not user_input:
        continue

    conversation_context += f"\nUser: {user_input}\n"
    print("\nAgent is thinking...\n")

    task = Task(
        description=(
            f"Based on the conversation history:\n{conversation_context}\n\n"
            f"Current user request: {user_input}\n\n"
            f"Please help the user with their Ashby related request."
        ),
        expected_output="A helpful response addressing the user's request",
        agent=toolkit_agent,
    )

    crew = Crew(
        agents=[toolkit_agent],
        tasks=[task],
        verbose=False,
    )

    result = crew.kickoff()
    response = str(result)

    conversation_context += f"Agent: {response}\n"
    print(f"Agent: {response}\n")
What's happening:
  • You build a simple chat loop and keep a running context
  • Each user turn becomes a Task handled by the same agent
  • Crew executes the task and returns a response

Run the application

python
if __name__ == "__main__":
    main()
What's happening:
  • Standard Python entry point so you can run python crewai_ashby_agent.py

Complete Code

Here's the complete code to get you started with Ashby and CrewAI:

python
# file: crewai_ashby_agent.py
from crewai import Agent, Task, Crew, LLM
from crewai_tools import MCPServerAdapter  # optional
from composio import Composio
from dotenv import load_dotenv
import os
from crewai.mcp import MCPServerHTTP

load_dotenv()

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 LLM
    llm = LLM(
        model="gpt-5-mini",
        api_key=os.getenv("OPENAI_API_KEY"),
    )

    # Create Ashby assistant agent
    toolkit_agent = Agent(
        role="Ashby Assistant",
        goal="Help users interact with Ashby through natural language commands",
        backstory=(
            "You are an expert assistant with access to Ashby tools. "
            "You can perform various Ashby operations on behalf of the user."
        ),
        mcps=[
            MCPServerHTTP(
                url=url,
                streamable=True,
                cache_tools_list=True,
                headers={"x-api-key": os.getenv("COMPOSIO_API_KEY")},
            ),
        ],
        llm=llm,
        verbose=True,
        max_iter=10,
    )

    print("Chat started! Type 'exit' or 'quit' to end the conversation.\n")
    print("Try asking the agent to perform Ashby operations.\n")

    conversation_context = ""

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

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

        if not user_input:
            continue

        conversation_context += f"\nUser: {user_input}\n"
        print("\nAgent is thinking...\n")

        task = Task(
            description=(
                f"Based on the conversation history:\n{conversation_context}\n\n"
                f"Current user request: {user_input}\n\n"
                f"Please help the user with their Ashby related request."
            ),
            expected_output="A helpful response addressing the user's request",
            agent=toolkit_agent,
        )

        crew = Crew(
            agents=[toolkit_agent],
            tasks=[task],
            verbose=False,
        )

        result = crew.kickoff()
        response = str(result)

        conversation_context += f"Agent: {response}\n"
        print(f"Agent: {response}\n")

if __name__ == "__main__":
    main()

Conclusion

You now have a CrewAI agent connected to Ashby through Composio's Tool Router. The agent can perform Ashby operations through natural language commands. Next steps:
  • Add role-specific instructions to customize agent behavior
  • Plug in more toolkits for multi-app workflows
  • Chain tasks for complex multi-step operations

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

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 CrewAI?

Yes, you can. CrewAI 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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