🤖AI Agents Guide
TutorialsComparisonsReviewsExamplesIntegrationsUse CasesTemplatesGlossary
Get Started
🤖AI Agents Guide

Your comprehensive resource for understanding, building, and implementing AI Agents.

Learn

  • Tutorials
  • Glossary
  • Use Cases
  • Examples

Compare

  • Tool Comparisons
  • Reviews
  • Integrations
  • Templates

Company

  • About
  • Contact
  • Privacy Policy

© 2026 AI Agents Guide. All rights reserved.

Home/Integrations/How to Integrate AI Agents with Monday.com
IntegrationMonday.comintermediate9 min readSetup: 20-25 minutes

How to Integrate AI Agents with Monday.com

Step-by-step guide to connecting AI agents with Monday.com. Learn how to automate item creation, status updates, workload reporting, and board management using LangChain, n8n, and the Monday.com GraphQL API.

Workflow board representing Monday.com AI agent project management automation
Photo by Helloquence on Unsplash
By AI Agents Guide Team•February 28, 2026

Table of Contents

  1. What AI Agents Can Do With Monday.com Access
  2. Setting Up Monday.com API Access
  3. Option 1: No-Code with n8n
  4. Slack-to-Monday Item Creation
  5. Option 2: LangChain with Python
  6. Build Monday.com Tools
  7. Monday.com Operations Agent
  8. Rate Limits and Best Practices
  9. Next Steps
Team planning session representing Monday.com workflow automation
Photo by Dylan Gillis on Unsplash

Monday.com's flexible board system handles everything from sales pipelines to engineering sprints — and AI agents make it even more dynamic. By connecting agents to Monday.com, teams can automate the tedious work of populating boards, updating statuses, and generating reports, letting human team members focus on the work that actually needs their judgment.

For operations, marketing, and product teams already using Monday.com as their work OS, AI integration delivers the intelligent automation layer that transforms it from a tracking tool into an active workflow participant.

What AI Agents Can Do With Monday.com Access#

Item Lifecycle Automation

  • Create new items from emails, Slack messages, or form submissions with all relevant columns populated
  • Update item status columns when work completes or conditions change
  • Move items between boards as they progress through workflow stages
  • Archive completed items automatically after a grace period

Workload and Capacity Management

  • Identify team members with the most items in progress
  • Flag items with approaching deadlines and no recent updates
  • Redistribute work when someone's capacity is overloaded
  • Generate capacity reports for sprint planning

Reporting and Insights

  • Generate board summaries showing completion rates and bottlenecks
  • Create end-of-week status digests for leadership
  • Track SLA compliance for customer-facing boards
  • Alert when key milestones are at risk

Setting Up Monday.com API Access#

pip install requests langchain langchain-openai python-dotenv

Get your API token from Profile → Admin → API or Profile → Developers.

export MONDAY_API_TOKEN="your-monday-api-token"

Test your connection:

import os
import requests

MONDAY_API_URL = "https://api.monday.com/v2"
HEADERS = {
    "Authorization": os.getenv("MONDAY_API_TOKEN"),
    "Content-Type": "application/json"
}

def run_query(query: str, variables: dict = None) -> dict:
    """Execute a Monday.com GraphQL query."""
    payload = {"query": query}
    if variables:
        payload["variables"] = variables
    response = requests.post(MONDAY_API_URL, json=payload, headers=HEADERS)
    response.raise_for_status()
    return response.json()

# Test: Get your boards
result = run_query("{ boards { id name } }")
for board in result.get("data", {}).get("boards", []):
    print(f"Board: {board['name']} (ID: {board['id']})")

Option 1: No-Code with n8n#

Slack-to-Monday Item Creation#

  1. Slack Trigger: Listen for messages in #requests channel
  2. OpenAI: "Extract from this Slack message: item name, assignee, priority (high/medium/low), due date if mentioned"
  3. HTTP Request: POST to https://api.monday.com/v2 with GraphQL mutation to create item
  4. Slack: Reply in thread with Monday.com item URL

The GraphQL mutation for n8n's HTTP Request node:

mutation CreateItem($boardId: ID!, $name: String!, $columnValues: JSON) {
  create_item(board_id: $boardId, item_name: $name, column_values: $columnValues) {
    id url
  }
}

Option 2: LangChain with Python#

Build Monday.com Tools#

import json
from langchain.tools import tool

MONDAY_API_URL = "https://api.monday.com/v2"


@tool
def list_boards() -> str:
    """List all accessible Monday.com boards with their IDs and item counts."""
    query = """
    {
      boards(limit: 20) {
        id
        name
        items_count
        state
      }
    }"""
    result = run_query(query)
    boards = result.get("data", {}).get("boards", [])
    if not boards:
        return "No boards found"
    lines = ["Available boards:"]
    for board in boards:
        if board.get("state") == "active":
            lines.append(f"  - {board['name']} (ID: {board['id']}, {board.get('items_count', 0)} items)")
    return "\n".join(lines)


@tool
def get_board_items(board_id: str, status_filter: str = None) -> str:
    """
    Get items from a Monday.com board. status_filter: filter by status label (e.g., 'In Progress').
    """
    query = """
    query GetItems($boardId: ID!) {
      boards(ids: [$boardId]) {
        items_page(limit: 30) {
          items {
            id
            name
            state
            column_values {
              id
              text
              type
            }
          }
        }
      }
    }"""
    result = run_query(query, {"boardId": board_id})
    board = result.get("data", {}).get("boards", [{}])[0]
    items = board.get("items_page", {}).get("items", [])

    if not items:
        return f"No items found on board {board_id}"

    lines = [f"Board items ({len(items)} shown):"]
    for item in items:
        if item.get("state") == "deleted":
            continue
        # Get status and date columns
        cols = {cv["id"]: cv["text"] for cv in item.get("column_values", []) if cv.get("text")}
        status = cols.get("status", "No status")
        if status_filter and status_filter.lower() not in status.lower():
            continue
        col_summary = " | ".join(f"{k}: {v}" for k, v in list(cols.items())[:3])
        lines.append(f"  [{item['id']}] {item['name']} | {col_summary}")
    return "\n".join(lines)


@tool
def create_item(board_id: str, item_name: str, group_id: str = None,
                column_values: dict = None) -> str:
    """
    Create a new item on a Monday.com board.
    column_values: dict mapping column IDs to values.
    Example: {"status": {"label": "In Progress"}, "date4": {"date": "2026-03-15"}}
    """
    mutation = """
    mutation CreateItem($boardId: ID!, $groupId: String, $name: String!, $values: JSON) {
      create_item(
        board_id: $boardId,
        group_id: $groupId,
        item_name: $name,
        column_values: $values
      ) {
        id name url
      }
    }"""
    variables = {
        "boardId": board_id,
        "name": item_name,
        "values": json.dumps(column_values or {})
    }
    if group_id:
        variables["groupId"] = group_id

    result = run_query(mutation, variables)
    item = result.get("data", {}).get("create_item", {})
    return f"Item created: '{item.get('name')}' (ID: {item.get('id')}) — {item.get('url', '')}"


@tool
def update_item_status(item_id: str, board_id: str, status_label: str,
                       column_id: str = "status") -> str:
    """
    Update the status of a Monday.com item.
    status_label: the label text (e.g., 'Done', 'In Progress', 'Blocked').
    """
    mutation = """
    mutation UpdateStatus($boardId: ID!, $itemId: ID!, $columnId: String!, $value: JSON!) {
      change_column_value(
        board_id: $boardId,
        item_id: $itemId,
        column_id: $columnId,
        value: $value
      ) {
        id
      }
    }"""
    variables = {
        "boardId": board_id,
        "itemId": item_id,
        "columnId": column_id,
        "value": json.dumps({"label": status_label})
    }
    run_query(mutation, variables)
    return f"Item {item_id} status updated to '{status_label}'"


@tool
def add_update_to_item(item_id: str, message: str) -> str:
    """Add an update/comment to a Monday.com item visible to the whole team."""
    mutation = """
    mutation AddUpdate($itemId: ID!, $body: String!) {
      create_update(item_id: $itemId, body: $body) {
        id
      }
    }"""
    result = run_query(mutation, {"itemId": item_id, "body": message})
    update_id = result.get("data", {}).get("create_update", {}).get("id")
    return f"Update added to item {item_id} (Update ID: {update_id})"

Monday.com Operations Agent#

from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4o", temperature=0.1)
tools = [list_boards, get_board_items, create_item, update_item_status, add_update_to_item]

prompt = ChatPromptTemplate.from_messages([
    ("system", """You are an operations assistant with access to Monday.com.

When creating items:
1. List available boards first to find the right destination
2. Use clear, actionable item names
3. Set status and date columns when information is available

When reporting:
1. Fetch current board state before summarizing
2. Quantify items by status (not started, in progress, done, blocked)
3. Flag items that are overdue or blocked

Always add updates as comments rather than changing item names — comments preserve history."""),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}"),
])

agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True, max_iterations=6)

Rate Limits and Best Practices#

Monday.com APIValue
Complexity limit5,000,000 points/minute
Request timeout60 seconds
Pagination500 items max per page

Best practices:

  • Fetch column IDs first: Run a schema query at agent startup to map column names to IDs — don't hardcode column IDs across different boards
  • Use column_values efficiently: Only request the column_values fields you need by specifying column IDs in the query
  • Paginate large boards: Use items_page with cursor-based pagination for boards with 500+ items
  • Batch mutations: Monday.com supports running multiple mutations in a single GraphQL request by naming them — use this for bulk status updates

Next Steps#

  • AI Agents Asana Integration — Asana-based project management automation
  • AI Agents Slack Integration — Create Monday items from Slack without leaving the chat
  • AI Agents Linear Integration — Developer-focused project management alternative
  • Tool Calling in AI Agents — How agents interact with Monday.com's GraphQL API

Related Integrations

How to Integrate AI Agents with Airtable

Step-by-step guide to connecting AI agents with Airtable. Learn how to automate record creation, data enrichment, workflow triggers, and database management using LangChain, n8n, and the Airtable REST API.

How to Integrate AI Agents with Asana

Step-by-step guide to connecting AI agents with Asana. Learn how to automate task creation, project updates, workload analysis, and deadline tracking using LangChain, n8n, and the Asana REST API.

AI Agents + Google BigQuery: Setup Guide

Step-by-step guide to connecting AI agents with Google BigQuery. Learn how to automate SQL queries, build analytics pipelines, detect anomalies, and generate business reports using LangChain, n8n, and the BigQuery Python SDK.

← Back to All Integrations