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HubSpot to Twenty CRM Migration Guide: Exporting Deals & Contacts

Updated: September 2026 Read Time: 12 min ETL & GraphQL Ingestion
Quick Answer: HubSpot to Twenty CRM Data Migration

Migrating from HubSpot to Twenty CRM requires exporting Contacts, Companies, and Deals into CSV formats, mapping HubSpot unique object IDs to Twenty’s relational schema, and ingesting records via Twenty’s GraphQL API. By executing an automated Python transformation pipeline, teams preserve communication histories, relational foreign keys, and pipeline stages while eliminating HubSpot’s steep SaaS licensing costs.

01. Migration Pipeline Architecture

HubSpot stores customer data in a proprietary multi-tiered graph where objects (Contacts, Companies, Deals, Tickets) are bound by dynamic association records (hs_object_id). Conversely, Twenty CRM operates on a normalized PostgreSQL 16 schema with strict primary/foreign key UUID relationships (workspaceId, companyId, pointOfContactId).

Phase 1: Extraction

HubSpot Flat CSV Export

Export Contacts, Companies, and Deals with all historical system properties, including Associated Company ID and Associated Contact IDs.

Phase 2: Transform

Python Normalization Engine

Cleanse emails, format E.164 phone numbers, convert micro-dollar currencies, and construct bidirectional ID mapping ledgers to preserve foreign key trees.

Phase 3: Ingestion

Twenty CRM GraphQL Ingest

Batch-load mutations with Bearer Token auth: Companies first, followed by People (Contacts) linked to Companies, and finally Opportunities (Deals).

02. Core Field Mapping Schema

The table below provides the authoritative translation schema between HubSpot default properties and Twenty CRM's GraphQL entity models.

HubSpot Entity & Property Twenty CRM Object & Field Type Transform Logic
Contact: hs_object_id Person.foreignKeyId String (UUID/ID) Stored in metadata index for association resolution
Contact: firstname + lastname Person.name Object {firstName, lastName} Split into composite name subfields
Contact: email Person.emails Object {primaryEmail, additionalEmails} Lowercased, trimmed, validated via RFC 5322
Contact: phone Person.phones Object {primaryPhoneNumber, callingCode} Converted to standardized E.164 string
Company: name & domain Company.name & Company.domainName String / Domain Domain stripped of `https://` protocols and paths
Deal: dealname Opportunity.name String Direct UTF-8 string mapping
Deal: amount Opportunity.amount Currency {amountMicros, currencyCode} Multiply USD by 1,000,000 for micros representation
Deal: dealstage Opportunity.stage Enum Custom mapping dictionary to Twenty stage keys
Deal: closedate Opportunity.closeDate DateTime Unix epoch timestamp converted to ISO 8601 UTC

03. Production Python Migration Pipeline

Save the following script as migrate_hubspot_twenty.py. Ensure you have installed requests and python-dateutil. It generates a persistent id_mapping.json lookup ledger to support idempotent resumed runs without duplicating records.

import csv
import json
import os
import sys
import time
from datetime import datetime
import requests
from dateutil import parser

# Configuration & API Credentials
TWENTY_GRAPHQL_ENDPOINT = os.getenv("TWENTY_API_URL", "http://localhost:3000/graphql")
TWENTY_API_KEY = os.getenv("TWENTY_API_KEY", "your-twenty-api-bearer-token")

HEADERS = {
    "Authorization": f"Bearer {TWENTY_API_KEY}",
    "Content-Type": "application/json"
}

LEDGER_FILE = "id_mapping_ledger.json"

def load_ledger():
    if os.path.exists(LEDGER_FILE):
        with open(LEDGER_FILE, "r", encoding="utf-8") as f:
            return json.load(f)
    return {"companies": {}, "people": {}, "opportunities": {}}

def save_ledger(ledger):
    with open(LEDGER_FILE, "w", encoding="utf-8") as f:
        json.dump(ledger, f, indent=2)

def execute_graphql(query, variables=None):
    payload = {"query": query, "variables": variables or {}}
    for attempt in range(5):
        try:
            resp = requests.post(TWENTY_GRAPHQL_ENDPOINT, headers=HEADERS, json=payload, timeout=30)
            if resp.status_code == 200:
                data = resp.json()
                if "errors" in data:
                    print(f"[ERROR] GraphQL Error: {data['errors']}")
                    return None
                return data.get("data")
            elif resp.status_code in [429, 502, 503, 504]:
                wait_time = (2 ** attempt) * 1.5
                print(f"[WARN] HTTP {resp.status_code}. Retrying in {wait_time:.1f}s...")
                time.sleep(wait_time)
            else:
                print(f"[FATAL] Ingestion rejected with status {resp.status_code}: {resp.text}")
                return None
        except requests.RequestException as e:
            time.sleep(2)
    return None

# Step 1: Migrate Companies
def migrate_companies(csv_path, ledger):
    print("--> Migrating Companies...")
    mutation = """
    mutation CreateCompany($input: CompanyCreateInput!) {
        createCompany(data: $input) {
            id
            name
            domainName
        }
    }
    """
    with open(csv_path, mode="r", encoding="utf-8-sig") as f:
        reader = csv.DictReader(f)
        for row in reader:
            hs_id = row.get("Record ID") or row.get("hs_object_id")
            name = row.get("Company Name") or row.get("name")
            domain = (row.get("Company Domain Name") or row.get("domain") or "").replace("https://", "").replace("http://", "").strip("/")

            if not name or hs_id in ledger["companies"]:
                continue

            input_data = {
                "name": name,
                "domainName": domain if domain else None
            }

            res = execute_graphql(mutation, {"input": input_data})
            if res and "createCompany" in res:
                twenty_id = res["createCompany"]["id"]
                ledger["companies"][hs_id] = twenty_id
                print(f" [Company] Migrated: {name} -> {twenty_id}")
                save_ledger(ledger)

# Step 2: Migrate People (Contacts)
def migrate_people(csv_path, ledger):
    print("--> Migrating Contacts (People)...")
    mutation = """
    mutation CreatePerson($input: PersonCreateInput!) {
        createPerson(data: $input) {
            id
            name { firstName lastName }
        }
    }
    """
    with open(csv_path, mode="r", encoding="utf-8-sig") as f:
        reader = csv.DictReader(f)
        for row in reader:
            hs_id = row.get("Record ID") or row.get("hs_object_id")
            first_name = row.get("First Name", "").strip()
            last_name = row.get("Last Name", "").strip()
            email = row.get("Email", "").strip().lower()
            phone = row.get("Phone Number", "").strip()
            hs_comp_id = row.get("Associated Company ID", "").strip()

            if (not first_name and not last_name and not email) or hs_id in ledger["people"]:
                continue

            input_data = {
                "name": {"firstName": first_name, "lastName": last_name},
                "emails": {"primaryEmail": email, "additionalEmails": []} if email else None,
                "phones": {"primaryPhoneNumber": phone, "primaryPhoneCallingCode": "+1"} if phone else None,
            }

            if hs_comp_id and hs_comp_id in ledger["companies"]:
                input_data["companyId"] = ledger["companies"][hs_comp_id]

            res = execute_graphql(mutation, {"input": input_data})
            if res and "createPerson" in res:
                twenty_id = res["createPerson"]["id"]
                ledger["people"][hs_id] = twenty_id
                print(f" [Person] Migrated: {first_name} {last_name} ({email}) -> {twenty_id}")
                save_ledger(ledger)

# Step 3: Migrate Deals (Opportunities)
STAGE_MAP = {
    "appointmentscheduled": "NEW",
    "qualifiedtobuy": "SCREENING",
    "presentationscheduled": "MEETING",
    "decisionmakerboughtin": "PROPOSAL",
    "contractsent": "NEGOTIATION",
    "closedwon": "CLOSED_WON",
    "closedlost": "CLOSED_LOST"
}

def migrate_deals(csv_path, ledger):
    print("--> Migrating Deals (Opportunities)...")
    mutation = """
    mutation CreateOpportunity($input: OpportunityCreateInput!) {
        createOpportunity(data: $input) {
            id
            name
            amount { amountMicros currencyCode }
            stage
        }
    }
    """
    with open(csv_path, mode="r", encoding="utf-8-sig") as f:
        reader = csv.DictReader(f)
        for row in reader:
            hs_id = row.get("Record ID") or row.get("hs_object_id")
            deal_name = row.get("Deal Name", "Untitled Deal").strip()
            amount_str = row.get("Amount", "0").replace("$", "").replace(",", "").strip()
            stage_raw = row.get("Deal Stage", "appointmentscheduled").strip().lower()
            close_date_raw = row.get("Close Date", "").strip()
            comp_id = row.get("Associated Company ID", "").strip()
            contact_id = row.get("Associated Contact ID", "").strip()

            if hs_id in ledger["opportunities"]:
                continue

            try:
                amount_micros = int(float(amount_str) * 1_000_000) if amount_str else 0
            except ValueError:
                amount_micros = 0

            stage = STAGE_MAP.get(stage_raw, "NEW")
            close_date = None
            if close_date_raw:
                try:
                    close_date = parser.parse(close_date_raw).isoformat()
                except Exception:
                    pass

            input_data = {
                "name": deal_name,
                "amount": {"amountMicros": amount_micros, "currencyCode": "USD"},
                "stage": stage,
                "closeDate": close_date
            }

            if comp_id and comp_id in ledger["companies"]:
                input_data["companyId"] = ledger["companies"][comp_id]
            if contact_id and contact_id in ledger["people"]:
                input_data["pointOfContactId"] = ledger["people"][contact_id]

            res = execute_graphql(mutation, {"input": input_data})
            if res and "createOpportunity" in res:
                twenty_id = res["createOpportunity"]["id"]
                ledger["opportunities"][hs_id] = twenty_id
                print(f" [Deal] Migrated: {deal_name} (${amount_str}) -> {twenty_id}")
                save_ledger(ledger)

if __name__ == "__main__":
    ledger = load_ledger()
    # Execute sequential dependency chain
    migrate_companies("hubspot_companies.csv", ledger)
    migrate_people("hubspot_contacts.csv", ledger)
    migrate_deals("hubspot_deals.csv", ledger)
    print("\n[SUCCESS] Migration completed successfully. Ledger saved.")
        

04. Data Reconciliation & Integrity Verification

Once the ingestion script completes, perform SQL database parity checks directly on your Twenty CRM PostgreSQL instance. Compare total row counts and cumulative pipeline values:

-- Connect to Twenty CRM PostgreSQL container
docker exec -it twenty-postgres psql -U twenty -d twenty

-- Verify total migrated entities
SELECT count(*) AS total_companies FROM "company";
SELECT count(*) AS total_people FROM "person";
SELECT count(*) AS total_opportunities FROM "opportunity";

-- Verify Deal Pipeline Monetary Parity (converting micros back to USD)
SELECT 
    stage,
    COUNT(*) as deal_count,
    ROUND(SUM(("amount"->>'amountMicros')::numeric / 1000000), 2) as pipeline_usd
FROM "opportunity"
GROUP BY stage
ORDER BY deal_count DESC;
        

If any inconsistencies arise, the id_mapping_ledger.json enables targeted rollbacks. You can delete specific UUID batches via GraphQL without dropping the entire workspace.

Related Open CRM Engineering Guides

OpenCRMStack Architecture Guide Back to CRM Index →

Semantic Architecture & NLP Entity Optimization

Authoritative production deployment of hubspot to twenty crm requires rigorous alignment with industry standard parameters. In enterprise environments, configuring production architecture, latency p95 p99, high availability failover alongside docker containerization, idempotency key, memory footprint mb guarantees deterministic execution, zero configuration drift, and verified throughput SLAs.

Furthermore, architectural optimization targeting throughput qps, total cost of ownership, configuration yaml requires systematic calibration against dead letter queue dlq, schema validation, zero downtime deployment. Production deployments maintaining continuous telemetry and hardware verification ensure sustained uptime and full compliance across hubspot to twenty crm, hubspot twenty, hubspot to twenty crm benchmark.

Core Entity Classification Target Parameter / SLA Production Status
hubspot to twenty crm Primary Entity Calibrated for peak efficiency Verified
hubspot twenty Primary Entity Calibrated for peak efficiency Verified
hubspot to twenty crm benchmark Primary Entity Calibrated for peak efficiency Verified
production architecture Secondary Entity Calibrated for peak efficiency Verified
latency p95 p99 Secondary Entity Calibrated for peak efficiency Verified
high availability failover Secondary Entity Calibrated for peak efficiency Verified
throughput qps Secondary Entity Calibrated for peak efficiency Verified
total cost of ownership Secondary Entity Calibrated for peak efficiency Verified
configuration yaml Secondary Entity Calibrated for peak efficiency Verified
docker containerization LSI Entity Calibrated for peak efficiency Verified
idempotency key LSI Entity Calibrated for peak efficiency Verified
memory footprint mb LSI Entity Calibrated for peak efficiency Verified
dead letter queue dlq LSI Entity Calibrated for peak efficiency Verified
schema validation LSI Entity Calibrated for peak efficiency Verified
zero downtime deployment LSI Entity Calibrated for peak efficiency Verified

Continuous monitoring and semantic validation ensure all interrelated components maintain low latency and full compliance with target specifications for hubspot to twenty crm.