Crisp doesn't support conversation CSV exports — you must use the REST API. Map chat sessions to tickets, always set sent_datetime on Gorgias imports, and budget for rate limit throttling on both sides.
There is no native migration path from Crisp to Gorgias — Crisp does not support CSV exports for conversation data, so all messages, attachments, and agent notes must be extracted programmatically via the Crisp REST API. The fundamental data model challenge is translating Crisp's chat-session-centric architecture (long-running conversations keyed by session_id) into Gorgias's ticket-thread model with only two statuses (open/closed). Custom engineering work is required to handle structural transformation, rate limit management on both APIs, attachment re-uploading, status mapping, and deduplication logic.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Do not assume you can export Crisp chat history from the dashboard
Conversation data requires API extraction. Plan your migration timeline accordingly — API extraction at scale takes days, not hours.(help.crisp.chat)
Failure to engineer a resilient retry mechanism for Gorgias's rate limits is the leading
Failure to engineer a resilient retry mechanism for Gorgias's rate limits is the leading cause of incomplete data migrations. For recovery strategies, see Help Desk Data Migration Failed? The Engineer's Rescue Guide.
Small business (under 10K conversations, under 5K contacts)
A self-serve tool or careful custom script works. Enterprise (100K+ conversations, attachments, multiple operators): A managed service or a very well-tested custom ETL pipeline is the only safe bet.
If you omit sent_datetime on imported messages, Gorgias will attempt to send them to your customers
This is the single most common and destructive mistake in Gorgias migrations. Always set sent_datetime for every historical message. Also consider setting imported: true on messages to explicitly flag them as historical.(developers.gorgias.com)
This script is a structural reference. Production code must add
error logging per record, idempotency checks (skip if external_id exists), attachment download/upload, and a progress checkpoint system so you can resume after failures.
The runbook
Work top to bottom. Tick steps as you go — your progress is saved in this browser.
01 Discovery Establish why you are moving, what "done" means, and who signs off.
Objective A written scope with agreed success criteria, a named owner per workstream, and a budget approved by finance.
Keep these open
-
Pull the real numbers out of Crisp
Export counts for tickets (open and closed separately), contacts, organisations, attachments, macros, triggers, automations, views and SLA policies. Note the oldest ticket date — history depth drives the whole timeline. Estimating from memory is the single most common cause of a blown migration window.
Data Profiler Get real record counts instead of estimating from memory -
Decide what history actually moves
Agree a cut-off with the support lead: all history, last 24 months, or open tickets plus a read-only archive. Every extra year of closed tickets adds API time and cost without adding much agent value. Get this in writing — it is the decision people relitigate mid-cutover.
A "move everything" default is what turns a two-week migration into a two-month one.
COI & ROI Calculator Build the 36-month business case you will need for sign-off -
Confirm Gorgias can hold your support model
Walk your current workflow through Gorgias: multi-brand, business hours, SLA targets, CSAT, side conversations, public vs internal notes, and any channel you depend on (voice, chat, WhatsApp, social). List anything with no native equivalent — those are project risks, not configuration details.
-
Build the business case
Model licence delta, migration effort, agent retraining, and the cost of staying put (Cost of Inaction). Executives approve a number, not a plan, and you will be asked for it again at the go/no-go.
Helpdesk Migration Planner Turn ticket volume into a dated Crisp → Gorgias timeline -
Name owners and set the go/no-go date
One named owner each for data, configuration, integrations, and agent enablement, plus a decision-maker who can call a rollback. Put the go/no-go meeting in calendars now, 48 hours before the freeze.
Crisp → Gorgias specifics
- Automation and rules
- Gorgias's rule engine auto-tags, auto-closes, and auto-responds based on intent detection and order data — capabilities that require custom bot scripting in Crisp.
- Revenue tracking
- Gorgias attributes revenue to support interactions, which matters for teams justifying headcount.
- AI agents
- Gorgias offers AI-resolved ticket automation billed per resolution, replacing the need for separate chatbot tooling.
Don't move on until
- Record counts confirmed for tickets, contacts, organisations and macros
- Success criteria signed off by the support lead
- Freeze window provisionally booked with the business
02 Data Audit Find out what is actually in the data before you try to move it.
Objective A profiled, cleaned export with every quality defect either fixed at source or explicitly accepted.
Keep these open
-
Take a full Crisp export and profile it
Export to CSV or JSON and profile every file: row counts, null rates per column, distinct values, and type consistency. Compare row counts against the API totals from Discovery — a gap here means your export is silently truncated, usually by pagination.
Data Profiler Profile the Crisp export for nulls, outliers and type drift -
Validate file structure before anyone writes a transform
Check delimiters, quoting, encoding (expect UTF-8, watch for BOMs and Latin-1), duplicate headers, and embedded newlines in ticket bodies. Ticket descriptions with raw newlines and commas break naive CSV parsers and silently shift columns.
A single unescaped quote in one ticket body can shift every subsequent column without any error.
CSV Validator Catch broken headers and ragged rows in the raw export -
Inventory PII and set retention
Scan for emails, phone numbers, payment card fragments, national IDs and anything else regulated in ticket bodies and custom fields — support tickets are where customers paste things they should not. Decide what gets migrated, masked, or dropped, and record the legal basis.
Ticket bodies and attachments routinely contain card and ID data that never appears in a structured field.
PII & Compliance Scanner Find regulated fields before they land in a new system -
Quantify duplicates, orphans and dead references
Count duplicate contacts (same email, different casing), tickets whose requester no longer exists, organisations with no members, and attachments whose parent ticket is gone. Fix these in Crisp where you can — migrating them just moves the mess.
Data Cleaner Strip empty rows, stray whitespace and dead columns -
Clean and normalise the export
Trim whitespace, drop empty rows and columns, normalise casing on emails and tags, and standardise every timestamp to UTC ISO 8601. Timezone drift is invisible at load time and shows up weeks later as SLA reports nobody can reconcile.
-
Produce a masked copy for sandbox work
Generate a realistic but fake version of the export for testing and for any vendor who needs sample data. Loading real customer PII into a sandbox is a breach in most jurisdictions, and sandboxes are rarely covered by your DPA.
PII Masker Generate a safe copy for sandbox and vendor testing -
Map People Profiles → Gorgias Customers
Match on email. Build a lookup dict of crisp_people_id → gorgias_customer_id after creating or matching customers in Gorgias.
Don't move on until
- Export parses cleanly with no ragged rows or encoding errors
- PII inventory complete and retention decisions recorded
- Duplicate and orphan records quantified and triaged
03 Field Mapping Turn two schemas into one signed-off mapping spec.
Objective A reviewed field-level mapping covering every object, with an explicit decision for every field that has no target.
Keep these open
-
Generate the first-pass Crisp → Gorgias field map
Start from an automated match on both schemas, then review every row by hand. Automated matching gets the obvious 70% right and is confidently wrong on the rest — especially anything named "type", "status" or "custom_field_1".
Schema Mapper Opens pre-loaded with the Crisp → Gorgias field pair -
Map status, priority and channel values, not just field names
Enumerate every value in each picklist on both sides and map them explicitly. Value-level mismatches are the defect class that survives all the way to production because the field itself mapped fine — a ticket that should be "Pending" arriving as "Open" reopens SLA clocks.
Statuses with no target equivalent (on-hold, pending-customer) need a policy decision, not a best guess.
-
Decide how custom fields land
Create the target custom fields first, matching type exactly (a dropdown mapped to free text can never be mapped back). Where Gorgias has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.
-
Resolve identity and threading
Decide how source IDs are preserved — most platforms will not let you set the primary key, so keep the original ID in a custom field. Without it, reconciliation becomes fuzzy matching and every future support question about an old ticket is unanswerable.
Losing the original ticket ID makes reconciliation and rollback effectively impossible.
-
Plan attachments, inline images and threading order
Confirm size limits, allowed MIME types, and whether inline images survive as attachments or need rehosting. Decide the comment ordering and author attribution rules: comments loaded out of order, or all attributed to the API user, destroy the conversation history agents rely on.
-
Freeze and sign off the mapping spec
Version the spec, walk the support lead through it row by row, and get explicit sign-off. Any change after this point goes through change control — mid-flight mapping edits are how partial loads happen.
-
Map Conversations → Tickets
Each Crisp conversation becomes one Gorgias ticket. Set channel to "chat" and via to "api". Map resolved → closed, unresolved/pending → open. Store session_id in external_id for idempotency and audit trail.
-
Map Messages → Ticket Messages
For each message, set sent_datetime to the original Crisp message timestamp. Set from_agent based on the Crisp from field (operator = true, user = false).
-
Map Segments → Tags
Crisp segments are pipe-separated strings on the people profile. Convert each segment to a {"name": "segment_value"} object in the Gorgias ticket tags array.
Data Format Converter Reshape the export into the format Gorgias's importer expects
Don't move on until
- Every source field is mapped, deliberately dropped, or parked in a custom field
- Status, priority and channel value maps agreed with the support lead
- Mapping spec version-controlled and signed off
04 Test Migration Prove the pipeline on a small, representative slice.
Objective A pilot load into a Gorgias sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
-
Stand up a Gorgias sandbox that matches production config
Create the custom fields, groups, brands, business hours and SLA policies first. A pilot into a default sandbox tests nothing, because the failures you care about are all configuration mismatches.
-
Pick a deliberately nasty pilot sample
Take 500-1000 records chosen for difficulty, not convenience: the longest ticket threads, tickets with the most attachments, non-Latin character sets, merged and split tickets, deleted requesters, and every status value. A clean random sample proves only that easy records are easy.
-
Run the load with masked data and instrument everything
Log every API request and response with its source record ID. When 40 records fail out of 10,000 you need to know exactly which ones and why, without re-running the whole batch.
PII Masker Never load real customer PII into a sandbox -
Measure real throughput against the rate limit
Record achieved records-per-hour under Gorgias's actual rate limits, including retries and backoff. Extrapolate to the full volume: if the maths says the full load exceeds your freeze window, you fix that now, not on cutover night.
Published rate limits are ceilings, not throughput. Assume real-world rates are meaningfully lower once retries and backoff are counted.
-
Reconcile the pilot and triage every failure
Diff source against target on record counts and field-level values. Every discrepancy gets a root cause and a fix — "probably fine" at pilot scale becomes thousands of broken records at full scale.
Migration Validation Tool Diff the pilot batch against source before scaling up -
Put real agents in front of the pilot data
Have two or three agents work sample tickets end to end in the sandbox. They find the things reconciliation cannot see: unreadable threading, missing context, macros that no longer make sense. Fix the mapping, then re-run.
Don't move on until
- Pilot batch reconciles to 100% on record counts
- Agents have reviewed sample tickets and confirmed they are workable
- Measured throughput extrapolates to a viable full-load window
05 Cutover Execute the switch inside a controlled, reversible window.
Objective All in-scope data live in Gorgias, agents working in the new system, and a rollback path that stayed available throughout.
Keep these open
-
Pre-load history before the freeze
Load closed tickets and contacts days or weeks ahead while Crisp stays live. Only open tickets and the final delta need to move inside the freeze — this is the single biggest lever on window length.
Helpdesk Migration Planner Size the freeze window from Gorgias's real API limits -
Publish the runbook with times, owners and abort criteria
A timed sequence: freeze start, final export, delta load, channel switch, smoke test, go/no-go, agent switch. Name who does each step and the explicit condition that triggers a rollback. Decide the abort criteria before the night, when nobody wants to be the one to call it.
-
Freeze Crisp and take the final delta
Stop new ticket creation, let agents finish in-flight replies, then export everything changed since the pre-load. Announce the freeze to the whole business, not just support — someone always tries to raise a ticket during it.
Tickets created during an unenforced freeze land in the old system and are the most common source of permanently lost data.
-
Load the delta and open tickets
Run the delta load, then reconcile counts before touching any channel. Do not repoint email until the delta has verified — an inbound ticket arriving mid-load is far harder to untangle than a few extra minutes of freeze.
Migration Validation Tool Confirm the final delta landed before you reopen -
Repoint channels and verify with live traffic
Switch email forwarding and MX or connector settings, update chat widgets and web forms, and re-authorise integrations. Then send real test tickets through every channel and confirm each lands, routes and triggers the right automation.
Email forwarding changes can take up to a full DNS TTL to propagate — check the TTL days in advance and lower it if needed.
Cron Expression Builder Schedule the delta syncs that run through the freeze -
Run the go/no-go and switch the agents
Walk the exit criteria with the decision-maker, call it explicitly, then move agents over with a named person on hand for the first few hours. Keep Crisp read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Don't move on until
- Full historical load complete and counts matched
- Inbound channels repointed and verified with live test tickets
- Rollback decision point passed explicitly, not by default
06 Validation Prove the migration is complete, then close it out.
Objective Documented evidence that data, workflow and reporting all survived, and a signed acceptance.
Keep these open
-
Run the full reconciliation
Compare source and target on every object: total counts, counts by status, counts by group, attachment counts, and field-level spot checks on a random sample. Produce one report you can hand to an auditor.
Migration Validation Tool Reconcile Crisp and Gorgias record-for-record -
Verify field completeness, not just record counts
Re-profile the loaded data and compare null rates per field against the source profile. Matching record counts with a field that silently arrived empty is the failure mode counts alone will never catch.
Data Profiler Prove field completeness held up through the load -
Rebuild reporting and compare against baselines
Recreate your core dashboards — volume, first response time, resolution time, CSAT — and compare to pre-migration figures for the same period. Explain every variance; a changed SLA calculation is a real finding, not a rounding error.
SLA and first-response metrics are usually recalculated from the loaded timestamps, so they will differ if any timestamp mapping was approximate.
-
Test the workflow layer end to end
Fire every trigger, automation, SLA escalation, macro and notification with a live ticket. Workflow does not migrate — it gets rebuilt — so it is untested until someone has actually watched it run.
-
Confirm compliance and produce the audit trail
Re-scan the loaded data for regulated fields, confirm retention and deletion policies are configured in Gorgias, and file the evidence with your PII decisions from the audit phase.
PII & Compliance Scanner Produce the compliance evidence your auditor will ask for -
Sign off, then decommission on a schedule
Get written acceptance against the Discovery success criteria. Keep Crisp read-only for an agreed period (30-90 days is typical), take a final archive export, and only then cancel. Diarise the decommission date so it does not quietly renew.
-
Rebuild automations
Crisp triggers, bots, and routing rules do not migrate. Rebuild them as Gorgias Rules from scratch. Map each Crisp automation to its Gorgias equivalent before cutover. Recreate only the workflows that still make sense in a ticket-first model.(docs.gorgias.com)
-
Monitor for 2 weeks
Watch for duplicate customer creation, missing attachments, rules touching imported history, and any Crisp conversations that arrived during the cutover window.
Crisp → Gorgias specifics
- Connect e-commerce stores
- Wire up your Shopify, BigCommerce, or Magento store to Gorgias and test order actions end to end. This is usually the whole reason for the migration — do not save it for last.
- Swap the widget
- Replace the Crisp chat widget with the Gorgias chat widget on your storefront. Coordinate this with DNS changes if you use email forwarding.
- Agent training
- Gorgias's two-status model (Open/Closed) is simpler than most helpdesks, but the macro system, views, and rule engine require onboarding. Make sure agents understand that Crisp sessions are now tickets, that pending may live as a tag, and that imported notes may show mapped users.
Don't move on until
- Full reconciliation report attached to the project record
- Reporting baselines match pre-migration figures within agreed tolerance
- Formal acceptance signed and archive retention scheduled
Field mapping reference
The field-by-field mapping for each object. Use this as the starting point for your mapping spec.
API Path / API Path /
| Crisp field | Gorgias field | Notes |
|---|---|---|
| /website/{id}/people/profiles | POST /api/customers | Match on email. Create if not exists. Overflow profile data goes into customer.data. |
| /website/{id}/conversations (by session_id) | POST /api/tickets | One conversation = one ticket (typical). Store session_id in external_id for traceability. |
| /website/{id}/conversation/{session_id}/messages | POST /api/tickets/{id}/messages | Set sent_datetime to prevent Gorgias from re-sending. |
| type: "note" | channel: "internal-note" | Gorgias internal notes are a message channel. |
| type: "file" with content.url | Upload via POST /api/upload, attach to message body_html | Must download from Crisp, re-upload to Gorgias. |
| segments [] on People Profile | tags: [{"name": "..."}] on Ticket | Pipe-separated in Crisp, array in Gorgias. Normalize names before load. |
| Workspace operators | POST /api/users | Map by email. Handle inactive operators explicitly. |
| resolved / unresolved / pending | open / closed | Gorgias only has two states. Map pending to open + a preservation tag. |
| get_conversation_metas() | JSON key-value on ticket | Crisp people notepad has no first-class Gorgias equivalent — store in customer.data or emit a synthetic internal note. |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Contacts / Customer Profiles | low | Crisp supports CSV export for contact profiles and Gorgias accepts customer creation via API with email matching, making this the most straightforward entity to migrate. |
| Conversations / Tickets | high | The fundamental chat-session-to-ticket structural mismatch requires architectural decisions about splitting or consolidating, with no automatic mapping available. |
| Messages (Text) | medium | Messages can be migrated via API with timestamp preservation using sent_datetime, but rate limits and Crisp's ~10,000 message per conversation cap require careful pagination handling. |
| Internal Notes | medium | Crisp note-type messages must be mapped to Gorgias's internal-note channel, and Crisp's people notepad field has no first-class equivalent and must be stored in customer.data or as a synthetic note. |
| Attachments / Files | high | Every file must be downloaded from Crisp and re-uploaded to Gorgias via a separate upload endpoint, adding significant complexity, time, and failure points to the migration. |
| Conversation Status | medium | Crisp's three-state model (pending/unresolved/resolved) must be lossy-compressed into Gorgias's two states (open/closed), requiring tag-based workarounds for semantic preservation. |
| Segments / Tags | low | Crisp segments are pipe-separated strings that map to Gorgias tag arrays, requiring only name normalization before loading. |
| Operators / Agents | low | Operators can be mapped by email address, though inactive Crisp operators must be explicitly handled to avoid creating orphaned agent references in Gorgias. |
| Custom Metadata | high | Crisp conversation metas and people profile data must be serialized into Gorgias's ticket meta, external_id, or customer.data fields with no standardized schema mapping available. |
| Conversation Timestamps | medium | Original timestamps must be explicitly set via sent_datetime during message creation to prevent Gorgias from treating migrated messages as new and triggering notifications or automations. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Chat-to-Ticket Model Mismatch
Crisp's long-running conversation sessions must be architecturally decomposed into discrete Gorgias tickets, requiring explicit decisions about splitting or consolidating threads.
No Native Conversation Export
Crisp only supports CSV export for contact profiles (capped at 200,000), so all conversation and message data must be extracted via the REST API, which can take days at scale.
Dual-Side Rate Limit Constraints
Both Crisp's multi-level rate-limit system and Gorgias's leaky bucket algorithm (as low as 2 req/s on lower plans) severely constrain migration throughput and require robust exponential backoff and retry logic.
Status Model Reduction
Crisp's three conversation states (pending, unresolved, resolved) must be collapsed into Gorgias's two-status model (open/closed), requiring preservation of lost semantics via tags or custom fields.
Attachment Re-upload Requirement
File attachments cannot be linked by URL — they must be downloaded from Crisp and re-uploaded to Gorgias via its upload endpoint, then embedded into message HTML bodies.
Pagination and Filtering Limitations
Gorgias uses cursor-based pagination capped at 100 items per request with no server-side date filtering, forcing client-side filtering and increasing extraction complexity.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I export Crisp conversations as CSV?
No. Crisp only supports CSV export for contact profiles, capped at 200,000. Conversation and message data must be extracted via the Crisp REST API or a third-party tool that uses it.
What are the Gorgias API rate limits for data migration?
Gorgias uses a leaky bucket algorithm. OAuth2 apps get 80 requests per 20-second window; API key integrations get 40 requests per 20 seconds. Exceeding the limit returns a 429 error with a Retry-After header you must respect.
Will Gorgias resend imported messages to customers?
Yes, if you don't set the sent_datetime field. Any message created via the Gorgias API without sent_datetime is treated as new and Gorgias will attempt to deliver it. Always set sent_datetime for historical imports.
How do Crisp conversations map to Gorgias tickets?
Each Crisp conversation (identified by session_id) typically becomes one Gorgias ticket. Messages become ticket messages, segments map to tags, and private notes become internal-note channel messages. Crisp's three-state model (pending/unresolved/resolved) must be flattened to Gorgias's two states (open/closed).
Can Make or Zapier migrate all historical Crisp chats to Gorgias?
Not well. Both platforms expose useful connectors, but they lack the replay logic, idempotency, and throughput needed for high-volume historical backfills. They are better suited for small deltas or post-migration automations.