building-flows

Building Flows

A flow moves data from one or more source systems to one or more destination systems. It runs on a schedule, in response to events (webhooks, listeners), or when triggered by another flow. Flows are the primary way integrations get work done in Celigo.

A flow has page generators (exports that fetch data) and page processors (imports and lookups that process each record). Processors run sequentially in a flat list, or conditionally through routers that branch records to different paths. These processing pipeline mechanics -- routers, branches, page processors, response mapping -- are shared with APIs and tools (see building-apis and building-tools).

What Starts a Flow

Flows start themselves -- this is the biggest thing that separates them from APIs (invoked by an HTTP caller) and tools (invoked by a consumer). Every flow begins with one or more page generators, of two kinds:

• Scheduled exports -- the flow runs on a cron cadence and each run pulls from the source: everything (full sync), only what changed since the last run (delta sync), records matching a query, or files landed in an FTP/SFTP/S3 folder. The right primitive for batch work: nightly reconciliations, hourly delta syncs, backfills, off-peak windows.
• Listeners -- the source pushes to the flow. A webhook fires (or a NetSuite/Salesforce native real-time event triggers) and the payload immediately starts flowing. No schedule; the flow runs as events arrive. The right primitive for event-driven work ("when X happens, do Y"), especially when latency matters.

A flow can mix both, and multi-generator designs are common:

• Real-time plus batch safety net -- a listener catches events as they fire; a scheduled export reconciles at off-peak hours, catching up after webhook outages
• Consolidating sources -- customers from Salesforce AND HubSpot, each with its own generator, feeding the same downstream pipeline
• Different slices of the same source -- one export pulls new records, another pulls updated records, when the API exposes them separately

If the requirement is "every night at 2 AM, do X" or "when a webhook arrives, do Y" -- that lives on a flow. APIs and tools have no schedule and no listener; they only run when invoked.

Fetched Data Needs a Downstream Consumer

A common design mistake: ending a flow on an import that fetches data back from a remote system (a preview call, a query, a lookup-shaped POST) and relying on response mapping to capture the result. Response mapping makes fields visible to the NEXT step -- if no next step exists, the captured data is discarded when the run ends and nobody sees it.

When the requirement says "preview / estimate / retrieve / fetch / check / look up", the design needs at least one of:

• A write-back import to the source system (most common) -- e.g. source export -> preview import -> update import that writes the captured fields onto the source record
• A persistent destination the user named (file to S3/SFTP, email, database)
• A router or AI agent step that consumes the captured data within the same run

A two-step export -> fetch-shaped-import flow with nothing after it is a smell -- re-read the intent for where the fetched data should end up. The same applies in reverse: capturing a created record's ID via response mapping is only useful if a later step writes it somewhere.

Flow Topologies

Linear Flows

A flat pageProcessors[] list with no routers. One or more page generators feed records through a sequential chain of page processors. Each processor is either an import (type: "import") or a lookup export (type: "export"). Records pass through every step in order. Unique to flows -- APIs and tools always use routers.

Branching Flows

Page generators feed records into routers[] instead of pageProcessors[]. Each router evaluates records against branch conditions and routes them to matching branches. Branches contain their own pageProcessors[] and can chain to other routers via nextRouterId.

Two routing modes (shared with APIs and tools):

• Input filters (routeRecordsUsing: "input_filters") -- S-expression rules on each branch; last branch can omit filter as a catch-all
• Script-based (routeRecordsUsing: "script") -- a JavaScript function returns the branch name

Flows support both first_matching_branch and all_matching_branches routing. APIs only support first_matching_branch. Tools support first_matching_branch only.

A flow uses EITHER pageProcessors (linear) OR routers (branching) at the top level -- not both.

When a branching flow needs linear steps before the branch point (e.g., a lookup enrichment or AI classification that all branches depend on), use a pass-through router: a single-branch router with nextRouterId pointing to the branching router. Omit routeRecordsTo and routeRecordsUsing on the pass-through router -- including them makes it appear as a filter-based branch in the UI. The API defaults are sufficient.

Abstract / Instance Flows

A template/inheritance model. An abstract flow (isAbstract: true) defines the complete graph but cannot execute. Instance flows (_abstractFlowId) inherit the graph and customize via an overrides object (connections, schedules, mappings, filters).

Use when the same flow structure is deployed across multiple regions, tenants, or environments with different connections or parameters.

Quick Reference

Flow Type Decision Matrix

Minimum Required Fields

Every flow needs at minimum:

• name -- display name
• _integrationId -- parent integration
• disabled: true -- always create disabled
• pageGenerators[] -- at least one entry with _exportId
• Either pageProcessors[] (linear) or routers[] (branching) -- never both

Which Schemas to Read

Always read:

• request.yml -- base flow fields
• page-generator.yml -- export sources, per-generator schedules, delta coordination
• page-processor.yml -- import/export steps with responseMapping and hooks

Add for branching flows:

• router.yml -- routing strategy, record distribution mode
• branch.yml -- input filters, per-branch processors, chaining

Add if response mapping is needed:

• response-mapping.yml -- extract/generate pairs for carrying data between steps

All available schemas (in references/schemas/):

• Base fields (all flows): request.yml
• Response shape: response.yml
• Page generators: page-generator.yml
• Page processors: page-processor.yml
• Response mapping: response-mapping.yml
• Routers: router.yml
• Branches: branch.yml
• Abstract flow helpers: overrides-helper.yml
• Instance overrides: overrides.yml
• Cloning: clone-request.yml, clone-response.yml

Related Skills

• configuring-connections > Quick Reference -- connection types and auth methods for page generators and processors
• configuring-exports > Quick Reference -- building exports used as page generators and lookup processors
• configuring-imports > Quick Reference -- building imports used as page processors
• writing-mappings > Mapper 2.0 Workflow -- field mappings on imports and response mapping between steps
• writing-scripts > Data Pipeline Hooks -- preSavePage, preMap, postMap, postSubmit, postResponseMap hooks
• writing-handlebars > Quick Reference -- dynamic values in URIs, filters, delta tokens, SQL queries
• troubleshooting-flows > Diagnostic Workflow -- diagnosing flow failures, errors, and performance issues

How to Build a Flow

1. Plan the flow

Before creating anything, decide what kind of operation this is:

Decision tree:

• Modifying an existing flow's step config (export settings, import mappings, scripts) -- work on the step directly, not the flow. Use celigo exports set, celigo imports set, or the relevant skill (configuring-exports, configuring-imports, writing-scripts, writing-mappings)
• Modifying an existing flow's structure (add/remove steps, change schedule, rename) -- GET the flow, modify the structure, PUT it back. Don't rebuild from scratch
• Building a new flow where every step is known -- build directly, bottom-up (skip to step 2)
• Any ambiguity about what steps are needed -- design first. List every system, every data direction, every step before writing any JSON

Design checklist (when ambiguity exists):

• What source systems? What destination systems?
• What data moves between them, in which direction?
• How often? (cron schedule, webhook trigger, on-demand)
• What happens when a step fails? (proceedOnFailure, error notifications)
• Do downstream steps need data from upstream responses? (response mapping)
• Is this a one-off or a reusable template? (abstract/instance flow)
• Sandbox or production? (never mix -- sandbox: true flows only use sandbox: true connections)

2. Identify the integration

Every flow belongs to an integration (the container). Find or create the integration first.

celigo integrations list

3. Check for existing patterns

Before building from scratch, check what already exists in the account and marketplace.

# Search for similar resources in the account index
celigo account search "<keyword>"
 
# Show what an existing resource uses and what uses it

The account index auto-refreshes when stale (>4 hours). Force a fresh snapshot with celigo account snapshot.

4. Build the connections, exports, and imports

Flows reference existing resources. Build bottom-up: connections first, then exports and imports that use those connections, then the flow that wires them together.

For every step, match the adaptor to the target application -- raw HTTP is the fallback, not the default. Use the native adaptor when one exists (NetSuite, Salesforce, databases, FTP/S3); otherwise check for a pre-built HTTP connector (550+ apps: celigo http-connectors list) and build the connection from it; hand-write HTTP config from public API docs only when no connector exists or it doesn't cover the endpoint. See configuring-exports > Check for a pre-built connector and configuring-imports > Check for a pre-built connector.

See configuring-exports and configuring-imports for how to build each resource.

5. Choose the topology

Abstract/instance flows: Abstract flows are reusable templates that cannot run directly. Instance flows inherit the abstract's structure and override specific fields (connections, filters, schedules). Use when the same integration pattern repeats across tenants or regions. Create with isAbstract: true. Top-level pageProcessors are automatically wrapped into a single-branch router. Instance flows reference the abstract via _abstractFlowId and specify overrides -- they do NOT use the normal scaffolding process. _integrationId is NOT inherited and must be set explicitly on the instance.

6. Design the step sequence

For each step, decide:

• Type -- import (write to destination) or export (lookup for enrichment)
• Response mapping -- what data from this step's response do downstream steps need? Only add response mapping when downstream steps need fields that aren't already in the record or when field names need to change. If the lookup returns fields with the same names the downstream step expects, skip the response mapping -- it adds complexity without value.
• proceedOnFailure -- should the pipeline continue if this step fails?
• Hooks -- does this step need a postResponseMap script?

7. Build the flow JSON

Reference the schemas listed in the Quick Reference above for exact field schemas.

8. Configure scheduling

Pair schedule (6-field cron) with timezone (IANA). Omit both for listener/webhook/realtime flows. timezone defaults to UTC, which is usually wrong for human-facing schedules ("9 AM every weekday" should survive daylight saving).

Individual page generators can override the flow schedule via their own schedule field (e.g. one source syncs hourly, another nightly, in the same flow).

9. Set the runtime controls

Flow-level and per-step switches that change production behavior. APIs and tools have none of these -- they are flow-only.

10. Configure chaining (if needed)

• _runNextFlowIds -- trigger other flows when this one completes. The classic use is multi-stage pipelines: "after the customer-master sync finishes, run the orders sync." When a requirement says "X has to happen, then Y", chain two focused flows rather than building one big one
• _runNextExportIds -- more granular: trigger specific exports inside other flows instead of the whole flow

11. Create disabled, verify, enable

Always create with disabled: true. Verify the structure with celigo flows get. Enable only after verification.

CLI Commands

Pre-Submit Checklist

Before creating or updating a flow, verify:

• _integrationId references a real integration (confirm with celigo integrations get <id>)
• disabled: true is set for initial creation -- an enabled flow with a schedule runs immediately
• schedule is 6-field cron with seconds: "? */5 * * * *" (first field is always ?)
• pageProcessors[] and routers[] are mutually exclusive -- a flow uses one or the other, never both
• Router IDs are unique within the flow (random alphanumeric, e.g., "N8Q9NX24Sj5")
• Every branch nextRouterId references an existing router id in the same flow

Gotchas

1. PUT erases omitted fields. Always GET first, modify, then PUT. The set command handles this automatically.
2. pageProcessors and routers are mutually exclusive. A flow uses one or the other at the top level. Setting both causes validation errors.
3. Router IDs must be unique within a flow. Use random alphanumeric strings (e.g., "N8Q9NX24Sj5"). nextRouterId must reference an existing router id in the same flow.
4. Create flows with disabled: true. An enabled flow with a schedule will run immediately. Enable only after verification.
5. Build order matters. Connection -> Export -> Import -> Flow. The API rejects references to non-existent resources.
6. Schedule is 6-field cron with seconds. Format: "? minute hour dayOfMonth month dayOfWeek". The first field is always ?. Common mistake: using 5-field cron without the seconds position.
7. Instance flows cannot define structure. Do not set pageGenerators, pageProcessors, or routers on instance flows -- these are inherited from the abstract flow. All customizations go through overrides.
8. Instance flow overrides is full-replace on PUT. Omitting an override entry removes it. Always GET, merge changes, then PUT.
9. Empty pageProcessors: [] in a branch is the discard pattern. Records matching that branch are dropped. A branch with no inputFilter serves as a catch-all.
10. responseMapping uses Transformation 1.0 syntax (extract/generate pairs), not expression-based transforms. Lookup export responses use data[0].fieldName; import responses use _json.fieldName.
11. Don't add unnecessary transforms on lookups. If the lookup returns fields with the same names the downstream import expects, skip the transform -- the data flows through as-is. Only add a transform when you need to rename fields, reshape nested structures, or drop fields. An identity transform (e.g., errorId -> $.errorId) adds complexity for no benefit.

Common Errors