n8n Workflows — Theory Reference
Here’s the thing: this repo contains n8n workflows, and this README is a compact, theory-only guide explaining how those workflows are built, how they behave, and which design choices matter. No setup, no commands — just the concepts you need to reason about, extend, or review the workflows.
- What a workflow is (conceptually)
A workflow is a directed graph of nodes that transform, route, and act on data. Think of it as a pipeline where each node receives input items (structured JSON), performs a unit of work, and emits output items for downstream nodes. The graph defines order and branching; data flows along the connections.
What this implies:
Work is split into small, testable units (nodes).
Data at any point is a list of items (records) where each item is a JSON object.
Nodes should be focused on one responsibility: fetch, transform, filter, or deliver.
- Core building blocks
Triggers: entry points that start a workflow. Typical types: webhooks (external events), schedules (cron), and manual triggers (developer runs). Triggers define when the graph is evaluated.
Nodes: the processing units. They can call external APIs, run logic, parse/format data, or route items. Each node accepts input items and returns output items.
Credentials: secure connectors/configurations used by nodes to authenticate with external systems. Credentials are separate from node logic and should be treated as secrets.
Connections (edges): define data flow and branching. The shape of the graph determines execution order and parallelism.
Expressions / Parameters: dynamic fields that compute values at runtime, often referencing incoming item data or workflow-level variables.
- Data model and transformations
Every node works with an array of items. Each item is a JSON object with fields.
Transformations can be:
mapping fields (rename, convert types),
enriching items (calling APIs, joining data),
filtering or splitting into batches.
Preserve shape clarity: use consistent field names and nested structures where appropriate to avoid ambiguous merges downstream.
- Execution model (how nodes run)
Execution follows the directed connections: a node runs after its upstream nodes produce items.
Nodes often process items in batches or per-item depending on node implementation. Design with idempotency in mind.
Parallel branches execute independently; merges require careful handling (e.g., join logic or aggregation).
Side effects (writes, API calls) should be minimized or guarded so retries don’t produce duplicates.
- Error handling patterns
Errors can and will happen. Build workflows that are resilient:
Fail-fast vs graceful handling
Fail-fast: let the workflow stop on error and alert. Useful for critical sequences.
Graceful: catch, log, and continue for non-critical steps.
Common strategies
Use conditional checks to validate inputs before side-effecting nodes.
Wrap risky operations behind retries with exponential backoff (conceptually).
Route failed items to a dedicated error-handling branch or workflow for inspection and replay.
Add metadata (status, attempt count, error message) to items to enable safe retries.
Idempotency
Make actions idempotent where possible (e.g., include unique request IDs) so re-running a step does not duplicate effects.
- Security & credentials
Keep credentials separate from workflow logic and rotate them regularly.
Limit credential scopes — only grant the permissions the workflows need.
Protect webhook endpoints:
Use token-based validation or signed payloads.
Validate origin and payload contents before processing.
Treat any external input as untrusted: validate and sanitize before use.
- Observability & debugging (theory)
Maintain clear logging: record key events, inputs, outputs (or hashes of them to avoid secrets), and error contexts.
Track execution meta: timestamps, workflow run id, node durations — these make performance and failure analysis possible.
Use a central place for failed items or alerts so operators can triage without digging through runs.
For debugging, isolate a node with representative input and inspect the input/output shapes.
- Versioning, testing, and maintainability
Modularity: break complex flows into smaller workflows or reusable subflows (if supported). Smaller units are easier to test and reason about.
Documentation: every workflow should include a short description of intent, inputs expected, outputs produced, and failure modes.
Testing (theory):
Create representative sample inputs and expected outputs.
Test boundary conditions and error scenarios.
Validate that side effects are either mocked or safely repeatable.
Change management: keep a changelog for workflow behavior changes, especially where data contracts or external integrations change.
- Performance and scaling (design considerations)
Minimize unnecessary polling or frequent external calls; prefer event-driven triggers when possible.
Batch operations when APIs or downstream systems support it to reduce overhead.
Avoid long-lived synchronous blocking inside nodes; prefer asynchronous or queued processing for heavy tasks.
Consider rate limits for external services and implement backpressure or throttling logic in workflow design.
- Common use cases (conceptual)
Event-driven syncs: on webhook, transform and sync to another system.
Scheduled reporting: collect data, aggregate, and deliver via email or storage.
ETL: extract from multiple sources, transform data shapes, load into a data sink.
Alerting/notifications: evaluate conditions and deliver messages when thresholds are crossed.
Orchestration: coordinate multi-step business processes where each step is a node.
- Design principles (short)
Keep nodes focused and small.
Make workflows declarative: clearly show intent with minimal hidden logic.
Preserve observable state and add meaningful metadata to each item.
Assume failure: design for retries, idempotency, and clear fallbacks.
Treat credentials and secrets as first-class sensitive assets.