n8n Integration¶

Connect n8n automation workflows to Amazon Bedrock models through stdapi.ai's OpenAI-compatible or Anthropic-compatible interfaces. Existing OpenAI and Anthropic templates from the n8n marketplace need the credential repointed at your stdapi.ai instance—base URL and API key—and, only where a template's model name isn't one your deployment serves, the node's model changed to one that is, chosen from every provider in the catalogue rather than one vendor's list.

About n8n¶

🔗 Links: Website | GitHub | Documentation

n8n is a powerful workflow automation platform that enables you to connect any app with an API to build intelligent automations. With its intuitive visual interface, you can create complex AI-powered workflows without writing code, connecting Amazon Bedrock models to hundreds of services including Slack, Google Sheets, Salesforce, and more.

Key Features:

• ⭐ 190,000+ GitHub stars - Leading open-source workflow automation platform
• Hundreds of integrations - Pre-built nodes for popular services and APIs
• Visual no-code builder - Drag-and-drop interface with JavaScript customization
• Self-hosted or cloud - Deploy on your infrastructure or use n8n Cloud
• AI-native platform - Built-in OpenAI nodes work instantly with Amazon Bedrock via stdapi.ai
• Template marketplace - Thousands of pre-built workflows ready to use

Why n8n + stdapi.ai?¶

%%{init: {'flowchart': {'htmlLabels': true}} }%%
flowchart LR
  n8n["<img src='../styles/logo_n8n.svg' style='height:64px;width:auto;vertical-align:middle;' /> n8n"] --> stdapi["<img src='../styles/logo.svg' style='height:64px;width:auto;vertical-align:middle;' /> stdapi.ai"]
  stdapi --> bedrock["<img src='../styles/logo_amazon_bedrock.svg' style='height:64px;width:auto;vertical-align:middle;' /> Amazon Bedrock"]
  stdapi --> transcribe["<img src='../styles/logo_amazon_transcribe.svg' style='height:64px;width:auto;vertical-align:middle;' /> Amazon Transcribe"]
  stdapi --> polly["<img src='../styles/logo_amazon_polly.svg' style='height:64px;width:auto;vertical-align:middle;' /> Amazon Polly"]

Connect Your Own Instance¶

Point any running n8n instance—self-hosted or n8n Cloud—at your stdapi.ai gateway. Nothing below requires the AWS sample in Part 2.

Prerequisites¶

Configuration¶

OpenAI Nodes¶

Set Up Your Credentials¶

The foundation of any n8n integration is configuring your API credentials. This one-time setup unlocks all AI capabilities.

Configure Nodes¶

For each node, first select the credentials you previously created in the node parameters. Then, select the model you want to use. If you want to use a model that is not listed, you can enter its ID as an expression in the Model parameter.

Operation names below match V2 of the n8n OpenAI node (n8n 1.117.0 and later); older versions use slightly different names (e.g. Message a model instead of Generate a Model Response).

Chat Completions¶

Enables: Text generation and conversational AI in workflows.

Text Generation¶

Enables: Text generation using the OpenAI Responses or Chat Completions APIs.

Legacy Completions¶

Enables: raw prompt completion in LangChain-based chains, as an alternative to the chat-based nodes above.

Text Moderation¶

Enables: Content safety classification in workflows.

Embeddings¶

Enables: Vector embeddings for semantic search and RAG workflows.

Image Analysis¶

Enables: Image understanding and analysis in workflows.

Image Generation¶

Enables: Text-to-image creation in workflows.

Image Editing¶

Enables: Image transformation and editing in workflows.

Video Generation¶

Enables: Asynchronous text-to-video generation in workflows.

Audio Generation (TTS)¶

Enables: Text-to-speech audio generation in workflows.

Audio Transcription (STT)¶

Enables: Speech-to-text transcription in workflows.

Audio Translation¶

Enables: Translating speech in any supported language into English text.

Files¶

Enables: Upload files once and reference them across multiple chat completion requests without resending the raw bytes each time.

n8n calls the /v1/files endpoints (see Files API). Set Resource to "Files" in the OpenAI node for all operations below.

Bulk Runs (Batch API)¶

Enables: running thousands of chat completion or embedding requests asynchronously, at the Amazon Bedrock batch price rather than the synchronous one.

n8n ships no node for the Batch API — the OpenAI node's resources stop at text, images, audio and files — so drive it with HTTP Request nodes, as the Cohere routes are reached below:

1. Build the request set as JSONL, one line per item with its own custom_id, and upload it with purpose=batch (OpenAI/Upload a File when its Purpose list offers it, an HTTP Request against POST /v1/files otherwise)
2. POST /v1/batches with that file id, then poll GET /v1/batches/{id} behind a Wait node until it reports completed
3. Download the output file and match each line back to its custom_id — results may arrive in any order

Worth the extra nodes when a workflow classifies, enriches or summarizes a large backlog overnight and nothing is waiting on the answer. Interactive workflows stay on the synchronous nodes above.

Anthropic Nodes¶

Set Up Your Credentials¶

Configure Nodes¶

For each node, first select the credentials you previously created in the node parameters. Then, select the model you want to use. The model can be selected directly in the Model parameter for all supported nodes.

Chat Completions¶

Enables: Text generation and conversational AI in workflows.

Image Analysis¶

Enables: Image understanding and analysis in workflows.

Document Analysis¶

Enables: Document understanding and extraction in workflows.

Files¶

Enables: Upload files once and reference them across multiple Messages requests as document or image sources.

n8n calls the /anthropic/v1/files endpoints (see Anthropic Files API). Set Resource to "Files" in the Anthropic node for all operations below.

Prompt Resource¶

The Anthropic node's Prompt resource (Generate Prompt, Improve Prompt, Templatize Prompt) calls Anthropic's experimental prompt tools endpoints, which are not part of the Amazon Bedrock API surface and are not available through stdapi.ai. Use a Message a Model node with prompt-engineering instructions instead.

Known Issues¶

n8n's Cohere sub-nodes—Cohere Reranker (used by vector store nodes for hybrid search) and Embeddings Cohere—cannot be pointed at stdapi.ai. Their shared cohereApi credential exposes only an API key: its base URL is a hidden field pinned to Cohere's own endpoint, and both nodes build their client from the API key and the model alone, so that URL never reaches the request anyway.

• Reranking: use an HTTP Request node against POST /cohere/v2/rerank (see Cohere Rerank API), or a framework with a configurable reranker base URL—see RAG Pipelines.
• Embeddings: use the Embeddings OpenAI node described above, which reaches the same Amazon Bedrock models through /v1/embeddings; an HTTP Request node against POST /cohere/v2/embed (see Cohere Embed API) works too, but its output cannot be fed to a vector store sub-node.

Deploy the Full Stack on AWS¶

The Terraform sample below is one worked example of a credible AWS deployment, not the only architecture that works. stdapi.ai's gateway is a normal HTTP service, and n8n can run wherever you already operate it—your own ECS, EKS, EC2, another cloud, or a laptop. The sample's README documents where you would reasonably diverge from what it builds (networking, worker capacity, database, queue broker).

Architecture¶

The diagram below is the topology the sample builds: n8n and the stdapi.ai gateway both on ECS Fargate in one VPC you own, with n8n's own workflow state in Aurora PostgreSQL alongside them.

%%{init: {'flowchart': {'htmlLabels': true, 'nodeSpacing': 20, 'rankSpacing': 40, 'subGraphTitleMargin': {'top': 8, 'bottom': 10}}} }%%
flowchart TB
  user["👤 Your users<br/>(browser)"]

  subgraph public["Your VPC · public subnets"]
    alb["<img src='../styles/logo_amazon_load_balancing.svg' style='height:40px;width:auto;vertical-align:middle;' /> Application Load Balancer<br/>HTTPS · ACM certificate"]
  end

  subgraph private["Your VPC · private app subnets — no inbound route from the internet"]
    n8n["<img src='../styles/logo_n8n.svg' style='height:40px;width:auto;vertical-align:middle;' /> n8n<br/>ECS Fargate"]
    stdapi["<img src='../styles/logo.svg' style='height:40px;width:auto;vertical-align:middle;' /> stdapi.ai<br/>ECS Fargate"]
    aurora["<img src='../styles/logo_amazon_aurora.svg' style='height:40px;width:auto;vertical-align:middle;' /> Aurora PostgreSQL<br/>n8n workflows · credentials · executions"]
    egress["<img src='../styles/logo_amazon_vpc.svg' style='height:40px;width:auto;vertical-align:middle;' /> NAT gateways · one per AZ<br/>multi-region Bedrock access"]
  end

  subgraph regional["AWS service endpoints · your account, the regions you configure"]
    bedrock["<img src='../styles/logo_amazon_bedrock.svg' style='height:40px;width:auto;vertical-align:middle;' /> Amazon Bedrock"]
    transcribe["<img src='../styles/logo_amazon_transcribe.svg' style='height:40px;width:auto;vertical-align:middle;' /> Amazon Transcribe"]
    polly["<img src='../styles/logo_amazon_polly.svg' style='height:40px;width:auto;vertical-align:middle;' /> Amazon Polly"]
    comprehend["<img src='../styles/logo_amazon_comprehend.svg' style='height:40px;width:auto;vertical-align:middle;' /> Amazon Comprehend"]
    s3["<img src='../styles/logo_amazon_s3.svg' style='height:40px;width:auto;vertical-align:middle;' /> Amazon S3<br/>SSE-KMS"]
    cw["<img src='../styles/logo_amazon_cloudwatch.svg' style='height:40px;width:auto;vertical-align:middle;' /> Amazon CloudWatch<br/>logs · metrics"]
    bedrock ~~~ polly ~~~ s3
    transcribe ~~~ comprehend ~~~ cw
  end

  user -->|"HTTPS · TLS 1.2+"| alb
  alb -->|"HTTP · private subnet"| n8n
  n8n -->|"OpenAI + Anthropic dialects · API key<br/>HTTP over Cloud Map private DNS<br/>no public endpoint"| stdapi
  n8n -->|"TLS, no cert verification<br/>security-group restricted"| aurora
  stdapi --> egress
  egress -->|"HTTPS · SigV4"| bedrock
  egress -->|"HTTPS · SigV4"| transcribe
  egress -->|"HTTPS · SigV4"| polly
  egress -->|"HTTPS · SigV4"| comprehend
  egress -->|"HTTPS · SigV4"| s3
  egress --> cw

Two properties are worth reading off the picture. n8n is the only service with a public address — the ALB forwards nothing but n8n traffic, and the stdapi.ai gateway has no listener of its own, reachable only through Cloud Map private DNS inside the VPC. Customer data then splits in two: n8n's own workflow definitions, credentials and execution history live in Aurora, inside the account boundary, while whatever a workflow sends to a model passes through the gateway straight to Amazon Bedrock and the other AWS AI services behind it — no third party sits between your workflows and your models.

What Each AWS Service Does Here¶

Security Measures in This Flow¶

• Authentication — n8n reaches the gateway with a stdapi.ai API key that Terraform generates (api_key_create = true) and seeds directly into n8n's own credential store at first start.
• Encryption in transit — HTTPS with TLS 1.2+ from the browser to the ALB; HTTP from n8n to the gateway, confined to the private subnet and reachable only through Cloud Map private DNS; TLS without certificate verification from n8n to Aurora, a connection that never leaves the VPC and is already restricted by security group; HTTPS with SigV4 from the gateway to each AWS service.
• Encryption at rest — SSE-KMS on the gateway's S3 bucket and CloudWatch Logs; separately, encrypted Aurora storage and the Postgres secret under the VPC module's own KMS key.
• Least privilege — each ECS task assumes its own role; the gateway's role carries no permission for Aurora or its Secrets Manager secret, and n8n's role carries none for Amazon Bedrock, Transcribe, Polly or Comprehend.
• Content policy — a Bedrock guardrail configured on the gateway applies to every route n8n uses, including the mapping Text Moderation already documents for omni-moderation-latest.
• Data handling — the gateway is stateless and holds request bodies in memory only, so nothing a workflow sends to a model is persisted outside Amazon Bedrock's own call; n8n's own workflow data — credentials, executions — stays in Aurora inside the account boundary.

What's Included¶

Deploy n8n + stdapi.ai together, with a credential and thirteen sample workflows already imported:

📦 stdapi-ai/samples/getting_started_n8n

What's included:

• n8n on ECS Fargate, backed by Aurora PostgreSQL
• stdapi.ai gateway connected to Amazon Bedrock, preconfigured as both an OpenAI and an Anthropic n8n credential
• Thirteen sample workflows — one per stdapi.ai route family — imported automatically on first start
• Owner account pre-provisioned non-interactively, no signup screen to click through
• HTTPS-only ALB on your own domain (required — n8n's session cookie needs it)
• No local image build — the official n8nio/n8n image is pulled directly from Docker Hub

Deploy:

git clone https://github.com/stdapi-ai/samples.git
cd samples/getting_started_n8n/terraform
tofu init
tofu apply

What It Costs to Run¶

Read a model's price before a workflow sends anything to it with GET /model_pricing. Setting COST_TRACKING=true additionally puts a per-request cost on each usage entry — estimated from published AWS prices, not read back from your invoice.

What to Watch¶

The gateway writes one structured request event per call, carrying the request id, path, status code, execution_time_ms, the model that served it (model_id), and the token, character and second counts AWS billed. A workflow that fires on a schedule has no one watching its output in real time, so whether its calls are succeeding matters more than for an interactive chat session:

fields model_id, path, status_code
| filter type = "request" and status_code >= 400
| stats count(*) as failures by model_id, path, status_code
| sort failures desc

Turning on CLOUDWATCH_METRICS republishes the same counts as CloudWatch metrics in the stdapi namespace, dimensioned by Model, so an alarm can fire on a rising failure count without a scheduled query.

Next Steps¶