# Compare sequential, agent-based, and parallel LLM processing: n8n workflow

> Started by another system calling it. It calls services over plain HTTP rather than through connectors.

Source: https://millionrounds.com/workflow/compare-sequential-agent-based-and-parallel-llm-processing (Millionrounds; every figure on the page links to its filing or article)

[Automation library](https://millionrounds.com/workflows) · [AI assistants](https://millionrounds.com/workflows?category=AI+assistants#library)

# Compare sequential, agent-based, and parallel LLM processing

Started by another system calling it. It calls services over plain HTTP rather than through connectors. It puts Claude behind a retrieval step, so answers are drawn from documents you supply rather than from the model alone. 31 steps: large, and worth reading before you trust it with anything live.

Large Calls a model: Claude Retrieval

[Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fcompare-sequential-agent-based-and-parallel-llm-processing)

**31 steps** Large: read it before trusting it with anything live

**0** Branches: places the flow splits

**0** Loops or waits

**0** Services by connector: it calls them over plain HTTP

## The flow

Starts it 2 Model 14 Logic 13 Service 2

The steps in the order they run, from what starts it.

- 1 **When clicking ‘Test workflow’** Manual trigger *Starts it*
- 2 **Anthropic Chat Model** Lm Chat Anthropic *Model*
- 3 **Anthropic Chat Model1** Lm Chat Anthropic *Model*
- 4 **Anthropic Chat Model2** Lm Chat Anthropic *Model*
- 5 **Anthropic Chat Model3** Lm Chat Anthropic *Model*
- 6 **Simple Memory** Memory Buffer Window *Model*
- 7 **Anthropic Chat Model4** Lm Chat Anthropic *Model*
- 8 **Anthropic Chat Model5** Lm Chat Anthropic *Model*
- 9 **Webhook** Webhook *Starts it*
- 10 **CONNECT ME** No Op *Logic*
- 11 **CONNECT ME1** No Op *Logic*
- 12 **CONNECT ME2** No Op *Logic*
- 13 **HTTP Request** Http Request *Service*
- 14 **LLM Chain - Step 1** Chain Llm *Model*
- 15 **LLM Chain - Step 2** Chain Llm *Model*
- 16 **LLM Chain - Step 3** Chain Llm *Model*
- 17 **LLM Chain - Step 4** Chain Llm *Model*
- 18 **All LLM steps here - sequentially** Agent *Model*
- 19 **Clean memory** Memory Manager *Model*
- 20 **Basic LLM Chain4** Chain Llm *Model*
- 21 **Merge** Merge *Logic*
- 22 **Initial prompts1** Set *Logic*
- 23 **Merge2** Merge *Logic*
- 24 **Markdown** Markdown *Logic*
- 25 **Merge output with initial prompts1** Merge *Logic*
- 26 **Initial prompts** Set *Logic*
- 27 **Split Out1** Split Out *Logic*
- 28 **LLM steps - parallel** Http Request *Service*
- 29 **Merge output with initial prompts** Merge *Logic*
- 30 **Reshape** Set *Logic*
- 31 **Split Out** Split Out *Logic*

## How it's built

- **Starts on:** Another system calling it
- **Steps:** 31
- **Size:** Large: read it before trusting it with anything live
- **Connects to:** Nothing by connector; it calls services over plain HTTP
- **Model:** Claude
- **Retrieval:** Answers are drawn from documents you supply, not from the model alone

## Questions

What does it need to run?

An n8n account, and a key for Claude. You connect your own accounts after importing it; none are included.

What starts it?

It starts on another system calling it.

Does it use AI?

Yes. It calls Claude. Its answers draw on documents you give it, not on the model alone. Model calls are billed by the provider, not by n8n.

How long does it take to set up?

Take your time. At 31 steps it is large: read it before trusting it with anything live.

How do I import it?

Copy the workflow, open a new workflow in n8n and paste. The steps appear on the canvas, ready for your own accounts.

**Before you run it**

Nothing here has been tested by us. It is somebody's working automation, read and described from its own steps. Treat the connections it wants as a list of accounts it will touch, and read the 31 steps before you point it at anything that matters.

## Run it in n8n

- **Create your n8n account** Free to start. n8n is where the workflow runs. [Create an n8n account ↗](https://millionrounds.com/n8n)
- **Download the workflow** Free with a Millionrounds account. [Download free](https://millionrounds.com/login?next=%2Fapp%2Fworkflow%2Fcompare-sequential-agent-based-and-parallel-llm-processing)
- **Paste it into n8n** Open a new workflow in n8n and paste. Then connect your own accounts.

Stored with the author's traces removed: canvas notes, instance identifiers, saved credential names and any live document they had left behind.

## Workflows like it

Also ai assistants, with the same services where possible, closest in size first.

- [**Auto workflow positioning with OpenAI and in-memory vectors** 31 steps · calls a model · in-memory vectors](https://millionrounds.com/workflow/auto-workflow-positioning-with-openai-and-in-memory-vectors)
- [**Adaptive RAG strategy with Gemini and Qdrant** 30 steps · calls a model · Qdrant](https://millionrounds.com/workflow/adaptive-rag-strategy-with-gemini-and-qdrant)
- [**Adaptive RAG with Gemini and Qdrant:** 30 steps · calls a model · Qdrant](https://millionrounds.com/workflow/adaptive-rag-with-gemini-and-qdrant)
- [**Automate agile project setup with OpenAI and Jira** 32 steps · calls a model · Gmail, Jira](https://millionrounds.com/workflow/automate-agile-project-setup-with-openai-and-jira)
- [**Build an All-Source Knowledge Assistant with Claude and RAG** 30 steps · calls a model · Google Drive, Supabase vectors](https://millionrounds.com/workflow/build-an-all-source-knowledge-assistant-with-claude-and-rag)
- [**Create adaptive RAG chat agent with Gemini and Qdrant** 30 steps · calls a model · Qdrant](https://millionrounds.com/workflow/create-adaptive-rag-chat-agent-with-gemini-and-qdrant)