Automated Morning Briefing Email with n8n: Turn RAG + Embeddings into Your Daily Advantage

Every morning, you and your team wake up to a familiar challenge: too much information, not enough clarity. Slack threads, dashboards, tickets, emails, docs – the signal is there, but it is buried in noise. Manually pulling it all together into a focused briefing takes time and energy that you could spend on real work and strategic decisions.

This is where automation can change the game. In this guide, you will walk through a journey from scattered data to a calm, curated Morning Briefing Email, powered by n8n, vector embeddings, Supabase, Cohere, and an Anthropic chat model. You will not just build a workflow. You will create a system that turns raw information into daily momentum.

The workflow uses text splitting, embeddings, a Supabase vector store, a RAG (retrieval-augmented generation) agent, and simple alerting and logging. The result is a reliable, context-aware morning briefing that lands in your inbox automatically, so you can start the day aligned, informed, and ready to act.

From information overload to focused mornings

Before diving into nodes and configuration, it is worth pausing on what you are really building: a repeatable way to free your brain from manual status gathering. Instead of chasing updates, you receive a short, actionable summary that highlights what truly matters.

By investing a bit of time in this n8n workflow, you create a reusable asset that:

• Saves you from daily copy-paste and manual summarization
• Aligns your team around the same priorities every morning
• Scales as your data sources and responsibilities grow
• Becomes a foundation you can extend to other automations

Think of this Morning Briefing Email as your first step toward a more automated workday. Once you see how much time one workflow can save, it becomes easier to imagine a whole ecosystem of automations doing the heavy lifting for you.

Why this n8n architecture sets you up for success

There are many ways to send a daily email. This one is different because it is built for accuracy, context, and scale. The architecture combines vector embeddings, a Supabase vector index, and a RAG Agent so your summaries are not just generic AI text, but grounded in your real data.

Here is what this architecture gives you:

• Context-aware summaries using Cohere embeddings and a Supabase vector store, so the model pulls in the most relevant pieces of information.
• Up-to-date knowledge retrieval via a RAG Agent that blends short-term memory with retrieved documents, rather than relying on a static prompt.
• Scalability and performance through text chunking and vector indexing, which keep response times predictable as your data grows.
• Operational visibility with Google Sheets logging and Slack alerts, so you can trust this workflow in production and quickly spot issues.

You are not just automating an email. You are adopting a modern AI architecture that you can reuse for many other workflows: internal search, knowledge assistants, support summaries, and more.

The workflow at a glance

Before we go step by step, here is a quick overview of the building blocks you will be wiring together in n8n:

• Webhook Trigger – receives the incoming content or dataset you want summarized.
• Text Splitter – breaks long content into manageable chunks (chunkSize: 400, chunkOverlap: 40).
• Embeddings (Cohere) – converts each chunk into vectors using embed-english-v3.0.
• Supabase Insert – stores those vectors in a Supabase index named morning_briefing_email.
• Supabase Query + Vector Tool – retrieves the most relevant pieces of context for the RAG Agent.
• Window Memory – maintains a short history so the agent can stay consistent across runs if needed.
• Chat Model (Anthropic) – generates the final briefing text based on the retrieved context and instructions.
• RAG Agent – orchestrates retrieval, memory, and the chat model to produce the email body.
• Append Sheet – logs the final output in a Google Sheet tab called Log.
• Slack Alert – posts to #alerts when something goes wrong, so you can fix issues quickly.

Each of these pieces is useful on its own. Together, they form a powerful pattern you can replicate for other AI-driven workflows.

Building your Morning Briefing journey in n8n

1. Start with a Webhook Trigger to receive your data

Begin by creating an HTTP POST Webhook node in n8n and name it something like morning-briefing-email. This will be your entry point, where internal APIs, ETL jobs, or even manual tools can send content for summarization.

Once this is in place, you have a stable gateway that any system can use to feed information into your briefing pipeline.

2. Split long content into smart chunks

Next, add a Text Splitter node. Configure it as a character-based splitter with:

• chunkSize: 400
• chunkOverlap: 40

This balance is important. Smaller chunks keep embeddings efficient and retrieval precise, while a bit of overlap preserves context across chunk boundaries. You can always tune these numbers later, but this starting point works well for most use cases.

3. Turn text into embeddings with Cohere

Now it is time to give your workflow a semantic understanding of the text. Add an Embeddings node configured to use Cohere and select the embed-english-v3.0 model.

Make sure your Cohere API key is stored securely in n8n credentials, not hard-coded in the workflow. Each chunk from the Text Splitter will be passed to this node, which outputs high-dimensional vectors that capture meaning rather than just keywords.

These embeddings are the foundation of your retrieval step and are what allow the RAG Agent to pull in the most relevant context later.

4. Store vectors in a Supabase index

With embeddings in hand, add a Supabase Insert node to push the vectors into your Supabase vector index. Use an index named morning_briefing_email so you can easily reuse it for this workflow and related automations.

Alongside the vector itself, store useful metadata such as:

• Title
• Source (for example, which system or document it came from)
• Timestamp or date

This metadata helps later when you want to audit how a briefing was generated or trace a specific point back to its origin.

5. Retrieve relevant context with Supabase Query and the Vector Tool

When it is time to actually generate a morning briefing, you will query the same Supabase index for the most relevant chunks. Add a Supabase Query node configured for similarity search against morning_briefing_email.

Wrap this query with a Vector Tool node. The Vector Tool presents the retrieved documents in a format that the RAG Agent can easily consume. This is the bridge between your stored knowledge and the AI model that will write your briefing.

6. Add Window Memory and connect the Anthropic chat model

To give your workflow a sense of continuity, add a Window Memory node. This short-term conversational memory lets the RAG Agent maintain a small history, which can be helpful if you extend this workflow later or chain multiple interactions together.

Then, configure a Chat Model node using an Anthropic-based model. Anthropic models are well suited for instruction-following, which is exactly what you need for clear, concise morning briefings.

At this point, you have all the ingredients: context from Supabase, a memory buffer, and a capable language model ready to write.

7. Orchestrate everything with a RAG Agent

Now comes the heart of the workflow: the RAG Agent. This node coordinates three inputs:

• Retrieved documents from Supabase via the Vector Tool
• Window Memory history
• The Anthropic chat model

Configure the RAG Agent with a clear system prompt that defines the style and structure of your briefing. For example:

System: You are an assistant for Morning Briefing Email. Produce a short, actionable morning briefing (3-5 bullet points), include urgent items, outstanding tasks, and a short quick-glance summary.

This is where your workflow starts to feel truly transformative. Instead of a raw data dump, you get a focused, human-readable summary you can act on immediately.

8. Log every briefing and protect reliability with alerts

To keep a record of what is being sent, add an Append Sheet node and connect it to a Google Sheets document. Use a sheet named Log to store each generated briefing, along with any metadata you find useful. This gives you an audit trail and makes it easy to analyze trends over time.

Finally, add a Slack Alert node that posts to a channel such as #alerts whenever the workflow encounters an error. This simple step is what turns an experiment into a system you can trust. If something breaks, you will know quickly and can respond before your team misses their morning update.

Configuration tips to get the most from your automation

Once the basic pipeline is working, a few targeted tweaks can significantly improve quality and robustness.

• Chunk sizing: If your source documents are very long or very short, experiment with different chunkSize and chunkOverlap values. Larger chunks reduce the number of API calls but can blur the boundaries between topics. Smaller chunks increase precision at the cost of more calls.
• Rich metadata: Capture fields like source URL, timestamp, and author with each vector. This makes it easier to understand why certain items appeared in the briefing and to trace them back to the original data.
• Security best practices: Store all API keys (Cohere, Supabase, Anthropic, Google Sheets) in n8n credentials. Protect your webhook with access controls and request validation, such as an API key or HMAC signature.
• Rate limit awareness: Monitor your Cohere and Anthropic usage. For high-volume workloads, batch embedding requests where possible to stay within rate limits and keep costs predictable.
• Relevance tuning: Adjust how many nearest neighbors you retrieve from Supabase. Too few and you might miss important context, too many and you introduce noise. Iterating on this is a powerful way to improve briefing quality.

Testing your n8n Morning Briefing workflow

Before you rely on this workflow every morning, take time to test it end to end. Testing is not just about debugging. It is also about learning how the system behaves so you can refine it confidently.

1. Send a test POST payload to the webhook. For example:
   { "title": "Daily Ops", "body": "...long content...", "date": "2025-01-01" }
2. Check your Supabase index and confirm that vectors have been inserted correctly, along with the metadata you expect.
3. Trigger the RAG Agent and review the generated briefing. If it feels off, adjust the system prompt, tweak retrieval parameters, or fine-tune chunk sizes.
4. Verify that the Google Sheets Append node logs the output in the Log sheet and simulate an error to ensure the Slack Alert fires in #alerts.

Each test run is an opportunity to learn and improve. Treat this phase as a chance to shape the exact tone and depth you want in your daily emails.

Scaling your Morning Briefing as your needs grow

Once you see how effective this workflow is, you may want to expand it to more teams, more data sources, or more frequent runs. The architecture you have chosen is ready for that.

• Separate ingestion from summarization: If live ingestion becomes expensive or complex, move embeddings creation and vector insertion into a scheduled job. Your morning briefing can then query an already up-to-date index.
• Use caching for hot data: For information that changes slowly but is requested often, introduce caching to speed up retrieval and reduce load.
• Consider specialized vector databases: If you outgrow Supabase in terms of performance or scale, you can migrate to a dedicated vector database such as Pinecone or Milvus, as long as it fits your existing tooling and architecture.

The key is that you do not need to rebuild from scratch. You can evolve this workflow step by step as your organization and ambitions grow.

Troubleshooting: turning issues into improvements

Even well designed workflows hit bumps. When that happens, use these checks to quickly diagnose the problem and turn it into a learning moment.

• No vectors in Supabase? Confirm that the Embeddings node is using valid credentials and that the Text Splitter is producing non-empty chunks.
• Briefings feel low quality? Refine your system prompt, increase the number of retrieved neighbors, or adjust chunk sizes for better context.
• Rate limit errors from Cohere or Anthropic? Implement retry and backoff strategies in n8n and consider batching embedding requests.
• n8n workflow failures? Use n8n execution logs together with your Slack Alert node to capture stack traces and pinpoint where things are breaking.

Each fix you apply makes the workflow more resilient and prepares you for building even more ambitious automations in the future.

Prompt ideas to shape your Morning Briefing

Your prompts are where you translate business needs into instructions the model can follow. Here are two examples you can use or adapt:

Prompt (summary): Produce a 3-5 bullet morning briefing with: 1) urgent items, 2) key updates, 3) blockers, and 4) action requests. Use retrieved context and keep it under 150 words.

Prompt (email format): Write an email subject and short body for the team’s morning briefing. Start with a one-line summary, then list 3 bullets with actions and deadlines. Keep tone professional and concise.

Do not hesitate to experiment. Small prompt changes can dramatically shift the clarity and usefulness of your briefings.

From one workflow to a culture of automation

By building this n8n-powered Morning Briefing Email, you have created more than a daily summary. You have built a reusable pattern that combines a vector store, embeddings, memory, and a RAG Agent into a reliable, production-ready pipeline.

The impact is tangible: accurate, context-aware briefings that save time, reduce cognitive load, and keep teams aligned. The deeper impact is mindset. Once you see what a single well designed workflow can do, it becomes natural to ask, “What else can I automate?”

As you move this into production, make sure you:

• Protect your webhook with strong authentication and request validation
• Monitor usage and costs across Cohere, Supabase, and Anthropic
• Maintain a clear error-notification policy using Slack alerts and n8n logs

From here, you can branch out to automated weekly reports, project health summaries, customer support digests, and more, all built on the same RAG + embeddings foundation.

Call to action: Spin up this Morning Briefing workflow in your n8n instance and make tomorrow morning the first where your day starts with clarity, not chaos. If you want a downloadable n8n workflow export or guidance on configuring credentials for Cohere, Supabase, Anthropic, or Google Sheets, reach out to our team or leave a comment below. Use this template as your starting point, then iterate, refine, and keep automating.