n8n

Connect n8n to cognee’s AI memory engine and build powerful automation workflows with persistent knowledge graphs. Add data, process it into memory, and run semantic searches - all without writing code.

Why Use This Integration

No-Code Memory: Build AI memory pipelines using n8n’s visual workflow editor
Knowledge Graph Search: Query your data using natural language with graph-enhanced retrieval within n8n workflows
Workflow Automation: Combine cognee memory with 400+ n8n integrations
Self-Hosted Ready: Works with self-hosted n8n instances out of the box

Installation

The cognee node is available as an n8n community node. Install it directly from n8n:

Login to your self-hosted n8n
Go to Settings → Community Nodes
Click Install button to add a new node and search for n8n-nodes-cognee
Click Install to complete.

Or install via npm in your n8n instance directory:

npm install n8n-nodes-cognee

For self-hosted n8n, you can quickly spin up an instance following their documentation:Don’t forget to set your timezone. After setup, access n8n at localhost:5678.

Credentials Setup

Before using the cognee node in your workflows, configure your API credentials:

Get your API key and Base URL from your Cognee Cloud dashboard (API Keys page)
In your workflow, click the + icon and search for cognee — you’ll see the available resources (Add Data, Cognify, Search, Delete, Skill)
Click on the Add Data action to start
Create credentials of type Cognee API using the values below

Field	Value
Base URL	`https://tenant-xxx.aws.cognee.ai`
API Key	Your key from platform.cognee.ai (sent via the `X-Api-Key` header)

Do not include a trailing /api in the Base URL — the node appends it automatically. The connection test hits GET /health.

Quick Start

Build a complete Add → Cognify → Search workflow in minutes:

Step 1: Add Data

Add the cognee node to your workflow and configure it:

Resource: Add Data
Operation: Add
Dataset Name: my-dataset
Text Data: Your content to store

{
  "datasetName": "my-dataset",
  "text_data": ["Natural language processing is an interdisciplinary subfield of computer science and information retrieval."]
}

Step 2: Cognify

Process your data into cognee’s knowledge graph memory:

Resource: Cognify
Operation: Cognify
Datasets: my-dataset

Step 3: Search

Query your memory using natural language:

Resource: Search
Operation: Search
Search Type: GraphCompletion
Datasets: my-dataset
Query: Tell me about NLP
Top K: 10

Operations Reference

You can interact with your data cross platform. You can query a dataset that you already have in your Cognee Cloud or you can view and interact with your new data from n8n in Cognee Cloud thanks to your cognee credentials.

Add Data

Stores text content in a cognee dataset for later processing.

Parameter	Required	Description
Dataset Name	Yes	Name of the dataset to store data in
Text Data	Yes	One or more text strings to add

API Endpoint: POST /api/add_text Text strings added through the n8n node are stored under an auto-generated filename of the form text_<md5_hash>.txt, derived from an MD5 hash of the content. The n8n node does not expose a filename, label, or metadata override. If you need custom labels or arbitrary metadata per item, ingest via the Python SDK using the DataItem wrapper (DataItem(data=..., label=..., external_metadata=...)) — see DataItem metadata and labels.

Cognify

Transforms raw data into structured knowledge graph memory with embeddings.

Parameter	Required	Description
Datasets	Yes	One or more dataset names to process

API Endpoint: POST /api/cognify

Search

Queries the knowledge graph memory using semantic search.

Parameter	Required	Description
Search Type	Yes	Search algorithm to use
Datasets	Yes	Datasets to search within
Query	Yes	Natural language search query
Top K	No	Number of results to retrieve (default: 10)

API Endpoint: POST /api/search

Delete

Removes a whole dataset or a single data item.

Operation	Endpoint	Parameters
Delete Dataset	`DELETE /api/datasets/{datasetId}`	Dataset ID (UUID)
Delete Data	`DELETE /api/datasets/{datasetId}/data/{dataId}`	Dataset ID, Data ID (UUIDs)

Skill

The self-improving skill loop, targeting the /api/v1 endpoints. A weak run becomes a reviewable, approvable edit to a skill’s instructions.

Operation	Endpoint	Purpose
Ingest Skill	`POST /api/v1/skills`	Ingest a `SKILL.md` body as a dataset-scoped skill node
Review Skill	`POST /api/v1/search` (`AGENTIC_COMPLETION`)	Run an agentic completion with the skill loaded so you can grade it
Propose Improvement	`POST /api/v1/remember/entry`	Record a weak run and create a `SkillImprovementProposal` (returns `proposal_id`)
Get Proposal	`GET /api/v1/proposals/{proposalId}`	Review the `old`/`proposed` procedure diff before approving
Apply Improvement	`POST /api/v1/remember/entry` (`apply=true`)	Apply the approved proposal into the skill
Get Skill	`GET /api/v1/skills/{skillId}`	Return the full skill, including its `procedure` body

Loop wiring: Ingest Skill → Review Skill → (score in n8n) → Propose Improvement → Get Proposal (show diff for approval) → Apply Improvement → Get Skill.

The Add Data / Cognify / Search / Delete resources call Cognee Cloud’s /api/* endpoints. The Skill resource calls /api/v1/*, available on a self-hosted Cognee server today and rolling out to Cognee Cloud. Point the credential Base URL at whichever backend exposes the routes you need (e.g. http://localhost:8000 for self-hosted).

Search Types

Choose the search type that best fits your use case:

GraphCompletion

Combines graph traversal with vector search for comprehensive retrieval. Best for:

General-purpose queries
Finding related entities and relationships
Balanced accuracy and context

{ "searchType": "GRAPH_COMPLETION" }

ChainOfThought

Uses step-by-step reasoning over the knowledge graph. Best for:

Complex multi-hop questions
Queries requiring logical inference
Detailed explanations

{ "searchType": "GRAPH_COMPLETION_COT" }

RagCompletion

Standard RAG (Retrieval-Augmented Generation) search. Best for:

Simple factual queries
Direct document retrieval
Fast response times

{ "searchType": "RAG_COMPLETION" }

Learn more about search here

Use Cases

Customer Support Automation

Build a support bot that learns from your documentation:

Add FAQ documents, guides, and knowledge base articles
Cognify to build searchable memory
Search with customer questions to retrieve relevant answers

Connect to Slack, email, or chat integrations in n8n for end-to-end automation.

Document Intelligence Pipeline

Process and query large document collections:

Use n8n’s file triggers to watch for new documents
Extract text and Add to cognee datasets
Schedule periodic Cognify runs
Build search interfaces or connect to chatbots

Research Assistant

Create a research workflow that accumulates knowledge:

Pull data from RSS feeds, APIs, or web scraping nodes
Add relevant content to topic-specific datasets
Cognify to extract entities and relationships
Search across all research to find connections

Sales Intelligence

Build memory from sales interactions:

Connect CRM triggers (HubSpot, Salesforce)
Add meeting notes, emails, and deal information
Cognify to build relationship graphs
Search for context before calls: “What do we know about Acme Corp?”

Example Workflow

Here’s a complete workflow configuration:

{
  "nodes": [
    {
      "name": "Add Data",
      "type": "n8n-nodes-cognee.cognee",
      "parameters": {
        "resource": "addData",
        "operation": "add",
        "datasetName": "support_docs",
        "textData": ["FAQ: Reset password via account settings.", "Guide: Export data as CSV from dashboard."]
      }
    },
    {
      "name": "Cognify",
      "type": "n8n-nodes-cognee.cognee",
      "parameters": {
        "resource": "cognify",
        "operation": "cognify",
        "datasets": ["support_docs"]
      }
    },
    {
      "name": "Search",
      "type": "n8n-nodes-cognee.cognee",
      "parameters": {
        "resource": "search",
        "operation": "search",
        "searchType": "GRAPH_COMPLETION",
        "datasets": ["support_docs"],
        "query": "How do I export my data?",
        "topK": 5
      }
    }
  ]
}

Troubleshooting

Issue	Solution
401/403 errors	Verify your API key is correct and active
Connection errors	Check the Base URL and network access from your n8n host
Empty search results	Ensure you’ve run Cognify after adding data

GitHub Repository

View source code and contribute

n8n Documentation

Learn more about n8n community nodes

Overview

Cloud LLM Providers

Observability

Evaluation

Data Ingestion

Agent Frameworks

Coding Agents

No-Code & Low-Code

Why Use This Integration

Installation

Credentials Setup

Quick Start

Step 1: Add Data

Step 2: Cognify

Step 3: Search

Operations Reference

Add Data

Cognify

Search

Delete

Skill

Search Types

Use Cases

Example Workflow

Troubleshooting

GitHub Repository

n8n Documentation

​Why Use This Integration

​Installation

​Credentials Setup

​Quick Start

​Step 1: Add Data

​Step 2: Cognify

​Step 3: Search

​Operations Reference

​Add Data

​Cognify

​Search

​Delete

​Skill

​Search Types

​Use Cases

​Example Workflow

​Troubleshooting

GitHub Repository

n8n Documentation

Why Use This Integration

Installation

Credentials Setup

Quick Start

Step 1: Add Data

Step 2: Cognify

Step 3: Search

Operations Reference

Add Data

Cognify

Search

Delete

Skill

Search Types

Use Cases

Example Workflow

Troubleshooting