Dify

Connect Dify to cognee’s AI memory engine with a marketplace tool plugin. Add data, build a knowledge graph with cognify, and run semantic search directly from your Dify apps and workflows — no code required. There are two plugins depending on where your Cognee runs:

Plugin	Backend	Auth
Cognee (`cognee`)	Cognee Cloud (hosted)	API key
Cognee (Self-Hosted) (`cognee-sdk`)	Your own Cognee OSS server	Email + password

Cognee (Cloud)

Targets the hosted Cognee Cloud API.

Setup

Get your Base URL and API key from the Cognee Cloud dashboard.
Install the Cognee plugin from the Dify Marketplace.
Configure the plugin credentials:

Field	Value
`base_url`	`https://tenant-xxx.aws.cognee.ai/api`
`api_key`	Your Cognee API key

Tools

Tool	Description
Create Dataset	Create a new dataset (or return an existing one with the same name)
Add Data	Ingest text content into a dataset
Add File	Upload files (PDF, DOCX, TXT, …) into a dataset
Cognify	Process dataset(s) into a searchable knowledge graph
Search	Query the knowledge graph (14 search strategies, `top_k`, `only_context`, …)
Get Datasets	List all datasets for the authenticated user
Get Dataset Data	List all data items in a dataset
Delete Dataset	Permanently delete a dataset and its data
Delete Data	Delete a specific data item from a dataset

Cognee (Self-Hosted)

Targets a local or self-hosted Cognee OSS server. Tested with Cognee v0.5.5.

Run a Cognee server

services:
  cognee:
    image: cognee/cognee:0.5.5
    container_name: cognee-local
    ports:
      - "8000:8000"
    environment:
      - HOST=0.0.0.0
      - ENVIRONMENT=local
    volumes:
      - .env:/app/.env

pip install cognee==0.5.5
LLM_API_KEY=sk-your-openai-key-here python -m cognee.api.client

Provide your LLM_API_KEY (via .env for Docker) and start the server, then verify it:

curl http://localhost:8000/health   # should return HTTP 200

Setup

In the Dify plugins page, find Cognee (Self-Hosted) and configure:

Field	Value
`base_url`	`http://localhost:8000`
`user_email`	`default_user@example.com`
`user_password`	`default_password`

The plugin validates the configuration with a health check and login.

The plugin runs on your host (not inside Docker), so use localhost. If you run the plugin inside Docker too, use host.docker.internal. Change the default credentials before production use.

Tools

Tool	Description
Add Data	Add text data to a dataset
Cognify	Build memory from one or more datasets
Search	Search the knowledge graph for relevant information
Update Data	Replace an existing data item and re-integrate it into memory
Delete Dataset	Delete a dataset and all its data
Delete Data	Delete a specific data item

The self-hosted plugin authenticates with email + password (not an API key), adds an Update Data tool, and does not include the cloud-only Add File / Create Dataset tools.

Typical Workflow

Both plugins follow the same pattern inside a Dify app or workflow:

Add Data (or Add File) to ingest content into a dataset
Cognify to build the knowledge graph
Search before LLM calls to pull relevant context from memory
Delete Dataset / Delete Data to clean up

Trigger Cognee over HTTP (no plugin)

If you already run the conversational layer in Dify and just want to pull memory into a prompt with minimal setup, skip the marketplace plugin and call Cognee’s REST API directly from a Dify HTTP Request node. This works against any self-hosted Cognee OSS server — all routes live under /api/v1/*. See the HTTP API reference and the Deploy a REST API server guide.

1. Get a token

Unless the server runs with auth off (ENABLE_BACKEND_ACCESS_CONTROL=false), first exchange credentials for a JWT. POST /api/v1/auth/login expects form-encoded fields:

POST http://localhost:8000/api/v1/auth/login
Content-Type: application/x-www-form-urlencoded

username=default_user@example.com&password=default_password

The response is { "access_token": "...", "token_type": "bearer" }. Store access_token in a Dify variable and send it as Authorization: Bearer {{access_token}} on every later call. Tokens expire after JWT_LIFETIME_SECONDS (default 3600).

2. Search from your workflow

Add an HTTP Request node that queries memory before your LLM node, passing the user’s message as the query:

POST http://localhost:8000/api/v1/search
Authorization: Bearer {{access_token}}
Content-Type: application/json

{
  "search_type": "GRAPH_COMPLETION",
  "datasets": ["my-dataset"],
  "query": "{{sys.query}}",
  "top_k": 10
}

Set "only_context": true to get back just the retrieved context (instead of a Cognee-generated answer) and feed it into your own prompt. See search basics for the available search_type values.

To ingest and process data over HTTP too, use POST /api/v1/add (multipart form: data files + datasetName) followed by POST /api/v1/cognify (JSON: { "datasets": ["my-dataset"] }). Point the URL at whichever backend you deploy — http://localhost:8000 for a local server, or your hosted host name.

Cloud plugin source

View the Cognee Cloud Dify plugin

Self-hosted plugin source

View the self-hosted Dify plugin

Overview

Cloud LLM Providers

Observability

Evaluation

Data Ingestion

Agent Frameworks

Coding Agents

No-Code & Low-Code

Team Chat

Cognee (Cloud)

Setup

Tools

Cognee (Self-Hosted)

Run a Cognee server

Setup

Tools

Typical Workflow

Trigger Cognee over HTTP (no plugin)

1. Get a token

2. Search from your workflow

Cloud plugin source

Self-hosted plugin source

​Cognee (Cloud)

​Setup

​Tools

​Cognee (Self-Hosted)

​Run a Cognee server

​Setup

​Tools

​Typical Workflow

​Trigger Cognee over HTTP (no plugin)

​1. Get a token

​2. Search from your workflow

Cloud plugin source

Self-hosted plugin source

Cognee (Cloud)

Setup

Tools

Cognee (Self-Hosted)

Run a Cognee server

Setup

Tools

Typical Workflow

Trigger Cognee over HTTP (no plugin)

1. Get a token

2. Search from your workflow