Comparison · Build it yourself
Curiosity and Dify
Assemble the app, or build on the context
Curiosity is the system; Dify is an open source platform for building AI apps and agents.
Your data · your environment
Who each tool is for
Different problems, honestly stated
Curiosity
For industrial and regulated teams whose knowledge sits in engineering systems and structured records, where the answer depends on how those records connect, and where the deployment has to run on their own infrastructure.
Dify
An open source platform for building language model applications, agents and workflows on a visual canvas, with retrieval over uploaded documents and a choice of models. It can be self-hosted or used as a cloud service, and suits teams that want to assemble their own AI apps.
How Curiosity compares
Where the boundary falls
Dify makes the workflow quick to draw. What it leaves to you is the data underneath: connectors kept in sync, records joined across systems and permissions enforced on every query, which Curiosity ships.
The parts you would build
Connectors, incremental sync, a typed graph, hybrid retrieval and an interface come with the system rather than being the next quarter of work.
Permissions at query time
Access is synced from the source systems and enforced on every query, down to the record. In a framework that is code your team writes and maintains.
Your code still fits
Custom endpoints, AI tools and agents are written against the graph in C# or Python, so you keep the control a framework gives you.
Side by side
Feature comparison
The same five questions, answered for both.
Deployment
Cloud or self-hosted, ready to use
Self-hosted or vendor cloud
Data model
Managed context layer and knowledge graph
Workflows over uploaded documents
Industrial fit
Built for industrial and enterprise teams
Teams building their own AI apps
Data reach
70+ systems connected, and yours through a connector you write
Documents and tools you wire up
Residency
EU hosted, or entirely your own infrastructure
Your choice
Customization
What your team can change
Your own code, interface, data model and configuration. Dify as its own documentation described it in October 2026. Where it says Not available, the vendor documents no such capability.
Your own code
C# endpoints, AI tools and tasks, compiled in Studio
Python and JavaScript sandboxed code nodes, Python plugins
Your own interface
Your own front end in C#, deployed from git
Generated web apps, embeds, or your own via API
Data model
Typed node and edge schemas you define
Knowledge bases with metadata, no custom schema
Configuration as code
Every change committed to git, pushed to your remote
YAML DSL export and import, versions in app
APIs and SDKs
REST API, C# and Python library, connector SDK
REST API per app, difyctl CLI, plugin SDK
AI integration
What AI can build and reach
Whether AI can build on it, who reviews the result, and which models it runs. Dify as its own documentation described it in October 2026.
AI that builds
Sudo drafts endpoints, tools, schemas and screens
/create and /refine build workflows from a prompt
Review before it applies
Every change a diff a person approves, in a sandbox
Changes land in the draft until you publish
Model choice
Any major provider, or an OpenAI compatible endpoint
Marketplace providers, any OpenAI compatible endpoint
Models on your servers
Open weights on your servers, or air gapped
Self hosted, local models via the Ollama plugin
Assistant permissions
Built in tools search as the user, per record
Knowledge access set per workspace member, not end user
Agent protocols
MCP server and an OpenAI compatible API
MCP server and client
At Airbus, we developed the next generation of customer support AI assistance tools with Curiosity, enhancing our technical support’s ability to access and navigate vast data from multiple sources, including millions of documents.
Services Innovation Team
Airline Services at Airbus
Build it yourself
Other comparisons in this group
Neo4j
Curiosity gives you a finished context layer; Neo4j gives you a graph database.
CompareAmazon Neptune
Curiosity gives you a finished context layer; Neptune gives you a managed graph store.
CompareGraphwise GraphDB
Curiosity gives you a finished context layer; GraphDB gives you a semantic database.
CompareYour data. Your infrastructure.