Dify for Agents and Knowledge Bases
Dify is a platform for building applications based on language models. It lets you define agents, version prompts, and set up knowledge bases with retrieval-augmented generation without building the scaffolding from scratch.
At Coresis we use it when the problem is building product agents and the client's team needs to adjust content without depending on a developer for every change. It's the foundation of Labbu.
Main capabilities
Defined agents, not improvised ones
Every agent has a declared scope and an expected output format. That makes the result verifiable, which is the difference between a demo and a product.
Versioned prompts
Prompts are treated as configuration with history, not as loose text. You can roll back and compare behavior between versions.
Knowledge bases with RAG
Answers backed by the client's own documentation, updatable by the client's team without technical involvement.
Self-hosted deployment
It can be self-hosted, which means sensitive documentation never has to leave the project's infrastructure.
A license worth reviewing
Dify is distributed under a modified version of Apache 2.0 that doesn't allow operating a multi-tenant environment without written authorization or removing its branding from the console. It isn't OSI-approved open source, despite often being presented as such.
When we choose Dify
Dify fits when there are several scoped tasks to solve with specialized assistants and you want to iterate on prompts and knowledge quickly. It doesn't fit when the problem is governing delegated work with retries, recovery from partial failures, and evidence of every step: for that we use AquilaMesh, our own runtime.
Frequently asked questions
Is Dify open source?
Not strictly speaking. Its license is a modified version of Apache 2.0 with multi-tenant and branding restrictions. The code is visible, but those conditions matter if the deployment is going to serve several organizations.
Can it integrate with Drupal?
Yes. Drupal provides the content model and editorial management, and Dify exposes the agents via API. That's the combination we use in Labbu.
Does the data leave our infrastructure?
It depends on the model. Dify can be self-hosted, but if the agent queries a commercial model via API, that call leaves the perimeter. It's worth deciding this explicitly from the start.
Decision guide
How to evaluate Dify for agents and knowledge
Dify speeds up building applications with language models, flows, agents, and knowledge retrieval. It's useful for iterating with product teams, as long as evaluation, permissions, costs, data handling, and license restrictions are defined before putting the agent in front of real users.
Knowledge assistants
Answers backed by controlled documentation, with references and a declared scope.
Language-driven flows
Classification, extraction, or drafting within a process a person can review and approve.
Product iteration
Teams that need to compare prompts, models, and knowledge bases without deploying code for every adjustment.
How it fits into the architecture
Dify orchestrates the flow and exposes an API; documents live in a knowledge base; and the model can be your own or a provider's. The application needs to preserve identity, authorization, and a record of the source. In production you need evaluation sets, cost limits, handling of unsafe responses, and traces that don't leak sensitive data.
What to validate before adopting it
- Define allowed tasks and situations where it should abstain.
- Evaluate responses with real cases before and after every change.
- Separate secrets, personal data, and content authorized per user.
- Review license, model, storage, costs, and exit path.
When not to choose it
A deterministic integration doesn't get better by adding a model: for moving data between systems, n8n or code is usually clearer. If the product requires durable coordination, recovery from failures, and evidence of every step, it may need a specialized runtime. Dify's visual interface doesn't remove those obligations.
How Coresis applies it
Coresis uses Dify as the foundation for product agents in Labbu. For deterministic automation we combine it with n8n; for delegated work with greater control we evaluate AquilaMesh and the support of our AI agency.
Source and related technologies
Compare capabilities and limits in the official Dify documentation.