Dify App Wrapper

Problem

Dify exposes application patterns such as Workflow, RAG, and Agents, but manual use in the web UI is hard to fold into existing Python services, scheduled jobs, or automation pipelines.

How It Works

Dify Wrap presents Dify as a Python-callable integration layer:
- Workflow: trigger an existing workflow with structured input and read its output
- RAG: connect knowledge-base question flows
- Agents: call agent applications
- LLMOps / REST API: automate against platform interfaces

The typical flow is to issue a request from application code, assemble the parameters required by the Dify app, parse the response into structured data, and then use it for routing, storage, or notification. This makes Dify behave less like a demo-only tool and more like an embeddable system component.

Boundaries

It targets Dify and does not provide a standalone inference runtime; it assumes an available Dify service, configured apps, and access permissions. Private deployments still require endpoint, credential, and network setup. If the goal is only local model calls or moving away from Dify-managed apps, treat it as a Dify integration helper rather than a general-purpose LLM SDK.