Dify, n8n or custom development for an internal AI agent
Dify is a useful starting point for an AI application with models and knowledge. n8n starts from workflows that connect services and actions. Custom code becomes relevant when access rules, action control or operations exceed what the chosen platform can support. Test the choice on one real workflow.
updated 9 min read
Which workflow should you compare first?
Pick a task with a known input and outcome. An employee might ask for a contract status; the system reads the record, checks access and suggests the next action. List the systems, users, permitted actions and cases requiring human review. Build the same small workflow with each candidate and compare the entire request path.
- What data does the agent read, and on whose behalf?
- Which actions change data, and who approves them?
- Where do action logs, secrets and backups live?
- What happens on a retry or an external system failure?
When does Dify fit?
Dify provides a visual way to assemble AI applications, connect models and knowledge, test a workflow and publish it through an interface or API. It shortens the path to a model-centred use case. Self-hosting is possible, but hosting location alone does not define access to connected business systems.
If you need separate workspaces, SSO, specific roles or central governance, check each requirement against the chosen edition and version before committing to the architecture. Feature sets and licence terms change; a comparison article cannot replace that check.
When does n8n fit?
n8n builds workflows from events, actions and service integrations. It fits a process that receives an event, reads several systems, calls a model at a defined step and writes the result back. The workflow carries the task while the model handles text or judgement within it.
For an internal agent, verify workflow permissions, sharing, SSO, promotion between environments and secret handling separately. n8n documents some of these as plan-dependent. Test the exact deployment you intend to buy or host.
When is custom code justified?
Custom code is justified when the tested workflow needs controls the available edition cannot provide: user-scoped access, approval for specific actions, safe retries, a detailed audit trail or a particular deployment process. Write those requirements down and test them. A complex prompt alone is not a reason to build a platform.
n8n vs LangGraph, and where does CrewAI fit?
This is a choice between a visual workflow builder and a code framework. In n8n a process is assembled from nodes on a canvas, with AI steps placed between integrations. LangGraph is a Python library where the agent is written in code as a graph of steps over shared state. Its documentation positions it for long-running, stateful agents that resume after failures and pause for human approval.
CrewAI is also a Python code framework, built around role-based agents, their tasks and flows. Compare it with LangGraph rather than with n8n: both need developers and tests in a repository. Without engineers to maintain the code, a code framework becomes custom development with all of its costs.
| Question | n8n | LangGraph / CrewAI |
|---|---|---|
| Who changes the workflow | An analyst or engineer in a visual editor | A developer, through code and review |
| Main strength | Ready integrations and explicit steps between services | Complex agent logic, state, branching and step control |
| How to test | Workflow runs and exporting the workflow to version control | Regular tests in the repository plus an evaluation set |
| Main risk | Agent logic spreads across canvas nodes | You need a team to maintain the code |
What belongs in the total cost?
Include build and maintenance work, infrastructure, model usage, platform upgrades and incident response. A ready-made tool reduces initial work, while integrations and operations remain. Custom code requires more work up front and can enforce the controls your workflow needs. The result depends on how many workflows you run and what an error costs.