Dify vs Flowise vs Langflow: Which to Use for Production Agents

Dify vs Flowise vs Langflow: Which to Use for Production Agents

There is no single winner in Dify vs Flowise vs Langflow. The right choice depends on who will operate the agent, which license fits your use, and how much tracing you need to audit what the agent did. In practice, Dify looks most like an application platform with a console, Flowise like a lightweight visual builder in Node.js, and Langflow like a visual environment close to the Python ecosystem. In production, what matters is licensing, self-hosting, observability and action limits, not how easy it is to drag blocks around.

Key takeaways

• All three platforms speed up the prototype, but none replaces process decisions: what the agent can do, with which data, and when it hands off to a person.
• License is a selection criterion, not a legal detail for later. Read the license file of the exact version you will install, because the terms differ across the three and can change.
• Self-hosting gives you control over data, but it moves operations to your company: updates, backups, secrets, access and uptime.
• Observability decides whether you can explain a wrong answer. Require logs of inputs, steps, tool calls, cost and errors before you open the agent to customers.
• The best first step is a pilot with one real workflow, explicit limits and an agreed success metric. Only then decide whether to expand, adjust or stop.

What do these platforms do, and where do they differ?

Dify, Flowise and Langflow share the same idea: you build an agent workflow in a visual interface, connect a language model, a knowledge base and a few tools, and publish the result as an API or a chat. This cuts the code needed to reach a demo by a wide margin.

The differences show up in the shape of the product. Dify is usually presented as an AI application platform, with app management, a knowledge base, workflows and a logs area. Flowise is a visual builder written in Node.js, with chat flows and agent flows assembled on a canvas. Langflow is written in Python and leans on the library ecosystem of that world, which helps when your team already writes custom components.

A note on method: what is described here comes from each project's public documentation and general technical reading, not from our own measurements in a client environment. Features change quickly. Before you decide, confirm each item on the version you plan to install.

A useful reference for not confusing a tool with a solution is Anthropic's article on building effective agents. It argues for starting with the simplest pattern that solves the problem and adding autonomy only when the gain is clear. That holds for any visual platform.

Licensing: what to check before you choose

Licensing is where many platform decisions go wrong, because the technical team installs and tests the tool, and only months later does someone ask whether it can be used that way.

These are the points that tend to vary across projects like these, and that you should check in each project's repository and official documentation:

• Whether the license is a standard permissive one or a custom license with extra conditions.
• Whether there is a restriction on operating the platform as a service for multiple customers or multiple workspaces.
• Whether you must keep the tool's brand, logo or visual identification.
• Whether parts of the code, such as enterprise access and administration features, sit under separate commercial terms.
• Whether the vendor's paid hosted version has features the self-hosted version does not.

In our reading, the three projects are not in the same place on this point: Langflow has been distributed under a permissive license, Flowise under a permissive license with enterprise feature directories under commercial terms, and Dify under a custom license derived from a permissive one, with additional conditions. Treat this as a starting point, not legal advice. If you plan to resell, embed it in a product or serve several customers from one installation, ask for a legal review of the exact license text.

Your customer contracts matter here too. If the agent will use third-party models, align what is processed where, which connects to the AI clause in business contracts.

Self-hosting: who operates it, who updates it, where the data lives

Self-hosting is attractive for control and for data location. But the platform is only one part of the system. You become responsible for the database, file storage, task queue, certificates, backups, security updates and secrets management, such as API keys.

Before approving self-hosting, answer these questions:

• Who, inside the company or at a partner, updates the platform and tests the workflow after each update?
• Where are the model and integration keys stored, and who can read them?
• Who has access to the visual editor, which often allows changing the agent's behavior in production?
• Is there a separate environment to test changes before publishing them?
• Is there a restore plan if the database is lost?

Hosting the platform in Brazil does not settle the data question on its own, because the language model called by the workflow may be outside the country. In that case, the article on international data transfer under LGPD when using AI providers shows what to check. If the requirement is to keep everything inside your own infrastructure, the alternative is a local model, covered in running Ollama local models in a company, at the cost of operating and evaluating that model yourself.

Observability: can you explain what the agent did?

Observability, as defined in the OpenTelemetry observability primer, is the ability to understand a system's internal state from what it emits: logs, metrics and traces. For an agent, this means reconstructing an entire conversation: what the customer wrote, what context was retrieved, which tool was called, with which parameters, and what came back.

All three platforms offer some view of execution in the editor or console, and they usually integrate with third-party tracing tools. Confirm in the documentation of the version you use which integrations exist and whether they work in a self-hosted installation. Whatever you choose, require the following:

• A record of each run with date, input, output and conversation identifier.
• Detail for each step, including tool calls with parameters and results.
• Token counts and cost per run, so you can track spend by workflow.
• Error logging and response times.
• Defined retention: how long content is stored and who can read it, since conversation logs contain personal data.

That last point is often forgotten. The log that helps you debug is also a customer database, and it belongs in your data processing inventory. To standardize generative AI traces, there are the OpenTelemetry semantic conventions for generative AI, useful if you plan to send the data to your own monitoring tool.

What limits appear in production?

The visual editor hides complexity, but the complexity is still there. These are the most common limits that surface after the pilot.

Version control and publishing. Workflows built in the interface do not always handle change review, version comparison and rollback well. Check whether you can export the workflow, version it, and promote it from test to production in a controlled way.

Permissions. Ask whether there is a separation between who edits the workflow, who publishes it and who only reads logs. The tools the agent calls should have credentials with the least power needed, as the OWASP authorization cheat sheet suggests in general terms.

Loops and spend. An agent with access to tools can repeat steps endlessly. Set a cap on steps, time and spend per conversation, as described in AI agent infinite loop: step limits, time and budget caps.

Content security. Any text the agent reads, from an email, a web page or a document, may contain malicious instructions. See prompt injection protection for AI agents and the risk list in the OWASP Top 10 for LLM applications.

Evaluation. Visual platforms make manual testing easy, but a fixed set of test cases that runs on every change is something you have to build yourself.

Performance and scale. Confirm with a load test in your own scenario, not from the product page. Do not assume that what works for a few users will hold at peak.

How to decide: a checklist

Use the questions below in order. The first answer that rules out an option already saves you time.

• Will you resell it, embed it in a product or serve several customers from one installation? Then start by reading the license and ask for a legal review before any technical test.
• Is the operating team a business team that needs a console with application management, a knowledge base and ready-made logs? Dify tends to be the first to test.
• Is the team a JavaScript or TypeScript development team that wants something lightweight to embed in an existing service? Test Flowise.
• Does the team already write Python and want to build custom components that connect ecosystem libraries? Test Langflow.
• Is the process mostly deterministic, with AI in only one or two judgment steps? Consider a traditional workflow orchestrator, as in the n8n WhatsApp Cloud API workflow, and use AI as one step inside it.
• Do you need detailed auditing, or data that cannot leave your infrastructure? Demand the observability and hosting requirements above before comparing interface features.

Illustrative scenario: an electrical supplies distributor wants an agent that answers order questions and checks delivery status in its internal system. The IT team is small and knows Python. It builds the same workflow on two platforms over a few days, using a set of real, anonymized customer questions as the test set. It compares three things: whether it can see every call to the order system in the logs, whether it can restrict the tool to read-only access, and how much work it takes to update the platform. The choice comes out of that comparison, not from a feature list.

How the map, limit, pilot, hand over method applies

First, map: describe the current process, who does it, how long it takes and which errors occur, so you have a baseline. Second, limit: define which data the agent can see and which actions it can take, with human approval wherever there is risk. Third, pilot one real workflow, with logs and an agreed success metric. Finally, hand over: document, train whoever will operate it, and decide with evidence whether to expand, adjust or stop. The platform choice belongs in the limit and pilot stages, not at the start. A good reference for building the pilot's test cases is Anthropic's material on evaluating agents.

Frequently asked questions

Which of the three is best for someone just starting out?

It depends on who will operate it. For a business team that needs a console and application management, Dify is usually the most natural starting point. For development teams, Flowise or Langflow fit depending on the dominant language. Run a short test with the same workflow on the candidates.

Can I use any of them commercially without paying anything?

You cannot say that in general. The licenses have different conditions, and some restrict multi-tenant use or require keeping the brand. Read the license file of the installed version and, if the commercial use is significant, ask for a legal review. Infrastructure and model costs exist either way.

Does self-hosting these platforms solve LGPD compliance?

Not by itself. LGPD covers purpose, legal basis, minimization, security and data subject rights, and that depends on how the workflow uses data, including the models it calls. Self-hosting helps with control, but you still need to define log retention, access and any international transfer. The ANPD, Brazil's data protection authority, is the official source for guidance.

Is a low-code platform fit for production or only for prototypes?

It can work in production if the scope is limited and there is observability, permissions, testing and a named owner for operations. When the workflow requires strict versioning, automated tests on every change or very specific integrations, traditional code is usually safer. Many teams use the visual platform to validate the idea and migrate the critical parts later.

Sources

• Anthropic: Building effective agents
• OpenTelemetry: observability concepts
• OWASP Top 10 for LLM Applications
• Anthropic: Demystifying evals for AI agents