Picture an after-sales team for industrial pumps. About thirty people, half of them engineers who spend most of the year on customer sites. Every day the group chat fills with the same handful of questions. Which mechanical seal fits this model. What did we finally do about that unit that kept leaking oil. The answers exist somewhere, spread across more than two hundred PDF manuals, a few thousand maintenance records, and the memory of two or three senior technicians who are not getting any younger.
The technical lead built a Q&A page with FastGPT, loaded the manuals into it, and within a week the support staff were looking up part numbers in plain language instead of scrolling through PDFs. That should have been the end of the story.
Then came the quarterly meeting. The boss asked whether the same thing could be opened up to dealers, so they could log in and ask their own questions. The technical lead went back to the repository, opened the LICENSE file, and found Apache 2.0 followed by two additional conditions. One of them landed squarely on the phrase “multi-tenant SaaS.” He was not sure whether the dealer idea crossed that line, so he sent over the repository link and asked.
The shape of that question repeats across teams, even when the sticking point moves. Some people are stuck because scanned documents come out as garbage. Some are stuck because nobody in the company knows how to maintain the thing six months after it goes live. Some are stuck because corporate IT insists every system authenticate against the internal directory. This article is not a specification comparison. It is about which differences actually change the outcome when you decide to replace FastGPT.
One note before the stories start. Every team described below is a hypothetical assembled to make a decision visible. None of them corresponds to a specific company.
What you are really replacing
The first line of the FastGPT README (https://github.com/labring/FastGPT) describes it as an AI agent building platform, with data processing and model calls that work out of the box, plus Flow for visual workflow orchestration.
The knowledge base is still the center of gravity in that feature list. Multiple libraries can be reused and mixed. Chunks can be edited and deleted one at a time. Documents come in through manual entry, direct segmentation, or QA pair import, and the supported formats run from TXT, MD, HTML and PDF through Docx, PPTX, CSV and XLSX. It reads URLs and takes CSV batches. Retrieval is hybrid, with reranking on top.
That list is dense enough to blur, so pick one item and look at it closely. Chunk-level editing is the one people who have used it understand immediately. Document parsing fails constantly. Tables get sliced into fragments, page headers bleed into body text, two-column layouts get read straight across the gutter. An interface that lets you fix that one broken passage yourself is worth more than any adjective on a product page.
There is a second layer that gets less attention, and it is operational. A share window that needs no login. An iframe embed that takes one line. Conversation logs you can review and annotate in one place. When a department manager with no technical background wants to hang a Q&A box on her team’s page, these are the pieces she depends on. It is also why small teams find FastGPT comfortable the first time they use it. It does not hand you an API and wish you luck with the interface. The interface, the permissions and the sharing are already built.
Which means “replacing FastGPT” can mean two different projects. If all you want is the window that answers questions, there are many options and the bar can be low. If what you want is the whole package, something you can hand to someone else and expect them to use, the list gets shorter fast.
What the two extra conditions actually restrict
Apache 2.0 on its own is permissive. Commercial use, modification, redistribution, plus an explicit patent grant. Three of the four projects discussed here use it, and two of those three added conditions after the standard text.
FastGPT added two. The first says that without written authorization you may not use its source code to operate a multi-tenant SaaS service similar to FastGPT. The second says you may not remove or alter the LOGO and copyright notices in the console during use. The same LICENSE also spells out what is allowed: running it as a backend service for your own application, or delivering it to an enterprise as an application development platform, both count as permitted commercial use. Licensing questions go to [email protected], and the copyright holder is listed as Sealos.
One detail deserves to be pulled out on its own. The FastGPT README summarizes the two conditions as “permitted to be used commercially as a backend service, but not permitted to provide SaaS services.” That sentence is considerably narrower than the LICENSE text, which limits itself to multi-tenant SaaS similar to FastGPT rather than any contact with the word SaaS at all. When the question actually matters, the LICENSE text governs. The README is written for skimming. A summary that reads stricter than the original is not unusual, but it does scare people away from uses that were never prohibited.
For the pump team, the question sits right there. Opening a door for dealers, does that count as multi-tenant SaaS? If each dealer gets a separate deployment and a separate database, that is probably not the same thing. If it is one instance with accounts separated by permissions, then it needs a conversation. That judgment should not be made by the technical lead alone, and it should not be made by an article either.
Dify wants to be the front door for every AI app in the company
Change the scene. A cross-border ERP company, a bit over two hundred people, wants marketing, support and after-sales to each build their own AI applications without every department filing a separate request for a server. They picked Dify (https://github.com/langgenius/dify).
Dify describes itself as an open source LLM application development platform, and it puts AI workflows, the RAG pipeline, agent capabilities, model management and observability into a single interface. The divisions are clean. A visual canvas for building workflows and testing them in place. Connections to dozens of model providers, including anything that speaks the OpenAI API. A prompt IDE for tuning instructions and comparing how different models respond. A RAG pipeline that covers the path from document ingestion to retrieval, with extraction for common formats such as PDF and PPT available out of the box. Agents with their own sandbox, able to run commands, install software and handle files. LLMOps logging and annotation on top. Every one of those features has an API.
The list overlaps with FastGPT, but the feel is different. FastGPT’s entry point is the application. You create one, configure its knowledge base and its flow, and publish it. Dify’s entry point is closer to a workbench. You accumulate capabilities there first, then combine them into applications. The first suits “I need to build a Q&A tool.” The second suits “our company needs to have a pile of AI applications.”
The minimum configuration is stated plainly in the documentation: two CPU cores and 4 GiB of memory. Bring it up with Docker Compose, then open http://localhost/install to run the initialization. That is the lowest bar among the four, low enough for an ordinary cloud host and low enough to try on an idle office machine.
On licensing, Dify is also a modified Apache 2.0 with two conditions attached. The multi-tenant clause says one workspace counts as one tenant, and operating a multi-tenant environment from the source code requires written authorization. The frontend clause says that when you use its frontend, meaning the web directory in the source or the Docker web image, you may not remove or modify the LOGO and copyright notices in the console and in applications. It then adds a sentence that matters: uses that do not involve the frontend are not subject to this restriction. The copyright holder is LangGenius, Inc.
Put the two sets of terms side by side and the difference is not whether a restriction exists. It is where the boundary sits. Dify’s logo clause carries an exemption, so if you write your own frontend and only call its API, that clause does not apply to you. FastGPT’s corresponding clause has no such sentence. On multi-tenancy, both require written authorization, but one scopes it to services similar to FastGPT while the other scopes it to multi-tenant environments, and the second is wider.
Langflow is a box of blocks you can open in Python
The third scenario is a different kind of problem. A person with a data science background has a pile of technical documents in inconsistent formats. Before anyone talks about procurement, she wants to find out whether this chunking strategy and this retrieval setup actually work. She does not want to be boxed into a product’s data model on day one, so she uses Langflow (https://github.com/langflow-ai/langflow).
Langflow positions itself as a platform for building and deploying AI agents and workflows. What it offers is visual editing plus a built-in API and MCP service, so every workflow can become a tool that another system calls. It ships with support for mainstream models and vector stores. The interface is nodes and wires, and behind each node is Python. If you want to change something, you change the source. You can also export a flow as JSON and drop it into a Python application.
Installation is the lightest of the four. It is a Python package, and the README asks for Python 3.10 through 3.14. Once running, you reach it on port 7860 on the local machine. A single Docker container works too. Released versions ship with automatic login disabled, so you set an administrator password before the first start. There is also a desktop edition with the dependencies bundled, which spares you from managing a Python environment at all.
Its license is MIT. That is the least friction of the four. Commercial use, modification and closed-source distribution all work without extra conversation, as long as the copyright notice stays.
The cost sits on the other side. Langflow has no knowledge base management console in the FastGPT sense. There is no document list that anyone on the team can use, no chunk preview, no hit testing page. You build those out of nodes. What you get is a set of blocks and a canvas, and the result depends on what you assemble. For the data scientist, that is exactly right, because control is the point. For a team that wants the support supervisor to upload documents herself, it is too much machinery, because in the end somebody has to wrap the flow into a product.
This is where selection goes wrong most often. GitHub stars put these projects next to each other in a list, but Langflow and Dify are not the same category of thing. One is a toolbox. The other is an office.
RAGFlow is built for the cases where a wrong answer costs money
The last scenario is the least comfortable. An engineering contract review firm works with documents that are mostly scans, tables spanning multiple pages, and handwritten annotations in the margins. The Q&A system they want sits in a context where one wrong sentence can create liability. In that setting, roughly right and wrong carry the same weight.
RAGFlow (https://github.com/infiniflow/ragflow) aims at that ground. Its core claim is knowledge extraction built on deep document understanding, made specifically for complex unstructured data. In practice that means template-based chunking, where the split is visible, explainable and open to human correction. It means knowledge compilation, which turns documents and datasets into structured artifacts such as a wiki, a graph, a tree, a timeline or a mind map. It means agentic retrieval, where a complicated question gets decomposed first, then searched, then checked against the evidence, with four levels of thinking depth to choose from. And it means citations that trace back to a specific block of text.
The hardware bar is the highest of the four. The documentation recommends starting at four CPU cores, 16 GB of memory and 50 GB of free disk, with a note that real requirements depend on the document engine, the volume of data, parsing tasks and concurrency. Docker needs to be 24.0 or newer and Compose v2.26.1 or newer. A deployment pulls up a chain of dependencies underneath: a document engine, either Elasticsearch or Infinity, plus MySQL, MinIO, NATS and Kvrocks. If you choose Elasticsearch, you also have to raise the host’s vm.max_map_count to 262144 before anything works.
That chain looks intimidating the first time you read it. It is also the price of handling bad documents. Layout analysis, OCR and table recognition all need compute and components behind them.
The license is standard Apache-2.0 with no additional terms, which makes it cleaner than FastGPT and Dify on this point. One caution: the components underneath carry their own licenses. The document engine and the object storage each have their own terms, and a commercial deployment should confirm those separately.
Putting the four on one table
At this point the four stories have answered the same set of questions. How does it install. What can it do once installed. Who can legally take it and sell it. How long does a newcomer need before the thing works end to end. So the table compares those five dimensions and does not pile up feature checkboxes. Stars are left out on purpose, because they explain nothing here. At the time of writing Dify sits at just over 159,000 stars and Langflow at just over 156,000, numbers nearly touching, yet one is a platform for building applications and the other is a set of blocks for assembling flows. Ranking them together would only mislead.
| Project | License | Deployment | Focus of knowledge base handling | Onboarding bar | Best fit |
|---|---|---|---|---|---|
| FastGPT | Apache 2.0 with additional terms (multi-tenant SaaS requires authorization; console LOGO and copyright must stay) | One script brings up Docker Compose, default port 3000 | Chunk-level edit and delete, QA split import, hybrid retrieval with reranking, API knowledge bases | Low, no minimum configuration published, an ordinary cloud host is enough | Small teams that want a deliverable Q&A app quickly and need sharing and embedding |
| Dify | Modified Apache 2.0 (multi-tenant requires authorization; restrictions apply to the frontend only, pure API use is exempt) | Docker Compose, minimum 2 cores and 4 GiB | Full pipeline from document ingestion to retrieval, common format extraction works out of the box | Low to medium, more concepts than FastGPT | Mid-sized teams with several departments sharing one platform, needing workflows, agents and logs |
| Langflow | MIT | Python package or a single container, desktop edition available | No knowledge base console, chunking and retrieval are assembled from nodes | Medium, fast if you know Python, blocked if you do not | Teams with development capacity that need deep customization or embedding into their own systems |
| RAGFlow | Apache-2.0 | Docker Compose with several dependencies, 4 cores, 16 GiB and 50 GB suggested as a start | Deep document understanding, template chunking, visual intervention, traceable citations | High, requires tuning system parameters and configuring models and a document engine | Scenarios where documents are mostly scans and complex tables and a wrong answer is expensive |
Two things the table cannot show. The first is that the onboarding column is not about installation difficulty. It is about how long it takes to get from a fresh install to something you can put in front of another person. FastGPT and Dify both come up with a single compose file, but Dify carries more concepts. Workflows, applications, knowledge bases and model providers are four separate things, and working out how they relate takes a while.
The second is that all four projects are active. On the day this was written, all four repositories had commits. That matters, because it means you are unlikely to pick something that nobody maintains next year. The risk in this decision lives somewhere else.
The license column deserves a second pass
Each section above mentioned licensing once. Line the four up and look at them together.
The Apache 2.0 baseline is this: commercial use, modification, distribution and a patent grant, with the requirement that copyright and license notices stay in place. FastGPT and Dify both add two conditions on top, pointing the same direction, multi-tenancy needs authorization and the LOGO stays. The differences come in three places.
The first is where the LOGO clause applies. Dify includes the sentence about uses that do not involve the frontend, which means you can run its backend, write your own interface and put your own brand on it. FastGPT has no such exemption. Its clause says the LOGO and copyright in the console may not be removed or modified in the course of using FastGPT. If you want to deliver a white-label product to a customer, that clause stands in front of you and needs to be discussed.
The second is the definition of multi-tenancy. Dify equates tenant with workspace, one workspace is one tenant, and the standard is clear. FastGPT uses the phrase multi-tenant SaaS similar to FastGPT, and the word similar leaves room for interpretation. That room is not good for either side, because it means you have to go and ask.
The third is how permissive the whole agreement is. Langflow’s MIT imposes almost nothing beyond keeping the copyright notice, so commercial use, modification and closed-source distribution all pass. That is part of why it turns up as a foundation under commercial products. RAGFlow is plain Apache-2.0. If your business model is itself selling an AI platform to other enterprises, those two carry the least licensing friction.
There is a layer many people miss. These systems are not monoliths. Underneath them sit databases, vector stores, document engines and object storage. An open source license covers the project’s own code, and the components below have their own licenses, some of them split between community and commercial editions. RAGFlow’s document engine can be either Elasticsearch or Infinity, and those two do not carry the same terms. For a real commercial deployment, that layer has to be confirmed piece by piece. The Apache-2.0 badge on the project homepage is not the whole picture.
The same applies to everything written above. It reflects a reading of the LICENSE files, not legal advice, and it is not an interpretation binding on anyone. If you plan to turn one of these into a paid service, paying a lawyer for one review is cheaper than reading ten comparison articles.
Getting it installed is not the same as keeping it running
On hardware, official numbers are more reliable than any review, so here they are as published. Dify states a minimum of two cores and 4 GiB. RAGFlow recommends starting at four cores, 16 GiB and 50 GB of disk, and notes that the real requirement depends on the document engine, data volume and concurrency. The FastGPT README gives no minimum configuration, only the sequence of pulling the config and running compose. Langflow is a Python package and makes no hardware statement at all.
The gap between those numbers is not only money. Two cores and 4 GiB means the thing can live on the idle virtual machine the company already owns, no procurement required. Four cores and 16 GiB means requesting a dedicated machine, and possibly explaining to a budget owner why a question answering system needs that much. For teams watching costs, this column decides the outcome more often than the feature comparison does.
What gets underestimated more easily is the long run. RAGFlow carries the most dependencies of the four. FastGPT and Dify sit in the middle. Langflow has the fewest, and depends on you the most. More components is not automatically worse, because they buy the ability to process difficult documents. Fewer components means some capabilities have to be filled in by hand. The real cost arrives six months later. Who applies the upgrade. Whose logs do you read when something breaks. Who adjusts the chunking when the document format changes.
Then there is the question of where the model comes from, which has to be settled early. A team will typically connect a cloud provider API or run a self-hosted inference service. The hidden cost is that switching models usually means retuning prompts and retrieval parameters, and almost nobody budgets for that during selection.
Choosing by situation
If the goal is turning internal documents into a window that answers questions, quickly, and embedding that window into systems you already run, then the choice is between FastGPT and Dify. Installation difficulty is close. The difference is shape. If you want an application you can send out tomorrow, FastGPT already has sharing, embedding and conversation records. If you want a bench that many applications can grow from, Dify’s workflows, agents and logging suit collaboration better. On licensing, internal use is not a problem for either one. The additional terms target operating a multi-tenant service for outside users.
If the documents are mostly scans, contracts, drawings and tables that span pages, and a wrong answer creates liability, then RAGFlow’s deep document handling and traceable citations are the real differentiator. That is worth requesting the 16 GB machine for. The reverse also holds. If your documents are clean Markdown and web pages, putting RAGFlow in front of them is like buying a truck to pick up groceries.
If someone on the team writes Python and the plan is to embed this into your own product rather than use it as off-the-shelf software, then Langflow’s freedom and its MIT license will save a lot of conversations. The test is plain enough: can you assemble a working flow with it inside two weeks. If you cannot, it does not fit the team you have right now.
If the goal is selling the service itself, for example an industry Q&A platform sold downstream, then licensing belongs on the table first, not after the product is finished. In that order, Langflow and RAGFlow create the least friction. FastGPT and Dify mean contacting the vendor early for a commercial license and getting the boundaries written down.
One last point that may not be welcome. FastGPT itself is doing fine. The repository had new commits on the day this was written, and the feature set keeps moving. The real cost of switching platforms is not installation. It is data migration, retuned prompts, rebuilt flows, and a team learning a new interface. If your situation has not changed and you have only heard about another tool, that is not a need to replace anything. That is a reason to look around a little. The signals that actually justify a move tend to be specific: licensing blocks what you want to do, document handling cannot keep up, or the current shape cannot carry what you need to deliver.
Related reading
- umami vs matomo: A Deep Dive Into Self-Hosted Analytics in 2026
- OpenObserve vs SigNoz: Which Self-Hosted Observability Stack Fits a Small Team?
- Why Developers Are Moving Away from Auth0 to Self-Host SuperTokens
- When Documents Pile Up: WeKnora and the Open-Source Platforms Trying to Make Knowledge Actually Useful
- Sim vs n8n vs Dify: Which AI Agent Workflow Builder Fits a Small Team in 2026?
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