Dify vs FastGPT vs Flowise vs RAGFlow vs Glean

The fastest way to make a poor enterprise knowledge decision is to put every product with “AI search,” “knowledge base,” or “RAG” on one feature checklist. These products often operate at different layers. One ingests web pages, another parses documents, another orchestrates an application, and another searches across existing enterprise systems while preserving source permissions.

An enterprise knowledge system is better understood as a supply chain. Data enters through controlled connectors, crawlers, or search APIs. Parsing and indexing turn that data into retrievable evidence. An application layer decides when to search, call a tool, or ask a user for clarification. A conversation layer delivers the result through a website, support channel, or voice workflow. Long-term memory, when it is genuinely needed, sits beside those components rather than replacing them.

This guide does not name a universal winner. It identifies the layer your organization needs, then compares products that solve that specific problem. Draw the supply chain first:

┌──────────────────────────────────────────────────────────┐
│ Delivery     Support channels / web chat / human handoff  │
│              Botpress · Dialogflow                        │
├──────────────────────────────────────────────────────────┤
│ Application  Orchestration / workflows / agents / APIs    │
│              Dify · FastGPT (Flow) · Flowise              │
├──────────────────────────────────────────────────────────┤
│ Retrieval    Chunking / vector index / recall / rerank /  │
│              citations — FastGPT · RAGFlow · Glean        │
├──────────────────────────────────────────────────────────┤
│ Parsing      PDFs / tables / scans / layout understanding │
│              RAGFlow                                      │
├──────────────────────────────────────────────────────────┤
│ Data         Internal docs · SaaS connectors · web crawl  │
│              · memory — Glean connectors · Firecrawl ·    │
│              Bocha API · Mem0                             │
└──────────────────────────────────────────────────────────┘

One product can span several layers (FastGPT covers retrieval through application; Glean covers data connection through retrieval), but each project’s bottleneck usually sits in one layer. Name the bottleneck layer first, then compare who is strongest there.

Quick Answer

Choose Dify when you need a broader LLM application platform that combines knowledge retrieval, agents, workflows, model access, and API delivery.
Choose FastGPT when the primary goal is a practical enterprise knowledge assistant, especially for Chinese-language teams that value open-source deployment and a direct route from documents to a usable application.
Choose Flowise when developers want a visual workspace for composing and testing LangChain or LlamaIndex components before committing to a coded architecture.
Choose RAGFlow when document understanding is the hard part: complex PDFs, tables, scanned files, contracts, manuals, or research reports.
Consider Glean when knowledge already lives across many enterprise SaaS systems and employees need permission-aware discovery. Glean is an enterprise search platform, not a general-purpose RAG builder.

If the team cannot yet describe the target user, source systems, permitted actions, and success metric, do not start with procurement. Start with 100 real questions, representative documents, and at least two permission roles. A short controlled pilot will reveal more than a long generic feature matrix.

First Identify the Requirement Layer

Document Q&A or an application platform?

“Let employees ask questions about the policy handbook” is a document Q&A requirement. “Answer the question, check the employee’s record, and create a service request” is an application workflow. The first emphasizes parsing, retrieval, citations, and content maintenance. The second also requires tool calls, state, authorization, error recovery, and deployment interfaces.

FastGPT is relatively direct for knowledge applications. Dify covers a broader set of LLM applications and workflows. Flowise gives developers more freedom to experiment with individual components. Those positions overlap, but they are not identical.

Complex documents or cross-system discovery?

If the source set consists of difficult PDFs, tables, scanned pages, and layout-heavy reports, test document parsing before anything else. That points toward RAGFlow. If the information is distributed across cloud drives, collaboration suites, ticketing tools, chat, and CRM systems, the central challenge becomes connectors, identity synchronization, inherited permissions, and cross-source ranking. That is the problem Glean is designed to address.

Internal content or the public web?

Internal content should enter through permission-controlled sources with traceable ownership. Public web content is a separate path. Firecrawl can crawl selected public pages and convert them into cleaner, model-ready content. Bocha AI Search can provide a Chinese web search API for applications that need current public information. Firecrawl is a web-data ingestion component, while Bocha is a public search component. Neither is an internal enterprise knowledge platform.

Do you need memory or a customer-service runtime?

Mem0 manages long-term user and agent memory across sessions. It can preserve preferences, history, or task state, but it does not replace a governed document knowledge base. For customer-facing conversations, channel delivery and deterministic transactions may matter more than another retriever. Botpress focuses on extensible business chatbots and support experiences, while Dialogflow is better aligned with Google Cloud, voice, contact centers, and controlled multi-step flows. Both can use knowledge, but neither should be confused with a dedicated RAG engine.

Core Comparison

Tool	Correct role	Best fit	Main tradeoff	Deployment view
Dify	General LLM application and workflow platform	Teams building knowledge apps, agents, and business workflows together	Broad product surface; deep retrieval and production controls still require deliberate engineering	Evaluate managed service for speed or self-hosting for control
FastGPT	Enterprise knowledge Q&A and RAG application platform	Internal assistants, support knowledge, sales enablement, and Chinese-language deployments	Direct path to delivery, but complex state machines and large-scale governance may need supporting systems	Open-source self-hosting or official service
Flowise	Low-code LLM and RAG component orchestration	Developer prototypes, architecture experiments, and embeddable APIs	Flexible canvas; large flows, versioning, and production operations can become difficult	Open-source hosting or cloud service
RAGFlow	Document-understanding RAG engine	Layout-heavy files, traceable answers, contracts, reports, and scanned material	Strong document focus, with infrastructure and operational requirements to test early	Often best evaluated by a capable self-hosting team
Glean	Permission-aware enterprise search	Mid-sized and large organizations with knowledge across many SaaS tools	Strong connectors and identity model; not a general RAG workflow builder	Enterprise procurement and implementation with connector and compliance review

Tool-by-Tool Selection

Dify: a common application layer

Dify makes sense when retrieval is one part of a larger application. A workflow can classify a request, query a knowledge source, call an external API, route by condition, and format a final response. That makes it suitable for organizations that expect an internal knowledge assistant to grow into a collection of departmental AI applications. For a hands-on start, follow this site’s Dify knowledge bot walkthrough to stand up a minimal knowledge Q&A app before committing further.

Do not evaluate it only by how quickly a demonstration can be assembled. Ask how department-level access is enforced, how workflow versions move through environments, how rollbacks work, and who owns credentials for models, plugins, and external services. If difficult document parsing is the main constraint, Dify can remain the application layer while a specialized service handles ingestion and retrieval.

FastGPT: a direct route to enterprise knowledge Q&A

FastGPT combines document ingestion, chunking, retrieval, source references, visual flows, and API delivery in a path that is easy to map to an internal assistant. It is particularly relevant for policy search, product support, sales materials, operating procedures, and other use cases where the business team needs to understand and maintain the knowledge collection.

Its delivery speed does not eliminate content work. If three versions of the same policy remain active, retrieval may faithfully surface all three. Every production collection needs authoritative status, an owner, an effective date, and an archival rule.

Flowise: a visual engineering bench

Flowise is useful when developers need to compare loaders, splitters, vector stores, retrievers, models, tools, and memory approaches without rebuilding the entire application after each experiment. Its visual graph also helps product and engineering teams discuss architecture using the same artifact.

The graph should not become an excuse to skip software discipline. As a prototype grows, define node naming, secret handling, environment separation, regression tests, and release ownership. After the pilot, reconsider whether the most critical paths should remain visual or move into maintained code.

RAGFlow: fix document interpretation first

RAGFlow is strongest when layout carries meaning. A fixed-size text splitter can destroy relationships between table cells, headings, footnotes, and paragraphs before retrieval begins. Once that information is lost, changing the chat model will not reliably restore it.

A realistic RAGFlow trial should include poor scans, rotated pages, cross-page tables, repeated headers, attachments, and conflicting revisions. Inspect parsed structure and citation location, not just fluent answers. Measure processing time, storage, resource use, and incremental update behavior as well.

Glean: enterprise search, not a general RAG builder

Glean addresses where information lives, whether the current employee may see it, and which result is most relevant in organizational context. Its value is tied to enterprise connectors, identity and group synchronization, source-system permission inheritance, cross-application ranking, and governance.

It is therefore misleading to compare Glean with Dify or Flowise by counting workflow nodes. If the organization wants to design a customer-facing RAG product with arbitrary retrieval and action logic, Glean is not the natural general-purpose builder. If employees waste time searching across many authorized systems and permissions must remain consistent, Glean becomes a serious candidate.

Deployment, Permissions, and Data Governance

Deployment is not a single “cloud versus on-premises” checkbox. Draw the complete data path. Where do original files, parsed copies, embeddings, vectors, prompts, generated answers, logs, and backups live? Which items leave the organization’s environment, and which vendors or model providers process them?

Self-hosting can improve control, but it transfers patching, scaling, monitoring, backup, disaster recovery, and security response to the internal team. A managed service reduces operational setup, but the organization must review retention, deletion, processing regions, subprocessors, support access, and contractual controls. The right answer depends on operational capability as much as data sensitivity.

Permissions must be enforced before retrieval. A prompt that says “do not reveal confidential information” is not authorization. Build pilot identities for ordinary employees, department administrators, project guests, and deactivated users. Test results, snippets, generated summaries, citations, caches, exports, and permission-revocation latency for every role.

Content governance determines whether quality survives after launch. Every important document needs an owner, audience, authoritative status, review date, and retirement policy. Public web ingestion must respect site terms, copyright, and privacy obligations. Long-term user memory needs its own rules for consent, inspection, correction, deletion, and retention.

Evaluation Method: Beyond the Demo

Create the evaluation set from real tickets, search logs, employee questions, and failed support cases. Include simple facts, multi-section synthesis, table questions, ambiguous requests, unanswerable questions, outdated policies, permission traps, and prompt-injection attempts. For each item, record an acceptable answer, authoritative evidence, and the roles allowed to access it.

Evaluate four separate layers:

Parsing: Did the system preserve headings, tables, page relationships, and metadata?
Retrieval: Did it locate the right evidence and exclude irrelevant or unauthorized material?
Generation: Did the answer stay faithful to evidence, cite it correctly, and refuse when evidence was insufficient?
Task outcome: Did the complete experience reduce work without creating unsafe actions or extra verification burden?

Track citation correctness, retrieval success, appropriate refusal, latency, permission incidents, human escalation, and user correction. A polished answer can still be wrong. Conversely, a concise refusal with the right source path may be the safest successful result.

Cost Beyond the Subscription

Total cost includes initial and incremental parsing, embeddings, vector storage, reranking, generation calls, object storage, network traffic, logs, monitoring, data cleanup, identity integration, implementation, and ongoing content operations. One visible user question may trigger several searches and model calls. Cost per successfully resolved task is more meaningful than cost per API request.

Open source does not mean zero cost. A team without reliable operations may exchange a subscription fee for outages, delayed upgrades, and security debt. Managed services can also be economical during discovery because they allow the organization to disprove a weak use case quickly. Compare complete operating models, not license labels.

A Practical Rollout Plan

Constrain the scope. Choose one department, one knowledge domain, and one measurable task. Do not begin with an “enterprise brain.”
Prepare representative evidence. Collect real documents, historical questions, permission roles, and unanswerable cases. Remove obviously obsolete material.
Run a same-task comparison. Test two tools with the same data, model policy, user roles, and scoring rubric.
Launch read-only first. Require visible citations and prevent high-risk write actions during the first production phase.
Create an ownership loop. Let users report wrong sources, missing knowledge, and failed answers. Assign people to resolve those reports regularly.
Add adjacent layers selectively. Introduce Firecrawl, Bocha, Mem0, Botpress, or Dialogflow only after core retrieval is stable, and evaluate each addition independently.
Operationalize. Add monitoring, budget alerts, versioning, rollback, backup, permission audits, retention controls, and a vendor exit plan.

FAQ

Is Dify or FastGPT better for an enterprise knowledge base?

FastGPT is a strong first test when the goal is a direct internal knowledge assistant, particularly for Chinese-language deployment and self-hosting. Dify is a better fit when the same platform must support several kinds of LLM applications, agents, and workflows. Real retrieval, permission, and maintenance tests should decide the final choice.

Can RAGFlow replace Dify?

Not in a simple one-for-one sense. RAGFlow emphasizes document understanding and retrieval, while Dify emphasizes application orchestration and delivery. A difficult-document project can use specialized retrieval beneath an application platform.

Is Glean a RAG development platform?

Glean uses search and AI-answering capabilities, but its primary role is permission-aware enterprise search across connected systems. It is not a general-purpose canvas for building arbitrary RAG applications. Evaluate it for cross-system discovery, identity, connectors, and governance.

Where do Firecrawl and Bocha fit?

Firecrawl retrieves and cleans selected public web pages. Bocha provides Chinese public-web search results through an API. They are external information inputs. They do not provide internal document ownership, source permissions, or complete enterprise knowledge governance.

Does every knowledge assistant need Mem0?

No. A document Q&A system should first retrieve governed evidence reliably. Add a memory layer only when the product must preserve user preferences, relationship history, or long-running tasks across sessions. Memory also requires correction, deletion, isolation, and retention controls.

Why consider Botpress or Dialogflow for customer support?

RAG supplies evidence, but customer service also needs channel delivery, identity, deterministic transactions, failure recovery, and human handoff. Botpress is oriented toward extensible visual chatbots and web support. Dialogflow is more aligned with Google Cloud, voice, contact centers, and controlled multi-step flows.

Is self-hosting always safer?

No. It can reduce some third-party data exposure, but security still depends on patching, identity, secrets, logs, backups, network controls, and operational discipline. Poorly maintained self-hosting can be riskier than a well-governed managed service.

When is a RAG pilot ready for production?

It is ready for controlled production when citations and refusals meet the business threshold on real questions, role-based access tests pass, failures have a human path, latency and cost fit the budget, and named owners maintain the source content. A successful demonstration alone is not enough.

Official Sources and Verification

Dify: dify.ai with official docs and the open-source repository, access verification attempted 2026-07-24.
FastGPT: fastgpt.io and the open-source repository, access verification attempted 2026-07-24.
Flowise: flowiseai.com and the open-source repository, access verification attempted 2026-07-24.
RAGFlow: ragflow.io and the open-source repository, access verification attempted 2026-07-24.
Glean: glean.com and official product docs, access verification attempted 2026-07-24.
Firecrawl, Bocha, Mem0, Botpress, Dialogflow: official sites and docs, access verification attempted 2026-07-24.

Feature boundaries, deployment options, and commercial terms change frequently; this article pins no prices or configuration figures. At procurement time, the official docs and contract of that day govern.

Bottom Line

There is no context-free winner. Dify provides a broad application and workflow layer. FastGPT offers a direct route to enterprise knowledge Q&A. Flowise is a visual engineering environment for rapid composition. RAGFlow focuses on difficult document understanding. Glean provides permission-aware enterprise search across existing systems and should not be mistaken for a general RAG builder.

Firecrawl, Bocha, Mem0, Botpress, and Dialogflow can add public-web ingestion, Chinese search, long-term memory, or customer conversation delivery. They belong to different layers, and combining them only makes sense after the core requirement is clear. Define the layer, test with real data and real permissions, then include governance, evaluation, and operations in the decision. That sequence produces a durable knowledge system rather than an impressive but fragile demo.