Best Dify Alternatives in 2026

TL;DR

Sim is the best overall Dify alternative in 2026 for teams that need broader workflow automation, tool-using agents, permissive Apache 2.0 licensing, and MCP support in both directions. Choose n8n for integration-heavy technical automation, LangChain and LangGraph for code-first agent control, RAGFlow for document-heavy retrieval, and Langflow for Python-based visual LLM pipelines.

Dify remains a strong choice when prompt iteration, knowledge retrieval, and packaged LLM applications define most of the workload. Look beyond Dify when you need wider business-system automation or a standard permissive license without Dify's added multi-tenant and branding conditions.

Quick answer

• Best Dify alternative overall: Sim
• Best for broad technical automation: n8n
• Best for code-level agent control: LangChain and LangGraph
• Best for document-heavy RAG: RAGFlow
• Best for Python visual LLM pipelines: Langflow
• Best when packaged RAG and prompt iteration remain the priority: Dify

How we ranked the best Dify alternatives

This ranking evaluates each platform against the same five buyer-relevant criteria: license, workflow depth, agent and MCP capability, deployment control, and commercial cost. We ranked broader automation and agent-building capability first because this page is for buyers who have already identified a reason to look beyond Dify's LLM-app and RAG focus.

• Author: Andrew Caslow
• Affiliation: Sim
• Review basis: Official vendor documentation, pricing pages, license files, product documentation, and vendor-maintained repositories
• Facts checked: August 26, 2026
• Ranking rule: The highest-ranked product must provide the strongest overall fit across the published criteria, not merely the closest feature match to Dify

Prices, plan limits, product status, and license terms can change. All changing claims below were checked against primary vendor sources on the fact-check date.

What should you look for in a Dify alternative?

The right Dify alternative should solve the specific limitation that caused you to leave Dify without creating a larger licensing, deployment, or operational problem. Evaluate these five criteria before choosing a platform.

License and commercial rights

A standard permissive license creates fewer product-specific restrictions than Dify's modified Apache terms. Dify's license requires written authorization to operate a multi-tenant service from its source and prevents removal or modification of the Dify console logo and copyright notices. Compare those conditions with Apache 2.0, MIT, or a source-available fair-code license based on your intended use.

Workflow depth

Workflow depth measures whether a platform can coordinate APIs, business tools, structured data, branching, schedules, and event-driven processes in addition to model calls and retrieval. Dify may remain the better fit when the application is primarily a prompt, knowledge base, or chatbot experience.

Agent building and MCP

Strong agent tooling should support tool use, controlled execution, and interoperable deployment. Model Context Protocol support matters when workflows must consume external tools, publish capabilities to other AI applications, or do both.

Deployment and self-hosting

A credible self-hosting path should include documented infrastructure requirements and a clear distinction between open-source, cloud, and enterprise features. Also confirm whether production workflows can be exposed through APIs, chat interfaces, or MCP tools.

Pricing and operating cost

Compare the billing unit, not just the headline subscription. Credits, workflow executions, seats, traces, compute units, model tokens, and self-hosting infrastructure produce different cost curves.

Key facts at a glance

• Sim: Sim is Apache 2.0 open source, supports documented self-hosting, connects to 1,000+ integrations, works as an MCP client and server, and deploys workflows as APIs, chat pages, or MCP tools.
• n8n: n8n is source-available under Sustainable Use License Version 1.0 and is strongest for integration-heavy technical automation billed by completed workflow executions on its cloud plans.
• LangChain and LangGraph: LangChain and LangGraph are MIT-licensed code frameworks for developers who want explicit control over agent state, branching, retries, persistence, and human review.
• RAGFlow: RAGFlow is an actively maintained Apache 2.0 RAG engine and agent platform with Docker-based self-hosting and public cloud tiers.
• Langflow: Langflow is an MIT-licensed visual Python platform for LLM and RAG pipelines, and DataStax is now presented by IBM as an IBM company.

1. Sim

Best for

Best for: Teams that want visual workflow automation and AI agents in one permissively licensed workspace.

What it is

Sim combines deterministic workflow steps and model-driven agents in the same visual graph. Teams can connect 1,000+ integrations and keep predictable operations separate from decisions that require model judgment.

Sim is Apache 2.0 open source and has documented Docker and Kubernetes self-hosting. It supports MCP in both directions: agents can use tools from external MCP servers, and completed workflows can be deployed as MCP tools. A workflow can also be deployed as a REST API or hosted chat page.

This makes Sim a broader automation alternative rather than a clone of Dify. Dify remains more specialized around prompts, retrieval, and packaged LLM applications; Sim is designed for workflows that must coordinate AI decisions with business systems and repeatable operational logic.

Pros

• Apache 2.0 provides standard permissive rights for use, modification, redistribution, and self-hosting.
• One graph can combine fixed workflow logic with tool-using AI agents.
• Sim publishes support for 1,000+ integrations across business and developer services.
• MCP client and server support covers both consuming external tools and publishing workflows as tools.
• API, chat, and MCP deployment let one workflow serve multiple product surfaces.
• Ollama and vLLM can be used as local or BYOK model options; Sim's cost documentation lists those local model paths as free from Sim model charges.

Cons

• Credit usage varies with workflow design and non-model services, so teams should monitor consumption as volume grows.
• Enterprise administration features such as SSO and access control require a custom Enterprise plan.
• Teams focused almost entirely on document retrieval may prefer Dify's packaged RAG workflow or RAGFlow's document-first architecture.
• Code-first engineering teams may prefer LangGraph's explicit state and execution model over a visual workspace.

Pricing

As of August 2026, Sim pricing is Free at $0 with 1,000 credits per month; Pro at $25 per user per month with 6,000 monthly credits and a 50-credit daily refresh; Max at $100 per user per month with 25,000 monthly credits and a 200-credit daily refresh; and Enterprise at custom pricing with custom credits. The pricing page also offers a 15% annual-billing discount. Model-provider charges, local infrastructure, and other metered services should be considered separately from plan credits.

2. n8n

Best for

Best for: Engineering-led teams that need broad API, database, and business-tool automation and can work within a source-available fair-code license.

What it is

n8n is a visual automation platform for connecting APIs, databases, business software, custom code, and AI steps. Its strength is breadth: teams can place model calls and agent nodes inside operational workflows for jobs such as ticket routing, enrichment, notifications, document handling, and data synchronization.

n8n uses Sustainable Use License Version 1.0, a source-available fair-code license rather than an OSI-approved open-source license. The license supports internal business use and self-hosting but restricts scenarios such as charging others to access hosted n8n or selling a white-labeled n8n service without a commercial agreement. That distinction matters when the workflow engine is part of the product being sold rather than an internal tool.

Compared with Dify, n8n provides broader deterministic automation and a larger emphasis on integrations. Dify offers a more packaged experience for knowledge retrieval, prompt testing, and LLM applications.

Pros

• Broad technical and business automation reaches beyond chat and RAG applications.
• The Community Edition provides a self-hosting path for permitted use under the Sustainable Use License.
• JavaScript and Python support lets engineers implement logic beyond standard nodes.
• AI-agent and model nodes can feed results into downstream operational steps.
• Cloud plans are billed by full workflow execution rather than by the number of steps in a workflow.

Cons

• Sustainable Use License Version 1.0 is not OSI-approved and can be unsuitable for hosted, resale, or white-label product models.
• Self-hosting requires the operator to manage upgrades, security, backups, scaling, and reliability.
• Large visual workflows can become difficult to inspect and maintain without governance conventions.
• n8n is less specialized than Dify or RAGFlow for document ingestion, retrieval configuration, and grounded RAG applications.

Pricing

As of August 2026, n8n pricing lists Starter at €20 per month for 2,500 monthly workflow executions, Pro at €50 per month for 10,000 monthly executions, and Business at €667 per month for 40,000 monthly executions, with those prices billed annually. Enterprise uses custom pricing. Business is a self-hosted tier, while Enterprise can support cloud or self-hosted deployment. The free Community Edition can be self-hosted under n8n's license, but infrastructure and maintenance costs remain.

3. LangChain and LangGraph

Best for

Best for: Engineering teams that want code-level control over agent state, branching, retries, persistence, and human approval.

What it is

LangChain and LangGraph are MIT-licensed Python and TypeScript frameworks for building LLM applications and agents. LangChain supplies higher-level model, tool, retrieval, and agent abstractions. LangGraph provides a graph-based runtime for explicitly defining state, nodes, edges, checkpoints, interrupts, and execution paths.

Unlike Dify's visual application builder, these frameworks require code and leave more of the interface, data pipeline, infrastructure, and deployment architecture to the engineering team. That extra work buys direct control over how an agent resumes after interruption, handles failures, requests human input, and moves through decision branches.

LangGraph Platform was renamed LangSmith Deployment. LangSmith adds observability and managed deployment options around the open-source frameworks, but it is a separate commercial service rather than part of the MIT license grant for the core projects.

Pros

• MIT licensing provides standard permissive rights for the core LangChain and LangGraph frameworks.
• LangGraph's explicit state model supports controlled branching, persistence, and resumable execution.
• Human-in-the-loop interrupts can be built into the application flow.
• Python and TypeScript support fits common AI application stacks.
• Engineers can choose their own retrieval layer, database, model provider, user interface, and hosting architecture.

Cons

• Teams must write and maintain code rather than editing a shared visual application.
• Dify provides more packaged prompt, knowledge-base, and RAG application features out of the box.
• Production use may require separate choices for ingestion, retrieval, evaluation, tracing, deployment, authentication, and user experience.
• Non-engineers cannot safely modify agent behavior as easily as they can in a visual workflow workspace.

Pricing

As of August 2026, the core LangChain and LangGraph frameworks are free to use under their MIT licenses, while LangSmith pricing lists a Developer plan at $0 for one seat and a Plus plan at $39 per seat per month. Plus includes 10,000 base traces and one free Serverless Small deployment; additional usage is metered. LangSmith lists LangChain Usage Units at $1.50 per LCU and LangSmith Usage Units at $1.00 per LSU. Enterprise pricing is custom. Model, storage, database, and infrastructure charges remain separate.

4. RAGFlow

Best for

Best for: Teams building document-heavy RAG applications that need deep parsing, retrieval, citations, agents, and an Apache 2.0 self-hosting path.

What it is

RAGFlow is an Apache 2.0 open-source RAG engine and agent platform. Its product focus is document understanding: parsing source files, applying chunking strategies, combining retrieval methods, reranking results, and returning grounded answers with citations.

RAGFlow is the closest specialist in this list to buyers who value Dify's retrieval capabilities but want a more document-first RAG engine. It also includes agent and workflow capabilities, but its clearest differentiation is the retrieval pipeline rather than broad business automation.

The project provides official Docker Compose self-hosting documentation. As of the fact check, the documented minimum was four x86 CPU cores, 16 GB of RAM, 50 GB of disk, Docker 24 or later, and Docker Compose 2.26.1 or later. RAGFlow v0.27.0 was released on August 19, 2026, providing a current maintenance signal.

Pros

• Apache 2.0 provides a standard permissive license for the open-source project.
• Document parsing, chunking, hybrid retrieval, reranking, and citations are central product capabilities.
• Official Docker Compose documentation supports self-hosting.
• The project's release history maintained an active release cadence in August 2026.
• Cloud, open-source, and Enterprise deployment paths give teams room to change operating models.

Cons

• The documented self-hosting minimum is heavier than a small application runtime.
• RAGFlow is less suited than Sim or n8n to broad operational automation across many business tools.
• The vendor states that cloud and local open-source experiences are not identical, including differences in API availability and Enterprise capabilities.
• Teams with lightweight chatbot or prompt-testing needs may find the document pipeline more infrastructure than they require.

Pricing

As of August 2026, RAGFlow pricing lists Free at $0 per month with five apps, one team member, 0.1 GB of dataset storage, and 500 monthly credits; Starter at $29 per month with 50 apps, five members, 5 GB, and 5,000 monthly credits; Pro at $129 per month with unlimited apps, 20 members, 50 GB, and 20,000 monthly credits; and Enterprise at custom pricing. The public page showed promotional strike-through reference prices for Starter and Pro, so buyers should confirm the current checkout price. Self-hosting still requires infrastructure, model, storage, and operational spend.

5. Langflow

Best for

Best for: Python-focused teams that want a visual builder for LLM and RAG pipelines while retaining the ability to edit component code.

What it is

Langflow is an MIT-licensed visual platform for assembling models, prompts, vector stores, tools, agents, and other LLM application components. Python developers can edit component code, define inputs and outputs, export flows, and expose applications through APIs.

DataStax acquired Langflow's creator, and IBM now presents DataStax as an IBM company. That ownership gives Langflow a different continuity and enterprise ecosystem than an independent community project, while the open-source Langflow repository remains available under the MIT License.

Compared with Dify, Langflow gives Python teams more direct component-level customization. Dify provides a more packaged product experience for prompt iteration, knowledge management, application publishing, and experimentation.

Pros

• MIT licensing provides standard permissive rights for the open-source project.
• Python developers can modify component code and behavior directly.
• A visual graph makes model, prompt, tool, and retrieval relationships easier to inspect than code alone.
• Node-level execution data can help teams inspect inputs, outputs, and timing.
• Self-hosting gives operators control over infrastructure and data location.

Cons

• Teams generally need to build or connect the end-user interface separately.
• Custom component code runs inside the deployment environment and requires security and stability controls.
• Dify offers a more packaged RAG application and experiment-management experience.
• Visual flexibility does not remove the need for Python expertise when debugging custom components or production behavior.

Pricing

As of August 2026, the Langflow open-source project can be self-hosted without a software license fee under the MIT License; infrastructure, storage, model, and maintenance costs still apply. A current public managed-service price was not verified from a primary vendor pricing page during this review, so buyers should confirm any hosted Langflow or IBM service pricing directly with the vendor before purchase.

Flowise status: According to its official repository, Flowise is no longer maintained, reaches end of life on August 31, 2026, and has an archived repository; its Apache 2.0 code remains available to fork.

Dify alternatives compared

Sim ranks first because it offers the strongest overall combination of standard permissive licensing, visual automation, agent building, integrations, MCP interoperability, and deployment options. The table uses the same criteria for every ranked product.

Which Dify alternative fits your team?

Choose the product whose strongest capability matches the reason you are leaving Dify. A platform that wins on licensing may not win on retrieval depth, and a framework that wins on agent control may require substantially more engineering.

• Choose Sim for broader automation plus agents. Sim is the best fit when workflows must coordinate business tools, structured data, deterministic steps, and AI judgment. Its Apache 2.0 license, 1,000+ integrations, MCP client-and-server support, and API, chat, and MCP deployment options make it the broadest option in this ranking.
• Choose n8n for integration-heavy technical automation. n8n is the stronger fit when engineering teams prioritize APIs, databases, business applications, and high-volume operational workflows over packaged RAG tooling. Confirm that Sustainable Use License Version 1.0 permits the intended commercial model.
• Choose LangChain and LangGraph for full code-level control. LangGraph is the best fit when developers need to define state, branching, retries, persistence, and human review directly in Python or TypeScript and are prepared to assemble the surrounding application stack.
• Choose RAGFlow for document-heavy retrieval. RAGFlow is the strongest specialist when parsing, chunking, hybrid recall, reranking, citations, and grounded document answers matter more than broad business automation.
• Choose Langflow for Python visual pipelines. Langflow is the best fit when a Python team wants visual composition but still needs to customize components and execution behavior in code.
• Stay with Dify for packaged LLM and RAG applications. Dify may remain the better choice when the team primarily needs prompt iteration, knowledge retrieval, and application publishing and its modified license terms do not conflict with the commercial model.

When should you look beyond Dify?

Look beyond Dify when you need either broader workflow automation or license terms that better match a commercial hosting or redistribution plan. Dify's visual builder centers on prompts, model calls, knowledge retrieval, and LLM applications. That focus is valuable, but it may not cover operational workflows that must coordinate many business systems and combine fixed logic with tool-using agents.

Dify's modified Apache terms add two especially important conditions: source-based multi-tenant operation requires written authorization, and the console logo and copyright notices cannot be removed or modified. Those terms do not make Dify unusable; they make license fit a product-design decision that should be reviewed before deployment.

Why does Sim lead this list?

Sim leads because it is the only ranked platform that combines Apache 2.0 licensing, visual deterministic automation, tool-using agents, 1,000+ integrations, two-way MCP support, and API, chat, and MCP deployment in one workspace. That combination directly addresses the two most common reasons to leave Dify: needing automation beyond LLM and RAG applications, and needing a standard permissive license for a broader commercial use case.

The recommendation follows the published criteria rather than claiming Sim is best for every workload. RAGFlow is stronger for deep document-centric retrieval. LangGraph gives engineers more direct control over code-defined state and execution. n8n is a strong choice for broad technical automation when its fair-code license fits. Dify remains a strong product for packaged prompt, knowledge, and RAG applications.

Sim wins the overall ranking because it covers the widest buyer need without forcing a choice between a visual automation layer and an agent-building layer. Teams can keep deterministic operations explicit, let agents handle judgment-heavy steps, consume external MCP tools, and publish completed workflows to other MCP-compatible applications.

For a focused head-to-head rather than a roundup, read Sim vs. Dify: Open-Source AI Workspace vs. LLM App and RAG Platform.

Related comparisons

• Comparing Sim and Dify directly: Sim vs. Dify: Open-Source AI Workspace vs. LLM App and RAG Platform
• Replacing n8n: Best n8n Alternatives
• Replacing LangGraph: Best LangGraph Alternatives
• Comparing open-source platforms: Open-Source AI Agent Platforms

FAQ

Is Dify open source?

Dify publishes source code under modified Apache License 2.0 terms, but the added restrictions mean it is not the standard Apache 2.0 license: operating a multi-tenant service requires written authorization, and the Dify console logo and copyright notices cannot be removed or modified.

Is Dify free?

Dify can be self-hosted from its published source subject to its modified Apache terms, but self-hosting still creates infrastructure and model costs, and the license adds conditions for multi-tenant commercial services and the Dify console branding.

What is the best open-source Dify alternative?

Sim is the best open-source Dify alternative for teams that need visual workflow automation and AI agents in one Apache 2.0 workspace, while RAGFlow is the stronger choice for document-heavy RAG applications.

How do Sim and Dify compare?

Sim is the better fit for broader business automation, tool-using agents, 1,000+ integrations, and MCP client-and-server workflows; Dify remains a strong fit for teams centered on prompt iteration, knowledge retrieval, and packaged LLM applications.

Is n8n open source?

n8n is source-available under Sustainable Use License Version 1.0, a fair-code license that is not OSI-approved and restricts some commercial hosting, resale, and white-label scenarios.

Can I self-host a Dify alternative?

Yes. Sim, n8n, LangChain and LangGraph, RAGFlow, and Langflow all provide self-hosting paths, but their licenses, infrastructure requirements, and included product features differ.

What replaced Flowise in this list?

RAGFlow replaced Flowise because RAGFlow is actively maintained, Apache 2.0 licensed, self-hostable, and focused on document RAG and agent applications; Flowise is no longer maintained, reaches end of life on August 31, 2026, and has an archived repository.