The best open-source chatbot platform depends on what you are building: a visual LLM workflow, a conversational AI system, or an interface for talking to models. Flowise and Dify focus on visual LLM applications; Rasa is aimed at production conversational AI; Open WebUI is a model chat interface; and Langflow is a visual agent-and-workflow builder. Botpress needs a special qualification: its current repository describes Botpress Cloud, not the former self-hosted open-source product.
Here are seven candidates, organized by practical fit rather than a universal score. The available product documentation does not establish a like-for-like 2026 feature, license, or price comparison for every option, so the table distinguishes what is documented from what remains unspecified.
Best open-source chatbot platforms at a glance
| Platform | Best fit | What the documentation establishes | Self-hosting and price |
|---|---|---|---|
| Flowise | Visual LLM and agent workflows with an embedded chat option | Assistant, Chatflow, and Agentflow builders; API/SDK access and embedded chat are described in its official documentation. | npm and Docker Compose setup are documented. Price: not stated in the official documentation. |
| Dify | LLM apps combining workflows, RAG, model management, and observability | The project describes these capabilities and a self-hosted Community Edition. | Docker Compose is documented; vendor-stated minimums are two CPU cores and 4 GiB RAM. Price: not stated in the project README. |
| Rasa | Conversational AI deployments that require on-premises or isolated environments | Rasa’s vendor-authored comparison page describes self-hosted, on-premises, and air-gapped deployment. | Specific current edition limits, terms, and price are not stated in the comparison page. |
| Open WebUI | A chat interface for local Ollama models or OpenAI API-connected models | The official repository description names Ollama and OpenAI API support. | Deployment details, license terms, and price are not established by the repository information available here. |
| Langflow | Visual construction of AI agents and workflows | The official repository describes building and deploying AI-powered agents and workflows. | License, deployment details, and price are not established by the repository information available here. |
| Botpress | Hosted visual bot building | The GitHub repository identifies the product as Botpress Cloud; Rasa says the older Botpress v12 self-hosted open-source product has been sunset. | Do not infer a current self-hosted product from the repository’s MIT-licensed packages. Cloud pricing is not stated in the repository or Rasa comparison. |
| LangChain | Teams evaluating a developer-oriented framework alongside conversational AI platforms | It is named as a comparator in Rasa’s vendor-authored comparison; the available information does not establish a full chatbot-builder feature set. | License, deployment options, edition limits, and price are not stated in the comparison page. |
“Not stated” means the cited material does not establish that detail; it does not mean a product has no such capability or plan. For an open-source decision, evaluate the exact repository, license, and hosted offering separately.
1. Flowise: visual LLM and agent workflows
Flowise is a visual development platform for AI agents and LLM workflows, rather than only a ready-made customer-facing chatbot. Its documentation describes Assistant, Chatflow, and Agentflow builders, along with API/SDK access and the ability to embed a chatbot. That combination suits a team that wants to compose workflows visually and then expose a chat experience in another application.
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What stands out
- Visual builders cover more than one workflow style: assistants, chatflows, and agentflows.
- API and SDK access make it possible to connect a built flow to other software; embedded chat provides a route to a web chat interface.
- The documented deployment paths include npm and Docker Compose, so a team can operate an instance rather than relying only on a hosted interface.
Setup and operating trade-offs
The official setup guide specifies Node.js v18.15.0 or v20 and above for the npm path. It documents a global npm installation followed by npx flowise start; its Docker Compose path runs from the project’s Docker folder. The guide warns that self-hosting requires technical skill for server setup, database backups, and updates. These are operating responsibilities, not one-time installation details. The guide does not establish a production capacity benchmark or a price: Flowise getting started.
Flowise is a strong fit when the priority is a visual LLM workflow with an embeddable chat surface. Before treating it as a production customer-service system, distinguish the workflow builder from the surrounding work of authentication, security configuration, integration maintenance, backups, and operations. The current license for the exact version is not established by the cited pages.
2. Dify: LLM applications with RAG and observability
Dify positions itself as an LLM application platform. Its project README describes visual workflows, retrieval-augmented generation (RAG), model management, observability, APIs, and a self-hosted Community Edition. It is a sensible candidate when a chatbot is one part of a broader LLM application and the team wants workflow and knowledge features in the same platform.
Deployment requirements
Dify’s README documents a Docker Compose quick start and requires Docker Compose v2.24.0 or later. It publishes a minimum machine requirement of at least two CPU cores and 4 GiB RAM. Those are vendor-stated installation minimums, not an independent performance benchmark or a guarantee of capacity for a particular workload. The same README describes the Community Edition as self-hosted: Dify project README.
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License and fit
Dify says its Dify Open Source License is based on Apache 2.0 with additional conditions. Apache 2.0 alone therefore does not describe the full terms. Read the current license text for the exact version and edition before relying on assumptions about commercial use or redistribution. The project README does not establish a comparable 2026 price or edition-by-edition feature matrix.
Choose Dify when workflows, RAG, model management, and observability are central requirements. The additional license conditions and the ongoing work of running a Docker Compose service are important parts of that choice.
3. Rasa: conversational AI with deployment control
Rasa belongs in a different category from a visual LLM flow builder: it is a conversational AI platform to consider when deployment control is central, including on-premises or air-gapped environments. Rasa’s own comparison page describes self-hosted, on-premises, and air-gapped deployment. Because that is vendor-authored comparison material, treat it as a description to confirm against the specific current edition rather than an independent evaluation: Rasa’s comparison of LangChain, Botpress, and Rasa.
What to establish for your deployment
- Which current edition supports the required deployment pattern.
- What the current Developer Edition limits are and which capabilities require enterprise terms.
- What infrastructure and operational prerequisites apply to the chosen edition.
- Which license and commercial terms apply to the exact software being deployed.
The cited comparison does not establish current edition limits, enterprise feature boundaries, or price. Rasa is most relevant to teams that need conversational AI and deployment choices beyond a hosted-only service, but it cannot be ranked above workflow builders on visual flow flexibility or RAG from the available evidence.
The Tool Desk
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4. Open WebUI: a chat interface for models
Open WebUI is a different kind of candidate: its official repository description says it supports Ollama and the OpenAI API. That makes it worth considering when the requirement is an interface for chatting with local Ollama models or API-connected models, rather than a full conversational AI authoring platform.
The available repository description does not establish a detailed feature list, license terms, deployment instructions, supported channels beyond model chat, or a price comparison. Do not assume that a model chat interface provides the workflow design, business-system integrations, or customer-service channel management that a support chatbot may need. Review the current official project material for the exact edition before selecting it.
5. Langflow: visual agent and workflow building
Langflow’s official repository describes a platform for building and deploying AI-powered agents and workflows. That makes it a relevant visual-builder option for teams exploring agent workflows, especially when their primary need is to construct AI behavior rather than select a prebuilt support-desk product.
The available project description does not establish the current license, deployment choices, chatbot channels, supported integrations, or pricing. Those gaps matter if the requirement is a public-facing support bot: an agent/workflow builder is not automatically a complete website chat, email, WhatsApp, or social-service system. Assess Langflow on workflow fit, then confirm that the exact project version supplies the deployment and user-facing interface your use case needs.
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6. Botpress: hosted visual bot building, not the old self-hosted option
Botpress can appear in open-source chatbot searches, but the distinction between its current hosted product and its former self-hosted version is essential. The GitHub repository identifies the product as Botpress Cloud and says its repository packages use the MIT License. Separately, Rasa’s comparison page says Botpress v12 self-hosted open source has been sunset and describes current Botpress as cloud-delivered. A license label for repository packages does not establish that the current hosted product can be self-hosted: Botpress repository; Rasa comparison.
Consider Botpress when hosted visual bot building is acceptable. If your requirement is self-hosting, air-gapped operation, or control of the deployed runtime, the cited sources do not establish that current Botpress Cloud meets it. Current cloud pricing and terms are not stated in those sources.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. LangChain: a developer-oriented option, not a verified turnkey chatbot builder
LangChain is named alongside Rasa and Botpress in Rasa’s vendor-authored comparison. The available information does not provide enough detail to treat it as a directly comparable, ready-to-deploy chatbot platform: it does not establish a product feature matrix, current license and edition terms, deployment options, or prices. Its place on this list is therefore as a developer-oriented candidate to assess, not as a validated substitute for a full customer-facing chat system.
For a team considering it, the decision hinges on whether a developer-led framework approach is acceptable in place of a packaged visual chatbot builder. The cited comparison does not establish which channels, workflow tooling, or hosting terms apply, so no claim about those specifics is warranted here: Rasa’s comparison page.
Best Value
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How to choose the right platform
Start with the artifact you need to build
- Visual LLM flows and an embedded chat interface: start with Flowise.
- An LLM application with workflows, RAG, model management, and observability: compare Dify.
- Conversational AI with on-premises or air-gapped deployment requirements: investigate Rasa’s exact current edition and terms.
- A chat interface for Ollama or OpenAI API models: assess Open WebUI.
- Visual agent and workflow construction: consider Langflow.
- Hosted visual bot authoring: Botpress may fit if cloud delivery is acceptable; do not assume its current offering is self-hostable.
- A developer-led framework approach: investigate LangChain independently; the cited comparison does not establish that it is a turnkey chatbot platform.
Check the requirements that often get confused
- Define the runtime. Decide whether the bot may run in a vendor cloud, must run on infrastructure you manage, or must operate in an isolated or air-gapped environment. A GitHub repository or an open-source package license alone does not prove that a hosted product is self-hostable.
- Map the user-facing channels. Specify whether the bot must serve website chat, messaging apps, or other channels. The cited material explicitly establishes embedded chat for Flowise and model chat for Open WebUI, but does not provide a uniform channel comparison across all seven products.
- Separate model and knowledge needs. If you require RAG or knowledge ingestion, Dify’s README explicitly describes RAG. For other platforms, the available information here does not establish equivalent capabilities.
- Inspect the exact license and edition. Check the repository license and hosted-service terms separately. This is particularly important for Dify’s Apache 2.0-based license with additional conditions and for Botpress’s distinction between MIT-licensed repository packages and Botpress Cloud.
- Budget for operations. Self-hosting means maintaining servers, backups, updates, and security configuration. Flowise’s guide explicitly calls out setup, database backup, and maintenance; Dify’s stated machine minimums are only vendor-published starting requirements, not a sizing recommendation for your workload.
What “open source” and “self-hosted” mean in this comparison
These labels should not be treated as interchangeable. A project may publish source code while offering a hosted product with separate terms; a package license does not settle whether the hosted service can be run locally; and a self-hosted edition may have different capabilities or limits from a paid enterprise edition. Dify’s stated license includes additional conditions beyond Apache 2.0. Botpress’s repository describes Botpress Cloud and MIT-licensed packages, while Rasa says Botpress v12’s self-hosted open-source product has been sunset. For commercial use, redistribution, or a deployment requirement, read the current license and terms for the exact version and offering.
Frequently Asked Questions
Frequently Asked Questions
Can I self-host Flowise or Dify?
Both publish self-hosting paths in the cited official material. Flowise documents npm and Docker Compose deployment; Dify documents a Docker Compose quick start. Self-hosting still requires someone to maintain the instance.
Which option is best for a chatbot that must work without internet access?
Rasa is the candidate in this comparison whose vendor-authored page describes air-gapped deployment. Confirm that the current edition and its terms support your exact requirements before designing around it.
Does an MIT-licensed repository mean I can self-host Botpress Cloud?
No. The repository’s MIT license statement applies to its packages; the repository identifies the product as Botpress Cloud, and Rasa says the older Botpress v12 self-hosted open-source product has been sunset.
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Are any 2026 prices established for these platforms?
No comparable 2026 prices are established by the cited material. It supports some deployment and product distinctions, but not a like-for-like price table.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
