Recommended Free Tools
There is no single best open-source chatbot platform for every project. Flowise is the clearest fit for visually building LLM workflows and agents with an embedded chat option; Dify is aimed at building broader LLM applications with workflows, retrieval-augmented generation (RAG), model management, and observability; and Rasa is worth evaluating when enterprise conversational AI must run in a self-hosted, on-premises, or air-gapped environment. The other options below serve different needs—or require extra care before calling the current offering open source.
This comparison ranks six named platforms by how well the available product information supports a particular use case, not by a universal feature score. A chatbot interface, an agent-workflow builder, and a conversational AI platform are different kinds of tools. That distinction matters as much as a feature checklist.
Quick comparison: which platform fits your project?
| Platform | Best fit | What it is | Deployment and open-source qualification | Pricing information |
|---|---|---|---|---|
| Flowise | Visual LLM agents and workflows with an embedded chat option | Generative AI development platform with Assistant, Chatflow, and Agentflow builders | Official setup documentation covers local npm and Docker Compose deployment. Check the license for the specific version. | Not stated in the official documentation. |
| Dify | LLM applications that combine workflows, RAG, model management, and observability | Broader LLM application platform with a self-hosted Community Edition | README documents Docker Compose deployment. Its stated license is Apache 2.0-based with additional conditions. | Not stated in the project README. |
| Rasa | Enterprise conversational AI with self-hosted, on-premises, or air-gapped deployment needs | Conversational AI platform | Rasa’s vendor-authored comparison describes these deployment options; edition limits and terms need to be read directly. | Not stated in the comparison page. |
| Langflow | Visual construction of AI agents and workflows | AI agent and workflow builder | Its official repository describes the product, but the available information does not establish license or deployment details. | Not stated in the official repository information summarized here. |
| Open WebUI | A chat interface for local or API-based models | Interface supporting Ollama and the OpenAI API | The available official repository description does not establish license or deployment details. | Not stated in the official repository information summarized here. |
| Botpress | Hosted visual bot building | The current repository identifies the product as Botpress Cloud | The repository says its packages use the MIT License, but that does not establish that the current hosted product can be self-hosted. Rasa says the former Botpress v12 self-hosted open-source offering has been sunset. | Not stated in the repository or Rasa comparison. |
“Not stated” means the linked official material does not establish a price; it does not mean the product is free. The available information also does not support a uniform comparison of paid plans, channel coverage, or current edition limits across these projects.
What “open-source chatbot platform” can mean
Some projects help you design the model-backed workflow behind an assistant. Others provide a chat interface for models, or a platform for building conversational applications. These categories overlap, but they are not interchangeable: a visual workflow builder does not automatically provide every customer-service channel, and a chat interface is not necessarily a full chatbot development platform.
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- Agent and LLM workflow builders: Flowise and Langflow focus on visually creating AI-powered workflows or agents. Flowise also documents an embedded chatbot capability.
- LLM application platforms: Dify brings together workflow building, RAG, model management, observability, and APIs.
- Conversational AI platforms: Rasa is positioned for conversational AI and is the option in this comparison whose vendor-authored material specifically describes on-premises and air-gapped deployment.
- Chat interfaces: Open WebUI is described as an interface for Ollama and OpenAI API models. That is a different starting point from a platform for designing a full chatbot application.
- Hosted bot builders: Botpress is presented in its current repository as Botpress Cloud. A repository license and a hosted service’s deployment model are separate questions.
Before adopting any project as “open source,” distinguish the source repository and its license from the edition or hosted service you intend to use. For commercial use, redistribution, or self-hosting, the terms for the exact version and offering govern.
1. Flowise: best-supported starting point for visual LLM workflows
Flowise describes itself as an open-source generative AI development platform for building AI agents and LLM workflows. Its official documentation identifies Assistant, Chatflow, and Agentflow builders, along with API and SDK access and an embedded chatbot capability. That makes it a strong first option when the goal is to assemble a model-backed workflow visually and expose it through chat, rather than to select a conventional live-chat help desk.
Setup and operating requirements
The official getting-started guide supports Node.js v18.15.0 or v20 and above, a global npm installation, and launching the service with npx flowise start. It also documents a Docker Compose path run from the project’s Docker folder. Follow the current Flowise getting-started guide for the exact installation commands and deployment steps.
Flowise explicitly cautions that self-hosting takes technical skill for instance setup, database backups, and maintenance. Running it yourself gives your team responsibility for keeping the service and its environment operational; it is not a maintenance-free alternative to a hosted service. The docs do not establish current pricing or a complete license analysis for every version, so consult the terms attached to the specific release you plan to use.
The Tool Desk
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- Good fit: teams that want visual agent or LLM workflow construction and an embedded chatbot, and can operate a Node.js or Docker deployment.
- Not established by the cited material: a complete list of supported customer-service channels, a uniform catalog of integrations, production security requirements, or pricing.
2. Dify: best-supported fit for a broader LLM application stack
Dify is broader than a chat widget builder. Its project README describes a platform for LLM applications with visual workflows, RAG, model management, observability, and APIs, as well as a self-hosted Community Edition. Consider it when an assistant needs to combine a knowledge workflow and model configuration with an application-building environment, rather than only a chat surface.
Self-hosting requirements
The Dify README documents a Docker Compose quick start and specifies Docker Compose v2.24.0 or later. It gives minimum machine requirements of at least two CPU cores and 4 GiB of RAM. Those are vendor-published setup minimums, not an independent performance benchmark or a guarantee that a particular workload will run well at that size.
Rank #2
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- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
License and fit
Dify describes its Dify Open Source License as based on Apache 2.0 with additional conditions. Read the current license text for the precise version and edition before relying on assumptions about commercial use or redistribution. The README’s Community Edition label also does not establish that every feature or deployment option is available under identical terms.
- Good fit: builders who want workflows, RAG, model management, observability, and APIs in one LLM application platform, with a documented self-hosted route.
- Trade-off: operating the self-hosted version means maintaining the Docker-based deployment, and the additional license conditions need attention.
- Not established by the cited material: a full channel matrix, current paid-plan prices, or uniform feature availability across editions.
3. Rasa: consider it for deployment-controlled conversational AI
Rasa’s comparison page describes self-hosted, on-premises, and air-gapped deployment for its conversational AI platform. That makes it relevant when infrastructure control is a primary requirement, particularly for organizations that cannot send an assistant’s runtime to a standard hosted environment. The deployment claims here come from Rasa’s own comparison material, not an independent evaluation.
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The cited comparison does not settle the current Developer Edition’s limits, enterprise feature boundaries, deployment prerequisites, or license terms. Those details determine whether a specific Rasa edition is suitable for a given workload; consult the current edition documentation and terms rather than treating the deployment description as proof that every capability is included in every edition.
Rasa’s page also discusses Botpress and LangChain, but it is a vendor-authored comparison. Use it to identify the deployment distinction it describes, not as a neutral ranking of competing products. See Rasa’s comparison page.
- Good fit: teams evaluating conversational AI where self-hosted, on-premises, or air-gapped operation is central.
- Trade-off: the available information does not provide a complete, independently verified account of current editions, limits, and terms.
4. Langflow: a visual agent and workflow builder to evaluate
Langflow’s official repository describes building and deploying AI-powered agents and workflows. That supports considering it for visual construction of AI systems, but does not by itself establish that it supplies the customer-facing chatbot channels, license terms, or deployment choices a particular project needs.
The available repository description is not detailed enough to compare Langflow’s license, operational requirements, integrations, or edition boundaries with Flowise or Dify. Its position here is therefore narrower: it is a named workflow-building option, not a verified substitute for a complete support-chat platform. Check the current official repository and project documentation for the exact product and terms before basing a deployment decision on it.
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5. Open WebUI: a chat interface for supported models
The official repository description identifies Open WebUI as a chat interface supporting Ollama and the OpenAI API. That can suit a project whose immediate need is a conversational interface for those model routes. It is not enough evidence to characterize it as a full chatbot application platform with workflow, retrieval, or customer-service channel features.
The available information does not establish its current license, deployment requirements, complete provider list, or prices. Treat it as an interface candidate and inspect the current repository and documentation for those specifics; do not infer its terms or operational profile from the product category alone.
6. Botpress: hosted visual bot building, not a confirmed self-hosted choice
Botpress needs a more careful qualification than its repository’s MIT reference might suggest. The current Botpress repository identifies the product as Botpress Cloud and says repository packages use the MIT License. Rasa’s vendor-authored comparison says Botpress v12, the self-hosted open-source version, has been sunset and describes current Botpress as cloud-delivered.
Those facts do not establish that the current hosted Botpress product can be deployed from source by a customer. A license applying to repository packages should not be generalized into a self-hosting claim for the current cloud offering. Botpress may be relevant when hosted visual bot building is the aim, but it is not the strongest match in this list for a requirement to operate the current product on your own infrastructure.
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How to choose a platform for your chatbot
Start with the system you need to operate, not the longest feature list. A support assistant that answers from internal documents, a controlled conversational workflow, and a local-model chat interface can all be called chatbots but require different software.
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1. Decide what you are building
- Choose a workflow or agent builder if you need to visually assemble model calls and logic; Flowise and Langflow are the candidates in this comparison.
- Choose a broader LLM application platform if you need workflow construction alongside RAG, model management, and observability; Dify’s README describes those capabilities.
- Choose a conversational AI platform if deployment control for a conversational system is the defining requirement; Rasa’s vendor-authored material describes self-hosted, on-premises, and air-gapped deployment.
- Choose a chat interface if you primarily need a front end for supported models; Open WebUI is described for Ollama and OpenAI API.
2. Decide where it must run
Separate three questions: can you deploy the project yourself, can the precise edition you want be self-hosted, and are you able to maintain it? Flowise documents npm and Docker Compose routes and warns about the upkeep involved. Dify documents a Docker Compose route and machine minimums. Rasa’s comparison describes constrained deployment environments. For Botpress, the current cloud offering and the sunset of v12 make the old self-hosting assumption unsafe.
3. Check the knowledge and model requirements
If retrieval from documents, model selection, APIs, or observability are key, Dify’s documented feature set is the most explicit match in this group. If you need to construct agent flows visually, Flowise and Langflow are relevant. The available information does not establish comparable integration catalogs or model-provider coverage for all six platforms, so do not assume their capabilities are interchangeable.
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Read the license file for the version you will deploy, the terms for the edition you will use, and the service terms if you are choosing a hosted product. This is especially important for Dify’s additional license conditions and for interpreting Botpress’s MIT-licensed repository packages alongside a cloud-delivered current product. A project label, repository license, or feature in a hosted edition does not automatically answer the others.
5. Account for operating work
Self-hosting shifts responsibility for deployment and maintenance to the operator. Flowise specifically names setup, database backups, and maintenance as technical requirements. Dify’s published CPU and RAM minimums are starting requirements from its own README, not workload-sizing guidance. Neither figure substitutes for planning backups, updates, access control, monitoring, or capacity for your actual use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the available comparisons do—and do not—establish
The official materials support distinguishing platform types, identifying documented deployment routes for Flowise and Dify, and noting the deployment claims Rasa makes about its platform. They do not establish a complete independent 2026 leaderboard, comparable pricing, performance benchmarks, or a full feature and license matrix for all the named projects. The sensible conclusion is to choose by product category and deployment constraints, then assess the exact version and edition on its published terms.
Frequently Asked Questions
Is a visual LLM workflow builder the same thing as a customer-service chatbot?
No. A workflow builder helps create the logic behind an AI agent or application; a customer-service chatbot may additionally need an end-user interface, support-specific workflows, and channels such as a website widget or messaging apps. The platform descriptions here do not establish a common channel set across products.
Best Value
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- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
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- HD Display: Connect to the HDMI port to stream or mirror content to an external monitor in resolutions of up to 4K@30Hz. Note: The USB-C ports do not support video output.
- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
Do the listed open-source platforms all have a free plan?
The cited material does not establish a comparable free-plan or pricing schedule for these six products. A self-hosted edition, a source repository, and a hosted service are different offerings; the existence of one does not establish the price or limits of another.
Can I use the Dify minimum server size as a production recommendation?
No. Dify’s stated minimum of two CPU cores and 4 GiB of RAM is a vendor-published setup minimum. It is not a performance benchmark or workload-specific production sizing recommendation.
Does an MIT-licensed repository mean Botpress Cloud is self-hostable?
No. The repository’s statement about MIT-licensed packages is distinct from the deployment model of the current Botpress Cloud offering. The repository does not establish that the current hosted product can be self-hosted.
Frequently Asked Questions
Is a visual LLM workflow builder the same thing as a customer-service chatbot?
No. A workflow builder helps create the logic behind an AI agent or application; a customer-service chatbot may additionally need an end-user interface, support-specific workflows, and channels such as a website widget or messaging apps. The platform descriptions here do not establish a common channel set across products.
Do the listed open-source platforms all have a free plan?
The cited material does not establish a comparable free-plan or pricing schedule for these six products. A self-hosted edition, a source repository, and a hosted service are different offerings; the existence of one does not establish the price or limits of another.
Can I use the Dify minimum server size as a production recommendation?
No. Dify’s stated minimum of two CPU cores and 4 GiB of RAM is a vendor-published setup minimum. It is not a performance benchmark or workload-specific production sizing recommendation.
Does an MIT-licensed repository mean Botpress Cloud is self-hostable?
No. The repository’s statement about MIT-licensed packages is distinct from the deployment model of the current Botpress Cloud offering. The repository does not establish that the current hosted product can be self-hosted.
Quick Recap
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