Best Open Source AI Chatbot Frameworks in 2026

Most "best chatbot frameworks" lists lump together SaaS chatbot builders, self-hosted chat UIs, and actual conversation frameworks. That makes comparison nearly impossible. Before looking at individual tools, it helps to understand the two architectural camps that define this space right now.

Traditional NLU frameworks like Rasa train intent classifiers and entity extractors on your own data. You define conversation flows, train a model, and deploy a pipeline that handles dialogue management without calling an external LLM at inference time. This gives you full control over latency, cost, and data privacy. The tradeoff is upfront training effort and the brittleness of intent-based systems when users go off-script.

LLM-native platforms skip the training step entirely. They route user messages to a large language model (hosted or local) and use prompt engineering, tool calling, and retrieval-augmented generation to produce responses. Setup is faster. Conversations feel more natural. But you depend on model quality, and costs scale with token usage.

A third category worth noting: chat UIs and interfaces like Open WebUI and Jan. These are not chatbot frameworks in the strict sense. They do not provide conversation design tools, NLU training, or dialogue management. Instead, they give you a polished self-hosted frontend for interacting with LLMs. They appear on this list because many teams deploy them as customer-facing chatbots by connecting them to fine-tuned models or RAG pipelines.

Here is a quick comparison of all nine frameworks covered in this guide:

Helpful references: Fast.io Workspaces, Fast.io Collaboration, and Fast.io AI.

These four projects all connect to large language models, but they solve different problems. Open WebUI and Jan are primarily chat interfaces. LibreChat adds agent capabilities. HuggingChat focuses on open-weight model access.

1. Open WebUI

Open WebUI is the most popular self-hosted AI chat interface, with over 135,000 GitHub stars and 282 million container downloads. It provides a ChatGPT-style web UI that connects to Ollama for local models or any OpenAI-compatible API.

Key strengths:

• Built-in RAG engine for document-grounded conversations
• Voice and video call support with multiple speech providers
• Model builder for creating custom agents without code
• Python function calling for extending chatbot capabilities
• Single pip or Docker command to deploy

Limitations:

• Not a conversation design tool. You get a chat UI, not dialogue flows or intent management.
• No built-in channel integrations (Slack, WhatsApp, etc.) without custom work.

Best for: Teams that want a private, self-hosted ChatGPT replacement with RAG capabilities.

2. Jan

Jan runs LLMs entirely offline on your desktop. It has 41,900+ GitHub stars and supports downloading models directly from Hugging Face, plus optional cloud API connections to OpenAI, Anthropic, and Groq.

Key strengths:

• True offline operation with no data leaving your machine
• Cross-platform desktop app (Windows, macOS, Linux)
• Supports both local models (Llama, Gemma, Qwen) and cloud APIs
• Clean interface for managing multiple models and conversations

Limitations:

• Desktop-only. Not designed for web deployment or team use.
• Performance depends on your local hardware (GPU recommended for larger models).

Best for: Individual developers or security-conscious users who want full local control.

3. LibreChat

LibreChat unifies multiple AI providers in a single self-hosted interface. With 33,900+ GitHub stars, it has grown rapidly since its 2023 launch. It supports OpenAI, Anthropic, Google, Azure, and dozens of other providers through a single UI.

Key strengths:

• AI Agents with tool use and Model Context Protocol (MCP) support
• Code Interpreter for running Python in conversations
• Multi-user authentication with role-based access
• Conversation search and artifact management
• Active community with over 9,000 Discord members

Limitations:

• More complex to self-host than simpler chat UIs (requires MongoDB).
• Agent capabilities are newer and still maturing compared to dedicated agent frameworks.

Best for: Teams that need one interface across multiple AI providers, with growing agent capabilities.

4. HuggingChat (chat-ui)

HuggingChat is Hugging Face's open source chat application, built with SvelteKit. The chat-ui codebase (10,700+ GitHub stars) gives you access to the latest open-weight models from Meta, Mistral, and others.

Key strengths:

• Smart routing ("Omni") automatically picks the best model for each query
• Web search integration for real-time information
• Custom assistants with system prompts
• No account required to start chatting
• Direct access to new open-weight models as they launch

Limitations:

• Fewer self-hosting features than Open WebUI or LibreChat.
• Primarily designed around Hugging Face's own inference infrastructure.

Best for: Developers who want to test and deploy the newest open-weight models quickly.

These three projects provide actual conversation design tools: dialogue management, NLU training, or structured bot-building workflows. If you need more than a chat UI and want to design specific conversation flows, this is where to look.

5. Rasa

Rasa remains the most widely deployed open source conversational AI framework, with 20,000+ GitHub stars and a decade of production use. It provides a complete ML pipeline for intent classification, entity extraction, and dialogue management.

Key strengths:

• Full NLU pipeline you train on your own data, no external API calls at inference
• Dialogue policies that learn from conversation examples, not just rules
• Enterprise-grade deployment with Kubernetes support
• Active open source community at rasa.community
• Optional LLM integration layer (CALM) added in recent versions for hybrid approaches

Limitations:

• Steeper learning curve than LLM-native tools. You need training data and ML pipeline understanding.
• The open source version (Rasa Open Source) receives fewer updates since the company shifted focus to Rasa Pro.

Best for: Enterprise teams with ML expertise who need deterministic, auditable conversation flows.

6. Botpress

Botpress pivoted to an LLM-native architecture in 2025, building its custom inference engine (LLMz) that coordinates agent behavior internally. With 15,000+ GitHub stars and a $25M Series B, it bridges visual bot building with AI-powered conversation.

Key strengths:

• Visual flow builder combined with LLM reasoning for flexible conversation design
• Agent Router for multi-agent coordination
• Built-in integrations for Slack, WhatsApp, Telegram, and web
• Supports both proprietary (GPT-4o, Claude) and open models (Llama 3)
• JavaScript sandbox for custom logic within flows

Limitations:

• The open source version and the cloud platform have diverged. Some features are cloud-only.
• LLMz engine adds a layer of abstraction that can be hard to debug.

Best for: Teams that want visual chatbot building with LLM intelligence and channel integrations out of the box.

7. DeepPavlov

DeepPavlov is a research-grade NLP framework from the Moscow Institute of Physics and Technology. With 7,000+ GitHub stars, it provides pretrained models for question answering, named entity recognition, sentiment analysis, and dialogue systems.

Key strengths:

• Extensive pretrained model library covering dozens of NLP tasks
• Strong multilingual support, particularly for Russian and European languages
• Configuration-driven development that separates model architecture from training code
• Built on PyTorch and Transformers for modern model support

Limitations:

• Smaller community than Rasa or Botpress. Documentation can lag behind releases.
• More suited to NLP research than production chatbot deployment.

Best for: Research teams and multilingual projects that need fine-grained NLP model control.

Give your chatbot persistent file storage

Fast.io provides 50GB free storage with MCP server access and built-in RAG. Upload files, index them for retrieval, and let your chatbot answer questions from stored documents. No credit card required.

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Not every project needs a full conversation framework. These two tools solve specific chatbot use cases: customer support and lead capture.

8. Chatwoot

Chatwoot is an open source customer engagement platform with 21,000+ GitHub stars. Think of it as a self-hosted Intercom or Zendesk. While not a chatbot framework per se, it provides the infrastructure for deploying AI-powered support chatbots across multiple channels.

Key strengths:

• Omnichannel support: website live chat, email, Twitter, WhatsApp, Telegram, and more
• AI-assisted responses for support agents
• Canned responses, automations, and team collaboration features
• Self-hosted with full data ownership
• Available in 30+ languages

Limitations:

• The AI chatbot capabilities are supplementary to the human support workflow, not standalone.
• Building custom conversation flows requires integrating external NLU or LLM tools.

Best for: Support teams that want open source live chat with AI assistance, not a standalone chatbot builder.

9. Typebot

Typebot replaces static forms with step-by-step chat interfaces. With 9,800+ GitHub stars, it provides a drag-and-drop builder for creating conversational flows that collect data, qualify leads, and route inquiries.

Key strengths:

• Visual drag-and-drop flow builder with no coding required
• Embeddable chat widget for any website
• Integrations with Google Sheets, webhooks, Zapier, and AI providers
• Self-hostable with Docker
• Conditional logic, file uploads, and payment collection

Limitations:

• Designed for structured data collection, not free-form conversation.
• LLM integration exists but is not the primary architecture.

Best for: Marketing teams and solo founders who need conversational forms for lead capture and qualification.

The right choice depends on what you are actually building.

Building a customer support chatbot? Start with Chatwoot if you want a full support platform, or Botpress if you need custom conversation flows with LLM reasoning. Rasa makes sense if your team has ML expertise and you need deterministic responses for regulated industries.

Deploying a private ChatGPT for your team? Open WebUI is the fast path. Install it with Docker, connect to Ollama for local models, and you have a private AI chat in minutes. LibreChat is the better choice if you need multi-provider support or agent capabilities.

Running models offline on your laptop? Jan is purpose-built for this. Download models from Hugging Face, run them locally, and keep everything on your machine.

Collecting leads through conversational forms? Typebot handles this without requiring you to learn a chatbot framework.

Doing NLP research? DeepPavlov gives you the most pretrained model variety and fine-grained control.

Where your chatbot stores its files

One gap that most frameworks leave open is persistent file storage. When a chatbot agent generates a report, collects uploaded documents, or builds a knowledge base, those files need a home that outlasts the conversation session.

Local disk works for prototypes. For production, teams typically choose between S3-compatible object storage, cloud drives like Google Drive or Dropbox, or purpose-built agent workspaces.

Fast.io is one option worth evaluating for this layer. It provides 50GB of free storage with MCP server access, so chatbot agents can read, write, and organize files through a standard API. Intelligence Mode auto-indexes uploaded files for retrieval-augmented generation, which means your chatbot can answer questions about stored documents without a separate vector database. When the bot finishes its work, you can transfer workspace ownership to a human team member.

The free tier includes 5,000 AI credits per month, five workspaces, and no credit card requirement, which makes it practical for prototyping chatbot storage before committing to infrastructure.