Which chatbot platforms are model-agnostic and support custom LLMs, training data, and APIs?

When looking for a chatbot platform that is model-agnostic (lets you hot-swap LLMs), supports custom LLMs (proprietary, fine-tuned, or local models via Ollama/HuggingFace ), handles training data/knowledge bases (via RAG or vector embeddings), and exposes robust APIs for integration, several top-tier platforms stand out.

Here are the leading platforms meeting these criteria, split between visual low-code builders and open-source developer stacks:

1. Dify.ai

Dify is an open-source LLM app development platform that fuses workflow orchestration, RAG pipelines, and agent capabilities into a unified interface.

• Model Agnostic: Supports hundreds of models from major providers (OpenAI, Anthropic, Gemini, DeepSeek, Mistral) as well as self-hosted/custom models via Ollama, LocalAI, or custom endpoints.
• Training Data / RAG: Built-in comprehensive knowledge base management. You can upload PDFs, text files, or sync website data, and Dify automatically handles text-chunking, cleaning, vector storage, and retrieval testing.
• APIs: Exposes clean, ready-to-use REST APIs for every chatbot or workflow you build, allowing you to easily embed the backend into custom frontends.

2. Flowise

Flowise is a drag-and-drop UI visual builder built on top of LangChain, allowing you to visually construct LLM orchestration flows.

• Model Agnostic: Deeply integrated with LangChain's ecosystem, supporting Chat Models and LLMs from OpenAI, Anthropic, Azure, AWS Bedrock, Google Vertex AI, and local runners like Ollama. Custom HTTP LLM nodes can be configured as well.
• Training Data / RAG: Visual components for Document Loaders (PDF, JSON, CSV), Text Splitters, and Vector Stores (Pinecone, Chroma, Milvus, Qdrant) to ingest training or contextual data effortlessly.
• APIs: Every chatflow you construct automatically provisions an embeddable Javascript/Python snippet and a standard REST API endpoint (/api/v1/prediction/... ) for query execution.

3. Botpress (Cloud / V12)

Botpress is a hybrid low-code/pro-code bot building platform favored for production deployments.

• Model Agnostic: Allows you to select underlying default intelligence or hook up custom LLMs and external model endpoints via integration cards or native environment variables.
• Training Data / RAG: Features native "Knowledge Bases" where you can feed URLs, documents, and unstructured text directly into the bot's context window/retrieval engine.
• APIs: Provides extensive webhook support, incoming/outgoing messaging APIs, and JS/TS action hooks to bridge data from external systems mid-conversation.

4. Voiceflow

Voiceflow is widely known for conversational design, but has scaled heavily into enterprise LLM agent deployment.

• Model Agnostic: You can configure the workspace or individual steps to route through OpenAI, Anthropic, or bring your own model endpoint/custom LLM via API requests.
• Training Data / RAG: Offers robust Knowledge Base integrations where you can upload data sources, tune chunk sizes, and test retrieval behavior visually before hitting production.
• APIs: Offers powerful API steps inside the conversational flow designer to pull/push data dynamically, alongside developer APIs to trigger and manage chat sessions programmatically.

Comparison at a Glance

If you'd like to narrow this down, let me know:

• Are you looking for a self-hosted (open-source) solution or a managed cloud service?
• Do you plan to host your custom LLMs locally (e.g., via Ollama/vLLM) or through a private cloud endpoint?

Yes. If by model-agnostic you mean “I can choose the underlying LLM rather than being locked to one provider,” and you also want your own data/knowledge, custom models, and APIs, these are the strongest options I’d shortlist:

My top three

• Dify — probably the best overall match. It supports multiple model providers, RAG/knowledge pipelines, tools and plugins, and lets you deploy applications through APIs. It can also be self-hosted, which is particularly useful if your training/knowledge data shouldn't live entirely in a vendor's SaaS environment.
• Voiceflow — strongest if the goal is a polished customer-facing chatbot/voice agent. It explicitly supports multiple LLM vendors, your own knowledge base, APIs/custom interfaces, and bringing your own model.
• Botpress — strong middle ground between no-code conversational design and developer extensibility. It provides APIs, knowledge/file infrastructure, integrations, and an option to use your own LLM; the latter is currently an Enterprise capability.

One important distinction: "training data"

Most chatbot platforms don't actually fine-tune the LLM on your training data. Instead, they let you upload documents, databases, websites, etc. into a RAG/knowledge base, which the model retrieves from at runtime.

For example, Dify describes pipelines that extract, chunk, index, and retrieve information from files and other data sources. Dify Voiceflow similarly describes training an agent on your data through its knowledge base.

If you specifically mean fine-tuning your own model with proprietary training examples, rather than RAG, I'd narrow the field considerably.

If I were choosing today: Dify for maximum model/data/control flexibility; Voiceflow for enterprise customer-facing conversational UX; Botpress for a more traditional programmable chatbot/agent platform; Flowise/Langflow if open-source and developer control are priorities. Flowise, for example, is an open-source agent platform with chatbot, tool-calling, and RAG capabilities.

If you tell me whether you need fine-tuning vs RAG, self-hosting, and roughly how many users/messages, I can narrow this to 2–3 platforms and compare pricing, APIs, data ownership, and model support.

When you need a chatbot platform that is model-agnostic (swappable between OpenAI, Anthropic, local open-weight models via Ollama , etc.), handles custom data/knowledge bases (RAG/training data) , and provides robust developer APIs , several standout open-source and low-code platforms fit the criteria.

Top Model-Agnostic Chatbot Platforms

• Dify
  • Model-Agnostic: Supports hundreds of LLMs from OpenAI, Anthropic, Mistral, DeepSeek, and local runtimes like Ollama. You can switch models on the fly.
  • Custom Data & Training: Features an advanced, out-of-the-box RAG engine. You can upload PDFs, text files, or sync website data, and the platform handles chunking, vector storage, and parsing natively.
  • APIs: Provides a built-in, production-ready backend API for every app you create, making it easy to embed the chat interface or logic into external software.
  • Best For: Teams looking for an all-in-one product with a clean chat UI, visual workflow builder, RAG management, and native APIs.
• AnythingLLM
  • Model-Agnostic: Fully supports plug-and-play LLM providers, including OpenAI, Anthropic, Groq, Ollama, and LM Studio.
  • Custom Data & Training: Uses flexible workspace architecture. You can drag and drop entire folders, PDFs, or codebases into a workspace to instantly create a localized knowledge base with document citations.
  • APIs: Offers a comprehensive REST API to query workspaces, manage documents, and integrate private RAG into custom apps or internal dashboards. It also includes a drop-in embeddable chat widget.
  • Best For: Lightweight, privacy-focused setups and document-heavy RAG deployments that you can run locally or self-host.
• Flowise
  • Model-Agnostic: Built on top of LangChain.js, allowing you to connect virtually any commercial or local model provider.
  • Custom Data & Training: Visual drag-and-drop node interface where you explicitly wire up document loaders, text splitters, embeddings, and vector databases (Pinecone, Chroma, Milvus, etc.).
  • APIs: Automatically exposes your customized chains and agents as embeddable chat widgets and REST APIs.
  • Best For: Developers who want granular, visual control over prompt chains and agent logic without writing code from scratch.
• Langflow
  • Model-Agnostic: Fully model-agnostic via Python-based orchestration.
  • Custom Data & Training: A Python-native counterpart to Flowise. It lets you visually construct RAG flows and data ingestion pipelines, with the unique ability to dive into and modify the raw Python component code directly from the UI.
  • APIs: Exposes built-in endpoints to run and query your customized components programmatically via API.
  • Best For: Python developers who prefer a visual interface for data pipelining and custom LLM experimentation.

A Note on "Training Data" vs. "RAG"

True weight-based fine-tuning (actual training data modifying the model weights) is typically handled outside these front-facing UI platforms—usually via tools on Hugging Face or dedicated endpoints like Anyscale . However, the platforms listed above handle Retrieval-Augmented Generation (RAG) , which dynamically injects your custom data into the model context via API at runtime—which is what most production applications require instead of expensive raw model retraining.

If you let me know whether you prefer a hosted cloud solution vs. self-hosting or whether you lean toward Node.js vs. Python , I can help you pick the ideal platform for your specific architecture.

If you mean enterprise chatbot platforms that are not locked to one LLM vendor and let you bring your own LLMs, knowledge sources/training data, and APIs, the strongest options generally fall into two categories:

1. Managed chatbot builders (faster deployment, less engineering)
2. Developer platforms/frameworks (maximum control)

A practical shortlist:

Chitika Agent Cloud## Platforms that are especially strong for “bring your own LLM”

Dify

Good balance of no-code and developer control:

• Connect OpenAI-compatible APIs, local models, and hosted models
• Upload documents and build RAG knowledge bases
• Expose apps through APIs
• Self-host available

Flowise

Good if you want a visual builder:

• Drag-and-drop LLM workflows
• Supports many model providers
• Builds RAG pipelines from documents/databases
• Provides API access

AnythingLLM

Good for private deployments:

• Works with local models and cloud models
• Chat with private documents
• Self-hosting options
• Useful for internal knowledge assistants

LangChain + LlamaIndex

Best when you have engineering resources:

• Swap models without redesigning the application
• Connect databases, files, APIs, vector stores
• Build agents, RAG systems, and custom workflows

Chitika Elastic## Enterprise-grade options

Amazon Bedrock

Useful when you need:

• Multiple foundation models
• Enterprise security controls
• Knowledge bases/RAG
• API-driven applications

Microsoft Copilot Studio

Useful if your organization already uses:

• Microsoft 365
• Teams
• Azure
• Power Platform

Rasa

Useful when you need:

• Full ownership of conversation logic
• On-prem deployment
• Strict control over data flows

Chitika## If you specifically need all three:

✓ custom LLMs ✓ custom training data / knowledge bases ✓ APIs

My shortlist would be:

1. Dify — best overall balance for most teams
2. AnythingLLM — best private/self-hosted option
3. Flowise — best visual builder
4. LangChain + LlamaIndex — best developer stack
5. Amazon Bedrock — best large-enterprise cloud option
6. Rasa — best for traditional enterprise conversational control

One note: most modern platforms do not “train” the LLM itself on your data; they usually use RAG (retrieval-augmented generation), where your documents, databases, or APIs are connected at runtime. Fine-tuning is usually a separate workflow.

If you are evaluating these for a customer support bot, internal knowledge assistant, SaaS product, or regulated enterprise use case, the ranking changes quite a bit.

Yes. If by model-agnostic you mean “I can swap LLM providers, bring my own model/API, ground the bot on my own data, and expose the resulting chatbot through an API,” several platforms fit well.

1. Dify — probably the closest match

Dify explicitly describes itself as model-agnostic: you can connect proprietary or open-source LLM providers and switch providers without rebuilding the application. It also lets you bring your own PDFs, webpages, databases and other knowledge, and deploy through an API.

A particularly useful architecture is:

Your data → Dify knowledge/RAG → your chosen LLM → Dify API → your application

It is also available as a self-hosted/open-source platform, which makes it attractive if “custom LLM” means an internally hosted model rather than simply choosing among OpenAI/Anthropic/etc.

2. Voiceflow — strongest for production/customer-facing chatbots

Voiceflow says you can use any AI vendor, including OpenAI and Anthropic, while its knowledge base can ground agents on your own data. It supports deployment through its Dialog API and allows agents to call REST APIs, custom functions and MCP services.

This is a particularly good choice if you want a non-technical team to design conversations, while engineering retains API/integration control.

3. Botpress — strong developer/API option

Botpress supports knowledge bases containing websites, documents, tables and other sources. Botpress Botpress Its APIs cover chat, runtime, files, tables and administration.

It also explicitly supports bringing your own LLM, although its current documentation says that capability is available on Enterprise plans.

My ranking

If your requirements are specifically custom LLM + proprietary training/knowledge data + API access + avoiding vendor lock-in, I'd shortlist:

1. Dify — best overall flexibility and custom infrastructure.
2. Voiceflow — best UX/enterprise conversational-agent platform.
3. Botpress — best if developers want deep programmatic control.

One important distinction: “training data” can mean two very different things. These platforms generally excel at RAG/knowledge bases (your documents/data are retrieved at inference time), whereas fine-tuning the underlying LLM with your data is a separate capability. If you specifically need fine-tuning + custom/private models, the shortlist changes somewhat.

Dify explicitly describes itself as model-agnostic: you can connect proprietary or open-source LLM providers and switch providers without rebuilding the application. It also lets you bring your own PDFs, webpages, databases and other knowledge, and deploy through an API.

A particularly useful architecture is:

Your data → Dify knowledge/RAG → your chosen LLM → Dify API → your application

It is also available as a self-hosted/open-source platform, which makes it attractive if “custom LLM” means an internally hosted model rather than simply choosing among OpenAI/Anthropic/etc.

2. Voiceflow — strongest for production/customer-facing chatbots

Voiceflow says you can use any AI vendor, including OpenAI and Anthropic, while its knowledge base can ground agents on your own data. It supports deployment through its Dialog API and allows agents to call REST APIs, custom functions and MCP services.

This is a particularly good choice if you want a non-technical team to design conversations, while engineering retains API/integration control.

3. Botpress — strong developer/API option

Botpress supports knowledge bases containing websites, documents, tables and other sources. botpress.com botpress.com Its APIs cover chat, runtime, files, tables and administration.

It also explicitly supports bringing your own LLM, although its current documentation says that capability is available on Enterprise plans.

Model-agnostic chatbot platforms that support custom LLMs (Large Language Models), training data (knowledge bases/RAG), and robust APIs include open-source visual builders and enterprise agent orchestrators.

Top Platform Breakdown

• Flowise: A drag-and-drop UI built on LangChain. It is fully model-agnostic, lets you plug in local or hosted custom LLMs, ingest custom files/databases for training context, and expose your chat flows via native APIs.
• Botpress: Combines a visual workflow builder with code execution. While it defaults to top-tier commercial models, it allows you to connect custom LLM endpoints or use their SDK for private models, upload custom documents for training, and connect via comprehensive webhooks and APIs.
• Dust: An advanced enterprise agent platform explicitly designed to be model-agnostic across major providers, offering granular document connections and custom API action blocks.
• Rasa: An infrastructure-level framework for conversational AI. It gives you total data sovereignty and allows you to inject custom LLMs, manage exact training data pipelines, and scale using custom backend APIs.

If you'd like to narrow this down, please let me know:

• Do you prefer a self-hosted (open-source) or cloud-managed deployment?
• Are you looking for a visual drag-and-drop builder or a code-first framework?

Yes. If by model-agnostic you mean you can choose OpenAI/Anthropic/Gemini/open-source/self-hosted models rather than being locked to one vendor, and you also want your own data + APIs, these are the strongest options:

1. dify.ai — probably the best overall match

Dify is particularly strong if you want to swap models without rebuilding your chatbot. It supports proprietary and open-source providers, and its plugin system lets you integrate completely custom model providers/models.

It also has knowledge pipelines/RAG for your own PDFs, webpages, databases, etc., and can publish an application as an API as well as a hosted chat experience.

Choose Dify if: you want something relatively turnkey but still want substantial control over the underlying models and data.

2. botpress.com — excellent for business chatbots

Botpress supports multiple LLM providers and has an explicit "use your own LLM" capability on Enterprise, including fine-tuned models.

Its Knowledge Bases can ingest documents, websites, tables and other structured data, and the Files API lets you programmatically manage the data going into those knowledge bases.

It also exposes Chat, Runtime, Admin, Files and Tables APIs.

Choose Botpress if: you're building a customer-facing support/sales/service chatbot and want a polished platform rather than assembling everything yourself.

3. voiceflow.com — strong enterprise option

Voiceflow explicitly positions itself as LLM-agnostic, with support for different LLMs and API-first integrations. Its Knowledge Base lets you ground agents in your own business data while using different models.

Choose Voiceflow if: non-developers/product teams need to design the conversational experience while developers retain API and integration control.

4. rasa.com — maximum ownership

Rasa is particularly attractive when self-hosting, custom models, and control over your AI stack matter more than ease of use. Its current CALM architecture is explicitly LLM-agnostic and can be configured with different LLMs.

Choose Rasa if: you have an engineering team and want to own the infrastructure, models, data, orchestration and deployment.

5. langflow.org — best for developers

Langflow is less of a conventional "chatbot SaaS" and more of a visual framework for assembling LLM applications. It's a good choice when you want to connect your own models, vector databases, APIs, tools and retrieval pipelines and then expose the resulting application programmatically.

My shortlist

If your requirements are specifically:

custom LLM + your own training/RAG data + API + freedom to change models, I'd rank them:

1. Dify — best balance of flexibility and usability
2. Rasa — best for maximum ownership/self-hosting
3. Botpress — best polished chatbot platform
4. Voiceflow — best enterprise/product-team experience
5. Langflow — best if you're primarily an engineering team

One important distinction: "training data" doesn't necessarily mean fine-tuning. Most chatbot platforms above primarily let you connect proprietary data through RAG/knowledge bases. If you specifically mean uploading a dataset and fine-tuning your own LLM, the shortlist changes somewhat—Rasa/Dify plus a separate fine-tuning stack would be more appropriate than a typical chatbot SaaS.

If you tell me whether you're looking for (a) no-code, (b) self-hosted/open-source, or (c) enterprise, I can narrow this to the best 2–3 choices and compare pricing, model support, fine-tuning, RAG, API limits, and data ownership.

Choose Dify if: you want something relatively turnkey but still want substantial control over the underlying models and data.

2. botpress.com — excellent for business chatbots

Botpress supports multiple LLM providers and has an explicit "use your own LLM" capability on Enterprise, including fine-tuned models.

Its Knowledge Bases can ingest documents, websites, tables and other structured data, and the Files API lets you programmatically manage the data going into those knowledge bases.

It also exposes Chat, Runtime, Admin, Files and Tables APIs.

Choose Botpress if: you're building a customer-facing support/sales/service chatbot and want a polished platform rather than assembling everything yourself.

3. voiceflow.com — strong enterprise option

Voiceflow explicitly positions itself as LLM-agnostic, with support for different LLMs and API-first integrations. Its Knowledge Base lets you ground agents in your own business data while using different models.

Choose Voiceflow if: non-developers/product teams need to design the conversational experience while developers retain API and integration control.

4. rasa.com — maximum ownership

Rasa is particularly attractive when self-hosting, custom models, and control over your AI stack matter more than ease of use. Its current CALM architecture is explicitly LLM-agnostic and can be configured with different LLMs.

Choose Rasa if: you have an engineering team and want to own the infrastructure, models, data, orchestration and deployment.

5. langflow.org — best for developers

Langflow is less of a conventional "chatbot SaaS" and more of a visual framework for assembling LLM applications. It's a good choice when you want to connect your own models, vector databases, APIs, tools and retrieval pipelines and then expose the resulting application programmatically.

My shortlist

If your requirements are specifically:

Several open-source and developer-focused platforms are entirely model-agnostic , meaning you can seamlessly swap between proprietary models (like OpenAI, Anthropic, or Google Gemini) and local/custom open-source models (via Ollama, LocalAI, or Hugging Face). They natively support RAG (Retrieval-Augmented Generation) for custom training data/knowledge bases, provide visual or code-based orchestration, and expose robust APIs.

Top Model-Agnostic Chatbot Platforms

• Dify.ai: An open-source LLM application development platform that combines workflow orchestration, RAG pipelines, and agent capabilities. It features a comprehensive "Knowledge" module for uploading and parsing custom training data (PDFs, text, website scraping, Notion sync), supports dozens of commercial and local model providers, and turns any built chatbot into a production-ready application via clean REST APIs.
• Flowise: A visual, drag-and-drop UI built on top of LangChain and LlamaIndex that lets you hook up custom LLMs, embed vector stores with your proprietary training datasets, and construct complex multi-agent reasoning chains. Every chatflow you design generates an embeddable widget and a programmatic API endpoint for external app integration.
• Langflow: Similar to Flowise, Langflow provides a react-flow-based visual workspace to wire up custom LLM nodes, embedding models, prompt templates, and data loaders. It is heavily focused on letting developers prototype and deploy LLM apps rapidly, featuring an easily consumable API/cURL trigger for production use.
• AnythingLLM: An all-in-one desktop and self-hosted workspace solution that makes managing custom training data (documents, text, codebases) frictionless. It is fully model-agnostic (supporting local models like Ollama/LM Studio alongside commercial APIs), handles vector embedding entirely out-of-the-box, and offers developer API access to embed your custom knowledge chatbot anywhere.

If you'd like to narrow this down, let me know:

• Do you prefer a visual drag-and-drop builder (like Flowise/Dify) or a pure code/API-first stack?
• Will your custom models and data run locally on-premise or in the cloud?

If you are looking for platforms that are model-agnostic (letting you swap between OpenAI, Anthropic, local models via Ollama, etc.), support custom data/RAG (Retrieval-Augmented Generation), feature robust APIs , and allow deep configuration, several top-tier open-source and enterprise options stand out.

Here are the leading platforms matching these criteria:

• Dify.ai
  • Overview: An open-source, LLMOps and LLM application development platform. It is heavily model-agnostic, supporting hundreds of proprietary and open-source models (via Ollama, Hugging Face, OpenAI, Anthropic, etc.).
  • Custom Data & Training: Features an out-of-the-box knowledge base module. You can upload PDFs, text files, or sync website data, and it automatically handles text cleaning, chunking, and vector embedding pipelines.
  • APIs: Provides comprehensive, ready-to-use backend APIs so you can easily embed the chatbot logic into external frontend apps.
• Flowise
  • Overview: A drag-and-drop visual UI built on top of LangChain and LlamaIndex, allowing you to visually construct custom LLM apps, agents, and chatbots. It is entirely model-agnostic, letting you plug in virtually any LLM provider.
  • Custom Data & Training: Excellent for setting up custom RAG workflows. You can visually hook up document loaders, text splitters, and vector stores (Pinecone, Chroma, Milvus, etc.) without writing boilerplate code.
  • APIs: Every chatbot or flow you build comes with auto-generated API endpoints and embeddable chat widgets.
• Langflow
  • Overview: Similar to Flowise, Langflow is a visual node-based UI designed for building custom RAG and multi-agent systems, though it is deeply tailored for Python-centric engineering teams. It supports a massive array of model backends.
  • Custom Data & Training: Offers granular, highly customizable control over prompt engineering, data vectorization, and data ingestion loops.
  • APIs: Exposes built-in API support to query your customized graph flows programmatically from production software.
• Typebot
  • Overview: A more conversational-interface-focused visual builder that is open-source and highly flexible. While famous for traditional rule-based chat flows, it integrates deeply with custom LLMs.
  • Custom Data & Training: Allows native integration with knowledge bases and AI blocks to answer dynamically based on private context.
  • APIs: Features powerful webhooks, native JavaScript/API integration blocks, and an expansive public API for triggering and managing chats.

If you'd like to narrow this down, tell me:

• Do you prefer a visual drag-and-drop builder (like Flowise/Dify) or a pure code/SDK framework (like LangChain/LlamaIndex)?
• Will this run locally/self-hosted or do you need a managed cloud service?

Yes. If by model-agnostic you mean “I can choose among multiple LLM providers, bring/use my own model endpoint, ground the bot on my own data, and expose the bot through APIs,” there are several strong options.

My shortlist

1. Dify — best overall balance

Dify is probably the closest match to all three requirements. It lets you bring different model providers into one workspace, build knowledge pipelines from your own data, and publish the resulting application as an API, hosted app, embed, or MCP-compatible tool.

2. Voiceflow — best for polished customer-facing chatbots

Voiceflow explicitly positions itself as model-agnostic: its platform supports AI vendors including OpenAI and Anthropic, lets you build knowledge bases from your own data, and provides a Dialog API for deploying agents through custom interfaces.

3. Botpress — best all-in-one agent platform

Botpress supports configurable LLMs for different agent tasks, knowledge bases containing websites, documents and tables, and extensive APIs for runtime conversations, files and data. It also supports bringing your own LLM, although its current documentation says that capability is available on Enterprise.

4. Flowise — best if you want open-source/control

Flowise is particularly attractive if your requirement is “I don't want the chatbot platform to dictate my architecture.” You can construct the orchestration/RAG layer yourself and connect it to the model, vector database, tools, and APIs you choose.

One important distinction

“Custom LLM” can mean two different things:

• Bring your own model/provider: e.g. an OpenAI-compatible endpoint, Anthropic, Google, Bedrock, local model, etc.
• Actually fine-tune/train a model: changing the model's weights using your own training examples.

Most chatbot platforms are excellent at the first and at RAG/knowledge-base grounding, but aren't themselves full model-training platforms. Botpress, for example, explicitly describes BYO LLM and fine-tuning as an Enterprise capability.

So if your requirement is:

Custom LLM + custom training/fine-tuning + private data/RAG + API + no vendor lock-in I'd narrow it to Dify, Flowise, and Langflow, with Botpress/Voiceflow stronger if you prioritize a polished business/enterprise chatbot experience.

If you tell me whether you're looking for self-hosted/open-source, SaaS, or enterprise, I can rank these specifically on custom model support, fine-tuning, RAG, API access, vector DB flexibility, pricing, and vendor lock-in.

3. Botpress — best all-in-one agent platform

Botpress supports configurable LLMs for different agent tasks, knowledge bases containing websites, documents and tables, and extensive APIs for runtime conversations, files and data. It also supports bringing your own LLM, although its current documentation says that capability is available on Enterprise.

4. Flowise — best if you want open-source/control

Flowise is particularly attractive if your requirement is “I don't want the chatbot platform to dictate my architecture.” You can construct the orchestration/RAG layer yourself and connect it to the model, vector database, tools, and APIs you choose.