Most tools that call themselves "AI agent builders" build chatbots. Polished, capable chatbots — but chatbots. They answer questions. They pull from a knowledge base. They hold a conversation.
That's not an agent.
An agent takes actions. It reads your database, decides what to do, triggers a workflow, updates a record, sends an email, and reports back. The difference between a chatbot and an agent is the difference between someone who gives advice and someone who actually does the work.
This matters when you're choosing a platform. If you need a support bot that answers FAQs, half the tools on this list will work fine. If you need an AI that can process a refund, update a CRM, and send a confirmation email — your options narrow fast.
Here's how seven platforms stack up.
What Makes an Agent an Agent?
Before the comparisons, a quick definition. A real AI agent needs three things:
- Reasoning — It can interpret a request, break it into steps, and decide what to do.
- Tool access — It can call external systems, query databases, and trigger actions.
- Autonomy — It can execute a multi-step process without needing human input at every stage.
A chatbot has reasoning (thanks to the LLM) but limited or no tool access, and zero autonomy. It answers; it doesn't act.
Keep this framework in mind as you read through each platform.
1. Botpress
Botpress has been around since 2017, originally as an open-source chatbot framework. It's since pivoted to a cloud platform focused on building conversational AI with visual flow builders.
What it does: Visual conversation flow builder with AI-powered natural language understanding. Build multi-turn conversations, connect to knowledge bases, and deploy across web, WhatsApp, Messenger, and other channels.
LLM options: OpenAI GPT-4o and GPT-4 Turbo are the primary options. You can use Anthropic Claude through custom integrations. Model selection is somewhat limited compared to dedicated LLM platforms.
Pricing (2026):
- Free: Pay-as-you-go, $5 AI credit included
- Plus: $79/month — higher limits, analytics
- Team: $495/month — collaboration, SSO
Can it take actions? Partially. Botpress supports "Actions" — code blocks that can call external APIs. You can make HTTP requests, query databases, and trigger webhooks. But you're writing code to do it. There are no pre-built integrations with CRMs, email platforms, or databases that work out of the box. The action layer is capable but requires developer effort.
Best for: Teams with developer resources that want to build sophisticated conversational flows with custom backend integrations.
2. Voiceflow
Voiceflow started as a voice app builder (Alexa, Google Assistant) and evolved into a general-purpose conversation design platform. The visual canvas is one of the best in the category.
What it does: Drag-and-drop conversation design with a visual canvas. Build agents with branching logic, knowledge base retrieval, entity extraction, and multi-channel deployment. Strong collaboration features for teams.
LLM options: OpenAI models, Anthropic Claude, Google Gemini. Good flexibility here. You can also bring custom models through API integration.
Pricing (2026):
- Sandbox: Free — 50 AI tokens/month, 2 projects
- Pro: $60/editor/month — 5,000 AI tokens/month
- Teams: custom pricing
Can it take actions? Limited. Voiceflow supports API steps that can call external services, and you can use custom functions. But the strength is conversation design, not action execution. You'd typically have Voiceflow trigger a webhook that calls out to Zapier or a custom backend to actually do things. The agent itself doesn't directly manipulate databases or send emails.
Best for: Designers and product teams building polished conversational experiences with a focus on conversation quality and user experience.
3. CustomGPT
CustomGPT focuses on creating custom AI agents trained on your own content. Upload documents, websites, and files to create an AI that knows your business. Simple concept, straightforward execution.
What it does: Upload your content (PDFs, websites, documents, spreadsheets), and CustomGPT creates an agent that can answer questions based on that content. Deployable as a widget, full-page embed, or via API.
LLM options: Primarily OpenAI GPT-4o and GPT-4 Turbo. Model selection is limited — this isn't a platform for experimenting with different LLMs.
Pricing (2026):
- Standard: $49/month — 1,000 pages, 500 queries/month
- Premium: $99/month — 5,000 pages, 2,000 queries/month
- Enterprise: $499/month — 50,000 pages, 10,000 queries/month
Can it take actions? No. CustomGPT is purely a knowledge retrieval system. It answers questions from your content. It doesn't trigger workflows, update databases, or call APIs. It's a chatbot — a very good knowledge-base chatbot — but strictly a chatbot.
Best for: Companies that need a customer-facing FAQ/knowledge assistant built from existing documentation, with minimal setup.
4. Chatbase
Chatbase is similar to CustomGPT but with more customization options and a focus on lead generation. You train it on your data, embed it on your site, and it captures leads while answering questions.
What it does: Create AI chatbots from your data sources. Supports website scraping, PDF uploads, plain text, and Q&A pairs. Includes lead capture forms, conversation analytics, and handoff to human agents.
LLM options: OpenAI GPT-4o, GPT-4 Turbo, GPT-3.5. Claude is available on higher-tier plans. Limited but covers the main models.
Pricing (2026):
- Free: 30 message credits/month, 1 chatbot
- Hobby: $19/month — 2,000 credits, 2 chatbots
- Standard: $99/month — 10,000 credits, 5 chatbots
- Unlimited: $399/month — 40,000 credits, 10 chatbots
Can it take actions? Minimally. Chatbase recently added "Actions" that let you define custom API calls the chatbot can make during a conversation. You configure the API endpoint, parameters, and headers. It works, but it's primitive compared to a real workflow engine. You're configuring individual API calls, not building processes.
Best for: Marketing teams that want a lead-gen chatbot on their website, trained on company content, with minimal technical setup.
5. Relevance AI
Relevance AI positions itself as a "workforce of AI agents." The platform lets you build agents, give them tools (API calls, code execution, data transformations), and chain them together into multi-agent systems.
What it does: Build AI agents with custom tools. Each tool is a defined action — an API call, a data transformation, a code snippet. Agents reason about which tools to use and chain tool calls together. Supports multi-agent orchestration where agents can delegate to other agents.
LLM options: OpenAI, Anthropic Claude, Google Gemini, Cohere, and custom models via API. Strong LLM flexibility.
Pricing (2026):
- Free: 100 credits/day
- Pro: $19/month — 5,000 credits/month
- Business: $199/month — 100,000 credits/month
- Enterprise: custom pricing
Can it take actions? Yes — this is Relevance AI's core value proposition. The tools system lets you define arbitrary API calls, data lookups, web scraping, code execution, and more. Agents actually execute multi-step processes. The limitation is that you need to define each tool yourself. There's no pre-built integration library comparable to Zapier.
Best for: Technical teams building AI-powered internal tools and processes, particularly in sales ops, data enrichment, and research automation.
6. Stack AI
Stack AI provides a visual builder for creating AI workflows and agents. Think of it as a flow-based programming environment specifically designed for LLM applications.
What it does: Visual drag-and-drop builder for AI workflows. Connect LLM nodes, data processing nodes, API calls, and conditional logic into chains. Build chatbots, document processors, classification systems, and multi-step agents.
LLM options: OpenAI, Anthropic, Google, Meta Llama, Mistral, and more. Excellent model coverage. You can even compare model outputs side-by-side.
Pricing (2026):
- Free: 100 runs/month, 2 projects
- Developer: $199/month — 2,000 runs/month
- Business: $599/month — 5,000 runs/month
- Enterprise: custom pricing
Can it take actions? Partially. Stack AI workflows can call APIs, process data, and output results. But the focus is on AI processing pipelines — document analysis, data extraction, content generation. The workflows are powerful for data processing but aren't designed as business process automation. You won't find CRM integration nodes or email campaign nodes.
Best for: AI engineers and data teams building LLM-powered processing pipelines, not customer-facing chatbots or business process agents.
7. TinyCommand (TinyAgents)
TinyAgents is the AI agent builder inside TinyCommand. What makes it different isn't the agent builder itself — it's what the agent connects to.
What it does: Build AI agents with knowledge bases (upload PDF, DOCX, CSV files), custom tools, configurable guardrails, and six different embed modes (chat widget, full page, inline, popup, slide-over, and API). One-click publish to get a shareable link or embed code.
LLM options: Seven providers — OpenAI, Anthropic Claude, Google Gemini, Mistral, Cohere, Meta Llama, and Groq. Switch models per agent. This is the widest LLM selection on the list.
Pricing (2026):
- Free: $0/month — 1,000 credits (shared across all TinyCommand products)
- Basic: $19/month — 10,000 credits
- Professional: $49/month — 50,000 credits
- Agency: $149/month — 250,000 credits
Can it take actions? Yes — and this is the critical differentiator. Because TinyAgents lives inside TinyCommand alongside TinyWorkflows, TinyTables, TinyEmails, and TinyForms, agents can:
- Trigger TinyWorkflows — kick off a 20-step automation based on a conversation
- Read and write TinyTables — look up a customer record, update a status field, log an interaction
- Send TinyEmails — draft and send a personalized email with merge fields pulled from the conversation
- Accept TinyForms data — process form submissions that feed directly into agent conversations
The agent doesn't call external APIs to do these things. These systems are native. The data is already there.
Best for: Teams that want AI agents tightly integrated with their business operations — not just chatting, but actually executing workflows, managing data, and communicating with customers.
The Chatbot vs. Agent Spectrum
Here's a blunt summary of where each tool falls:
Pure chatbots (answer questions only):
- CustomGPT
- Chatbase
Chatbots with some action capability (limited API calls):
- Botpress
- Voiceflow
Agents that can take defined actions (tool-based):
- Relevance AI
- Stack AI
Agents integrated with a full business platform (native actions):
- TinyCommand (TinyAgents)
The further right you go on this spectrum, the more your AI can actually do. But the further right you go, the more you're committing to a specific ecosystem.
If you just need a knowledge-base chatbot on your website, CustomGPT or Chatbase will have you live in an afternoon. If you need an agent that can process an order, update inventory, notify the warehouse, and email the customer — you need something closer to TinyAgents.
The n8n and Zapier Factor
Worth mentioning: n8n has been investing heavily in AI agent capabilities. You can build agentic workflows where an LLM decides which tools to use from within an n8n workflow. Zapier has also released AI agent templates. Both are coming at the problem from the automation side — adding AI reasoning to existing workflow engines.
This is a legitimate approach. If you already use n8n or Zapier, their AI agent features may be enough. The trade-off is that these are automation tools with AI bolted on, not agent platforms built from the ground up. The conversational UX, knowledge base management, and embed options are more limited.
What to Ask Before Choosing
Skip the feature comparison matrices. Answer these four questions:
1. Does your agent need to just answer questions, or take actions?
If answer-only, save your money. CustomGPT at $49/month or Chatbase at $19/month will work.
2. What systems does your agent need to interact with?
If it's your own custom APIs, Relevance AI gives you the most flexibility. If it's standard business tools (CRM, email, databases), TinyCommand's native integration is faster to set up.
3. How technical is the person building the agent?
Botpress and Relevance AI assume some technical comfort. Chatbase and TinyAgents are designed for non-technical operators.
4. Do you need the agent standalone, or as part of a larger system?
If standalone, any dedicated platform works. If the agent is one part of a broader business process involving forms, data, automation, and email — TinyCommand makes the most sense because everything is already connected.
The AI agent space is moving fast. New tools appear every month. But the fundamental question stays the same: can your "agent" actually do things, or is it just a chatbot with good marketing?
Pick accordingly.
Try TinyCommand Free
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