TL;DR
- Best for enterprise conversational AI with full control: Rasa (open-source framework, used by T-Mobile, BNP Paribas, Autodesk, CALM framework for LLM + business logic, voice + text + omnichannel, on-premises deployment)
- Best for AI agents with forms, data, workflows, and email: TinyAgents (7 LLM providers, no code, natively connected to TinyForms, TinyTables, TinyWorkflows, TinyEmails)
- Pricing: Rasa has a free Developer Edition and enterprise Rasa Pro (contact sales). TinyCommand starts free from $19/mo.
- The core difference: Rasa is a conversational AI framework for engineering teams building production-grade chatbots and voice assistants with full control over models, data, and deployment. TinyAgents is a no-code AI tool for business teams adding intelligence to workflows. Rasa builds conversations. TinyAgents processes business data.
| Feature | TinyAgents | Rasa |
|---|---|---|
| Open source | ✗ | ✓ (full framework) |
| On-premise | ✗ | ✓ |
| No vendor lock-in | ✓ | ✓ |
| Requires engineering | ✗ | ✓ |
| Native forms | ✓ | ✗ |
| Data enrichment | ✓ | ✗ |
We used to run our lead enrichment through five different tools. With TinyCommand, it is just one flow.
Ankit Solanki, InVideo
Rasa is the leading open-source conversational AI framework. Enterprise customers like T-Mobile, BNP Paribas, Autodesk, and Swisscom use it to build chatbots and voice assistants that handle millions of conversations. The CALM (Conversational AI Language Model) framework combines LLMs with business logic for reliable, controllable AI agents. You get full visibility into what the agent does and why.
TinyAgents is not a conversational AI framework. It is a no-code AI tool inside TinyCommand that connects LLM intelligence to your forms, databases, workflows, and email. No NLU pipelines. No dialogue management. No voice gateways. Just AI as a workflow step that scores leads, drafts content, classifies data, and writes emails.
Rasa builds conversational experiences. TinyAgents automates business operations. The overlap is near zero.
Where Each Tool Wins
Where Rasa wins
Conversational AI framework. Build production chatbots and voice assistants with NLU, dialogue management, and action execution. TinyAgents is not conversational AI.
Full control. Open-source code. On-premises deployment. Your data stays on your infrastructure. Complete visibility into agent decisions.
CALM framework. LLMs + business logic for reliable, controlled AI conversations. Prevents hallucination in critical workflows.
Enterprise voice. Voice gateway with natural turn-taking. Phone-based AI assistants for banks, telecoms, healthcare.
Where TinyAgents wins
All-in-one platform. AI + forms + database + workflows + email. Rasa is a framework only.
No code, no ML. Business teams use AI without engineers. Rasa needs ML engineers and DevOps.
7 LLM providers. Claude, GPT-4, Gemini. Switch per task. Rasa uses its own CALM framework.
Free tier with all products. Rasa Developer Edition is free but needs engineering to use.
Minutes to deploy. Sign up, configure, live. Rasa takes months to build and deploy.
Conversational AI framework vs business AI agents
Rasa's CALM framework is its differentiator. It extends LLMs with your business logic so the AI agent follows rules, accesses databases, and performs actions reliably — not just generating text. When a banking customer asks to transfer money, Rasa's agent checks the balance, validates the recipient, confirms the amount, and executes the transfer. This requires tight integration with backend systems and guardrails that prevent the AI from hallucinating financial transactions.
The open-source Developer Edition lets you build and test on your machine. Rasa Pro adds enterprise features: voice gateway with turn-taking, enterprise RAG for knowledge base retrieval, and production deployment with monitoring. On-premises deployment means your conversation data never leaves your infrastructure — critical for banks, healthcare, and government.
But Rasa requires significant engineering investment. You need ML engineers to train NLU models, dialogue developers to design conversation flows, and DevOps to deploy and maintain the infrastructure. The learning curve is steep. The reward is complete control.
TinyAgents trades control for accessibility. No ML engineering. No NLU training. No deployment infrastructure. Select an LLM, write a prompt, connect to your data. The AI reasons about your business data and returns results. Use those results in your workflow — send an email, update a database, post to Slack. Done in minutes, not months.
For a fintech company building a voice assistant that handles 10 million banking conversations monthly with regulatory compliance, Rasa is the platform. For a startup that needs AI to qualify leads from form submissions and draft personalized outreach emails, TinyAgents does it without an engineering team.
Who should choose what
Choose TinyAgents if:
- You need AI for business operations, not conversational interfaces
- You want 7 LLM providers with no code and no ML engineering
- You need AI connected to forms, databases, workflows, and email
- You are a non-technical team adding AI to existing business processes
- You want a free tier with 5 products at $19/month paid
- Your AI use cases are analytical (scoring, classifying, drafting) not conversational
Choose Rasa if:
- You are building production-grade conversational AI (chatbots, voice assistants)
- You need full control over models, data, and deployment (on-premises option)
- Enterprise compliance requires conversation data to stay on your infrastructure
- You have ML engineers and dialogue developers on your team
- Voice AI with natural turn-taking is a requirement
- Companies like T-Mobile and BNP Paribas are your reference customers
This comparison also applies to
- Teams comparing TinyAgents with Dialogflow (Google conversational AI)
- Teams comparing TinyAgents with Microsoft Bot Framework
- Teams comparing TinyAgents with Botpress (open-source chatbot platform)
- Engineering teams evaluating open-source vs managed AI agent platforms
