Last updated: March 15, 2026

layout: default title: “Botpress vs Rasa AI Chatbot Framework Compared” description: “Choosing between Botpress and Rasa requires understanding how each framework approaches chatbot development, customization, and deployment. Both are” date: 2026-03-15 last_modified_at: 2026-03-15 author: theluckystrike permalink: /botpress-vs-rasa-ai-chatbot-framework/ categories: [comparisons] intent-checked: true voice-checked: true score: 8 reviewed: true tags: [ai-tools-compared, comparison, artificial-intelligence] —

Choosing between Botpress and Rasa requires understanding how each framework approaches chatbot development, customization, and deployment. Both are open-source platforms popular with developers building conversational AI, but they serve different needs and development philosophies.

This comparison focuses on practical implementation details to help you decide which framework fits your project.

Key Takeaways

Architecture and Design Philosophy

Botpress

Botpress uses a visual flow-builder approach combined with JavaScript/TypeScript for customization. The platform provides a web-based studio where you design conversation flows visually, then extend functionality through code. Botpress ships with a built-in NLU engine (Powered by Natural) but allows switching to external providers like Dialogflow or Watson.

// Botpress: Custom action in JavaScript
const axios = require('axios')

async function fetchWeatherInfo(event, args) {
  const { city } = args
  const response = await axios.get(
    `https://api.weather.example/v1/current`,
    { params: { city } }
  )

  return {
    temperature: response.data.temp,
    conditions: response.data.conditions,
    humidity: response.data.humidity
  }
}

Botpress excels at rapid prototyping. You can build a functional chatbot in hours using the visual editor, then layer in custom logic as needed. The platform handles hosting internally or lets you deploy to your own infrastructure using Docker.

Rasa

Rasa takes a code-first approach built entirely around Python. There is no visual flow builder—conversational logic lives in declarative YAML files and Python code. Rasa provides its own NLU and dialogue management components, giving you complete control over the machine learning pipeline.

# Rasa: Training data format (nlu.yml)
version: "3.1"

nlu:
- intent: check_weather
  examples: |
    - what's the weather in [London](city)
    - will it rain in [Paris](city) today
    - temperature in [Berlin](city)

- intent: book_flight
  examples: |
    - book a flight from [NYC](origin) to [LA](destination)
    - I need a ticket from [Seattle](origin) to [Portland](destination)

Rasa’s architecture separates NLU (natural language understanding) from dialogue management. This separation allows fine-tuned control over how conversations progress and how intents are classified.

Quick Comparison

Feature Botpress Rasa
AI Model See specs See specs
Code Completion Supported Supported
Context Window See documentation See documentation
IDE Support Multiple IDEs Multiple IDEs
Pricing See current pricing See current pricing
Language Support Multi-language Multi-language

Natural Language Understanding

Botpress NLU

Botpress includes a built-in NLU system that handles intent classification and entity extraction. The system trains on examples you provide through the studio interface. For most use cases, the default engine works without additional configuration.

// Botpress: Entity extraction in a flow
const extractOrderNumber = async (event) => {
  const nlu = event.nlu
  const orderEntity = nlu.entities.find(e => e.type === 'order_number')

  if (orderEntity) {
    return orderEntity.value
  }
  return null
}

You can enhance NLU accuracy by training with diverse examples and using the system’s built-in entity recognition for common types like dates, emails, and numbers.

Rasa NLU

Rasa offers multiple NLU pipeline options. The DIETClassifier handles both intent classification and entity extraction in a single model, which often produces better results than separated approaches. You can also use Transformers or fine-tuned models for more complex understanding.

# Rasa: NLU pipeline configuration
language: en

pipeline:
  - name: WhitespaceTokenizer
  - name: RegexFeaturizer
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: char_wb
    ngram_range: [2, 3]
  - name: DIETClassifier
    epochs: 100
    learning_rate: 0.001
  - name: EntitySynonymMapper

Rasa’s pipeline approach gives you granular control over each processing stage, which matters when you need to optimize for specific languages or domain terminology.

Conversation Management

Botpress Flows

Botpress uses a node-based conversation designer. Each node represents a bot response, and connections between nodes define the conversation flow. You can add conditions, execute code actions, and integrate external services at any node.

// Botpress: Conditional transition logic
const checkUserTier = async (event, state) => {
  const userTier = state.user.tier // 'free' | 'premium'

  if (userTier === 'premium') {
    return { nextNode: 'premium_support' }
  }
  return { nextNode: 'standard_support' }
}

The flow-based approach works well for structured conversations with clear paths. It becomes challenging when conversations require complex branching or context retention across many turns.

Rasa Stories

Rasa uses “stories” to define conversation paths. Stories represent example dialogues that teach the dialogue management model how to respond to different situations.

# Rasa: Story definition (stories.yml)
version: "3.1"

stories:
- story: Weather check flow
  steps:
  - intent: greet
  - action: utter_greet
  - intent: check_weather
  - action: utter_ask_city
  - intent: inform
    entities:
    - city: London
  - action: action_weather_lookup
  - action: utter_weather_response

Stories require more upfront design work than visual flows, but they scale better for complex conversations and handle unexpected user inputs more gracefully.

Deployment and Production Considerations

Botpress Deployment

Botpress offers cloud hosting with straightforward scaling. You deploy through their dashboard, and infrastructure concerns are handled for you. For self-hosting, Botpress provides Docker containers that run on your servers or Kubernetes clusters.

The cloud option reduces operational overhead significantly—updates and maintenance happen automatically.

Rasa Deployment

Rasa is entirely self-hosted. You run it on your infrastructure using Docker, Kubernetes, or serverless configurations. This gives you full data control and customization but requires DevOps expertise.

# Rasa: Running locally with Docker
docker run -it -p 5005:5005 rasa/rasa:latest-full \
  run --enable-api --cors "*"

Rasa Open Source is free, while Rasa Pro adds commercial features and support. The self-hosted model works well for organizations with strict data privacy requirements or those wanting to avoid vendor lock-in.

When to Choose Each Framework

Choose Botpress when:

Choose Rasa when:

Code Example: Basic Echo Bot

Here is a minimal implementation in each framework to illustrate the development experience.

Botpress

// Botpress: Simple echo bot (actions/echo.js)
module.exports = async function echo(event, args) {
  const userMessage = event.preview

  return {
    markdown: `You said: **${userMessage}**`
  }
}

Rasa

# Rasa: Simple echo bot (actions/actions.py)
from typing import Any, Text, Dict, List
from rasa_sdk import Action, Tracker
from rasa_sdk.executor import CollectingDispatcher

class ActionEcho(Action):
    def name(self) -> Text:
        return "action_echo"

    def run(
        self,
        dispatcher: CollectingDispatcher,
        tracker: Tracker,
        domain: Dict[Text, Any],
    ) -> List[Dict[Text, Any]]:
        user_message = tracker.latest_message.get("text")
        dispatcher.utter_message(text=f"You said: {user_message}")
        return []

Both implementations accomplish the same task, but the development experience differs significantly—Botpress uses JavaScript in a visual environment, while Rasa uses Python with code-first definitions.

Frequently Asked Questions

Can I use the first tool and the second tool together?

Yes, many users run both tools simultaneously. the first tool and the second tool serve different strengths, so combining them can cover more use cases than relying on either one alone. Start with whichever matches your most frequent task, then add the other when you hit its limits.

Which is better for beginners, the first tool or the second tool?

It depends on your background. the first tool tends to work well if you prefer a guided experience, while the second tool gives more control for users comfortable with configuration. Try the free tier or trial of each before committing to a paid plan.

Is the first tool or the second tool more expensive?

Pricing varies by tier and usage patterns. Both offer free or trial options to start. Check their current pricing pages for the latest plans, since AI tool pricing changes frequently. Factor in your actual usage volume when comparing costs.

How often do the first tool and the second tool update their features?

Both tools release updates regularly, often monthly or more frequently. Feature sets and capabilities change fast in this space. Check each tool’s changelog or blog for the latest additions before making a decision based on any specific feature.

What happens to my data when using the first tool or the second tool?

Review each tool’s privacy policy and terms of service carefully. Most AI tools process your input on their servers, and policies on data retention and training usage vary. If you work with sensitive or proprietary content, look for options to opt out of data collection or use enterprise tiers with stronger privacy guarantees.

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