Build your first agent in just a few minutes with [Rasa Copilot](https://hello.rasa.com/?utm_source=docs&utm_medium=referral&utm_campaign=docs_cta). On this page This quickstart takes you from a simple setup to a fully functional AI agent in just a few minutes. ## Requirements Before getting started, make sure that you've already: - [x] [**Created a License Key**](/content/docs/pro/installation/licensing/index.html) - [x] [**Installed Rasa**](/content/docs/pro/installation/python/index.html) ## What are you building? In this tutorial, you will build an LLM-powered assistant that can complete a money transfer, reliably executing your business logic while allowing for fluid conversation. Here are some of the conversations your assistant will be able to handle, just by defining the money transfer process: - Happy path - All at once - Change of mind ### Happy path User: I want to send money Bot: Who would you like to send money to? User: to Jen Bot: How much would you like to send? User: $50 Bot: Please confirm: you want to transfer $50.0 to Jen? User: yes Bot: All done. $50.0 has been sent to Jen. Bot: Is there anything else I can help you with?! ### All info given User: I need to send $50 Bot: Who would you like to send money to? User: Jen Bot: Please confirm: you want to transfer $50.0 to Jen? User: actually no 60 Bot: Ok, I corrected the amount. Bot: Please confirm: you want to transfer $60.0 to Jen? User: yes. Bot: All done. $60.00 has been sent to Jen. Bot: Is there anything else I can help you with? ### Change of mind ## Following This Tutorial You'll need a free Rasa [Developer Edition license](/content/docs/pro/installation/licensing/index.html) This tutorial contains a mix of explanations and instructions. Whenever there are instructions you need to follow, you'll see this 'Action Required' label: ### Action Required This assistant is powered by [an LLM that we fine-tuned and uploaded to huggingface](https://huggingface.co/rasa/command-generator-llama-3.1-8b-instruct). For convenience, this tutorial will use a deployment that we host and make available for users working through the tutorial. If you prefer, you don't have to use any 3rd party API and just [run this model yourself](/content/docs/pro/deploy/deploy-fine-tuned-model/index.html) or use [another LLM](/content/docs/reference/config/components/llm-configuration/index.html) ## Setup ### Action Required For new users, the easiest way to get started is the [Developer Quickstart](/content/docs/learn/quickstart/pro/index.html) — get a license, install Rasa Pro, and set up MCP in your IDE. You can also [install Rasa Pro locally](/content/docs/pro/installation/overview/index.html) and use your own machine. To code along with this tutorial, navigate to an empty directory in your terminal, and run: ```bash rasa init --template tutorial ``` If you've completed the [Developer Quickstart](/content/docs/learn/quickstart/pro/index.html), you already have your license and MCP set up. If you've installed Rasa locally, set your Rasa license in an environment variable: - Linux/MacOS ```shell export RASA_LICENSE="your-rasa-pro-license-key" ``` - Windows ```shell setx RASA_LICENSE your-rasa-license-key ``` The variable will now be correctly set when you create a new cmd prompt window. Remember to replace `your-rasa-license-key` with the your actual license key. ## Overview Open up the project folder in your IDE to see the files that make up your new project. In this tutorial you will primarily work with the following files: - `data/flows.yml` - `domain.yml` - `actions/actions.py` ## Testing your money transfer flow ### Action Required Train your assistant by running: ```bash rasa train ``` Now, try telling the assistant that you'd like to transfer some money to a friend. Start talking to it in the browser by running: ```bash rasa inspect ``` ### info When you run the `rasa inspect` command in a GitHub Codespace, you'll see a notification that your application is available on port 5005. Click 'Open in Browser' to access the inspector and start chatting. ### info This template bot responds to chitchat by generating a response. If you want to disable this, delete the file `data/patterns.yml` and re-train. ## Understanding your money transfer flow. The file `data/flows.yml` contains the definition of a `flow` called `transfer_money`. Let's look at this definition to see what is going on: ### flows.yml ```yaml flows: transfer_money: description: Help users send money to friends and family. steps: - collect: recipient - collect: amount description: the number of US dollars to send - action: utter_transfer_complete ``` The two key attributes of the `transfer_money` flow are the `description` and the `steps`. The `description` is used to help decide _when_ to activate this flow. But it is also helpful for anyone who inspects your code to understand what is going on. If a user says "I need to transfer some money", the description helps Rasa understand that this is the relevant flow. The `steps` describe the business logic required to do what the user asked for. The first step in your flow is a `collect` step, which is used to fill a `slot`. A `collect` step sends a message to the user requesting information, and waits for an answer. ## Collecting Information in Slots `Slots` are variables that your assistant can read and write throughout a conversation. Slots are defined in your `domain.yml` file. For example, the definition of your `recipient` slot looks like this: ### domain.yml ```yaml slots: recipient: type: text mappings: - type: from_llm # ... ``` Slots can be used to store information that users provide during the conversation, or information that has been fetched via an API call. First, you're going to see how to store information provided by the end user in a slot. To do this, you define a `collect` step like the first step in your flow above. ### flows.yml Rasa will look for a `response` called `utter_ask_recipient` in your domain file and use this to phrase the question to the user. ### domain.yml ```yaml responses: utter_ask_recipient: - text: "Who would you like to send money to?" ``` After sending this message, Rasa will wait for a response from the user. When the user responds, Rasa will try to use their answer to fill the slot `recipient`. Read about [slot validation](/content/docs/reference/primitives/flow-steps/#slot-validation/index.html) to learn how you can run extra checks on the slot values Rasa has extracted. The diagram below summarizes how slot values are used to collect and store information, and how they can be used to create branching logic. ### Descriptions in collect steps The second `collect` step includes a description of the information your assistant will request from the user. Descriptions are optional, but can help Rasa extract slot values more reliably. ### flows.yml ## Action Steps The third `step` in your `transfer_money` flow is not a `collect` step but an `action` step. When you reach an action step in a flow, your assistant will execute the corresponding action and then proceed to the next step. It will not stop to wait for the user's next message. For now, this is the final step in the flow, so there is no next step to execute and the flow completes. ### flows.yml ## Branching Logic Slots are also used to build branching logic in flows. ### Action Required You're going to introduce an extra step to your flow, asking the user to confirm the amount and the recipient before sending the transfer. Since you are asking a yes/no question, you can store the result in a boolean `slot` which you will call `final_confirmation`. In your domain file, add the definition of the `final_confirmation` slot and the corresponding response: `utter_ask_final_confirmation`. Also add a response to confirm the transfer has been cancelled. ### domain.yml ```yaml slots: recipient: type: Text mappings: - type: from_llm # ... final_confirmation: type: bool mappings: - type: from_llm ``` ### domain.yml ```yaml responses: utter_ask_recipient: - text: "Who would you like to send money to?" # ... utter_ask_final_confirmation: - text: "Please confirm: you want to transfer {amount} to {recipient}?" utter_transfer_cancelled: - text: "Your transfer has been cancelled." ``` Notice that your confirmation question uses curly brackets `{}` to include slot values in your response. Add a `collect` step to your flow for the slot `final_confirmation`. This step includes a `next` attribute with your branching logic. The expression after the `if` key will be evaluated to true or false to determine the next step in your flow. The `then` and `else` keys can contain either a list of steps or the `id` of a step to jump to. In this case, the `then` key contains an `action` step to inform the user their transfer was cancelled. The `else` key contains the id `transfer_successful`. ### flows.yml ```yaml flows: transfer_money: description: Help users send money to friends and family. steps: - collect: recipient - collect: amount description: the number of US dollars to send - collect: final_confirmation next: - if: not slots.final_confirmation then: - action: utter_transfer_cancelled next: END - else: transfer_successful - action: utter_transfer_complete id: transfer_successful ``` To try out the updated version of your assistant, run `rasa train`, and then `rasa inspect` to talk to your assistant. It should now ask you to confirm before completing the transfer. ## Integrating an API call An `action` step in a flow can describe two types of actions. If the name of the action starts with `utter_`, then this action sends a message to the user. The name of the action has to match the name of one of the `responses` defined in your domain. The final step in your flow contains the action `utter_transfer_complete`, and this response is also defined in your domain. Responses can contain buttons, images, and custom payloads. You can learn more about everything you can do with responses [here](/content/docs/reference/primitives/responses/index.html). The second type of `action` is a custom action. The name of a custom action starts with `action_`. You are going to create a custom action, `action_check_sufficient_funds`, to check whether the user has enough money to make the transfer, and then add logic to your flow to handle both cases. Your custom action is defined in the file `actions/actions.py`. To learn more about custom actions, go [here](/content/docs/reference/primitives/custom-actions/index.html). Your `actions.py` file should look like this: ### actions.py ```python from typing import Any, Text, Dict, List from rasa_sdk import Action, Tracker from rasa_sdk.executor import CollectingDispatcher from rasa_sdk.events import SlotSet class ActionCheckSufficientFunds(Action): def name(self) -> Text: return "action_check_sufficient_funds" def run(self, dispatcher: CollectingDispatcher, tracker: Tracker, domain: Dict[Text, Any]) -> List[Dict[Text, Any]]: # hard-coded balance for tutorial purposes. in production this # would be retrieved from a database or an API balance = 1000 transfer_amount = tracker.get_slot("amount") has_sufficient_funds = transfer_amount <= balance return [SlotSet("has_sufficient_funds", has_sufficient_funds)] ``` Slots are the primary way to pass information to and from custom actions. In the `run()` method above, you access the value of the `amount` slot that was set during the conversation, and you pass information back to the conversation by returning a `SlotSet` event to update the `has_sufficient_funds` slot. ### Action Required Now you are going to make three additions to your `domain.yml`. You will add a top-level section listing your custom actions. You will add the new boolean slot `has_sufficient_funds`, and you will add a new response to send to the user in case they do not have sufficient funds. ### domain.yml ```yaml actions: - action_check_sufficient_funds slots: # ... has_sufficient_funds: type: bool mappings: - type: controlled responses: # ... utter_insufficient_funds: - text: "You do not have enough funds to make this transaction." ``` Now you are going to update your flow logic to handle the cases where the user does or does not have enough money in their account to make the transfer. Notice that your `collect: final_confirmation` step now also has an id so that your branching logic can jump to it. ### flows.yml ```yaml flows: transfer_money: description: Help users send money to friends and family. steps: - collect: recipient - collect: amount description: the number of US dollars to send - action: action_check_sufficient_funds next: - if: not slots.has_sufficient_funds then: - action: utter_insufficient_funds next: END - else: final_confirmation - collect: final_confirmation id: final_confirmation next: - if: not slots.final_confirmation then: - action: utter_transfer_cancelled next: END - else: transfer_successful - action: utter_transfer_complete id: transfer_successful ``` ## Testing your Custom Action ### Action Required Double check that in the file `endpoints.yml`, that the section for your custom action server is uncommented: ### endpoints.yml ```yaml action_endpoint: actions_module: actions ``` In the terminal, stop and restart the inspector by running `rasa inspect`. When you reach the "check_funds" step in your flow, Rasa will call the custom action `action_check_sufficient_funds`. We have hardcoded the user's balance to be `1000`, so if you try to send more, the assistant will tell you that you don't have enough funds in your account. At this point you have experience using some of the key concepts involved in building with Rasa. Congratulations! ## Adding Voice to Your Assistant Now that you've built a text-based assistant that handles money transfers, let's expand its capabilities to support voice interactions. Here's a demo of what you'll be building, Rasa Voice Inspector - YouTube Tap to unmute ### What You'll Need for Voice To enable voice capabilities, you'll need: - An API key from [Deepgram](https://deepgram.com/) for both speech recognition and speech synthesis. - Alternatively, you can use any of our other supported [Speech Integrations](/content/docs/reference/integrations/speech-integrations/index.html). ### Setting Up Your Voice Assistant ### Action Required Configure your speech service API keys: - Linux/MacOS ```shell export DEEPGRAM_API_KEY="your-deepgram-api-key" ``` - Windows ```shell setx DEEPGRAM_API_KEY your-deepgram-api-key ``` This will apply to future command prompt windows, so you will need to open a new one to use these variables. ### Testing Your Voice Assistant ### Action Required To test your voice assistant in the browser: ```bash rasa inspect --voice ``` Try saying "I want to send money to Sarah" and experience how your assistant handles the voice interaction. The voice inspector allows you to speak to your assistant and hear its responses, simulating a phone call experience. ## Next Steps Now you are ready to apply what you've learned to building your own assistant. 1. Create a new project by running: ```text rasa init --template calm ``` 2. Choose which LLM you want to use. This tutorial used an LLM that Rasa fine-tuned for this use case. For new projects created with `rasa init --template calm`, Rasa defaults to a general-purpose OpenAI chat model (`gpt-5.1-2025-11-13`) that doesn't require fine-tuning. You can configure which LLM to use by editing [the config.yml file in your project](/content/docs/reference/config/components/llm-configuration/index.html). When you're ready, you can [fine-tune your own model](/content/docs/pro/customize/fine-tuning-llm/index.html). 3. Start writing your own [flows](/content/docs/reference/primitives/flows/index.html) and [custom actions](/content/docs/reference/primitives/custom-actions/index.html).