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How can I implement context memory in a chatbot using Rasa?

Asked on Nov 23, 2025

Answer

Implementing context memory in a chatbot using Rasa involves utilizing the tracker store to maintain conversation state and context. Rasa's tracker keeps track of user inputs, bot actions, and conversation history, which can be used to manage context effectively.

<!-- BEGIN COPY / PASTE -->
    # Example of a custom action using Rasa's tracker
    from rasa_sdk import Action
    from rasa_sdk.events import SlotSet

    class ActionRememberUserInfo(Action):
        def name(self):
            return "action_remember_user_info"

        def run(self, dispatcher, tracker, domain):
            user_name = tracker.get_slot('user_name')
            if not user_name:
                user_name = tracker.latest_message.get('text')
                return [SlotSet('user_name', user_name)]
            dispatcher.utter_message(text=f"Hello again, {user_name}!")
            return []
    <!-- END COPY / PASTE -->

Additional Comment:

Rasa uses slots to store and retrieve information throughout the conversation.
The tracker object provides access to the conversation history and current state, which can be utilized in custom actions.
Ensure that your domain file defines the necessary slots and that your stories or rules account for context management.
Consider using forms for more complex data collection scenarios where context is crucial.

✅ Answered with Chatbot best practices.

Ask any question about Chatbots here... and get an instant response.

How can I implement context memory in a chatbot using Rasa?

Asked on Nov 23, 2025

Answer

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