Contextual Memory Tool

The Contextual Memory Tool allows your Agent to store and retrieve structured information across conversations, enabling it to "remember" past interactions, user preferences, and relevant data. This transforms your Agent from a simple assistant into a truly contextual and personalized companion.

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🔑 Key Capabilities

• Custom Properties: You decide what kind of information should be stored (e.g., names, preferences, conversation summaries, tool usage).

• Typed Data: Each property can have a type (string, number, boolean), making the memory precise and structured.

• Natural Language Descriptions: Define, in plain language, what the Agent should store, when to store it, and in which format.

• Sensitive Flag: Mark certain data as sensitive so it won’t be visible to human support agents if a chat is escalated.

• Retrieval Across Sessions: Data stored can be retrieved in future interactions, creating continuity across multiple conversations.

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🛠️ How to Configure

When creating a Contextual Memory Tool, you can:

1. Add Properties

   • Example:

     • interactions (string) → A list with the main points of the conversation.

     • toolCallHistory (string) → A bullet list of all tools called and their results.

     • people (string, sensitive) → Names, roles, or preferences mentioned by the user.

2. Describe Each Property Clearly

   • Example:

     • "A list of people, their names and/or nicknames, professional roles, contact details, and preferences."

     • "A summary of the last tool calls and what was learned from them."

3. Choose the Data Type

   • string for free text (e.g., notes, names, feedback).

   • number for numerical values (e.g., age, budget, score).

   • boolean for true/false data (e.g., subscription active: true/false).

4. Mark Sensitive Data (optional)

   • Toggle this if the information is private and should not be shared when the chat is escalated to a human.

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🌍 Example Use Cases

• Customer Support: Remember user account details, previous issues, and preferences to avoid asking the same questions again.

• Sales Assistant: Store budget ranges, product interests, and negotiation history to personalize follow-ups.

• Learning Agent: Track what topics the user studied, their skill level, and which tools they’ve used successfully.

• Internal Tools: Store agent’s own usage history, making it smarter in how it calls tools over time.

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📌 Best Practices

• Be specific in property descriptions so the Agent knows exactly when and how to save information.

• Use concise names for properties (people, preferences, history) to make them easy to reuse.

• Mark sensitive data whenever it involves personal or confidential information.

• Use memory in combination with Tickets and Knowledge Base to ensure smooth human handover and reliable long-term records.

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👉 With Contextual Memory, your Agents evolve beyond scripted bots, becoming adaptive, continuous, and deeply personalized in every interaction.