> For the complete documentation index, see [llms.txt](https://docs.zaia.app/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zaia.app/tools/available-tools/contextual-memory-tool.md).

# 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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### Contextual Memory in Squads

Memory access depends on the Squad management mode:

* **Hierarchical mode**: only the Squad Manager uses Contextual Memory.
* **Horizontal mode**: every Agent in the Squad can use Contextual Memory.

This behavior is defined by the selected management mode. Configuring the tool does not change access, routing, or memory persistence.

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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**
   * New tools include `interacoes` and `chamadasDeTool` by default.
   * `chamadasDeTool` is marked as sensitive by default.
   * 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.