### USER I want to build an LLM agent that leverages tool usage and reasoning in order to act as an Email Triage Agent. I imagine having a list of categories (or tags i guess since I’m on gmail?) that the LLM agent can then sort the emails into their respective folders by using the tools available to them, but I want to make sure that the agent has all the tools it could need in order to get all the information and context about the inbox and its emails, and how to allow it to organize the data in a way that is safe to my data. What tools should I include with the agent ### ASSISTANT
Thought for 20 seconds > Okay, the user wants to build an LLM agent for email triage. Let me break down what they need. First, they mentioned using Gmail, so the agent needs to interact with Gmail's API. That's a must. But they also want to make sure the agent has all the necessary tools to gather info and organize emails safely. > > Hmm, so what are the key tools here? Well, the Gmail API is the obvious one. It allows the agent to read, label, and manage emails. But they might need more. Maybe a natural language processing tool to analyze the email content for keywords or sentiment. That could help in categorizing emails into tags like 'Urgent' or 'Follow-up'. > > Wait, the user mentioned safety. So data security is important. They need to ensure that the agent doesn't expose sensitive info. Maybe encryption for data in transit and at rest. Also, access controls to limit who can access the data. Oh, and maybe a logging system to track actions taken by the agent, so they can audit if needed. > > What about the LLM itself? It needs to reason through the emails. So the agent should have a way to process the email content, maybe using some NLP models for classification. But they need to make sure the model is trained on the right data for their specific categories. Maybe they can fine-tune a model on their own email data for better accuracy. > > Also, the agent needs to handle different types of emails. Like, some might have attachments. So a tool to parse attachments could be necessary. Maybe extract text from PDFs or images using OCR tools. But that might complicate things. Should they include that as a tool or is it optional? > > Another thought: the agent should be able to prioritize emails. So maybe a tool to rank emails based on urgency or sender. But how does that integrate with the existing tools? Maybe the LLM can reason about the priority based on content and sender info. > > Wait, the user wants the agent to organize emails into folders. So the Gmail API's labeling feature is crucial. The agent needs to create and manage labels. Also, maybe the ability to move emails between labels. But they need to handle the API rate limits and ensure that the agent doesn't get blocked. > > What about user preferences? The agent should allow customization of categories. So maybe a configuration tool where the user can define their own tags or categories. The agent needs to adapt to these changes dynamically. > > Data safety is another concern. They mentioned making sure the data is safe. So the agent should not store any email content locally. All processing should be done in-memory or in a secure environment. Maybe using encrypted storage if necessary, but the user should be aware of the risks. > > Also, the agent needs to handle errors. Like, if an email can't be categorized, it should be flagged for manual review. So an error handling tool or a way to escalate issues. Maybe a feedback loop where the user can correct the agent's categorization, which can then be used to retrain the model. > > Wait, the user might not have mentioned it, but maybe they need a way to test the agent. So a testing tool or a sandbox environment where the agent can process emails without affecting the real inbox. That would be important for development and debugging. > > Another aspect: the agent should be able to understand the context of the email. For example, knowing that an email from a specific sender is usually urgent. So the agent needs access to sender information and possibly a database of known senders and their typical email types. But that might be part of the training data for the LLM. > > Oh, and the agent should be able to handle different languages if the user receives emails in multiple languages. So the NLP tools should support multilingual processing, or the LLM should be multilingual. > > Wait, the user is on Gmail, so the agent must integrate with the Gmail API. They need to set up OAuth 2.0 for authentication. That's a security measure to ensure only the authorized user can access their emails. The agent should handle the OAuth flow securely, maybe using a service account or a secure token storage. > > In summary, the tools needed are: Gmail API for email management, NLP tools for content analysis, encryption for data security, access controls, logging, configuration for categories, error handling, testing environment, multilingual support, and maybe OCR for attachments. The LLM itself should be integrated to perform the reasoning and categorization based on these tools' outputs.
Building an **Email Triage Agent** using an LLM requires a combination of tools to securely access, analyze, and organize emails while ensuring data safety. Below is a structured list of **essential tools** and **recommendations** to achieve this: --- ### **1. Core Tools for Email Interaction** #### **a. Gmail API (or equivalent for other email services)** - **Purpose**: Access and manage emails, labels, and folders. - **Key Features**: - Read/Write emails (including headers, body, attachments). - Create, update, and delete labels (folders). - Move emails between labels. - Search for emails based on criteria (sender, subject, date, etc.). - **Security**: Use **OAuth 2.0** for secure authentication. Avoid storing credentials in plaintext. #### **b. Email Content Parser** - **Purpose**: Extract structured data from emails (e.g., sender, subject, body, attachments). - **Tools**: - **Python's `email` module** or **BeautifulSoup** for parsing raw email content. - **OCR tools** (e.g., **Tesseract** or **Google Cloud Vision**) for extracting text from PDFs/images in attachments. - **Attachment downloader** to handle file attachments (e.g., ZIP, PDF, etc.). --- ### **2. NLP & Reasoning Tools** #### **a. LLM Integration (e.g., Llama, GPT, etc.)** - **Purpose**: Perform **reasoning**, **classification**, and **intent detection** on email content. - **Use Cases**: - Classify emails into categories (e.g., "Urgent", "Follow-up", "Billing"). - Detect priority levels (e.g., "High", "Low"). - Extract key information (e.g., deadlines, contacts, tasks). - **Training**: Fine-tune the LLM on your specific categories and email data for better accuracy. #### **b. Sentiment Analysis Tool** - **Purpose**: Assess the tone of the email (e.g., "Urgent", "Friendly", "Negative"). - **Tools**: Use pre-trained models like **BERT** or **DistilBERT** for sentiment scoring. #### **c. Entity Recognition Tool** - **Purpose**: Identify key entities (e.g., names, dates, locations) in the email body. - **Tools**: **spaCy**, **Stanford NLP**, or **Hugging Face Transformers**. --- ### **3. Data Security & Privacy Tools** #### **a. Encryption** - **In Transit**: Use **TLS/SSL** for all API communications. - **At Rest**: Encrypt stored data (e.g., logs, metadata) using **AES-256**. - **Tools**: **OpenSSL**, **Python's `cryptography` library**. #### **b. Access Control** - **Purpose**: Restrict access to sensitive data (e.g., email content). - **Tools**: - **RBAC (Role-Based Access Control)** for user permissions. - **IAM (Identity and Access Management)** for API key security. #### **c. Data Anonymization** - **Purpose**: Remove sensitive details (e.g., names, addresses) from email content before processing. - **Tools**: **Anonymization libraries** or custom regex rules. --- ### **4. Workflow & Organization Tools** #### **a. Label/Category Management** - **Purpose**: Define and manage custom labels/tags (e.g., "Work", "Personal", "Spam"). - **Tools**: - **User interface** for configuring categories. - **Dynamic label creation** via API (e.g., "Project X", "Client A"). #### **b. Prioritization Engine** - **Purpose**: Rank emails by urgency or importance. - **Tools**: - **Rule-based prioritization** (e.g., "Email from [boss@company.com] is high priority"). - **Machine learning model** to predict priority based on content. #### **c. Error Handling & Logging** - **Purpose**: Track misclassifications or errors for debugging. - **Tools**: - **Centralized logging** (e.g., **ELK Stack**, **Grafana Loki**). - **Feedback loop**: Allow users to correct misclassified emails and retrain the model. --- ### **5. Additional Tools for Enhanced Functionality** #### **a. Attachment Handling** - **Purpose**: Process or flag attachments (e.g., malware scans, file type checks). - **Tools**: - **Antivirus integration** (e.g., **ClamAV**, **Google Safe Browsing API**). - **File type validation** (e.g., reject unsupported formats). #### **b. Calendar & Task Integration** - **Purpose**: Sync emails with calendars or task managers. - **Tools**: - **Google Calendar API** or **Outlook Calendar API**. - **Task management tools** (e.g., **Todoist**, **Notion**). #### **c. Sandbox Environment** - **Purpose**: Test the agent without affecting the real inbox. - **Tools**: - **Mock Gmail API** or **email testing tools** (e.g., **Mailosaur**, **MailHog**). --- ### **6. Compliance & Legal Tools** - **Purpose**: Ensure the agent complies with data protection laws (e.g., GDPR, CCPA). - **Tools**: - **Data minimization**: Only process necessary email data. - **User consent management** for data usage. - **Audit logs** for transparency. --- ### **7. User Interface (Optional)** - **Purpose**: Provide a way for users to configure the agent, view results, or correct classifications. - **Tools**: - **Web dashboard** (e.g., **React**, **Vue.js**). - **Command-line interface (CLI)** for advanced users. --- ### **Implementation Workflow** 1. **Authentication**: Set up OAuth 2.0 for Gmail API access. 2. **Data Extraction**: Use the Gmail API to fetch emails and parse content. 3. **Analysis**: Feed parsed data to the LLM for classification and reasoning. 4. **Action**: Use the Gmail API to move emails to appropriate labels. 5. **Security**: Encrypt data and enforce access controls. 6. **Feedback Loop**: Allow users to correct classifications and retrain the model. --- ### **Safety Considerations** - **Avoid storing raw email content** unless encrypted. - **Use secure APIs** (e.g., Gmail API with HTTPS). - **Limit the agent’s permissions** to only necessary functions (e.g., read/write emails, not access contacts). - **Regularly audit logs** to detect unauthorized activity. By combining these tools, your LLM agent can securely and efficiently triage emails while maintaining data privacy and user control. Let me know if you’d like a sample code snippet or architecture diagram!