Trello
This document outlines the Fana <> Trello integration for task management.
What's New?
The integration allows users to add, update, or remove tasks on Trello lists, boards, and cards using natural language processing capabilities provided by FANA LLM, facilitated by a system of intelligent agents.
Features
Adding new tasks to existing lists
Creating new lists and boards
Updating task details (e.g., due dates, descriptions)
Removing tasks
Querying task information
Integration Architecture
User Input: Users provide natural language commands or queries.
Agent Processing: a. Reasoning Agents: Analyze the user input to understand the context and intent. b. Decision Maker Agents: Determine the appropriate action based on the analysis from Reasoning Agents. c. Action Agents: Execute the decided action, which may include triggering the HANDLE_TRELLO_TASKS action.
HANDLE_TRELLO_TASKS Action: If identified as a Trello task, this action is triggered.
Rust Backend Processing: a. The action is received by the Rust backend. b. The
services::trello.rsmodule processes the action. c. FANA LLM interprets the user's intent and extracts relevant information.Trello API Interaction: Based on the interpreted command, the system interacts with the Trello API to perform the requested action.
Response: The system provides feedback to the user about the action taken or information retrieved, which is then processed by the agents for appropriate presentation to the user.
Agent System Implementation
The Trello integration is facilitated by a sophisticated agent system:
Reasoning Agents: These agents analyze user input, context, and historical data to understand the user's intent and the broader implications of the request.
Decision Maker Agents: Based on the analysis from Reasoning Agents, these agents determine the best course of action. They decide whether a Trello action is appropriate and, if so, what specific action should be taken.
Action Agents: These agents are responsible for executing the decisions made. In the case of Trello tasks, they trigger the HANDLE_TRELLO_TASKS action, which is then processed by the Rust backend.
HANDLE_TRELLO_TASKS Action
When the Decision Maker agents determine that a user input should result in a Trello task action, they instruct the Action agents to trigger the HANDLE_TRELLO_TASKS action. This action is then processed by the Rust backend, specifically in the services::trello.rs module.
Backend Implementation
The Trello integration is implemented as two modules in the Rust backend:
FANA LLM Module: Responsible for natural language processing and intent extraction.
Trello Service Module: Located at
services::trello.rs, this module handles the interaction with the Trello API based on the processed user intent.
Usage Examples
Adding a Task
User: "Add a new task called 'Prepare quarterly report' to my 'Work' board, due next Friday."
System: Reasoning Agents analyze the input and recognize it as a task creation request. Decision Maker Agents determine it's a Trello task and decide to create it. Action Agents trigger the HANDLE_TRELLO_TASKS action. The Rust backend processes the request, and the services::trello.rs module interacts with the Trello API to create a new card with the specified details.
Updating a Task
User: "Change the due date of the 'Prepare quarterly report' task to next Monday."
System: The agent system processes the request, recognizes it as a task update, and triggers the HANDLE_TRELLO_TASKS action. The Rust backend identifies the task and updates its due date using the Trello API through the services::trello.rs module.
Removing a Task
User: "Delete the task 'Old project brainstorming' from my 'Ideas' list."
System: The agent system analyzes the request, determines it's a task deletion, and triggers the HANDLE_TRELLO_TASKS action. The services::trello.rs module in the Rust backend locates the specified task and removes it using the Trello API.
Querying Tasks
User: "What tasks are due this week on my 'Work' board?"
System: The agent system processes the query, recognizes it as a task information request, and triggers the HANDLE_TRELLO_TASKS action. The Rust backend retrieves the relevant information from Trello through the services::trello.rs module and returns it to the agent system for presentation to the user.
Setup and Configuration
Trello API Key: Obtain a Trello API key and token.
FANA LLM Integration: Set up the FANA LLM system and configure it to interact with the Trello API.
User Authentication: Implement a secure method for users to authenticate and authorize the integration to access their Trello boards.
Rust Backend Configuration: Ensure the
services::trello.rsmodule is properly set up and configured to handle Trello API interactions.Agent System Setup: Configure the Reasoning, Decision Maker, and Action agents to correctly identify and process Trello-related tasks and trigger the HANDLE_TRELLO_TASKS action when appropriate.
Error Handling
The integration should include robust error handling to manage issues such as:
Invalid user commands
Trello API rate limits
Network connectivity problems
Task not found errors
Error handling should be implemented both in the agent system and the Rust backend (services::trello.rs) to ensure a smooth user experience.
Security Considerations
Ensure all communication between the agent system, Rust backend, and Trello API is encrypted.
Implement proper authentication and authorization mechanisms.
Regularly audit and update the integration to address any security vulnerabilities.
Future Feature Details
[Future feature details remain the same as in the previous version]
Conclusion
This Trello-FANA LLM integration provides a powerful, natural language interface for managing Trello tasks. By leveraging FANA LLM's capabilities and implementing the integration through a sophisticated agent system and Rust backend, users can efficiently interact with their Trello boards using conversational commands. The proposed future features will further enhance the functionality and user experience of this integration.
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