âī¸Agents
Paal AI Agents and System Features
Overview
Paal AI is a robust platform that allows users to create, deploy, and manage AI agents, workflows, and resources. It enables advanced automation, real-time monitoring, and customizable tools to enhance productivity and scalability.
Visit: agents.paal.ai
1. Overview Page
Purpose
The Overview Page serves as the central hub for monitoring activity, managing agents, resources, and media, and accessing critical insights like usage and performance.
Features
Plan Usage:
Tracks available credit balance and consumption percentage.
Click "View Usage" for detailed insights into credit consumption.
Agent Stats Overview:
Agent Activity: Visualizes calls/tasks performed in the last 24 hours.
Tokens Generated: Displays the number of input/output tokens processed over time.
Media Storage:
Categorizes stored media by type (e.g., Documents, Images, Videos, YouTube links, Audio).
Enables file viewing and management directly from the dashboard.
My Agents:
Lists active agents with details like name, category, and creation date.
Allows managing, editing, and deploying agents.
My Models:
Displays deployed AI models and provides a "Deploy Model" option for adding new ones.
Credit Grants USD
Tracks Allocated and Available Credits:
Displays the total allocated credits, the remaining balance, and their expiration dates.
Purchase Additional Credits:
Click "Get Credits with PAAL" to buy more credits.
2. My Agents and Agent Marketplace
My Agents
Key Features:
Categorizes agents as Chat Agents or File Agents.
Provides quick actions:
Edit: Modify agent settings.
Unpublish: Change an agentâs availability.
Delete: Permanently remove agents.
Agent Details:
Displays agent type, category, and status (e.g., Public, Pending).
Allows filtering and searching for specific agents.
Agent Management:
Create Agent: Add new agents with custom configurations.
View All: Access a complete list of agents with timestamps for tracking changes.
Agent Marketplace
Categories:
Includes agents for Marketing, Web3, IoT, Data Analytics, and more.
Agent Details:
Displays community ratings, category, and creator information.
Provides quick deployment options using the Add button.
Actions:
Search: Find agents by name or type.
Copy ID: Use for integrations and workflows.
3. Playground
Purpose
A sandbox environment to test agents, workflows, and custom prompts in real-time.
Key Features
Multi-Agent Interaction:
Activate multiple agents simultaneously for collaborative tasks.
Preset Management:
Save commonly used setups for faster execution.
Execution Logs:
Monitor step-by-step execution plans and logs.
Output Options:
Download logs or view execution breakdowns.
Steps to Use
Add Agents:
Choose agents from Chat, File, or Default categories.
Input Prompts:
Enter text, document links, or audio/video URLs for processing.
Run and Analyze:
Review execution logs and plans.
Refine workflows using presets or modify agents.
4. Agents Flow Builder
Purpose
An intuitive drag-and-drop tool to automate workflows using triggers, LLMs, and delivery nodes.
Key Features
Triggers:
Initiate workflows based on events like Cron Schedules.
LLM Nodes:
Integrate language models like GPT-4 Turbo with custom prompts.
Delivery:
Send results through Slack, Webhooks, Emails, or Phones.
Advanced Options:
Enable memory for context retention.
Map outputs to specific delivery nodes.
Steps to Build Workflows
Add Nodes:
Drag Trigger, LLM, and Delivery nodes to the canvas.
Configure Nodes:
Set prompt instructions, assign agents, and choose delivery methods.
Save and Test:
Link nodes, save the workflow, and test execution.
Example Workflow: Automated Twitter Bot
Purpose: Automate tweets combining data from Binance price feeds, GambleGPT picks, news, and weather updates.
Workflow:
Trigger Node: Set to run every 30 minutes.
Data Sources:
Binance Agent: Fetch BTC and ETH prices.
GambleGPT: Get sports betting picks.
News Agent: Pull trending headlines.
Weather Agent: Retrieve weather updates.
LLM Node: Combine inputs into an engaging tweet using GPT-4 Turbo.
Delivery Node: Post formatted output to Twitter.
Example Tweet: "đ BTC: $45,000, ETH: $3,200 đĨ Lakers vs. Heat: +7 đ° Tech breakthrough đ Weather: Sunny, 25°C #Crypto #Sports #News"
5. Bring Your Own Model (BYOM)
Deploying a Model
Search Hugging Face models by tags or keywords.
Configure parameters like context length and repository tokens.
Click Deploy to integrate the model.
Deploying an Endpoint
Assign deployed models to scalable endpoints.
Configure instance types (Inference 1x, 2x, or 3x) and scaling options.
Deploy endpoints with automatic cost-saving features like Scale-to-Zero.
6. API Key Management
API Keys
Features:
Create and manage secure API keys.
Monitor usage and delete unused keys.
Best Practices:
Keep keys confidential and rotate them regularly.
IoT (MQTT) Credentials
Features:
Generate and manage credentials for IoT devices.
Best Practices:
Use unique credentials per device and delete unused ones.
7. Media Management
Storage
Purpose:
Manage uploaded files for agents to process.
Features:
Sort files by date, size, or type.
Assign files to agents.
Usage
Purpose:
Track storage activity and costs.
Features:
Monitor API requests and file processing.
View cost breakdowns for efficient management.
8. Documentation
Contents
Agent Types:
Explains Knowledge Agents, IoT Agents, and REST API Agents.
Integration Guides:
Step-by-step instructions for deploying and customizing agents.
FAQs
1. What are Paal AI Agents?
Agents extend AI model functionality by enabling data processing, automation, and contextual interactions.
2. How can I integrate an agent?
Use the Agent ID for API-based integrations or deploy agents directly via the marketplace.
3. What is BYOM?
Bring Your Own Model lets you deploy custom AI models and endpoints for specialized tasks.
4. How do I monitor costs?
Check the Usage section in Media Management for detailed cost breakdowns.
Last updated
