# Add HubSpot CRM Object
Source: https://docs.agent.ai/actions/add_hubspot_crm_object
## Overview
Create a new CRM object, such as a contact or company, directly within HubSpot.
### Use Cases
* **Data Entry Automation**: Add new leads or companies during workflows.
* **Campaign Management**: Create CRM objects for marketing initiatives.
## Configuration Fields
### Object Type
* **Description**: Select the type of HubSpot object to add.
* **Options**: Company, Contact
* **Required**: Yes
### Object Properties
* **Description**: Enter object properties as key-value pairs, one per line.
* **Example**: "name=Acme Corp" or "email=[john@example.com](mailto:john@example.com)."
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the new HubSpot object.
* **Example**: "created\_contact" or "new\_company."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Add to List
Source: https://docs.agent.ai/actions/add_to_list
## Overview
The "Add to List" action lets you add items to an existing list variable. This allows you to collect multiple entries or build up data over time within your workflow.
### Use Cases
* **Data Aggregation**: Collect multiple responses or items into a single list
* **Iterative Storage**: Track user selections or actions throughout a workflow
* **Building Collections**: Create lists of related items step by step
* **Dynamic Lists**: Add user-provided items to predefined lists
## Configuration Fields
### Input Text
* **Description**: Enter the text to append to the list.
* **Example**: Enter what you want to add to the list
1. Can be a fixed value: "Sample item"
2. Or a variable: \{\{first\_task}}
3. Or another list: \{\{additional\_tasks}}
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the updated list.
* **Example**: "task\_list" or "user\_choices."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
## **Example: Example Agent for Adding and Using Lists**
See this [simple Task Organizer Agent](https://agent.ai/agent/lists-agent-example). It collects an initial task, creates a list with it, then gathers additional tasks and adds them to the list. The complete list is then passed to an AI for analysis.
# Click Go to Continue
Source: https://docs.agent.ai/actions/click_go_to_continue
#
## Overview
The "Click Go to Continue" action adds a button that prompts users to proceed to the next step in the workflow.
### Use Cases
* **Workflow Navigation**: Simplify user progression with a clickable button.
* **Confirmation**: Add a step for users to confirm their readiness to proceed.
## Configuration Fields
### Variable Value
* **Description**: Set the display text for the button.
* **Example**: "Proceed to Next Step" or "Continue."
* **Required**: Yes
# Continue or Exit Workflow
Source: https://docs.agent.ai/actions/continue_or_exit_workflow
## Overview
Evaluate conditions to decide whether to continue or exit the workflow, providing control over the process flow.
### Use Cases
* **Conditional Completion**: End a workflow if certain criteria are met.
* **Dynamic Navigation**: Determine the next step in the workflow based on user input or data.
## Configuration Fields
### Condition Logic
* **Description**: Define the condition logic using Jinja template syntax.
* **Example**: "if user\_age > 18" or "agent\_control = 'exit'."
* **Required**: Yes
# Convert File
Source: https://docs.agent.ai/actions/convert_file
## Overview
Convert uploaded files to different formats, such as PDF, TXT, or PNG, within workflows.
### Use Cases
* **Document Management**: Convert user-uploaded files to preferred formats.
* **Data Transformation**: Process files for compatibility with downstream actions.
## Configuration Fields
### Input Files
* **Description**: Select the files to be converted.
* **Example**: "uploaded\_documents" or "images."
* **Required**: Yes
### Show All Conversion Options
* **Description**: Enable to display all available conversion options.
* **Required**: Yes
### Convert to Extension
* **Description**: Specify the desired output file format.
* **Example**: "pdf," "txt," or "png."
* **Required**: No
### Output Variable Name
* **Description**: Assign a variable name to store the converted files.
* **Example**: "converted\_documents" or "output\_images."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Create Blog Post
Source: https://docs.agent.ai/actions/create_blog_post
## Overview
Generate a blog post with a title and body, allowing for easy content creation and publishing.
### Use Cases
* **Content Marketing**: Draft blog posts for campaigns or updates.
* **Knowledge Sharing**: Create posts to share information with your audience.
## Configuration Fields
### Title
* **Description**: Enter the title of the blog post.
* **Example**: "5 Tips for Better Marketing" or "Understanding AI in Business."
* **Required**: Yes
### Body
* **Description**: Provide the content for the blog post, including text, headings, and relevant details.
* **Example**: "This blog covers the top 5 trends in AI marketing..."
* **Required**: Yes
# End If/Else/For Statement
Source: https://docs.agent.ai/actions/end_statement
## Overview
Mark the end of a conditional statement or loop to clearly define process boundaries within the workflow.
### Use Cases
* **Workflow Clarity**: Ensure conditional branches or loops are properly closed.
* **Error Prevention**: Avoid unintended behavior by marking the end of logical constructs.
## Configuration Fields
* **None Required**: This action serves as a boundary marker and does not require additional configuration.
# Enrich with Breeze Intelligence
Source: https://docs.agent.ai/actions/enrich_with_breeze_intelligence
## Overview
Gather enriched company data using Breeze Intelligence for deeper analysis and insights.
### Use Cases
* **Company Research**: Retrieve detailed information about a specific company for due diligence.
* **Sales and Marketing**: Enhance workflows with enriched data for targeted campaigns.
## Configuration Fields
### Domain Name
* **Description**: Enter the domain of the company to retrieve enriched data.
* **Example**: "hubspot.com" or "example.com."
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the enriched company data.
* **Example**: "company\_info" or "enriched\_data."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# For Loop
Source: https://docs.agent.ai/actions/for_loop
## Overview
For Loops allow your agent to repeat actions for each item in a list or a specific number of times. This saves you from having to create multiple copies of the same steps and makes your workflow more efficient.
### Use Cases
* **Process Multiple Items**: Apply the same steps to each item in a list
* **Repeat Actions**: Perform the same task multiple times
* **Build Cumulative Results**: Gather information across multiple iterations
* **Process User Lists**: Handle user-provided lists of items or requests
## **How For Loops Work**
A For Loop repeats the same actions for each item in your list. Think of it like an assembly line:
1. The loop takes one item from your list
2. It puts that item in a variable you can use
3. It performs all the actions you've added to the loop
4. Then it takes the next item and repeats the process until it's gone through every item
## **Creating a For Loop**
### **Step 1. Add the For Loop Action**
1. In the Actions tab, click "Add action"
2. Select "For Loop" from the Run Process tab
### Step 2. Configuration Fields
1. **List to loop over**
* **Description**: Enter a list to loop over or a fixed number of iterations.
* **Example:**
1. A variable containing a list (like \{\{topics\_list}})
2. A number of times to repeat (like 3)
3. A JSON array (like \["item1", "item2", "item3"])
* **Required**: Yes
2. **Loop Index Variable Name**
* **Description**: Name the variable that will count your loops (this counter starts at 0 and increases by 1 each time through the loop)
* **Example**: loop\_index
1. If you're looping 3 times, this variable will be 0 during the first loop, 1 during the second loop, and 2 during the third loop
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
### **Step 3. Add Actions Inside the Loop**
After your For Loop action, add the steps you want to repeat for each item.
### **Step 4: End the Loop**
After all the actions you want to repeat, add an "End If/Else/For Statement" action to mark where your loop ends.
## **Example: For Loop Example Agent**
See this [simple example agent](https://agent.ai/agent/for-loop-agent-template) which uses a For Loop:
1. Gets a list of 3 topics from the user
2. Loops through each topic, one by one
3. For each topic:
* Uses AI to generate an explanation
* Adds the explanation to a cumulative output
4. Displays all topic explanations to the user when complete
# Format Text
Source: https://docs.agent.ai/actions/format_text
## Overview
Apply formatting to text, such as changing case, removing characters, or truncating, to prepare it for specific uses.
### Use Cases
* **Text Standardization**: Convert inputs to a consistent format.
* **Data Cleaning**: Remove unwanted characters or HTML from text.
## Configuration Fields
### Format Type
* **Description**: Select the type of formatting to apply.
* **Options**: Make Uppercase, Make Lowercase, Capitalize, Remove Characters, Trim Whitespace, Split Text By Delimiter, Join Text By Delimiter, Remove HTML, Truncate
* **Example**: "Make Uppercase" for standardizing text.
* **Required**: Yes
### Characters/Delimiter/Truncation Length
* **Description**: Specify the characters to remove or delimiter to split/join text, or length for truncation.
* **Example**: "@" to remove mentions or "5" for truncation length.
* **Required**: No
### Input Text
* **Description**: Enter the text to format.
* **Example**: "Hello, World!"
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the formatted text.
* **Example**: "formatted\_text" or "cleaned\_data."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Generate Image
Source: https://docs.agent.ai/actions/generate_image
## Overview
Create visually engaging images using AI models, with options for style, aspect ratio, and detailed prompts.
### Use Cases
* **Creative Design**: Generate digital art, illustrations, or concept visuals.
* **Marketing Campaigns**: Produce images for advertisements or social media posts.
* **Visualization**: Create representations of ideas or concepts.
## Configuration Fields
### Model
* **Description**: Select the AI model to generate images.
* **Options**: DALL-E 3, Playground v3, FLUX 1.1 Pro, Ideogram.
* **Example**: "DALL-E 3" for high-quality digital art.
* **Required**: Yes
### Style
* **Description**: Choose the style for the generated image.
* **Options**: Default, Photo, Digital Art, Illustration, Drawing.
* **Example**: "Digital Art" for a creative design.
* **Required**: Yes
### Aspect Ratio
* **Description**: Set the aspect ratio for the image.
* **Options**: 9:16, 1:1, 4:3, 16:9.
* **Example**: "16:9" for widescreen formats.
* **Required**: Yes
### Prompt
* **Description**: Enter a prompt to describe the image.
* **Example**: "A futuristic cityscape" or "A serene mountain lake at sunset."
* **Required**: Yes
### Output Variable Name
* **Description**: Provide a variable name to store the generated image.
* **Example**: "generated\_image" or "ai\_image."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
***
# Get Assigned Company
Source: https://docs.agent.ai/actions/get_assigned_company
## Overview
Fetch the assigned company data from HubSpot for targeted workflows.
### Use Cases
* **Lead Management**: Automatically retrieve the assigned company for a contact.
* **Reporting**: Use company data for analysis or dashboards.
## Configuration Fields
### Output Variable Name
* **Description**: Provide a variable name to store the assigned company data.
* **Example**: "assigned\_company" or "company\_object."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get Bluesky Posts
Source: https://docs.agent.ai/actions/get_bluesky_posts
## Overview
Fetch recent posts from a specified Bluesky user handle, making it easy to monitor activity on the platform.
### Use Cases
* **Social Media Analysis**: Track a user's recent posts for sentiment analysis or topic extraction.
* **Competitor Insights**: Observe recent activity from competitors or key influencers.
## Configuration Fields
### User Handle
* **Description**: Enter the Bluesky handle to fetch posts from.
* **Example**: "jay.bsky.social."
* **Required**: Yes
### Number of Posts to Retrieve
* **Description**: Specify how many recent posts to fetch.
* **Options**: 1, 5, 10, 25, 50, 100
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the retrieved posts.
* **Example**: "recent\_posts" or "bsky\_feed."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get Data from Builder's Knowledge Base
Source: https://docs.agent.ai/actions/get_data_from_builders_knowledgebase
## Overview
Fetch semantic search results from the builder's knowledge base, enabling you to use structured data for analysis and decision-making.
### Use Cases
* **Content Retrieval**: Search for specific information in a structured knowledge base, such as FAQs or product documentation.
* **Automated Assistance**: Power AI agents with relevant context from internal resources.
## Configuration Fields
### Query
* **Description**: Enter the search query to retrieve relevant knowledge base entries.
* **Example**: "Latest sales strategies" or "Integration instructions."
* **Required**: Yes
### Builder Knowledge Base to Use
* **Description**: Select the knowledge base to search from.
* **Example**: "Product Documentation" or "Employee Handbook."
* **Required**: Yes
### Max Number of Document Chunks to Retrieve
* **Description**: Specify the maximum number of document chunks to return.
* **Example**: "5" or "10."
* **Required**: Yes
### Qualitative Vector Score Cutoff for Semantic Search Cosine Similarity
* **Description**: Set the score threshold for search relevance.
* **Example**: "0.2" for broad results or "0.7" for precise matches.
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the search results.
* **Example**: "knowledge\_base\_results" or "kb\_entries."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get Data from User's Uploaded Files
Source: https://docs.agent.ai/actions/get_data_from_users_uploaded_files
## Overview
Retrieve semantic search results from user-uploaded files for targeted information extraction.
### Use Cases
* **Data Analysis**: Quickly retrieve insights from reports or project files uploaded by users.
* **Customized Searches**: Provide tailored responses by extracting specific data from user-uploaded files.
## Configuration Fields
### Query
* **Description**: Enter the search query to find relevant information in uploaded files.
* **Example**: "Revenue breakdown" or "Budget overview."
* **Required**: Yes
### User Uploaded Files to Use
* **Description**: Specify which uploaded files to search within.
* **Example**: "Recent uploads" or "project\_documents."
* **Required**: Yes
### Max Number of Document Chunks to Retrieve
* **Description**: Set the maximum number of document chunks to return.
* **Example**: "5" or "10."
* **Required**: Yes
### Qualitative Vector Score Cutoff for Semantic Search Cosine Similarity
* **Description**: Adjust the score threshold for search relevance.
* **Example**: "0.2" for broad results or "0.5" for specific results.
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the search results.
* **Example**: "file\_search\_results" or "upload\_data."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get HubSpot CRM Object
Source: https://docs.agent.ai/actions/get_hubspot_crm_object
## Overview
Retrieve specific CRM objects like contacts or companies from HubSpot to use in workflows.
### Use Cases
* **Customer Insights**: Retrieve detailed information about a contact or company for targeted actions.
* **Lead Assignment**: Use CRM data to inform lead distribution.
## Configuration Fields
### Object Type
* **Description**: Select the type of HubSpot object to retrieve.
* **Options**: Company, Contact
* **Required**: Yes
### Query (optional)
* **Description**: Specify search criteria to filter HubSpot objects.
* **Example**: "contact email" or "company domain."
* **Required**: No
### Output Variable Name
* **Description**: Provide a variable name to store the HubSpot object.
* **Example**: "retrieved\_company" or "contact\_info."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get HubSpot Object Properties
Source: https://docs.agent.ai/actions/get_hubspot_object_properties
## Overview
Retrieve object properties from HubSpot CRM, such as company or contact details.
### Use Cases
* **Data Analysis**: Use property data for insights or decision-making.
* **Workflow Automation**: Leverage CRM properties to inform next steps.
## Configuration Fields
### Object Type
* **Description**: Select the type of HubSpot object to retrieve properties from.
* **Options**: Companies, Contacts, Deals, Products, Tickets
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the retrieved object properties.
* **Example**: "company\_properties" or "contact\_properties."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get HubSpot Owners
Source: https://docs.agent.ai/actions/get_hubspot_owners
## Overview
Fetch a list of HubSpot owners to manage assignments or contacts.
### Use Cases
* **Team Management**: Assign contacts or deals to specific owners.
* **Resource Allocation**: Distribute leads based on available owners.
## Configuration Fields
### Output Variable Name
* **Description**: Provide a variable name to store the list of HubSpot owners.
* **Example**: "owners\_list" or "hubspot\_owners."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get Instagram Followers
Source: https://docs.agent.ai/actions/get_instagram_followers
## Overview
Retrieve a list of top followers from a specified Instagram account for social media analysis.
### Use Cases
* **Audience Insights**: Understand the followers of an Instagram account for marketing purposes.
* **Engagement Monitoring**: Track influential followers.
## Configuration Fields
### Instagram Username
* **Description**: Enter the Instagram username (without @) to fetch followers.
* **Example**: "fashionblogger123."
* **Required**: Yes
### Number of Top Followers
* **Description**: Select the number of top followers to retrieve.
* **Options**: 10, 20, 50, 100
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the followers data.
* **Example**: "instagram\_followers" or "top\_followers."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get Instagram Profile
Source: https://docs.agent.ai/actions/get_instagram_profile
## Overview
Fetch detailed profile information for a specified Instagram username.
### Use Cases
* **Competitor Analysis**: Understand details of an Instagram profile for benchmarking.
* **Content Creation**: Identify influencers or collaborators.
## Configuration Fields
### Instagram Username
* **Description**: Enter the Instagram username (without @) to fetch profile details.
* **Example**: "travelguru."
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the profile data.
* **Example**: "instagram\_profile" or "profile\_data."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
***
# Get LinkedIn Activity
Source: https://docs.agent.ai/actions/get_linkedin_activity
## Overview
Retrieve recent LinkedIn posts from specified profiles to analyze professional activity and engagement.
### Use Cases
* **Recruitment**: Monitor LinkedIn activity for potential candidates.
* **Industry Trends**: Analyze posts for emerging topics.
## Configuration Fields
### LinkedIn Profile URLs
* **Description**: Enter one or more LinkedIn profile URLs, each on a new line.
* **Example**: "[https://linkedin.com/in/janedoe](https://linkedin.com/in/janedoe)."
* **Required**: Yes
### Number of Posts to Retrieve
* **Description**: Specify how many recent posts to fetch from each profile.
* **Options**: 1, 5, 10, 25, 50, 100
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store LinkedIn activity data.
* **Example**: "linkedin\_activity" or "recent\_posts."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get LinkedIn Profile
Source: https://docs.agent.ai/actions/get_linkedin_profile
## Overview
Retrieve detailed information from a specified LinkedIn profile for professional insights.
### Use Cases
* **Candidate Research**: Gather details about a LinkedIn profile for recruitment.
* **Lead Generation**: Analyze profiles for sales and marketing.
## Configuration Fields
### Profile Handle
* **Description**: Enter the LinkedIn profile handle to retrieve details.
* **Example**: "[https://linkedin.com/in/johndoe](https://linkedin.com/in/johndoe)."
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the LinkedIn profile data.
* **Example**: "linkedin\_profile" or "professional\_info."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get Recent Tweets
Source: https://docs.agent.ai/actions/get_recent_tweets
## Overview
Fetch recent tweets from a specified Twitter handle, enabling social media tracking and analysis.
### Use Cases
* **Real-time Monitoring**: Track the latest activity from a key influencer or competitor.
* **Sentiment Analysis**: Analyze recent tweets for tone and sentiment.
## Configuration Fields
### Twitter Handle
* **Description**: Enter the Twitter handle to fetch tweets from (without the @ symbol).
* **Example**: "elonmusk."
* **Required**: Yes
### Number of Tweets to Retrieve
* **Description**: Specify how many recent tweets to fetch.
* **Options**: 1, 5, 10, 25, 50, 100
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the retrieved tweets.
* **Example**: "recent\_tweets" or "tweet\_feed."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get Twitter Users
Source: https://docs.agent.ai/actions/get_twitter_users
## Overview
Search and retrieve Twitter user profiles based on specific keywords for targeted social media analysis.
### Use Cases
* **Influencer Marketing**: Identify key Twitter users for promotional campaigns.
* **Competitor Research**: Find relevant profiles in your industry.
## Configuration Fields
### Search Keywords
* **Description**: Enter keywords to find relevant Twitter users.
* **Example**: "AI experts" or "marketing influencers."
* **Required**: Yes
### Number of Users to Retrieve
* **Description**: Specify how many user profiles to retrieve.
* **Options**: 1, 5, 10, 25, 50, 100
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the retrieved Twitter users.
* **Example**: "twitter\_users" or "social\_media\_profiles."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Get User File
Source: https://docs.agent.ai/actions/get_user_file
## Overview
The "Get User File" action allows users to upload files for processing, storage, or review.
### Use Cases
* **Resume Collection**: Upload resumes in PDF format.
* **File Processing**: Gather data files for analysis.
* **Document Submission**: Collect required documentation from users.
## Configuration Fields
### User Prompt
* **Description**: Provide clear instructions for users to upload files.
* **Example**: "Upload your resume as a PDF."
* **Required**: Yes
### Required?
* **Description**: Mark this checkbox if file upload is necessary for the workflow to proceed.
* **Required**: No
### Output Variable Name
* **Description**: Assign a variable name for the uploaded files.
* **Example**: "user\_documents" or "uploaded\_images."
* **Validation**:
* Only letters, numbers, and underscores (\_) are allowed.
* No spaces, special characters, or dashes.
* **Regex**: `^[a-zA-Z0-9_]+$`
* **Hint**: This variable will be used to reference the files in subsequent steps.
* **Required**: Yes
### Show Only Files Uploaded in the Current Session
* **Description**: Restrict access to files uploaded only during the current session.
* **Required**: No
# Get User Input
Source: https://docs.agent.ai/actions/get_user_input
## Overview
The "Get User Input" action allows you to capture dynamic responses from users, such as text, numbers, URLs, and dropdown selections. This action is essential for workflows that require specific input from users to proceed.
### Use Cases
* **Survey Form**: Collect user preferences or feedback.
* **Authentication**: Prompt for email addresses or verification codes.
* **Customized Workflow**: Ask users to select options to determine the next steps.
## Configuration Fields
### Input Type
* **Description**: Choose the type of input you want to capture from the user.
* **Options**:
* **Text**: Open-ended text input.
* **Number**: Numeric input only.
* **Yes/No**: Binary selection.
* **Textarea**: Multi-line text input.
* **URL**: Input limited to URLs.
* **Website Domain**: Input limited to domains.
* **Dropdown (single)**: Single selection from a dropdown.
* **Dropdown (multiple)**: Multiple selections from a dropdown.
* **Multi-Item Selector**: Allows selecting multiple items.
* **Multi-Item Selector (Table View)**: Allows selecting multiple items in a table view.
* **Radio Select (single)**: Single selection using radio buttons.
* **HubSpot Portal**: Select a portal.
* **HubSpot Company**: Select a company.
* **Knowledge Base**: Select a knowledge base.
* **Hint**: Select the appropriate input type based on your data collection needs. For example, use "Text" for open-ended input or "Yes/No" for binary responses.
* **Required**: Yes
### User Prompt
* **Description**: Write a clear prompt to guide users on what information is required.
* **Example**: "Please enter your email address" or "Select your preferred contact method."
* **Required**: Yes
### Default Value
* **Description**: Provide a default response that appears automatically in the input field.
* **Example**: "[example@domain.com](mailto:example@domain.com)" for an email field.
* **Hint**: Use this field to pre-fill common or expected responses to simplify input for users.
* **Required**: No
### Required?
* **Description**: Mark this checkbox if this input is mandatory.
* **Example**: Enable if a response is essential to proceed in the workflow.
* **Required**: No
### Output Variable Name
* **Description**: Assign a unique variable name for the input value.
* **Example**: "user\_email" or "preferred\_contact."
* **Validation**:
* Only letters, numbers, and underscores (\_) are allowed.
* No spaces, special characters, or dashes.
* **Regex**: `^[a-zA-Z0-9_]+$`
* **Hint**: This variable will be used to reference the input value in subsequent steps.
* **Required**: Yes
# Get User KBs and Files
Source: https://docs.agent.ai/actions/get_user_knowledge_base_and_files
## Overview
The "Get User Knowledge Base and Files" action retrieves information from user-selected knowledge bases and uploaded files to support decision-making within the workflow.
### Use Cases
* **Content Search**: Allow users to select a knowledge base to search from.
* **Resource Management**: Link workflows to specific user-uploaded files.
## Configuration Fields
### User Prompt
* **Description**: Provide a prompt for users to select a knowledge base.
* **Example**: "Choose the knowledge base to search from."
* **Required**: Yes
### Required?
* **Description**: Mark as required if selecting a knowledge base is essential for the workflow.
* **Required**: No
### Output Variable Name
* **Description**: Assign a variable name to store the knowledge base ID.
* **Example**: "selected\_kb" or "kb\_source."
* **Validation**:
* Only letters, numbers, and underscores (\_) are allowed.
* No spaces, special characters, or dashes.
* **Regex**: `^[a-zA-Z0-9_]+$`
* **Hint**: This variable will be used to reference the knowledge base in subsequent steps.
* **Required**: Yes
# Get User List
Source: https://docs.agent.ai/actions/get_user_list
## Overview
The "Get User List" action collects a list of items entered by users and splits them based on a specified delimiter or newline.
### Use Cases
* **Batch Data Input**: Gather a list of email addresses or item names.
* **Bulk Selection**: Allow users to input multiple options in one field.
## Configuration Fields
### User Prompt
* **Description**: Write a clear prompt to guide users on what information is required.
* **Example**: "Enter the list of email addresses separated by commas."
* **Required**: Yes
### List Delimiter (leave blank for newline)
* **Description**: Specify the character that separates the list items.
* **Example**: Use a comma (,) for "item1,item2,item3" or leave blank for newlines.
* **Required**: No
### Required?
* **Description**: Mark this checkbox if this input is mandatory.
* **Example**: Enable if a response is essential to proceed in the workflow.
* **Required**: No
### Output Variable Name
* **Description**: Assign a unique variable name for the input value.
* **Example**: "email\_list" or "item\_names."
* **Validation**:
* Only letters, numbers, and underscores (\_) are allowed.
* No spaces, special characters, or dashes.
* **Regex**: `^[a-zA-Z0-9_]+$`
* **Hint**: This variable will be used to reference the list in subsequent steps.
* **Required**: Yes
# Get Variable from Database
Source: https://docs.agent.ai/actions/get_variable_from_database
## Overview
Retrieve stored variables from the agent's database for use in workflows.
### Use Cases
* **Data Reuse**: Leverage previously stored variables for decision-making.
* **Trend Analysis**: Access historical data for analysis.
## Configuration Fields
### Variable
* **Description**: Specify the variable to retrieve from the database.
* **Example**: "user\_input" or "order\_status."
* **Required**: Yes
### Retrieval Depth
* **Description**: Choose how far back to retrieve the data.
* **Options**: Most Recent Value, Historical Values
* **Example**: "Most Recent Value" for the latest data.
* **Required**: Yes
### Historical Data Interval (optional)
* **Description**: Define the interval for historical data retrieval.
* **Options**: Hour, Day, Week, Month, All Time
* **Example**: "Last 7 Days" for weekly data.
* **Required**: No
### Number of Items to Retrieve (optional)
* **Description**: Enter the number of items to retrieve from historical data.
* **Example**: "10" to fetch the last 10 entries.
* **Required**: No
### Output Variable Name
* **Description**: Assign a variable name to store the retrieved data.
* **Example**: "tracked\_values" or "historical\_data."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Google News Data
Source: https://docs.agent.ai/actions/google_news_data
## Overview
Fetch news articles based on queries and date ranges to stay updated on relevant topics or trends.
### Use Cases
* **Market Analysis**: Track news articles for industry trends.
* **Brand Monitoring**: Stay updated on mentions of your company or competitors.
## Configuration Fields
### Query
* **Description**: Enter search terms to find news articles.
* **Example**: "AI advancements" or "global market trends."
* **Required**: Yes
### Since
* **Description**: Select the timeframe for news articles.
* **Options**: Last 24 hours, 7 days, 30 days, 90 days
* **Required**: Yes
### Location
* **Description**: Specify a location to filter news results (optional).
* **Example**: "New York" or "London."
* **Required**: No
### Output Variable Name
* **Description**: Assign a variable name to store the news data.
* **Example**: "news\_data" or "articles."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# If/Else Statement
Source: https://docs.agent.ai/actions/if_else
## Overview
If/Else statements create decision points in your workflow. They evaluate a condition and direct your agent down different paths based on whether that condition is true or false.
### Use Cases
* Create branching workflows based on user inputs or data values
* Implement decision logic to handle different scenarios
* Personalize responses to different types of users
* Apply different processing based on data characteristics
## **How to Use If/Else Statements**
### **Step 1: Add the Action**
1. In your agent's Actions tab, click "Add action"
2. Select the "If/Else Statement" option
### **Step 2: Configure the First Condition**
1. Leave the "Is Else Statement?" checkbox **unchecked** for your first condition
2. Enter your condition in the field
3. Add the actions you want to run when this condition is TRUE
### **Writing Conditions**
Conditions must evaluate to true or false. Common formats include:
* **Comparing numbers**: variable > 100
* **Checking equality**: variable == "specific value"
* **Multiple conditions**: variable1 > 10 && variable2 == "active"
### **Step 3: Add Additional Paths (Else If)**
1. Add another "If/Else Statement" action
2. Check the "Is Else Statement?" checkbox to connect it to the previous condition
3. Enter your next condition
4. Add the actions you want to run when this condition is TRUE
5. Repeat this step to add as many alternative paths as needed
### **Step 4: Add a Default Path (Else)**
1. Add an "If/Else Statement" action after your other conditions
2. Check the "Is Else Statement?" checkbox
3. Leave the Conditional Statement field **blank**
4. Add the actions to run when no other conditions are met
### **Step 5: End the Statement**
1. After all your conditional paths, add the "**End If/Else/For Statement**" action
## **Example: IF/ELSE Example Agent**
See [this simple example](https://agent.ai/agent/IF-ELSE-Example) agent to learn how to use If/Else statements:
1. We collect a budget amount from the user
2. We evaluate three budget ranges
3. Each path provides different output based on the budget amount
# Invoke Other Agent
Source: https://docs.agent.ai/actions/invoke_other_agent
## Overview
Trigger another agent to perform additional processing or data handling within workflows.
### Use Cases
* **Multi-Agent Workflows**: Delegate tasks to specialized agents.
* **Cross-Functionality**: Utilize existing agent capabilities for enhanced results.
## Configuration Fields
### Agent ID
* **Description**: Enter the ID of the agent to invoke.
* **Example**: "agent\_123" or "data\_processor."
* **Required**: Yes
### Parameters
* **Description**: Specify parameters for the agent as key-value pairs, one per line.
* **Example**: "action=update" or "user\_id=567."
* **Required**: No
### Output Variable Name
* **Description**: Assign a variable name to store the agent's response.
* **Example**: "agent\_output" or "result\_data."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Invoke Web API
Source: https://docs.agent.ai/actions/invoke_web_api
## Overview
The Invoke Web API action allows your agents to make RESTful API calls to external systems and services. This enables access to third-party data sources, submission of information to web services, and integration with existing infrastructure.
### Use Cases
* **External Data Retrieval**: Connect to public or private APIs to fetch real-time data
* **Data Querying**: Search external databases or services using specific parameters
* **Third-Party Integrations**: Access services that expose information via REST APIs
* **Enriching Workflows**: Incorporate external data sources into your agent's processing
## **How to Configure Web API Calls**
### **Add the Action**
1. In the Actions tab, click "Add action"
2. Select the "Advanced" category
3. Choose "Invoke Web API"
## Configuration Fields
### URL
* **Description**: Enter the web address of the API you want to connect to (this information should be provided in the API documentation)
* **Example**: [https://api.nasa.gov/planetary/apod?api\_key=DEMO\_KEY](https://api.nasa.gov/planetary/apod?api_key=DEMO_KEY)
* **Required**: Yes
### Method
* **Description**: Choose how you want to interact with the API
* **Options:**
* **GET**: Retrieve information (most common)
* **POST**: Send information to create something new
* **PUT**: Update existing information
* **HEAD**: Check if a resource exists without retrieving it
* **Required**: Yes
### Headers (JSON)
* **Description**: Think of these as your "ID card" when talking to an API..
* **Example**: Many APIs need to know who you are before giving you information. For instance, for the X (Twitter) API, you’d need: \{"Authorization": "Bearer YOUR\_ACCESS\_TOKEN"}. The API's documentation will usually tell you exactly what to put here.
* **Required**: No
### Body (JSON)
* **Description**: This is the information you want to send to the API.
* Only needed when you're sending data (POST or PUT methods).
* **Example**: when posting a tweet with the X API, you'd include: \{"text": "Hello world!"}.
* When using GET requests (just retrieving information), you typically leave this empty.
* The API's documentation will specify exactly what format to use
* **Required**: No
### Output Variable Name
* **Description**: Assign a variable name to store the API response.
* **Example**: "api\_response" or "rest\_data."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
## **Using API Responses**
The API response will be stored in your specified output variable. You can access specific data points using dot notation:
* \{\{variable\_name.property}}
* \{\{variable\_name.nested.property}}
## **Example:** RESTful API Example Agent
See [this simple Grant Search Agent ](https://agent.ai/agent/RESTful-API-Example)that demonstrates API usage:
1. **Step 1**: Collects a research focus from the user
2. **Step 3**: Makes a REST API call to a government grants database with these keywords
3. **Step 5**: Presents the information to the user as a formatted output
This workflow shows how external APIs can significantly expand an agent's capabilities by providing access to specialized data sources that aren't available within the Agent.ai platform itself.
# Post to Bluesky
Source: https://docs.agent.ai/actions/post_to_bluesky
## Overview
Create and post content to Bluesky, allowing for seamless social media updates within workflows.
### Use Cases
* **Social Media Automation**: Share updates directly to Bluesky.
* **Marketing Campaigns**: Schedule and post campaign content.
## Configuration Fields
### Bluesky Username
* **Description**: Enter your Bluesky username/handle (e.g., username.bsky.social).
* **Required**: Yes
### Bluesky Password
* **Description**: Enter your Bluesky account password.
* **Required**: Yes
### Post Content
* **Description**: Provide the text content for your Bluesky post.
* **Example**: "Check out our latest product launch!"
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the post result.
* **Example**: "post\_response" or "bluesky\_post."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Save To Google Doc
Source: https://docs.agent.ai/actions/save_to_google_doc
## Overview
Save text content as a Google Doc for documentation, collaboration, or sharing.
### Use Cases
* **Documentation**: Save workflow results as structured documents.
* **Team Collaboration**: Share generated content via Google Docs.
## Configuration Fields
### Title
* **Description**: Enter the title of the Google Doc.
* **Example**: "Project Plan" or "Meeting Notes."
* **Required**: Yes
### Body
* **Description**: Provide the content to be saved in the Google Doc.
* **Example**: "This document outlines the key objectives for Q1..."
* **Required**: Yes
# Search Bluesky Posts
Source: https://docs.agent.ai/actions/search_bluesky_posts
## Overview
Search for Bluesky posts matching specific keywords or criteria to gather social media insights.
### Use Cases
* **Keyword Monitoring**: Track specific terms or hashtags on Bluesky.
* **Trend Analysis**: Identify trending topics or content on the platform.
## Configuration Fields
### Search Query
* **Description**: Enter keywords or hashtags to search for relevant Bluesky posts.
* **Example**: "#AI" or "climate change."
* **Required**: Yes
### Number of Posts to Retrieve
* **Description**: Specify how many posts to fetch.
* **Options**: 1, 5, 10, 25, 50, 100
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the search results.
* **Example**: "bluesky\_search\_results" or "matching\_posts."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Search Results
Source: https://docs.agent.ai/actions/search_results
## Overview
Fetch search results from Google or YouTube for specific queries, providing valuable insights and content.
### Use Cases
* **Market Research**: Gather data on trends or competitors.
* **Content Discovery**: Find relevant articles or videos for your workflow.
## Configuration Fields
### Query
* **Description**: Enter search terms to find relevant results.
* **Example**: "Best AI tools" or "Marketing strategies."
* **Required**: Yes
### Search Engine
* **Description**: Choose the search engine to use for the query.
* **Options**: Google, YouTube
* **Required**: Yes
### Number of Results to Retrieve
* **Description**: Specify how many results to fetch.
* **Options**: 1, 5, 10, 25, 50, 100
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the search results.
* **Example**: "search\_results" or "google\_data."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Send Message
Source: https://docs.agent.ai/actions/send_message
## Overview
Send messages to specified recipients, such as emails with formatted content or notifications.
### Use Cases
* **Customer Communication**: Notify users about updates or confirmations.
* **Team Collaboration**: Share workflow results via email.
## Configuration Fields
### Message Type
* **Description**: Select the type of message to send.
* **Options**: Email
* **Required**: Yes
### Send To
* **Description**: Enter the recipient's address.
* **Example**: "[john.doe@example.com](mailto:john.doe@example.com)."
* **Required**: Yes
### Output Formatted
* **Description**: Provide the message content, formatted as needed.
* **Example**: "Hello, your order is confirmed!" or formatted HTML for emails.
* **Required**: Yes
# Call Serverless Function
Source: https://docs.agent.ai/actions/serverless_function
## Overview
Serverless Functions allow your agents to execute custom code in the cloud without managing infrastructure. This powerful capability enables complex operations and integrations beyond what standard actions can provide.
### Use Cases
* **Custom Logic Implementation**: Execute specialized code for unique business requirements
* **External System Integration**: Connect with third-party services and APIs
* **Advanced Data Processing**: Perform complex calculations and transformations
* **Extended Functionality**: Add capabilities not available in standard Agent.ai actions
## **How Serverless Functions Work**
Serverless Functions in Agent.ai:
1. Run in AWS Lambda (fully managed by Agent.ai)
2. Support Python and Node.js
3. Automatically deploy when you save the action
4. Generate a REST API endpoint for programmatic access
## **Creating a Serverless Function**
1. In the Actions tab, click "Add action"
2. Select the "Advanced" category
3. Choose "Call Serverless Function"
## Configuration Fields
### Language
* **Description**: Select the programming language for the serverless function.
* **Options**: Python, Node
* **Required**: Yes
### Serverless Code
* **Description**: Write your custom code.
* **Example**: Python or Node script performing custom logic.
* **Required**: Yes
### Serverless API URL
* **Description**: Provide the API URL for the deployed serverless function.
* **Required**: Yes (auto-generated upon deployment)
### Output Variable Name
* **Description**: Assign a variable name to store the result of the serverless function.
* **Example**: "function\_result" or "api\_response."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
### **Deploy and Save**
1. Click "Deploy to AWS Lambda"
2. After successful deployment, the API URL will be populated automatically
### **Using Function Results**
The function's output is stored in your specified variable name. You can access specific data points using dot notation, for example:
* \{\{variable\_name.message}}
* \{\{variable\_name.input}}
## **Example: Serverless Function Agent**
See [this simple Message Analysis Agent](https://agent.ai/agent/serverless-function-example) that demonstrates how to use Serverless Functions:
1. **Step 1**: Get user input text message
2. **Step 2**: Call a serverless function that analyzes:
* Word count
* Character count
* Sentiment (positive/negative/neutral)
3. **Step 3**: Display the results in a formatted output
This sample agent shows how Serverless Functions can extend your agent's capabilities with custom logic that would be difficult to implement using standard actions alone.
# Set Variable
Source: https://docs.agent.ai/actions/set_variable
## Overview
Set or update variables within the workflow to manage dynamic data and enable seamless transitions between steps.
### Use Cases
* **Dynamic Data Storage**: Assign user inputs or calculated values to variables for later use.
* **Data Management**: Update variables based on workflow logic.
## Configuration Fields
### Output Variable Name
* **Description**: Name the variable to be set or updated.
* **Example**: "user\_email" or "order\_status."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
### Variable Value
* **Description**: Enter the value to assign to the variable.
* **Example**: "approved" for status updates or "[john.doe@example.com](mailto:john.doe@example.com)" for email storage.
* **Required**: Yes
# Show User Output
Source: https://docs.agent.ai/actions/show_user_output
## Overview
The "Show User Output" action displays information to users in a visually organized way. It lets you present data, results, or messages in different formats to make them easy to read and understand.
### Use Cases
* **Real-time Feedback**: Display data summaries or workflow outputs to users.
* **Interactive Reports**: Present results in a structured format like tables or markdown.
## **How to Configure**
### **Step 1: Add the Action**
1. In the Actions tab, click "Add action"
2. Select "Show User Output" from the options
## Step 2: Configuration Fields
### Heading
* **Description**: Provide a heading for the output display.
* **Example**: "User Results" or "Analysis Summary."
* **Required**: No
### Output Formatted
* **Description**: Enter the formatted output in HTML, JSON, or Markdown.
* **Example**:
1. Can be text: "Here are your results"
2. Or a variable: \{\{analysis\_results}}
3. Or a mix of both: "Analysis complete: \{\{analysis\_results}}"
* **Required**: Yes
### Format
* **Description**: Choose the format for output display.
* **Options**: Auto, HTML, JSON, Table, Markdown, Audio, Text, JSX
* **Example**: "HTML" for web-based formatting.
* **Required**: Yes
## **Output Formats Explained**
### **Auto**
Agent.AI will try to detect the best format automatically based on your content. Use this when you're unsure which format to choose.
### **HTML**
Displays content with web formatting (like colors, spacing, and styles).
* Example: \Results\
\Your information is ready.\
* Good for: Creating visually structured content with different text sizes, colors, or layouts
* Tip: When using AI tools like Claude or GPT, you can ask them to format their responses in HTML
### **Markdown**
A simple way to format text with headings, lists, and emphasis.
* Example: # Results\n\n- First item\n- Second item
* Good for: Creating organized content with simple formatting needs
* Tip: You can ask AI models to output their responses in Markdown format for easier display
### **JSON**
Displays data in a structured format with keys and values.
* Example: \{"name": "John", "age": 30, "email": "[john@example.com](mailto:john@example.com)"}
* Good for: Displaying data in an organized, hierarchical structure
* To get a specific part of a JSON string, use dot notation:
* \{\{user\_data.name}} to display just the name
* \{\{weather.forecast.temperature}} to display a nested value
* For array items, use: \{\{items.0}} for the first item, \{\{items.1}} for the second, etc.
* Tip: You can request AI models to respond in JSON format when you need structured data
### **Table**
Shows information in rows and columns, like a spreadsheet.
* **Important**: Tables requires a very specific format:
1\) A JSON array of arrays:
```
[
["Column 1", "Column 2", "Column 3"],
["Row 1 Data", "More Data", "Even More"],
["Row 2 Data", "More Data", "Even More"]
]
```
2\) Or a CSV:
```
Column 1,Column 2,Column 3
Row 1 Data,More Data,Even More
Row 2 Data,More Data,Even More
```
See [this example agent](https://agent.ai/agent/Table-Creator) for table output format.
### **Text**
Simple plain text without any special formatting. What you type is exactly what the user sees.
* Good for: Simple messages or information that doesn't need special formatting
### **Audio**
Displays an audio player to play sound files. See [this agent](https://agent.ai/agent/autio-template) as an example.
### **JSX**
For technical users who need to create complex, interactive displays.
* Good for: Interactive components with special styling needs
* Requires knowledge of React JSX formatting
# Store Variable to Database
Source: https://docs.agent.ai/actions/store_variable_to_database
# Overview
Store variables in the agent's database for tracking and retrieval in future workflows.
### Use Cases
* **Historical Tracking**: Save variables for analysis over time.
* **Data Persistence**: Ensure key variables are available across workflows.
## Configuration Fields
### Variable
* **Description**: Specify the variable to store in the database.
* **Example**: "user\_input" or "order\_status."
* **Required**: Yes
***
# Update HubSpot CRM Object
Source: https://docs.agent.ai/actions/update_hubspot_crm_object
## Overview
Modify properties of existing CRM objects, like contacts or companies, within HubSpot.
### Use Cases
* **Data Correction**: Update CRM information to ensure accuracy.
* **Workflow Automation**: Automatically update deal stages or lead information.
## Configuration Fields
### Object Type
* **Description**: Select the type of HubSpot object to update.
* **Options**: Company, Contact
* **Required**: Yes
### Object ID
* **Description**: Enter the ID of the HubSpot object to update.
* **Example**: "12345" for a company or "67890" for a contact.
* **Required**: Yes
### Property Identifier
* **Description**: Specify the property to update.
* **Example**: "name" for company name or "email" for contact email.
* **Required**: Yes
### Value
* **Description**: Enter the new value for the specified property.
* **Example**: "Acme Corp Updated" for a company name or "[jane.doe@example.com](mailto:jane.doe@example.com)" for a contact email.
* **Required**: Yes
# Use GenAI (LLM)
Source: https://docs.agent.ai/actions/use_genai
## Overview
Invoke a language model (LLM) to generate text based on input instructions, enabling creative and dynamic text outputs.
### Use Cases
* **Content Generation**: Draft blog posts, social media captions, or email templates.
* **Summarization**: Generate concise summaries of complex documents.
* **Customer Support**: Create personalized responses or FAQs.
## Configuration Fields
### LLM Engine
* **Description**: Select the language model to use for generating text.
* **Options**: Auto Optimized, GPT-4o, Claude Opus, Gemini 2.0 Flash, and more.
* **Example**: "GPT-4o" for detailed responses or "Claude Opus" for creative writing.
* **Required**: Yes
### Instructions
* **Description**: Enter detailed instructions for the language model.
* **Example**: "Write a summary of this document" or "Generate a persuasive email."
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the generated text.
* **Example**: "llm\_output" or "ai\_generated\_text."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Wait for User Confirmation
Source: https://docs.agent.ai/actions/wait_for_user_confirmation
## Overview
The "Wait for User Confirmation" action pauses the workflow until the user explicitly confirms to proceed.
### Use Cases
* **Decision Points**: Pause workflows at critical junctures to confirm user consent.
* **Verification**: Ensure users are ready to proceed to the next steps.
## Configuration Fields
### Message to Show User (optional)
* **Description**: Enter a message to prompt the user for confirmation.
* **Example**: "Are you sure you want to proceed?" or "Click OK to continue."
* **Required**: No
# Web Page Content
Source: https://docs.agent.ai/actions/web_page_content
## Overview
Extract text content from a specified web page for analysis or use in workflows.
### Use Cases
* **Data Extraction**: Retrieve content from web pages for structured analysis.
* **Content Review**: Automate the review of online articles or blogs.
## Configuration Fields
### URL
* **Description**: Enter the URL of the web page to extract content from.
* **Example**: "[https://example.com/article](https://example.com/article)."
* **Required**: Yes
### Mode
* **Description**: Choose between scraping a single page or crawling multiple pages.
* **Options**: Single Page, Multi-page Crawl
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the extracted content.
* **Example**: "web\_content" or "page\_text."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Web Page Screenshot
Source: https://docs.agent.ai/actions/web_page_screenshot
## Overview
Capture a visual screenshot of a specified web page for documentation or analysis.
### Use Cases
* **Archiving**: Save visual records of web pages.
* **Presentation**: Use screenshots for reports or presentations.
## Configuration Fields
### URL
* **Description**: Enter the URL of the web page to capture.
* **Example**: "[https://example.com](https://example.com)."
* **Required**: Yes
### Cache Expiration Time
* **Description**: Specify how often to refresh the screenshot.
* **Options**: 1 hour, 1 day, 1 week, 1 month
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the screenshot.
* **Example**: "web\_screenshot" or "page\_image."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# YouTube Channel Data
Source: https://docs.agent.ai/actions/youtube_channel_data
## Overview
Retrieve detailed information about a YouTube channel, including its videos and statistics.
### Use Cases
* **Audience Analysis**: Understand the content and engagement metrics of a channel.
* **Competitive Research**: Analyze competitors' channels.
## Configuration Fields
### YouTube Channel URL
* **Description**: Provide the URL of the YouTube channel to analyze.
* **Example**: "[https://youtube.com/channel/UC12345](https://youtube.com/channel/UC12345)."
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the channel data.
* **Example**: "channel\_data" or "youtube\_info."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# YouTube Search Results
Source: https://docs.agent.ai/actions/youtube_search_results
## Overview
Perform a YouTube search and retrieve results for specified queries.
### Use Cases
* **Content Discovery**: Find relevant YouTube videos for research or campaigns.
* **Trend Monitoring**: Identify trending videos or topics.
## Configuration Fields
### Query
* **Description**: Enter search terms for YouTube.
* **Example**: "Machine learning tutorials" or "Travel vlogs."
* **Required**: Yes
### Output Variable Name
* **Description**: Assign a variable name to store the search results.
* **Example**: "youtube\_results" or "video\_list."
* **Validation**: Only letters, numbers, and underscores (\_) are allowed.
* **Required**: Yes
# Convert file
Source: https://docs.agent.ai/api-reference/advanced/convert-file
api-reference/v1/v1.0.1_openapi.json post /action/convert_file
Convert a file to a different format.
# Convert file options
Source: https://docs.agent.ai/api-reference/advanced/convert-file-options
api-reference/v1/v1.0.1_openapi.json post /action/convert_file_options
Gets the full set of options that a file extension can be converted to.
# Invoke Agent
Source: https://docs.agent.ai/api-reference/advanced/invoke-agent
api-reference/v1/v1.0.1_openapi.json post /action/invoke_agent
Trigger another agent to perform additional processing or data handling within workflows.
# REST call
Source: https://docs.agent.ai/api-reference/advanced/rest-call
api-reference/v1/v1.0.1_openapi.json post /action/rest_call
Make a REST API call to a specified endpoint.
# Retrieve Variable
Source: https://docs.agent.ai/api-reference/advanced/retrieve-variable
api-reference/v1/v1.0.1_openapi.json post /action/get_variable_from_database
Retrieve a variable from the agent's database
# Store Variable
Source: https://docs.agent.ai/api-reference/advanced/store-variable
api-reference/v1/v1.0.1_openapi.json post /action/store_variable_to_database
Store a variable in the agent's database
# Enrich Company Data
Source: https://docs.agent.ai/api-reference/get-data/enrich-company-data
api-reference/v1/v1.0.1_openapi.json post /action/get_company_object
Gather enriched company data using Breeze Intelligence for deeper analysis and insights.
# Get Bluesky Posts
Source: https://docs.agent.ai/api-reference/get-data/get-bluesky-posts
api-reference/v1/v1.0.1_openapi.json post /action/get_bluesky_posts
Fetch recent posts from a specified Bluesky user handle, making it easy to monitor activity on the platform.
# Get Company Earnings Info
Source: https://docs.agent.ai/api-reference/get-data/get-company-earnings-info
api-reference/v1/v1.0.1_openapi.json post /action/company_financial_info
Retrieve company earnings information for a given stock symbol over time.
# Get Company Financial Profile
Source: https://docs.agent.ai/api-reference/get-data/get-company-financial-profile
api-reference/v1/v1.0.1_openapi.json post /action/company_financial_profile
Retrieve detailed financial and company profile information for a given stock symbol, such as market cap and the last known stock price for any company.
# Get Domain Information
Source: https://docs.agent.ai/api-reference/get-data/get-domain-information
api-reference/v1/v1.0.1_openapi.json post /action/domain_info
Retrieve detailed information about a domain, including its registration details, DNS records, and more.
# Get Instagram Followers
Source: https://docs.agent.ai/api-reference/get-data/get-instagram-followers
api-reference/v1/v1.0.1_openapi.json post /action/get_instagram_followers
Retrieve a list of top followers from a specified Instagram account for social media analysis.
# Get Instagram Profile
Source: https://docs.agent.ai/api-reference/get-data/get-instagram-profile
api-reference/v1/v1.0.1_openapi.json post /action/get_instagram_profile
Fetch detailed profile information for a specified Instagram username.
# Get LinkedIn Activity
Source: https://docs.agent.ai/api-reference/get-data/get-linkedin-activity
api-reference/v1/v1.0.1_openapi.json post /action/get_linkedin_activity
Retrieve recent LinkedIn posts from specified profiles to analyze professional activity and engagement.
# Get LinkedIn Profile
Source: https://docs.agent.ai/api-reference/get-data/get-linkedin-profile
api-reference/v1/v1.0.1_openapi.json post /action/get_linkedin_profile
Retrieve detailed information from a specified LinkedIn profile for professional insights.
# Get Recent Tweets
Source: https://docs.agent.ai/api-reference/get-data/get-recent-tweets
api-reference/v1/v1.0.1_openapi.json post /action/get_recent_tweets
This action fetches recent tweets from a specified Twitter handle.
# Get Twitter Users
Source: https://docs.agent.ai/api-reference/get-data/get-twitter-users
api-reference/v1/v1.0.1_openapi.json post /action/get_twitter_users
Search and retrieve Twitter user profiles based on specific keywords for targeted social media analysis.
# Google News Data
Source: https://docs.agent.ai/api-reference/get-data/google-news-data
api-reference/v1/v1.0.1_openapi.json post /action/get_google_news
Fetch news articles based on queries and date ranges to stay updated on relevant topics or trends.
# Search Bluesky Posts
Source: https://docs.agent.ai/api-reference/get-data/search-bluesky-posts
api-reference/v1/v1.0.1_openapi.json post /action/search_bluesky_posts
Search for Bluesky posts matching specific keywords or criteria to gather social media insights.
# Search Results
Source: https://docs.agent.ai/api-reference/get-data/search-results
api-reference/v1/v1.0.1_openapi.json post /action/get_search_results
Fetch search results from Google or YouTube for specific queries, providing valuable insights and content.
# Web Page Content
Source: https://docs.agent.ai/api-reference/get-data/web-page-content
api-reference/v1/v1.0.1_openapi.json post /action/grab_web_text
Extract text content from a specified web page or domain.
# Web Page Screenshot
Source: https://docs.agent.ai/api-reference/get-data/web-page-screenshot
api-reference/v1/v1.0.1_openapi.json post /action/grab_web_screenshot
Capture a visual screenshot of a specified web page for documentation or analysis.
# YouTube Channel Data
Source: https://docs.agent.ai/api-reference/get-data/youtube-channel-data
api-reference/v1/v1.0.1_openapi.json post /action/get_youtube_channel
Retrieve detailed information about a YouTube channel, including its videos and statistics.
# YouTube Search Results
Source: https://docs.agent.ai/api-reference/get-data/youtube-search-results
api-reference/v1/v1.0.1_openapi.json post /action/run_youtube_search
Perform a YouTube search and retrieve results for specified queries.
# YouTube Video Transcript
Source: https://docs.agent.ai/api-reference/get-data/youtube-video-transcript
api-reference/v1/v1.0.1_openapi.json post /action/get_youtube_transcript
Fetches the transcript of a YouTube video using the video URL.
# Convert text to speech
Source: https://docs.agent.ai/api-reference/use-ai/convert-text-to-speech
api-reference/v1/v1.0.1_openapi.json post /action/output_audio
Convert text to a generated audio voice file.
# Generate Image
Source: https://docs.agent.ai/api-reference/use-ai/generate-image
api-reference/v1/v1.0.1_openapi.json post /action/generate_image
Create visually engaging images using AI models, with options for style, aspect ratio, and detailed prompts.
# Use GenAI (LLM)
Source: https://docs.agent.ai/api-reference/use-ai/use-genai-llm
api-reference/v1/v1.0.1_openapi.json post /action/invoke_llm
Invoke a language model (LLM) to generate text based on input instructions, enabling creative and dynamic text outputs.
# Builder Overview
Source: https://docs.agent.ai/builder/overview
Learn how to get started with the Builder
The Agent.AI Builder is a no-code tool that allows users at all technical levels to build powerful agentic AI applications in minutes.
Once you sign up for your Agent.AI account, enable your Builder account by clicking on "Agent Builder" in the menu bar. Then, head over to the [Agent Builder](https://agent.ai/builder/agents) to get started.
## Create Your First Agent
To create an agent, click on the "Create Agent" modal. You can either start building an agent from scratch or start building from one of our existing templates.
Let's start by building an agent from scratch. Don't worry, it's easier than it sounds!
## Settings
The builder has 5 different sections: Settings, Triggers, Actions, Sharing, and Advanced. Most information is optional, so don't if you don't know what some of those words mean.
Let's start with the settings panel. Here we define how the agent will show up when users try to use it and how it will show up in the marketplace.
#### Required Information
The following information is required:
* Agent Name: Name your agent based on its function. Make this descriptive to reflect what the agent does (e.g., "Data Fetcher," "Customer Profile Enricher").
* Agent Description: Describe what your agent is built to do. This can include any specific automation or tasks it handles (e.g., "Fetches and enriches customer data from LinkedIn profiles").
* Agent Tag(s): Add tags that make it easier to search or categorize your agent for quick access.
#### Optional Information
The following information is not required, but will help people get a better understanding of what your agent can do and will help it stand out:
* Icon URL: You can add a visual representation by uploading an icon or linking to an image file that represents your agent's purpose.
* Sharing and Visibility:
* Private: unlisted, where only people with the link can use the agent
* User only: only the author can use this agent
* Public: all users can use this agent
* Expected Runtime: Gives users an indication as to how long the agent will take to run, on average. It also allows the builder to create a progress bar as the agent executes.
* Video Demo: Provide the public video URL of a live demo of your agent in action from Youtube, Loom, Vimeo, or Wistia, or upload a local recording. You can copy this URL from the video player on any of these sites. This video will be shown to Agent.AI site explorers to help better understand the value and behavior of your agent.
* Agent Username: This is the unique identifier for your agent, which will be used in the agent URL.
## Trigger
Triggers determine when the agent will run. You can set up the following trigger types:
#### **Manual**
Agents can always be run manually, but selecting ‘Manual Only’ ensures this agent can only be triggered directly from Agent.AI
#### **User configured schedule**
Enabling user configured schedules allows users of your agent to set up recurring runs of the agent using inputs from their previously defined inputs.
**How it works**
1. When a user runs your agent that has "User configured schedule" enabled, they will see an "Auto Schedule" button
2. Clicking "Auto Schedule" opens a scheduling dialog where:
* The inputs from their last run will be pre-filled
* They can choose a frequency (Daily, Weekly, Monthly, or Quarterly)
* They can review and confirm the schedule
3. After clicking "Save Schedule", the agent will run automatically on the selected schedule
**Note**: You can see and manage all your agent schedules in your [Agent Scheduled Runs](https://agent.ai/user/agent-runs). You will receive email notifications with outputs of each run as they complete.
#### **Enable agent via email**
When this setting is enabled, the agent will also be accessible via email. Users can just email the agent's email address and they'll get a reply with the full response directly.
#### **HubSpot Contact/Company Added**
Automatically trigger the agent when a new contact or company is added to HubSpot, a useful feature for CRM automation.
#### **Webhook**
By enabling a webhook, the agent can be triggered whenever an external system sends a request to the specified endpoint. This ensures your agent remains up to date and reacts instantly to new events or data.
**How to Use Webhooks**
When enabled, your agent can be triggered by sending an HTTP POST request to the webhook URL, it would look like:
```
curl -L -X POST -H 'Content-Type: application/json' \
'https://api-lr.agent.ai/v1/agent/and2o07w2lqhwjnn/webhook/ef2681a0' \
-d '{"user_input":"REPLACE_ME"}'
```
**Manual Testing:**
1. Copy the curl command from your agent's webhook configuration
2. Replace placeholder values with your actual data
3. Run the command in your terminal for testing
4. Your agent will execute automatically with the provided inputs
**Example: Webhook Example Agent**
See [this example agent ](https://agent.ai/agent/webhook-template)that demonstrates webhook usage. The agent delivers a summary of a YouTube video to a provided email address.
```
curl -L -X POST -H 'Content-Type: application/json' \
'https://api-lr.agent.ai/v1/agent/2uu8sx3kiip82da4/webhook/7a1e56b0' \
-d '{"user_input_url":"REPLACE_ME","user_email":"REPLACE_ME"}'
```
To trigger this agent via webhook:
* Replace the first "REPLACE\_ME" with a YouTube URL
* Replace the second "REPLACE\_ME" with your email address
* Paste and run in your terminal (command prompt)
* You'll receive an email with the video summary shortly
## Actions
In the SmartFlow section, users define the steps the agent will perform. Each action is a building block in your workflow, and the order of these actions will determine how the agent operates. Below is a breakdown of the available actions and how you can use them effectively.
Actions are grouped in categories, such as:
#### User Input
Capture a wide range of user responses with various input types, from simple text boxes and dropdowns to file uploads and multi-item selectors. For example, prompt users to submit URLs, answer Yes/No questions, or upload documents for review. These actions ensure flexible data collection, enabling interactions that are tailored to different user needs, such as gathering user feedback, survey responses, or receiving important files for processing.
#### Get Data
Access real-time information from a wide range of sources, such as extracting content from web pages, fetching social media data like recent tweets or YouTube videos, and retrieving news articles or Google Calendar events. For example, use these actions to keep users updated with the latest industry news, analyze competitor profiles, or compile social media statistics—providing comprehensive data to power smarter decisions and insights.
#### Access HubSpot
Seamlessly integrate with HubSpot's CRM to manage, update, or query CRM data directly within workflows. Retrieve contact information, add new companies, update deal properties, or pull HubSpot owners for targeted actions. For example, use this integration to update contact information, assign sales leads, or pull a list of recent deals—enhancing customer relationship management with precise, automated actions.
#### Use AI
Leverage AI-powered actions to enhance workflows with intelligent outputs, such as generating text, creating images, or synthesizing audio. For instance, use an AI language model to draft content based on user input, generate a product image, or convert text to speech for an audio message. These actions bring cutting-edge AI capabilities directly into workflows, enabling creative automation and smarter outputs.
#### Run Process
Control workflow logic with essential operations like checking conditions, setting variables, or prompting users for confirmation. Use actions such as ‘Set Variable’ to manage dynamic data flows, or ‘If/Else Statement’ to direct users down different paths based on logic outcomes. Whether it’s guiding a user to the next step based on their input or dynamically altering a process, these actions provide robust adaptability for automating complex workflows.
#### Create Output
Deliver meaningful, formatted results that can be communicated or saved for further use. Create engaging outputs like email messages, blog posts, Google Docs, or formatted tables based on workflow data. For example, send users a custom report via email, save generated content as a document, or display a summary table directly on the interface—ensuring results are clear, actionable, and easy to understand.
#### Advanced
Execute specialized, technical tasks that support advanced automation needs. Run serverless functions, invoke Python modules, or make direct web API calls to extend workflows beyond standard capabilities. For instance, fetch data from custom endpoints, process complex calculations using Python, or integrate external services via APIs—enabling deep customization, advanced data handling, and complex integrations.
We'll run through each available action in the Actions page.
## Sharing
Here, you define who can use your agent:
#### Just me
Only you can run and see the agent.
#### Anyone with the link
The agent is available to anyone with a direct link to it.
#### Specific users
Limit access to certain people or teams.
#### Public
Make the agent public for everyone.
## Advanced
Here are a few more advanced options:
#### Automatically generate sharable URLs
When this setting is enabled, user inputs will automatically be appended to browser urls as new parameters. Users can then share the URL with others, or reload the url themselves to automatically run your agent with those same values.
# LLM Models
Source: https://docs.agent.ai/llm-models
Agent.ai provides a number of LLM models that are available for use.
## **LLM Models**
Selecting the right Large Language Model (LLM) for your application is a critical decision that impacts performance, cost, and user experience. This guide provides a comprehensive comparison of leading LLMs to help you make an informed choice based on your specific requirements.
## How to Select the Right LLM
When choosing an LLM, consider these key factors:
1. **Task Complexity**: For complex reasoning, research, or creative tasks, prioritize models with high accuracy scores (8-10), even if they're slower or more expensive. For simpler, routine tasks, models with moderate accuracy (6-8) but higher speed may be sufficient.
2. **Response Time Requirements**: If your application needs real-time interactions, prioritize models with speed ratings of 8-10. Customer-facing applications generally benefit from faster models to maintain engagement.
3. **Context Needs**: If your application processes long documents or requires maintaining extended conversations, select models with context window ratings of 8 or higher. Some specialized tasks might work fine with smaller context windows.
4. **Budget Constraints**: Cost varies dramatically across models. Free and low-cost options (0-2 on our relative scale) can be excellent for startups or high-volume applications, while premium models (5+) might be justified for mission-critical enterprise applications where accuracy is paramount.
5. **Specific Capabilities**: Some models excel at particular tasks like code generation, multimodal understanding, or multilingual support. Review the use cases to find models that specialize in your specific needs.
The ideal approach is often to start with a model that balances your primary requirements, then test alternatives to fine-tune performance. Many organizations use multiple models: premium options for complex tasks and more affordable models for routine operations.
## Vendor Overview
**OpenAI**: Offers the most diverse range of models with industry-leading capabilities, though often at premium price points, with particular strengths in reasoning and multimodal applications.
**Anthropic (Claude)**: Focuses on highly reliable, safety-aligned models with exceptional context length capabilities, making them ideal for document analysis and complex reasoning tasks.
**Google**: Provides models with impressive context windows and competitive pricing, with the Gemini series offering particularly strong performance in creative and analytical tasks.
**Perplexity**: Specializes in research-oriented models with unique web search integration, offering free access to powerful research capabilities and real-time information.
**Other Vendors**: Offer open-source and specialized models that provide strong performance at minimal or no cost, making advanced AI accessible for deployment in resource-constrained environments.
## OpenAI Models
| Model | Speed | Accuracy | Context Window | Relative Cost | Use Cases |
| ------------ | :---: | :------: | :------------: | :-----------: | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| GPT-4o | 9 | 9 | 9 | 3 | • Multimodal assistant for text, audio, and images
• Complex reasoning and coding tasks
• Cost-sensitive deployments |
| GPT-4o-Mini | 10 | 8 | 9 | 1 | • Real-time chatbots and high-volume applications
• Long-context processing
• General AI assistant tasks where affordability and speed are prioritized |
| GPT-4 Vision | 5 | 9 | 5 | 5 | • Image analysis and description
• High-accuracy general assistant tasks
• Creative and technical writing with visual context |
| o1 | 6 | 10 | 9 | 4 | • Tackling highly complex problems in science, math, and coding
• Advanced strategy or research planning
• Scenarios accepting high latency/cost for superior accuracy |
| o1 Mini | 8 | 8 | 9 | 1 | • Coding assistants and developer tools
• Reasoning tasks that need efficiency over broad knowledge
• Applications requiring moderate reasoning but faster responses |
| o3 Mini | 9 | 9 | 9 | 1 | • General-purpose chatbot for coding, math, science
• Developer integrations
• High-throughput AI services |
| GPT-4.5 | 5 | 10 | 9 | 10 | • Mission-critical AI tasks requiring top-tier intelligence
• Highly complex problem solving or content generation
• Multi-modal and extended context applications |
## Anthropic (Claude) Models
| Model | Speed | Accuracy | Context Window | Relative Cost | Use Cases |
| ----------------------------- | :---: | :------: | :------------: | :-----------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Claude 3.7 Sonnet | 8 | 9 | 9 | 2 | • Advanced coding and debugging assistant
• Complex analytical tasks
• Fast turnaround on detailed answers |
| Claude 3.5 Sonnet | 7 | 8 | 9 | 2 | • General-purpose AI assistant for long documents
• Coding help and Q\&A
• Everyday reasoning tasks with high reliability and alignment |
| Claude 3.5 Sonnet Multi-Modal | 7 | 8 | 9 | 2 | • Image understanding in French or English
• Multi-modal customer support
• Research assistants combining text and visual data |
| Claude Opus | 6 | 7 | 9 | 9 | • High-precision analysis for complex queries
• Long-form content summarization or generation
• Enterprise scenarios requiring strict reliability |
## Google Models
| Model | Speed | Accuracy | Context Window | Relative Cost | Use Cases |
| ------------------------------ | :---: | :------: | :------------: | :-----------: | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Gemini 2.0 Pro | 7 | 10 | 8 | 5 | • Expert code generation and debugging
• Complex prompt handling and multi-step reasoning
• Cutting-edge research applications requiring maximum accuracy |
| Gemini 2.0 Flash | 9 | 9 | 10 | 1 | • Interactive agents and chatbots
• General enterprise AI tasks at scale
• Large-context processing up to \~1M tokens |
| Gemini 2.0 Flash Thinking Mode | 8 | 9 | 10 | 2 | • Improved reasoning in QA and problem-solving
• Explainable AI scenarios
• Tasks requiring a balance of speed and reasoning accuracy |
| Gemini 1.5 Pro | 7 | 9 | 10 | 1 | • Sophisticated coding and mathematical problem solving
• Processing extremely large contexts
• Use cases tolerating higher cost/latency for higher quality |
| Gemini 1.5 Flash | 9 | 7 | 10 | 1 | • Real-time assistants and chat services
• Handling lengthy inputs
• General tasks requiring decent reasoning at minimal cost |
| Gemma 7B It | 10 | 6 | 4 | 1 | • Italian-language chatbot and content generation
• Lightweight reasoning and coding help
• On-device or private deployments |
| Gemma2 9B It | 9 | 7 | 5 | 1 | • Multilingual assistant
• Developer assistant on a budget
• Text analysis with moderate complexity |
## Perplexity Models
| Model | Speed | Accuracy | Context Window | Relative Cost | Use Cases |
| ------------------------ | :---: | :------: | :------------: | :-----------: | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Perplexity | 10 | 7 | 4 | 1 | • Quick factual Q\&A with web citations
• Fast information lookups
• General knowledge queries for free |
| Perplexity Deep Research | 3 | 9 | 10 | 1 | • In-depth research reports on any topic
• Complex multi-hop questions requiring reasoning and evidence
• Scholarly or investigative writing assistance |
## Open Source Models
| Model | Speed | Accuracy | Context Window | Relative Cost | Use Cases |
| ---------------- | :---: | :------: | :------------: | :-----------: | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepSeek R1 | 7 | 9 | 9 | 1 | • Advanced reasoning engine for math and code
• Integrating into Retrieval-Augmented Generation pipelines
• Open-source AI deployments needing strong reasoning |
| Llama 3.3 70B | 8 | 9 | 9 | 1 | • Versatile technical and creative assistant
• High-quality AI for smaller setups
• Resource-efficient deployment |
| Mixtral 8×7B 32K | 9 | 8 | 8 | 1 | • General-purpose open-source chatbot
• Long document analysis and retrieval QA
• Scenarios needing both efficiency and quality on modest hardware |
# How Credits Work
Source: https://docs.agent.ai/marketplace-credits
Agent.ai uses credits to enable usage and reward actions in the community.
## **Agent.ai's Mission**
Agent.ai is free to use and build with.
As a platform, Agent.ai's goal is to build the world's best professional marketplace for AI agents.
## **How Credits Fit In**
Credits are an agent.ai marketplace currency with no monetary value. Credits cannot be bought, sold, or exchanged for money. They exist to enable usage of the platform and reward actions in the communiuty.
Generally speaking, running an agent costs 1 credit.
You can earn more credits by performing actions like completing your profile or referring new users.
If you ever do happen to hit your credit limit (most people won't) and can't run agents because you need more credits, let us know — we're happy to top you back up.
# MCP Server
Source: https://docs.agent.ai/mcp-server
Agent.ai provides an MCP server that is available for use.
## **Using the Model Context Protocol Server with Claude Desktop**
> A guide to integrating MCP-based tools with Claude and other AI assistants
## What is MCP?
Model Context Protocol (MCP) allows Claude and other AI assistants to access external tools and data sources through specialized servers. This enables Claude to perform actions like retrieving financial data, converting files, or managing directories.
## Setting Up MCP with Claude Desktop
Follow these steps to connect Claude Desktop to our MCP server:
### 1. Install Claude Desktop
Download and install the Claude desktop application from [claude.ai/download](https://claude.ai/download)
### 2. Access Developer Settings
1. Open Claude Desktop
2. Click on the Claude menu in the top menu bar
3. Select "Settings"
4. Navigate to the "Developer" tab
5. Click the "Edit Config" button
### 3. Configure the MCP Server
This will open your file browser to edit the `claude_desktop_config.json` file. Add our AgentAI MCP server configuration as shown below:
```json
{
"mcpServers": {
"agentai": {
"command": "npx",
"args": [
"-y",
"@agentai/mcp-server"
],
"env": {
"API_TOKEN": "YOUR_API_TOKEN_HERE"
}
}
}
}
```
Replace `YOUR_API_TOKEN_HERE` with your actual API token.
> **Note**: You can also set up multiple MCP servers, including the local filesystem server:
```json
{
"mcpServers": {
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/path/to/accessible/directory1",
"/path/to/accessible/directory2"
]
},
"agentai": {
"command": "npx",
"args": [
"-y",
"@agentai/mcp-server"
],
"env": {
"API_TOKEN": "YOUR_API_TOKEN_HERE"
}
}
}
}
```
### 4. Restart Claude Desktop
Save the configuration file and restart Claude Desktop for the changes to take effect.
## Using MCP Tools with Claude
Once configured:
1. Open a new conversation in Claude Desktop
2. Look for the "Tools" icon in the main chat window
3. Clicking this icon will display all available tools from your configured MCP servers
4. You can directly ask Claude to use these tools in your conversation
For example, typing "Give me the latest company financial details about HubSpot" will prompt Claude to:
* Identify the appropriate tool from available MCP servers
* Request your permission to use the tool
* Execute the request
* Provide the results in a formatted response
## MCP Server Package
Our MCP server is available as an NPM package at [https://www.npmjs.com/package/@agentai/mcp-server](https://www.npmjs.com/package/@agentai/mcp-server). The package provides the necessary infrastructure to connect Claude and other AI assistants to our API services.
## Security Considerations
* Claude will always request your permission before running any MCP tool
* You can grant permission for a single use or for the entire conversation
* Review each action carefully before approving
## Troubleshooting
If you encounter issues:
1. Verify your API token is correct
2. Ensure Claude Desktop has been restarted after configuration changes
3. Check that the NPM packages can be installed (requires internet connection)
4. Examine Claude's error messages for specific issues
## Using with Other MCP-Compatible Applications
This MCP server can be used with any application that supports the Model Context Protocol, not just Claude Desktop. The configuration process may vary by application, but the core functionality remains the same.
For additional help or to report issues, please contact our support team.
# Data Security & Privacy at Agent.ai
Source: https://docs.agent.ai/security-privacy
Agent.ai prioritizes your data security and privacy with full encryption, no data reselling, and transparent handling practices. Find out how we protect your information while providing AI agent services and our current compliance status.
## **Does Agent.ai store information submitted to agents?**
Yes, Agent.ai stores the inputs you submit and the outputs you get when interacting with our agents. This is necessary to provide you with a seamless experience and to ensure continuity in your conversations with our AI assistants.
## **How we handle your data**
* **We store inputs and outputs**: Your conversations and data submissions are stored to maintain context and conversation history.
* **We don't share or resell your data**: Your information remains yours—we do not sell, trade, or otherwise transfer your data to outside parties.
* **No secondary use**: The data you share is not used to train our models or for any purpose beyond providing you with the service you requested.
* **Comprehensive encryption**: All user data—both inputs and outputs—is fully encrypted in transit using industry-standard encryption protocols.
## **Third-party LLM providers and your data**
When you interact with agents on Agent.ai, your information may be processed by third-party Large Language Model (LLM) providers, depending on which AI model powers the agent you're using.
* **API-based processing**: Agent.ai connects to third-party LLMs via their APIs. When you submit data to an agent, that information is transmitted to the relevant LLM provider for processing.
* **Varying privacy policies**: Different LLM providers have different approaches to data privacy, retention, and usage. The handling of your data once it reaches these providers is governed by their individual privacy policies.
* **Considerations for sensitive data**: When building or using agents that process personally identifiable information (PII), financial data, health information, or company-sensitive information, we recommend:
* Reviewing the specific LLM provider's privacy policy
* Understanding their data retention practices
* Confirming their compliance with relevant regulations (HIPAA, GDPR, etc.)
* Considering data minimization approaches where possible
Agent.ai ensures secure transmission of your data to these providers through encryption, but we encourage users to be mindful of the types of information shared with agents, especially for sensitive use cases.
## **Our commitment to your privacy**
At Agent.ai, we believe that privacy isn't just a feature—it's a fundamental right. Our approach to data security reflects our core company values:
**Trust**: We understand that meaningful AI assistance requires sharing information that may be sensitive or confidential. We honor that trust by implementing rigorous security measures and transparent data practices.
**Respect**: Your data belongs to you. Our business model doesn't rely on monetizing your information—it's built on providing value through our services.
**Integrity**: We're straightforward about what we do with your data. We collect only what's necessary to provide our services and use it only for the purposes you expect.
## **Intellectual Property Rights for Agent Builders**
When you create an agent on Agent.ai, you retain full ownership of the intellectual property (IP) associated with that agent. Similar to sellers on marketplace platforms (Amazon, Etsy), Agent.ai serves as the venue where your creation is hosted and discovered, but the underlying IP remains your own. This applies to the agent's concept, design, functionality, and unique implementation characteristics.
* **Builder ownership**: You maintain ownership rights to the agents you build, including their functionality, design, and purpose
* **Platform hosting**: Agent.ai provides the infrastructure and marketplace for your agent but does not claim ownership of your creative work
* **Content responsibility**: As the owner, you're responsible for ensuring your agent doesn't infringe on others' intellectual property
For complete details regarding intellectual property rights, licensing terms, and usage guidelines, please review our [Terms of Service](https://www.agent.ai/terms). Our approach to IP ownership aligns with our broader commitment to respecting your rights and fostering an ecosystem where builders can confidently innovate.
## **Compliance and certifications**
Agent.ai does not currently hold specific industry certifications such as SOC 2, HIPAA compliance, ISO 27001, or other specialized security and privacy certifications. While our security practices are robust and our encryption protocols are industry-standard, organizations with specific regulatory requirements should carefully evaluate whether our current security posture meets their compliance needs. If your organization requires specific certifications for data handling, we recommend reviewing our security documentation or contacting our team to discuss whether our platform aligns with your requirements.
## **Security measures**
Our encryption and security protocols are regularly audited and updated to maintain the highest standards of data protection. We implement multiple layers of technical safeguards to ensure your information remains secure throughout its lifecycle on our platform.
If you have specific concerns about data security or would like more information about our privacy practices, please contact our support team who can provide additional details about our security infrastructure.
# Welcome
Source: https://docs.agent.ai/welcome
## What is Agent.AI?
Agent.AI is the #1 Professional Network For A.I. Agents (also, the only professional network for A.I. agents).
It is a marketplace and professional network for AI agents and the people who love them.
Here, you can discover, connect with and hire AI agents to do useful things.
Discover, connect with and hire AI agents to do useful things
Build advanced AI agents using an easy, extensible, no-code platform with data tools and access to frontier LLMS.
## Do I have to be a developer to build AI agents?
Not at all! Our platform is a no-code platform, where you can drag and drop various components together to build AI agents.
Our builder had dozens of actions that can grab data from various data sources (i.e. X. Bluesky, LinkedIn, Google) and use any frontier LLM (i.e. OpenAI's 4o and o1, Google's Gemini models, Anthropic's Claude models, as well as open source Meta Llama 3s and Mistral models) in an intuitive interface.
For those users looking for can code and are looking for more advanced functionality, you can even use third party APIs and write serverless functions to interact with your agent's steps.