LLM Actions

Overview

LLM Actions in Agent Studio provide built-in capabilities to leverage large language model (LLM) functionalities directly within your Compound Actions and Conversational Processes. These actions enable tasks such as summarization, reasoning, classification, data extraction, and content generation, allowing you to build workflows without custom integrations.

This documentation focuses on two key LLM Actions: generate_text_action and generate_structured_value_action. These actions are designed to help you process unstructured data, generate insights, and structure outputs efficiently.

generate_text_action

Description

The generate_text_action invokes an LLM to produce free-form text output based on user-provided input. This action is ideal for tasks requiring natural language generation, such as summarizing documents, generating responses, or performing step-by-step reasoning. Use this action when you need unstructured text results, like drafting emails, explaining concepts, or brainstorming ideas.

Input Parameters

Field	Type	Required	Description
`system_prompt`	`string`	❌	Defines the model's behavior or instructions. For example, "Act as a helpful assistant that summarizes technical articles."
`user_input`	`string`	✅	The primary context or query for the LLM to process.
`model`	`string`	❌	Specifies the LLM model to use. See the Model Reference for available options. Defaults to `gpt-4o-mini-2024-07-18`
`temperature`	`number`	❌	Control the randomness of the output. Higher values will make the output more random, while lower values will make it more focused and deterministic
`reasoning_effort`	`string`	❌	Optional reasoning effort argument. Can be set to one of "minimal" (only `gpt-5` models), `"low"`, `"medium"`, or `"high"`. Must be left empty for non-reasoning models such as `gpt-4.1`.

Output

Field	Type	Description
`generated_output`	`string`	The LLM-generated text response.

Usage Examples

Here are practical examples demonstrating various LLM abilities. Each includes a sample request schema for integration into a Compound Action.

Example 1: Text Summarization

Summarize a lengthy article or user query into a concise overview.

- action:
    action_name: mw.generate_text_action
    input_args:
      system_prompt: '''Summarize the following text in 3-5 bullet points, focusing on key takeaways.'''
      user_input: data.article_content  # e.g., a long blog post fetched from an API
      model: '''gpt-4o-mini'''
			temperature: 0.7
    output_key: summary_output

system_prompt: '''Summarize the following text in 3-5 bullet points, focusing on key takeaways.'''
user_input: data.article_content  # e.g., a long blog post fetched from an API
model: '''gpt-4o-mini'''
temperature: 0.7

Example 2: Content Generation

Generate creative or instructional content, such as drafting a user email.

- action:
    action_name: mw.generate_text_action
    input_args:
      system_prompt: '''Write a professional email response based on the user's complaint.'''
      user_input: data.user_complaint  # e.g., "My order is delayed by two weeks."
    output_key: email_draft

system_prompt: '''Write a professional email response based on the user's complaint.'''
user_input: data.user_complaint  # e.g., "My order is delayed by two weeks."

Example 3: Step-by-Step Reasoning

Guide the LLM through logical reasoning for problem-solving.

- action:
    action_name: mw.generate_text_action
    input_args:
      system_prompt: '''Solve the problem step by step, explaining your reasoning.'''
      user_input: '''What is the next number in the sequence: 2, 4, 8, 16?'''
      reasoning_effort: '''high'''	
			model: '''gpt-5-2025-08-07'''
    output_key: reasoning_output

system_prompt: '''Solve the problem step by step, explaining your reasoning.'''
user_input: '''What is the next number in the sequence: 2, 4, 8, 16?'''
reasoning_effort: '''high'''	
model: '''gpt-5-2025-08-07'''

generate_structured_value_action

Description

The generate_structured_value_action calls an LLM to extract or generate data in a predefined structured format (JSON schema). This is particularly useful for classification, entity extraction, or transforming unstructured input into queryable data.

Apply this action for tasks where output consistency is critical, such as tagging content, extracting key-value pairs, or categorizing user inputs.

Input Parameters

Field	Type	Required	Description
`payload`	`object`	✅	The data or text to analyze.
`output_schema`	`object`	✅	JSON Schema defining the expected output structure.
`system_prompt`	`string`	❌	Defines the model's behavior or instructions. For example, "Act as a helpful assistant that summarizes technical articles."
`model`	`string`	❌	Specifies the LLM model to use. Defaults to `"gpt-4o-mini-2024-07-18"`. IMPORTANT: This action is only compatible with `gpt-4o-mini-2024-07-18` and later and `gpt-4o-2024-08-06` and later.
`strict`	`string`	❌	Enforces schema adherence. Defaults to `false`; Can either be `true`or `false`
`output_schema_name`	`string`	❌	LLM-facing name for the schema (defaults to `extracted_value`)
`output_schema_description`	`string`	❌	Description of the schema for the LLM.
`reasoning_effort`	`string`	❌	Optional reasoning effort argument. Can be set to one of "minimal" (only `gpt-5` models), `"low"`, `"medium"`, or `"high"`. Must be left empty for non-reasoning models such as `gpt-4.1`.

❗️
additionalProperties: false must always be set in objects.
additionalProperties controls whether it is allowable for an object to contain additional keys / values that were not defined in the JSON Schema.

Output

Field	Type	Description
`generated_output`	`object`	Structured data matching the provided schema.

Usage Examples

Examples illustrate extraction, classification, and more. Include request schemas for easy implementation.

Example 1: Topic Classification (Existing Example, Expanded)

Classify a research abstract into predefined topics.

- action:
    action_name: mw.generate_structured_value_action
    input_args:
      payload: data.research_paper_abstract
      system_prompt: '''Given a research paper abstract and a list of topic options, output up to 5 topics that accurately apply to the paper.'''
      output_schema: >-
        {
            "type": "object",
            "properties": {
                "topic_tags": {
                    "type": "array",
                    "items": {
                      "type": "string",
                      "enum": data.topic_tag_options  # e.g., ["AI", "ML", "NLP"]
                    }
                }
            },
            "required": ["topic_tags"],
            "additionalProperties": false
        }
      strict: true
    output_key: classified_topics

payload: data.research_paper_abstract
system_prompt: '''Given a research paper abstract and a list of topic options, output up to 5 topics that accurately apply to the paper.'''
output_schema: >-
    {
        "type": "object",
        "properties": {
            "topic_tags": {
                "type": "array",
                "items": {
                    "type": "string",
                    "enum": data.topic_tag_options  # e.g., ["AI", "ML", "NLP"]
                }
            }
        },
        "required": ["topic_tags"],
        "additionalProperties": false
    }
strict: true

Expected Output

generated_output: {
  "topic_tags": ["LLM Capabilities", "Reinforcement Learning (RL)", "Reasoning"]
}

Example 2: Entity Extraction

Extract named entities like names, dates, and locations from text.

- action:
    action_name: mw.generate_structured_value_action
    input_args:
      payload: data.user_message  # e.g., "John Doe will arrive in New York on October 15, 2025."
      system_prompt: '''Extract entities such as persons, locations, and dates from the text.'''
      output_schema: >-
        {
            "type": "object",
            "properties": {
                "persons": {"type": "array", "items": {"type": "string"}},
                "locations": {"type": "array", "items": {"type": "string"}},
                "dates": {"type": "array", "items": {"type": "string"}}
            },
            "required": ["persons", "locations", "dates"],
            "additionalProperties": false
        }
      reasoning_effort: '''low'''
			model: '''gpt-5-2025-08-07'''
    output_key: extracted_entities

payload: data.user_message  # e.g., "John Doe will arrive in New York on October 15, 2025."
system_prompt: '''Extract entities such as persons, locations, and dates from the text.'''
output_schema: >-
    {
        "type": "object",
        "properties": {
            "persons": {"type": "array", "items": {"type": "string"}},
            "locations": {"type": "array", "items": {"type": "string"}},
            "dates": {"type": "array", "items": {"type": "string"}}
        },
        "required": ["persons", "locations", "dates"],
        "additionalProperties": false
    }
reasoning_effort: '''low'''
model: '''gpt-5-2025-08-07'''

Expected Output

generated_output: {
  "persons": ["John Doe"],
  "locations": ["New York"],
  "dates": ["October 15, 2025"]
}

Example 3: Sentiment Classification

Classify text sentiment with confidence scores.

- action:
    action_name: mw.generate_structured_value_action
    input_args:
      payload: data.customer_review
      system_prompt: '''Analyze the sentiment of the review and output the category with a confidence score.'''
      output_schema: >-
        {
            "type": "object",
            "properties": {
                "sentiment": {
                    "type": "string",
                    "enum": ["positive", "negative", "neutral"]
                },
                "confidence": {"type": "number"}
            },
            "required": ["sentiment", "confidence"],
            "additionalProperties": false
        }
    output_key: sentiment_analysis

payload: data.customer_review
system_prompt: '''Analyze the sentiment of the review and output the category with a confidence score.'''
output_schema: >-
    {
        "type": "object",
        "properties": {
            "sentiment": {
                "type": "string",
                "enum": ["positive", "negative", "neutral"]
            },
            "confidence": {"type": "number"}
        },
        "required": ["sentiment", "confidence"],
        "additionalProperties": false
    }

Expected Output

generated_output: {
  "sentiment": "positive",
  "confidence": 0.85
}

Model Reference

Model Availability Reference

Model	Capabilities				OpenAI Direct		Azure OpenAI
Model	Context	Max Output	Reasoning Effort	Live Search	US	EU	US	EU	CA	AU	Gov
gpt-5.2-2025-12-11	400K	128K	✓	—	✓	✓	✓	—	—	—	—
gpt-5.2	400K	128K	✓	—	✓	✓	—	—	—	—	—
gpt-5.1-2025-11-13	400K	128K	✓	—	✓	✓	✓	✓	—	—	✓
gpt-5.1-chat-latest	400K	128K	✓	—	✓	✓	—	—	—	—	—
gpt-5.1	400K	128K	✓	—	✓	✓	—	—	—	—	—
gpt-5-2025-08-07	400K	128K	✓	—	✓	✓	✓	✓	✓	✓	✓
gpt-5	400K	128K	✓	—	✓	✓	—	—	—	—	—
gpt-5-mini-2025-08-07	400K	128K	✓	—	✓	✓	✓	✓	✓	✓	✓
gpt-5-mini	400K	128K	✓	—	✓	✓	—	—	—	—	—
gpt-5-nano-2025-08-07	400K	128K	✓	—	✓	✓	✓	✓	✓	✓	✓
gpt-5-nano	400K	128K	✓	—	✓	✓	—	—	—	—	—
o4-mini-2025-04-16	200K	100K	✓	—	✓	✓	✓	✓	✓	✓	✓
o4-mini	1M	100K	✓	—	✓	✓	—	—	—	—	—
gpt-4.1-2025-04-14	1M	32K	—	—	✓	✓	✓	✓	✓	✓	✓
gpt-4.1	1M	32K	—	—	✓	✓	—	—	—	—	—
gpt-4.1-mini-2025-04-14	1M	32K	—	—	✓	✓	✓	✓	✓	✓	✓
gpt-4.1-mini	1M	32K	—	—	✓	✓	—	—	—	—	—
gpt-4.1-nano-2025-04-14	1M	32K	—	—	✓	✓	✓	✓	✓	✓	✓
gpt-4.1-nano	1M	32K	—	—	✓	✓	—	—	—	—	—
o3-2025-04-16	200K	100K	—	—	✓	✓	✓	✓	✓	✓	✓
o3	200K	100K	—	—	✓	✓	—	—	—	—	—
o3-mini-2025-01-31	200K	100K	—	—	✓	✓	✓	✓	✓	✓	✓
o3-mini	200K	100K	—	—	✓	✓	—	—	—	—	—
o1-2024-12-17	200K	100K	—	—	✓	✓	✓	✓	✓	✓	✓
o1	200K	100K	—	—	✓	✓	—	—	—	—	—
gpt-4o-2024-11-20	128K	16K	—	—	✓	✓	✓	✓	✓	✓	✓
gpt-4o	128K	16K	—	—	✓	✓	—	—	—	—	—
gpt-4o-mini-2024-07-18	128K	16K	—	—	✓	✓	✓	✓	✓	✓	✓
gpt-4o-mini	128K	16K	—	—	✓	✓	—	—	—	—	—
gpt-4o-search-preview	128K	16K	—	✓	✓	✓	—	—	—	—	—

Updated 19 days ago

Overview

generate_text_action

Description

Input Parameters

Output

Usage Examples

Example 1: Text Summarization

Example 2: Content Generation

Example 3: Step-by-Step Reasoning

generate_structured_value_action

Description

Input Parameters

additionalProperties: false must always be set in objects.

Output

Usage Examples

Example 1: Topic Classification (Existing Example, Expanded)

Expected Output

Example 2: Entity Extraction

Expected Output

Example 3: Sentiment Classification

Expected Output

Model Reference

`additionalProperties: false` must always be set in objects.