AI Generations in Agent Catalyst

Agent Catalyst integrates powerful AI models into workflows, enabling seamless text generation, structured data extraction, and reasoning tasks. This functionality empowers users to create dynamic, AI-driven processes tailored to their needs.

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Text Generation

Text generation allows users to create various types of content, such as summaries, narratives, or creative texts, using large language models (LLMs).

Example: Generating a Scientific Abstract

yaml <GenText  
  as="abstract"  
  model="openai:gpt-4"  
  message="Based on the following research paper, create a concise and detailed scientific abstract summarizing the findings: {research}" />  

Attributes for <GenText />

Attribute	Type	Description	Required
as	string	Unique variable name to store the result.	✅
model	string	AI provider and model name.	✅
stream	boolean	Enables response streaming (default: true).	❌
tools	array	List of tools the LLM can use.	❌
options	object	LLM-specific options for fine-tuning.	❌

The generated text is stored in the specified variable (e.g., abstract) and can be reused in later steps.

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Structured Data Generation

Structured data generation ensures that AI outputs conform to specific formats, making it ideal for extracting reliable, programmatic data.

Example: Extracting Conference Speakers

yaml <GenObject  
  as="speakers"  
  model="openai:gpt-4"  
  output="array"  
  schema={{  
    $.z.object({  
      name: $.z.string().describe('Full name of the speaker'),  
      company: $.z.string().describe('Company name'),  
      twitter: $.z.string().describe('Twitter profile').optional(),  
    })  
  }}  
  message="Extract all conference speakers and their details from the given webpage: {url}" />  

Attributes for <GenObject />

Attribute	Type	Description	Required
as	string	Unique variable name to store the result.	✅
model	string	AI provider and model name.	✅
output	string	Type of output: object, array, enum, or no-schema.	✅
schema	ZodType	Schema defining the data structure.	✅ (if output is object or array)
enum	array	List of possible values (if output is enum).	✅ (if output is enum)
schemaDescription	string	Additional guidance for the AI model.	❌
tools	array	List of tools the LLM can use.	❌

Schema Validation: Ensures the output adheres to the specified structure, making it reliable for automated workflows.

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Example Workflow: Generate Tweets for Conference Speakers

yaml <Loop  
  as="messages"  
  until={$.index === speakers.filter(s => !!s.twitter).length}  
  provide={{ speaker: speakers.filter(s => !!s.twitter)[$.index] }}>  

  Write a tweet thanking the speaker for their presentation. Make it fun, humorous, and use plenty of emojis.  

  Speaker: {speaker.name}  
  Company: {speaker.company}  
  Twitter: {speaker.twitter}  

  <GenText as="tweet" model="openai:gpt-4" />  
</Loop>

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AI Providers

Agent Catalyst supports multiple AI providers through integrations with SDKs like OpenAI, Anthropic, and Google. It also works with local open-source models using tools like Ollama.

Provider Configuration Example

javascript import { defineConfig } from '@agentsystem/core';  
import { openai } from '@ai-sdk/openai';  
import { anthropic } from '@ai-sdk/anthropic';  

export default defineConfig({  
  providers: {  
    openai,  
    anthropic,  
  },  
});

Custom Provider Setup

For custom configurations, you can create provider instances:

javascript import { createOpenAI } from '@ai-sdk/openai';  

export default defineConfig({  
  providers: {  
    openai: createOpenAI({  
      apiKey: process.env.OPENAI_API_KEY,  
      compatibility: 'strict',  
    }),  
    anotherProvider: createOpenAI({  
      apiKey: process.env.ANOTHER_API_KEY,  
      baseURL: 'https://api.example.com',  
    }),  
  },  
});

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Specifying Models

When using <GenText /> or <GenObject />, the model must be specified in the format providerId:modelId.

Example

yaml <GenText  
  as="outline"  
  model="openai:gpt-4"  
  message="Write an outline for a futuristic space adventure novel." />  

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