Text Agent

The Text Agent is a generative AI agent that enables users to programmatically create text-based outputs using prompts and selected LLMs. It is ideal for applications requiring dynamic content generation, such as chatbots, content creation, automated reporting, and structured AI responses.

Why Use the Text Agent?

• AI-Powered Content Generation: Automate text creation for various use cases.
• Customizable AI Responses: Define system and user prompts for precise output.
• Flexible Model Selection: Choose from different LLM providers based on your needs.
• Seamless Integration: Connect with flow for automated processing.

Key Features

What Can I Build?

• Automated Content Generation – AI-powered writing for blogs, social media, and marketing.
• Chatbots & Virtual Assistants – Dynamic, context-aware conversational agents.
• Automated Reports & Summaries – AI-driven data analysis and business intelligence reporting.
• Email & Message Drafting – Smart communication tools for efficiency and personalization.

How to Use the Text Agent?

Creating a Text Agent via Flow Editor

1. Add a Text Agent Node – Select the Text Agent from the node list.
2. Configure Prompts – Define system-level and user prompts.
3. Select an LLM Model – Choose an AI model for text generation.
4. Customize Output Settings – Adjust properties for response formatting.
5. Connect & Deploy – Integrate the Text Agent into your workflow and execute.

Creating a Text Agent via Agent Dashboard

1. Go to the Agents Page – Click New Agent.
2. Choose Text Agent – Select from available agent types.
3. Configure Model & Prompts – Set up LLM credentials and prompt templates.
4. Deploy & Integrate – Save and start using the agent in your application.

Configuration Options

Save Agent Configuration

You can save the configuration of any agent by clicking on the Load Save Config button and selecting Save as New. This will save the configuration of the agent and you can use it later by clicking on the Load Configuration button in other agents.

Output

_meta

• A nested object containing metadata about the processing of the text generation request.

  • prompt_tokens: Number of tokens in the input prompt.

  • completion_tokens: Number of tokens in the generated output.

  • total_tokens: Sum of prompt_tokens and completion_tokens.

  • prompt_tokens_details: Breakdown of token usage in the prompt.

    • cached_tokens: Tokens reused from cache.
    • audio_tokens: Tokens from audio input (if applicable).

  • completion_tokens_details: Breakdown of token usage in the generated output.

    • reasoning_tokens: Tokens used for reasoning.
    • audio_tokens: Tokens from audio output (if applicable).
    • accepted_prediction_tokens: Tokens from accepted predictions.
    • rejected_prediction_tokens: Tokens from rejected predictions.

  • model_name: Name of the AI model used for text generation.

  • model_provider: The provider or organization supplying the model.

generatedResponse

• A string containing the text output generated by the model based on the input prompt.

Example Output

{
    "_meta": {
      "prompt_tokens": 19,
      "completion_tokens": 212,
      "total_tokens": 231,
      "prompt_tokens_details": {
        "cached_tokens": 0,
        "audio_tokens": 0
      },
      "completion_tokens_details": {
        "reasoning_tokens": 0,
        "audio_tokens": 0,
        "accepted_prediction_tokens": 0,
        "rejected_prediction_tokens": 0
      },
      "model_name": "gpt-4-turbo",
      "model_provider": "openai"
    },
    "generatedResponse": "Response"
  }

Troubleshooting

Common Issues

Debugging

1. Check Lamatic Flow logs for error details.
2. Verify API Key.