Custom Agent Prompts

title: Custom Agent Prompts description: Learn how to customize agent prompts in KaibanJS to tailor AI agent behavior and responses for specific use cases.

Introduction

KaibanJS now supports custom agent prompts, allowing developers to fine-tune the behavior and responses of AI agents. This feature enables you to adapt agents to specific use cases or requirements, enhancing the flexibility and power of your multi-agent AI systems.

:::tip[Using AI Development Tools?] Our documentation is available in an LLM-friendly format at docs.kaibanjs.com/llms-full.txt. Feed this URL directly into your AI IDE or coding assistant for enhanced development support! :::

How to Implement Custom Prompts

To use custom prompts, you need to provide a

promptTemplates

object when initializing an agent. This object can contain one or more prompt types that you wish to customize.

Basic Usage

Here's a simple example of how to create an agent with custom prompts:

import { Agent } from 'kaibanjs';

const customPrompts = {
    SYSTEM_MESSAGE: ({ agent, task }) => `You are ${agent.name}, an AI assistant specialized in ${agent.role}. Your task is: ${task.description}`,
    INITIAL_MESSAGE: ({ agent, task }) => `Hello ${agent.name}, please complete this task: ${task.description}`,
};

const agent = new Agent({
    name: 'CustomAgent',
    role: 'Specialized Assistant',
    goal: 'Provide tailored responses',
    promptTemplates: customPrompts
});

Available Prompt Types

You can customize the following prompt types:

```
SYSTEM_MESSAGE
```
: Sets up the initial context and instructions for the agent.
```
INITIAL_MESSAGE
```
: Provides the task description to the agent.
```
INVALID_JSON_FEEDBACK
```
: Feedback when the agent's response is not in valid JSON format.
```
THOUGHT_WITH_SELF_QUESTION_FEEDBACK
```
: Feedback for a thought that includes a self-question.
```
THOUGHT_FEEDBACK
```
: Feedback for a general thought from the agent.
```
SELF_QUESTION_FEEDBACK
```
: Feedback for a self-question from the agent.
```
TOOL_RESULT_FEEDBACK
```
: Feedback after a tool has been used.
```
TOOL_ERROR_FEEDBACK
```
: Feedback when an error occurs while using a tool.
```
TOOL_NOT_EXIST_FEEDBACK
```
: Feedback when the agent tries to use a non-existent tool.
```
OBSERVATION_FEEDBACK
```
: Feedback for an observation made by the agent.
```
WEIRD_OUTPUT_FEEDBACK
```
: Feedback when the agent's output doesn't match the expected format.
```
FORCE_FINAL_ANSWER_FEEDBACK
```
: Forces the agent to return the final answer.
```
WORK_ON_FEEDBACK_FEEDBACK
```
: Provides feedback to the agent based on received feedback.

Take a look at the code of the prompts in the src/utils/prompts.js file.

Advanced Usage

For more complex scenarios, you can create dynamic prompts that utilize the full context of the agent and task:

const advancedCustomPrompts = {
    SYSTEM_MESSAGE: ({ agent, task }) => `
        You are ${agent.name}, a ${agent.role} with the following background: ${agent.background}.
        Your main goal is: ${agent.goal}.
        You have access to these tools: ${agent.tools.map(tool => tool.name).join(', ')}.
        Please complete the following task: ${task.description}
        Expected output: ${task.expectedOutput}
    `,
    TOOL_ERROR_FEEDBACK: ({ agent, task, toolName, error }) => `
        An error occurred while using the tool ${toolName}. 
        Error message: ${error}
        Please try an alternative approach to complete your task: ${task.description}
    `,
};

const advancedAgent = new Agent({
    name: 'AdvancedAgent',
    role: 'Multi-tool Specialist',
    background: 'Extensive experience in data analysis and problem-solving',
    goal: 'Provide comprehensive solutions using available tools',
    tools: [/* list of tools */],
    promptTemplates: advancedCustomPrompts
});

Best Practices

Maintain Consistency: Ensure your custom prompts align with the overall goals and context of your AI system.
Use Dynamic Content: Leverage the provided context (
```
agent
```
,
```
task
```
, etc.) to create more relevant and adaptive prompts.
Balance Flexibility and Structure: While customizing, maintain a structure that guides the agent towards completing tasks effectively.
Test Thoroughly: After implementing custom prompts, test your agents in various scenarios to ensure they behave as expected.

Conclusion

Custom agent prompts in KaibanJS offer a powerful way to tailor your AI agents' behavior and responses. By carefully crafting these prompts, you can create more specialized and effective multi-agent systems that are perfectly suited to your specific use cases.

:::tip[We Love Feedback!] Is there something unclear or quirky in the docs? Maybe you have a suggestion or spotted an issue? Help us refine and enhance our documentation by submitting an issue on GitHub. We're all ears! :::

title: Custom Agent Prompts description: Learn how to customize agent prompts in KaibanJS to tailor AI agent behavior and responses for specific use cases.

Introduction

How to Implement Custom Prompts

To use custom prompts, you need to provide a

promptTemplates

object when initializing an agent. This object can contain one or more prompt types that you wish to customize.

Basic Usage

Here's a simple example of how to create an agent with custom prompts:

import { Agent } from 'kaibanjs';

const customPrompts = {
    SYSTEM_MESSAGE: ({ agent, task }) => `You are ${agent.name}, an AI assistant specialized in ${agent.role}. Your task is: ${task.description}`,
    INITIAL_MESSAGE: ({ agent, task }) => `Hello ${agent.name}, please complete this task: ${task.description}`,
};

const agent = new Agent({
    name: 'CustomAgent',
    role: 'Specialized Assistant',
    goal: 'Provide tailored responses',
    promptTemplates: customPrompts
});

Available Prompt Types

You can customize the following prompt types:

```
SYSTEM_MESSAGE
```
: Sets up the initial context and instructions for the agent.
```
INITIAL_MESSAGE
```
: Provides the task description to the agent.
```
INVALID_JSON_FEEDBACK
```
: Feedback when the agent's response is not in valid JSON format.
```
THOUGHT_WITH_SELF_QUESTION_FEEDBACK
```
: Feedback for a thought that includes a self-question.
```
THOUGHT_FEEDBACK
```
: Feedback for a general thought from the agent.
```
SELF_QUESTION_FEEDBACK
```
: Feedback for a self-question from the agent.
```
TOOL_RESULT_FEEDBACK
```
: Feedback after a tool has been used.
```
TOOL_ERROR_FEEDBACK
```
: Feedback when an error occurs while using a tool.
```
TOOL_NOT_EXIST_FEEDBACK
```
: Feedback when the agent tries to use a non-existent tool.
```
OBSERVATION_FEEDBACK
```
: Feedback for an observation made by the agent.
```
WEIRD_OUTPUT_FEEDBACK
```
: Feedback when the agent's output doesn't match the expected format.
```
FORCE_FINAL_ANSWER_FEEDBACK
```
: Forces the agent to return the final answer.
```
WORK_ON_FEEDBACK_FEEDBACK
```
: Provides feedback to the agent based on received feedback.

Take a look at the code of the prompts in the src/utils/prompts.js file.

Advanced Usage

For more complex scenarios, you can create dynamic prompts that utilize the full context of the agent and task:

const advancedCustomPrompts = {
    SYSTEM_MESSAGE: ({ agent, task }) => `
        You are ${agent.name}, a ${agent.role} with the following background: ${agent.background}.
        Your main goal is: ${agent.goal}.
        You have access to these tools: ${agent.tools.map(tool => tool.name).join(', ')}.
        Please complete the following task: ${task.description}
        Expected output: ${task.expectedOutput}
    `,
    TOOL_ERROR_FEEDBACK: ({ agent, task, toolName, error }) => `
        An error occurred while using the tool ${toolName}. 
        Error message: ${error}
        Please try an alternative approach to complete your task: ${task.description}
    `,
};

const advancedAgent = new Agent({
    name: 'AdvancedAgent',
    role: 'Multi-tool Specialist',
    background: 'Extensive experience in data analysis and problem-solving',
    goal: 'Provide comprehensive solutions using available tools',
    tools: [/* list of tools */],
    promptTemplates: advancedCustomPrompts
});

Best Practices

Maintain Consistency: Ensure your custom prompts align with the overall goals and context of your AI system.
Use Dynamic Content: Leverage the provided context (
```
agent
```
,
```
task
```
, etc.) to create more relevant and adaptive prompts.
Balance Flexibility and Structure: While customizing, maintain a structure that guides the agent towards completing tasks effectively.
Test Thoroughly: After implementing custom prompts, test your agents in various scenarios to ensure they behave as expected.

Custom Agent Prompts

title: Custom Agent Prompts description: Learn how to customize agent prompts in KaibanJS to tailor AI agent behavior and responses for specific use cases.

Introduction

How to Implement Custom Prompts

Basic Usage

Available Prompt Types

Advanced Usage

Best Practices

Conclusion

Related Skills

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title: Custom Agent Prompts description: Learn how to customize agent prompts in KaibanJS to tailor AI agent behavior and responses for specific use cases.

Introduction

How to Implement Custom Prompts

Basic Usage

Available Prompt Types

Advanced Usage

Best Practices

Conclusion