Introduction to Custom Agent Tools and Skills

In the rapidly evolving world of generative AI and multi-agent systems, one size doesn't fit all. That's where custom agent tools and skills come into play. These tailored capabilities allow AI agents to perform specialized tasks, adapt to unique environments, and collaborate more effectively within a multi-agent setup.

Why Build Custom Tools and Skills?

1. Specialized Functionality: Off-the-shelf solutions may not address specific use cases or industry requirements.
2. Improved Performance: Custom tools can be optimized for particular tasks, leading to better efficiency and accuracy.
3. Competitive Edge: Unique capabilities can set your AI system apart in a crowded market.
4. Flexibility: Custom tools allow for easier updates and modifications as needs change.

Getting Started with Phidata

Phidata provides a robust framework for developing multi-agent systems. To begin creating custom tools and skills:

1. Install Phidata: pip install phidata
2. Set up a new project: phi init my_custom_agent_project
3. Navigate to the project directory: cd my_custom_agent_project

Designing Your Custom Tool

Let's create a simple custom tool that generates creative writing prompts:

from phidata import Tool

class WritingPromptGenerator(Tool):
    name = "writing_prompt_generator"
    description = "Generates creative writing prompts based on user input"

    def run(self, theme: str) -> str:

# In a real scenario, this could use a more sophisticated method
        prompts = {
            "mystery": "A detective finds an unusual object at a crime scene.",
            "romance": "Two strangers keep running into each other in unexpected places.",
            "sci-fi": "A team of scientists discovers a signal from deep space."
        }
        return prompts.get(theme.lower(), "A unexpected event changes everything.")

Implementing Custom Skills

Skills are more complex than tools and often involve a series of actions. Let's create a skill for analyzing and summarizing text:

from phidata import Skill
import nltk
from nltk.tokenize import sent_tokenize
from nltk.corpus import stopwords
from nltk.probability import FreqDist

nltk.download('punkt')
nltk.download('stopwords')

class TextAnalyzer(Skill):
    name = "text_analyzer"
    description = "Analyzes and summarizes text content"

    def execute(self, text: str) -> dict:
        sentences = sent_tokenize(text)
        words = nltk.word_tokenize(text.lower())
        stop_words = set(stopwords.words('english'))
        filtered_words = [word for word in words if word.isalnum() and word not in stop_words]
        
        freq_dist = FreqDist(filtered_words)
        most_common = freq_dist.most_common(5)
        
        return {
            "sentence_count": len(sentences),
            "word_count": len(words),
            "most_common_words": most_common,
            "summary": ' '.join(sentences[:3]) if len(sentences) > 3 else text
        }

Integrating Custom Tools and Skills

To use your new tools and skills in a Phidata agent:

from phidata import Agent
from your_custom_module import WritingPromptGenerator, TextAnalyzer

class CreativeWritingAssistant(Agent):
    name = "creative_writing_assistant"
    description = "An AI assistant for creative writing tasks"
    tools = [WritingPromptGenerator()]
    skills = [TextAnalyzer()]

    async def run(self, user_input: str):
        if "generate prompt" in user_input.lower():
            theme = user_input.split("for")[-1].strip()
            prompt = self.tools[0].run(theme)
            return f"Here's a writing prompt for you: {prompt}"
        elif "analyze text" in user_input.lower():
            text = user_input.split("analyze text")[-1].strip()
            analysis = self.skills[0].execute(text)
            return f"Text Analysis:\n{analysis}"
        else:
            return "I can generate writing prompts or analyze text. What would you like me to do?"

Testing Your Custom Agent

Now that we've created our custom agent with specialized tools and skills, let's test it:

assistant = CreativeWritingAssistant()

# Test the writing prompt generator
response = await assistant.run("Can you generate a prompt for mystery?")
print(response)

# Test the text analyzer
sample_text = "AI is revolutionizing the way we approach problem-solving and creativity. With advanced language models and multi-agent systems, we're unlocking new possibilities in various fields."
response = await assistant.run(f"Please analyze text: {sample_text}")
print(response)

Continuous Improvement

As you work with your custom tools and skills, you'll likely identify areas for improvement. Some tips for ongoing development:

1. Gather Feedback: Collect user input to understand how your tools and skills perform in real-world scenarios.
2. Iterate: Regularly update and refine your custom capabilities based on performance data and user needs.
3. Stay Updated: Keep an eye on new developments in AI and incorporate relevant advancements into your custom tools and skills.
4. Collaborate: Share your creations with the Phidata community and learn from others' innovations.

By building custom agent tools and skills, you're taking a significant step towards creating more powerful and specialized AI systems. As you continue to explore and experiment with Phidata and multi-agent systems, you'll discover endless possibilities for enhancing AI capabilities and solving complex problems in innovative ways.