basic-agent

name	basic-agent
description	Provides a foundational template for building AI agents using the ReAct pattern. Use for: quick prototyping of new agent ideas, understanding core agentic loops, and as a starting point for custom agent development.

Basic Agent Skill

This skill provides a basic agent template, demonstrating the ReAct (Reasoning and Acting) pattern. It is designed to be a simple, yet effective, starting point for developing AI agents.

Key Features

• ReAct Loop: Implements the fundamental Thought-Action-Observation cycle, enabling the agent to reason about its actions and learn from observations.
• Tool Integration: Offers a straightforward interface for incorporating custom tools, allowing the agent to interact with external systems or perform specific tasks.
• Context Management: Includes basic mechanisms for managing conversation history and maintaining context throughout interactions.

Usage Instructions

To utilize this basic agent skill, follow these steps:

1. Define Tools: Create Python functions that the agent can call to perform actions. These functions should be self-contained and clearly defined.
2. Initialize LLM: Configure your preferred Large Language Model (LLM) to serve as the agent's reasoning engine.
3. Run Agent: Instantiate the BasicAgent class with your LLM and tools, then invoke its run method with a user prompt to start the agent loop.

Example Implementation Sketch

class BasicAgent:
    def __init__(self, llm, tools):
        self.llm = llm
        self.tools = tools
        self.history = []

    def run(self, prompt):
        # The ReAct loop implementation goes here.
        # It involves: thinking, selecting a tool, executing the tool,
        # observing the result, and updating history.
        pass

Resources

• ReAct: Synergizing Reasoning and Acting in Language Models