gemini-tool-agent 0.1.3

A tool-aware agent powered by Gemini for structured query handling

Gemini Tool Agent

A lightweight, tool-aware Gemini agent to handle structured prompts and tool usage in conversations.

Overview

Gemini Tool Agent is a Python library that provides a simple interface for creating tool-aware agents powered by Google's Gemini AI models. It enables developers to define custom tools with structured input schemas and seamlessly integrate them into conversational flows.

Features

• Tool-aware conversation handling
• Structured prompt processing
• Automatic context management
• JSON response parsing
• Conversation history tracking

Installation

pip install gemini-tool-agent

Requirements

• Python 3.8 or higher
• Google Generative AI Python SDK (google-genai >= 0.3.2)

Usage

from gemini_tool_agent.agent import Agent

# Initialize the agent with your API key
agent = Agent(key="your-api-key")

# Define your tools
agent.tools = [
    {
        "name": "save_note",
        "description": "Save a note to the database",
        "input_schema": {
            "title": "string",
            "content": "string"
        }
    }
]

# Process a query that might use tools
response = agent.process_query("Save a note about AI agents")
print(response)

Response Format

The agent returns a structured response in JSON format:

{
  "needs_tool": true,
  "tool_name": "save_note",
  "needs_direct_response": true,
  "direct_response_first": false,
  "reasoning": "The query explicitly asks to save a note, which requires the save_note tool",
  "direct_response": "AI agents are software entities that can perform tasks autonomously..."
}

Advanced Usage

You can access the conversation history:

# Get the conversation history
history = agent.history

1### Tool Parameter Extraction

After identifying that a tool needs to be used, you can extract parameters from the conversation:

# First process the query to determine if a tool is needed
response = agent.process_query("Save a note titled 'AI Agents' with content about machine learning")

# If a tool is needed, extract the parameters
if response.get("needs_tool", False):
    tool_name = response.get("tool_name")
    tool_params = agent.process_use_tool(tool_name)
    
    # Now you can use the extracted parameters to execute the tool
    print(tool_params)
    # Output: {'tool_name': 'save_note', 'input': {'title': 'AI Agents', 'content': '...'}}  

Optimized Response Generation

The agent automatically handles large prompts for memory efficiency:

# For direct usage (normally used internally by the agent)
response_text = agent.generate_response(large_prompt)

# The method automatically optimizes prompts over 10,000 characters by:
# - Trimming conversation history to the most recent 15 lines when needed
# - Truncating large direct responses while preserving start and end content

License

MIT

Author

Paul Fruitful ([e-mail])