z007 0.1.0

Micro agent with tool support and MCP integration.

z007 ⚡ Fast Micro Agent

A lightning-fast, lightweight agent for interacting with LLM providers with built-in tool support and MCP (Model Context Protocol) integration.

Features

• ⚡ Lightning Fast: Quick setup with uvx z007 - start chatting instantly
• 🔧 Tool Support: Built-in calculator and custom tool integration
• 🔌 MCP Integration: Connect to Model Context Protocol servers
• 🎯 Multiple Providers: Currently supports AWS Bedrock (more coming soon)
• 🐍 Python API: Easy integration into your Python projects
• 🚀 Async: Fast, concurrent tool execution

Quick Start

Install and run with uvx (recommended)

# Install and run directly - fastest way to start!
uvx z007

# Or install globally  
uvx install z007
z007

Install as Python package

pip install z007

Usage

Command Line

# Start interactive chat
z007

# With custom model (AWS Bedrock)
z007 --model-id "anthropic.claude-3-sonnet-20240229-v1:0"

# With MCP configuration
z007 --mcp-config .vscode/mcp.json

Python API

Simple usage

import asyncio
from z007 import Agent

async def main():
    async with Agent(model_id="openai.gpt-oss-20b-1:0") as agent:
        response = await agent.run("What is 2+2?")
    print(response)

asyncio.run(main())

With tools

import asyncio
from z007 import Agent, create_calculator_tool

async def main():
    calculator = create_calculator_tool()
    async with Agent(
        model_id="openai.gpt-oss-20b-1:0",
        tools=[calculator]
    ) as agent:
        response = await agent.run("Calculate 15 * 23 + 7")
    print(response)

asyncio.run(main())

Using the Agent class

import asyncio
from z007 import Agent

async def main():
    async with Agent(
        model_id="openai.gpt-oss-20b-1:0",
        system_prompt="You are a helpful coding assistant."
    ) as agent:
        response = await agent.run("Write a Python function to reverse a string")
        print(response)

asyncio.run(main())

Custom Tools

Create your own tools by writing simple Python functions:

import asyncio
from z007 import Agent

def weather_tool(city: str) -> str:
    """Get weather information for a city"""
    # In a real implementation, call a weather API
    return f"The weather in {city} is sunny, 25°C"

def file_reader_tool(filename: str) -> str:
    """Read contents of a file"""
    try:
        with open(filename, 'r') as f:
            return f.read()
    except Exception as e:
        return f"Error reading file: {e}"

async def main():
    async with Agent(
        model_id="openai.gpt-oss-20b-1:0",
        tools=[weather_tool, file_reader_tool]
    ) as agent:
        response = await agent.run("What's the weather like in Paris?")
    print(response)

asyncio.run(main())

MCP Integration

Connect to Model Context Protocol servers for advanced capabilities:

1. Create .vscode/mcp.json:

{
  "servers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/project"]
    },
    "brave-search": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-brave-search"],
      "env": {
        "BRAVE_API_KEY": "${env:BRAVE_API_KEY}"
      }
    }
  }
}

2. Use with z007:

z007 --mcp-config .vscode/mcp.json

Or in Python:

from z007 import Agent

async with Agent(
    model_id="openai.gpt-oss-20b-1:0",
    mcp_config_path=".vscode/mcp.json"
) as agent:
    response = await agent.run("Search for recent news about AI")
    print(response)

Configuration

Environment Variables

For AWS Bedrock (default provider):

• AWS_REGION: AWS region (default: us-east-1)
• AWS_ACCESS_KEY_ID: AWS access key
• AWS_SECRET_ACCESS_KEY: AWS secret key

Supported Models

Current AWS Bedrock models:

• openai.gpt-oss-20b-1:0 (default)
• meta.llama3-70b-instruct-v1:0
• anthropic.claude-3-sonnet-20240229-v1:0
• Any AWS Bedrock model with tool support

Interactive Commands

When running z007 in interactive mode:

• /help - Show help
• /tools - List available tools
• /clear - Clear conversation history
• /exit - Exit

Development

Setup

# Clone and setup
git clone https://github.com/okigan/z007.git
cd z007
uv sync

Running locally

# Run the CLI
uv run z007

# Run with specific model
uv run z007 --model-id "anthropic.claude-3-sonnet-20240229-v1:0"

# Run examples
uv run python examples.py

# Run tests
uv run python test.py

Building and Publishing

# Build the package
uv build

# Install locally for testing
uv pip install dist/z007-0.1.0-py3-none-any.whl

# Publish to Test PyPI (recommended first)
uv publish --repository testpypi

# Publish to PyPI
uv publish

Development Tools

# Type checking
uv run mypy z007/

# Code formatting and linting
uv run ruff check z007/
uv run ruff format z007/

# Install in editable mode for development
uv pip install -e .

API Reference

z007.Agent

Main agent class for LLM interactions.

async with Agent(
    model_id="openai.gpt-oss-20b-1:0",
    system_prompt=None,
    tools=[],
    mcp_config=None,
    max_turns=10
) as agent:
    response = await agent.run("Your question")

z007.create_calculator_tool()

Creates a basic calculator tool function.

calculator = create_calculator_tool()
# Use with Agent

Requirements

• Python 3.9+
• LLM provider credentials (AWS for Bedrock)

License

MIT License

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z007 ⚡ Fast, lightweight, powerful. Get things done quickly! 🚀