minimal-harness 0.2.2

An exploration of making an agent sdk as lean as possible while being effective.

minimal-harness

Documentation: /docs

A lightweight Python agent harness for building LLM-powered agents with tool-calling support.

Latest version: 0.2.1

What This Project Is For

Minimal-harness is a lean framework for building agents that can call tools. It provides:

• Pluggable LLM providers - OpenAI, LiteLLM (optional), or implement your own
• Tool system - Create tools via decorators or directly; includes built-in tools (bash, file ops, grep, glob, ask_user)
• Interactive tools - Tools that pause execution to request user input mid-process
• Conversation memory - Tracks token usage across interactions
• Streaming callbacks - Real-time callbacks for chunks, tool start/end, execution start

How to Build an App

Project Structure

A typical app looks like this:

my-app/
├── cli.py          # Entry point with argparse
└── tools.py        # Your custom tools

1. Create Your Entry Point

import argparse
from minimal_harness.mhc import SimpleCli
from minimal_harness.tool.built_in import bash, read_file, ask_user
from minimal_harness.tool.registry import ToolRegistry

def main():
    parser = argparse.ArgumentParser(description="My AI agent")
    parser.add_argument("--base-url", required=True)
    parser.add_argument("--api-key", required=True)
    parser.add_argument("--model", default="qwen3.5-27b")
    args = parser.parse_args()

    # Register built-in tools
    registry = ToolRegistry.get_instance()
    registry.register(bash.bash_tool, bash.bash_handler)
    registry.register(read_file.read_file_tool, read_file.read_file_handler)
    registry.register(ask_user.ask_user_tool, ask_user.ask_user_first)

    # Run the CLI
    cli = SimpleCli(
        api_key=args.api_key,
        base_url=args.base_url,
        model=args.model,
    )
    cli.run()

if __name__ == "__main__":
    main()

2. Add Custom Tools

Use the @register_tool decorator to add your own tools:

from minimal_harness.tool.registration import register_tool

@register_tool(
    name="get_weather",
    description="Get weather for a location",
    parameters={
        "type": "object",
        "properties": {"location": {"type": "string"}},
        "required": ["location"],
    },
)
async def get_weather(location: str) -> str:
    return f"The weather in {location} is sunny."

The decorator auto-registers the tool. Just import it before cli.run().

3. Run

python cli.py --base-url https://api.openai.com/v1 --api-key sk-... --model gpt-4o

Or set environment variables:

export MH_BASE_URL=https://api.openai.com/v1
export MH_API_KEY=sk-...
export MH_MODEL=gpt-4o
python cli.py

Built-in Tools

Tool	Description
bash	Execute shell commands
read_file	Read file contents
create_file	Create new files
patch_file	Patch existing files
delete_file	Delete files
ask_user	Request user input

Environment Variables

Variable	Description
MH_BASE_URL	API base URL
MH_API_KEY	API key
MH_MODEL	Model name (default: qwen3.5-27b)

Running the Built-in CLI

mhc --base-url https://api.openai.com/v1 --api-key sk-... --model gpt-4o