minimal-harness 0.5.7

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: 0.5.7

What This Project Is For

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

• OpenAI/Anthropic-compatible API - Works with OpenAI, Anthropic, or any OpenAI-compatible API provider
• Multi-modal image input - Pass image URLs or base64 data to LLM providers supporting vision
• Tool system - Create tools via decorators; includes built-in tools (bash, file ops)
• Middleware hooks - Observe and intercept the agent lifecycle (agent start/end, LLM calls, tool execution, tool policy enforcement)
• AsyncIterator events - Real-time async iteration for chunks, tool start/end, execution events
• Conversation memory - Tracks token usage across interactions, auto-persists to disk
• ESC stop support - Gracefully stop LLM streaming and tool execution

Architecture

The framework uses an event-driven architecture with AsyncIterator-based event handling:

Agent (SimpleAgent) → AgentEvent (from types.py)

Event flow:

async for event in agent.run(
    user_input=[{"type": "text", "text": "..."}],
    memory=memory,
    tools=tools,
):
    if isinstance(event, LLMChunk):
        # handle chunk
    elif isinstance(event, ToolEnd):
        # handle tool result

All event types are defined in src/minimal_harness/types.py. No separate client event layer exists.

How to Build an App

Project Structure

A typical app looks like this:

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

1. Create Your Entry Point

import argparse
import asyncio
from openai import AsyncOpenAI

from minimal_harness.agent.simple import SimpleAgent
from minimal_harness.llm.openai import OpenAILLMProvider
from minimal_harness.memory import ConversationMemory
from minimal_harness.tool.built_in.bash import get_tools as get_bash_tools
from minimal_harness.types import (
    AgentStart,
    AgentEnd,
    LLMChunk,
    ToolStart,
    ToolEnd,
)

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()

    client = AsyncOpenAI(base_url=args.base_url, api_key=args.api_key)
    llm_provider = OpenAILLMProvider(client=client, model=args.model)
    agent = SimpleAgent(llm_provider=llm_provider, max_iterations=50)
    memory = ConversationMemory()
    tools = list(get_bash_tools().values())

    async def run():
        stop_event = asyncio.Event()
        context = {"user_id": "abc123"}  # passed to middleware hooks
        async for event in agent.run(
            user_input=[{"type": "text", "text": "What files are in the current directory?"}],
            stop_event=stop_event,
            memory=memory,
            tools=tools,
            context=context,
        ):
            if isinstance(event, AgentStart):
                print("Agent starting...")
            elif isinstance(event, LLMChunk):
                delta = event.chunk
                if delta and delta.content:
                    print(delta.content, end="", flush=True)
            elif isinstance(event, ToolStart):
                print(f"\n[Calling tool: {event.tool_call['function']['name']}]")
            elif isinstance(event, ToolEnd):
                print(f"\n[Tool result: {str(event.result)[:100]}...]")
            elif isinstance(event, AgentEnd):
                print(f"\n[Done in {event.time_taken:.2f}s]")
                break

    asyncio.run(run())

if __name__ == "__main__":
    main()

2. Add Custom Tools

Use the @register_tool decorator to add your own tools. You need a ToolRegistry instance:

from typing import AsyncIterator

from minimal_harness.tool.registration import register_tool
from minimal_harness.tool.registry import ToolRegistry

registry = ToolRegistry()

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

The decorator registers the tool with the provided registry. Pass the same registry to the harness when running.

Localized tool output: Tools can detect the user's language at runtime via get_current_locale():

from minimal_harness.agent.runtime import get_current_locale

async def my_tool() -> AsyncIterator[dict]:
    locale = get_current_locale()
    yield {"message": "你好" if locale == "zh" else "Hello"}

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

Middleware Hooks

Subclass Middleware to observe or intercept the agent lifecycle:

from minimal_harness.agent.middleware import Middleware
from minimal_harness.types import LLMEnd, ToolCall

class PolicyEnforcer(Middleware):
    async def should_allow_tool(
        self, tool_call: ToolCall, **kwargs
    ) -> bool | str:
        # Return False or a reason string to deny the tool
        if tool_call["function"]["name"] == "bash":
            return "bash is not permitted in this context"
        return True

    async def on_llm_end(self, event: LLMEnd) -> None:
        if event.usage:
            print(f"Tokens: {event.usage['total_tokens']}")

Pass middleware to SimpleAgent:

agent = SimpleAgent(
    llm_provider=llm_provider,
    middleware=[PolicyEnforcer()],
    max_iterations=50,
)

Multi-modal Image Input

Pass image URLs or base64-encoded image data as input content parts:

user_input = [
    {"type": "text", "text": "What's in this image?"},
    {
        "type": "image",
        "image_url": {"url": "https://example.com/photo.jpg"},
    },
]

For local images, encode as base64:

import base64

with open("photo.jpg", "rb") as f:
    data = base64.b64encode(f.read()).decode()

user_input = [
    {"type": "text", "text": "Describe this image"},
    {
        "type": "image",
        "data": data,
        "media_type": "image/jpeg",
    },
]

Built-in Tools

Tool	Description
bash	Execute shell commands with timeout and workdir support
local_file_operation	Read, write, patch, or delete files (4 universal modes)

Event Types

All events are defined in minimal_harness.types and consumed as a single AgentEvent union:

Event	Fields	Description
AgentStart	user_input, timestamp	Agent execution started
AgentEnd	response, time_taken, exceeded	Agent execution completed
LLMStart	messages, tools	LLM generation started
LLMChunk	chunk: LLMChunkDelta | None	LLM output chunk received
LLMEnd	content, reasoning_content, tool_calls, usage	LLM generation completed
ExecutionStart	tool_calls	Tool execution started
ExecutionEnd	results	Tool execution completed
ToolStart	tool_call	Tool call started
ToolProgress	tool_call, chunk	Tool intermediate progress
ToolEnd	tool_call, result	Tool call completed with result
MemoryUpdate	usage	Memory token usage updated

LLMChunkDelta contains content, reasoning, and tool_calls fields for provider-agnostic partial deltas.

Environment Variables

Variable	Description
MH_BASE_URL	API base URL
MH_API_KEY	API key
MH_MODEL	Model name (default: qwen3.5-27b)
MH_MAX_ITERATIONS	Max agent loop iterations (default: 50)
MH_THEME	TUI theme name (default: tokyo-night)

Stop Mechanism

Press ESC during execution to gracefully stop LLM streaming and tool execution.