Introduction
In this tutorial, we'll explore how to create and test a simple harness for an AI agent using Python. This tutorial is inspired by the ByteDance Seed's HarnessDev research, which evaluates how well large language models (LLMs) can engineer their own agent harnesses. A harness is a framework that helps an AI agent interact with its environment and execute tasks. We'll build a basic harness that can execute code and handle tasks like searching for information, simulating the kind of functionality that LLMs might use in real-world applications.
Prerequisites
Before starting this tutorial, you should have:
- A basic understanding of Python programming
- Python 3.7 or higher installed on your computer
- Access to a terminal or command line
- Optional: A code editor like VS Code or PyCharm
Step-by-Step Instructions
1. Setting Up Your Environment
First, we need to set up our working environment. We'll create a simple Python project directory and install necessary packages.
1.1 Create a Project Directory
Open your terminal and run the following commands:
mkdir harness_project
cd harness_project
This creates a new directory for our project and navigates into it.
1.2 Create a Virtual Environment
It's good practice to use a virtual environment to manage dependencies:
python3 -m venv harness_env
source harness_env/bin/activate # On Windows: harness_env\Scripts\activate
This creates a virtual environment named harness_env and activates it.
2. Installing Required Libraries
We'll need a few libraries to run our harness. The most important one is ipython, which allows us to execute code within our Python script.
2.1 Install Required Packages
pip install ipython
This installs the IPython package, which we'll use to execute code snippets in our harness.
3. Creating a Basic Harness
A harness is essentially a framework that allows an agent to interact with its environment. In our case, we'll build a simple harness that can:
- Execute code
- Search for information
- Return results
3.1 Create the Harness Class
Now, let's create a Python file called harness.py and define our harness class:
class SimpleHarness:
def __init__(self):
self.history = []
def execute_code(self, code):
"""Execute Python code and return the result"""
try:
# Use IPython to execute the code
from IPython import get_ipython
ipython = get_ipython()
result = ipython.run_cell(code)
return result.result
except Exception as e:
return str(e)
def search(self, query):
"""Simulate a search function"""
# For simplicity, we'll return a mock result
return f"Search results for: {query}"
def run_task(self, task):
"""Run a task using the harness"""
if "execute" in task.lower():
code = task.split("execute")[1].strip()
return self.execute_code(code)
elif "search" in task.lower():
query = task.split("search")[1].strip()
return self.search(query)
else:
return "Unknown task"
This class defines a basic harness with three main functions: execute_code, search, and run_task. The execute_code function uses IPython to run Python code snippets, and the search function simulates a search operation.
4. Testing the Harness
Now that we have our harness, let's test it by creating a simple script that uses it.
4.1 Create a Test Script
Create a file called test_harness.py with the following content:
from harness import SimpleHarness
# Create an instance of the harness
harness = SimpleHarness()
# Test the harness with a simple code execution task
print("Testing code execution:")
result = harness.run_task("execute print('Hello, World!')")
print(result)
# Test the harness with a search task
print("\nTesting search function:")
result = harness.run_task("search Python tutorials")
print(result)
This script creates an instance of our harness and tests it with two different tasks: executing a simple Python code snippet and performing a search.
4.2 Run the Test Script
In your terminal, run the following command:
python test_harness.py
You should see output similar to:
Testing code execution:
Hello, World!
Testing search function:
Search results for: Python tutorials
This shows that our harness is working correctly. It executed the code snippet and returned the search results.
5. Expanding the Harness
Our harness is basic, but we can expand it to include more functionality. For example, we can add a function to handle multiple tasks or integrate with external APIs.
5.1 Add a Task Queue
Let's enhance our harness by adding a simple task queue:
class EnhancedHarness(SimpleHarness):
def __init__(self):
super().__init__()
self.task_queue = []
def add_task(self, task):
"""Add a task to the queue"""
self.task_queue.append(task)
def process_queue(self):
"""Process all tasks in the queue"""
results = []
for task in self.task_queue:
result = self.run_task(task)
results.append(result)
self.task_queue.clear()
return results
This enhanced harness allows us to queue multiple tasks and process them all at once, simulating how a real agent might batch its work.
5.2 Test the Enhanced Harness
Update your test_harness.py to test the enhanced harness:
from harness import EnhancedHarness
# Create an instance of the enhanced harness
harness = EnhancedHarness()
# Add tasks to the queue
harness.add_task("execute print('Task 1')")
harness.add_task("search AI research")
# Process the queue
results = harness.process_queue()
for result in results:
print(result)
When you run this, you'll see that both tasks are processed in order.
Summary
In this tutorial, we've built a simple harness for an AI agent using Python. We started by setting up our environment, then created a basic harness class with functions to execute code and perform searches. We tested the harness with sample tasks and then enhanced it with a task queue. This demonstrates how LLMs might build and evolve their own harnesses, as described in the ByteDance Seed's HarnessDev research.
While our harness is simple, it illustrates the core concept of how an AI agent might interact with its environment and execute tasks. In real-world applications, harnesses would be more complex, integrating with APIs, databases, and other systems. The key takeaway is that harnesses allow agents to generalize their behavior across different tasks, as highlighted in the research where only 34 out of 64 changes generalized to held-out tasks.
