HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model

📖 Overview

HiAgent is a novel hierarchical working memory management framework for solving long-horizon agent tasks with large language models (LLMs). It introduces a hierarchical memory structure that helps LLMs better organize and utilize information during complex task solving.

Key features:

🧠 Hierarchical memory management with working memory and long-term memory
🎯 Effective for long-horizon tasks requiring multi-step planning
🔄 Dynamic memory updating and pruning mechanisms
📝 Structured memory format for better information organization
🤖 Compatible with various LLM backends

🤝 Special Thanks

We build this repo based on AgentBoard project. We would like to thank the authors for their excellent work.

🚀 Quick Start

🛠️ Build from source

Clone this repo
Create and activate virtual environment 🐍

conda create -n hiagent python=3.8.18
conda activate hiagent

Setup AgentBoard environment and data 📥

🔑 Setup environment

Before starting, please make sure you have configured cuda. If not, please configure it first.

If configured, you can check using the following commands:

Check version information 📊

nvcc -V

🛠️ Additional Setup

Download nltk library by running the following code: 📚

import nltk
nltk.download('punkt')
nltk.download('punkt_tab')

Create and configure ./agentboard/.env file, Environment Variables needed include: ⚡

PROJECT_PATH=
OPENAI_API_KEY=

🏃 Run script

If the configuration is correct and the code runs successfully, you should see a series of prompts in the terminal.

bash evaluate_model.sh

📊 Visualize results

HiAgent2024/HiAgent