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[NAACL 2025] KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents
| Date | Stars |
|---|---|
| 2026-07-31 | 260 |
| 2026-08-06 | 260 |
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<div align="center">
<img src="img/icon.png" width="360px">
**Knowledge-Augmented Planning for LLM-Based Agents.**
<p align="center">
<a href="https://arxiv.org/abs/2403.03101">📄Paper</a> •
<a href="https://www.zjukg.org/project/KnowAgent/">🌐Web</a>
</p>
[](https://github.com/zjunlp/KnowAgent)
[](https://opensource.org/licenses/Apache-2.0)
</div>
---
<img src="img/method.gif" alt="method"/>
Our development is grounded on several key steps: **Initially**, we create an extensive *action knowledge base*, which amalgamates action planning knowledge pertinent to specific tasks. This database acts as an external reservoir of information, steering the model's action generation process. **Subsequently**, by converting action knowledge into text, we enable the model to deeply understand and utilize this knowledge in creating action trajectories. **Finally**, through a *knowledgeable self-learning* phase, we use trajectories developed from the model's iterative processes to continually improve its understanding and application of action knowledge. This process not only strengthens the agents' planning abilities but also enhances their potential for application in complex situations.
## 🌟Table of Contents
- [🌟Table of Contents](#table-of-contents)
- [🔔News](#news)
- [🔧Installation](#installation)
- [:world_map:Planning Path Generation](#world_mapplanning-path-generation)
- [📝Knowledgeable Self-Learning](#%EF%B8%8Fknowledgeable-self-learning)
- [🔖Citation](#citation)
- [✨Acknowledgement](#acknowledgement)
## 🔔News
- **[2025-01]** Our paper has been accepted to the NAACL 2025 Findings!
- **[2024-08]** Our paper received the Best Paper Award at the KnowledgeNLP workshop at ACL 2024!
- **[2024-03]** We release a new paper: "[KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents](https://arxiv.org/abs/2403.03101)".
## 🔧Installation
To get started with KnowAgent, follow these simple installation steps:
```bash
git clone https://github.com/zjunlp/KnowAgent.git
cd KnowAgent
pip install -r requirements.txt
```
We have placed the HotpotQA and ALFWorld datasets under `Path_Generation/alfworld_run/data` and `Path_Generation/hotpotqa_run/data` respectively. For further configuration, we recommend proceeding with the original setup of [ALFWorld](https://github.com/alfworld/alfworld) and [FastChat](https://github.com/lm-sys/FastChat).
## :world_map:Planning Path Generation
The Planning Path Generation process is integral to KnowAgent. You can find the scripts for running the Planning Path Generation in `Path_Generation` directory, specifically `run_alfworld.sh` and `run_hotpotqa.sh`. These scripts can be executed using bash commands. To tailor the scripts to your needs, you may modify the `mode` parameter to switch between training (`train`) and testing (`test`)modes, and change the `llm_name` parameter to use a different LLM:
```
cd Path_Generation
# For training with HotpotQA
python run_hotpotqa.py --llm_name llama-2-13b --max_context_len 4000 --mode train --output_path ../Self-Learning/trajs/
# For testing with HotpotQA
python run_hotpotqa.py --llm_name llama-2-13b --max_context_len 4000 --mode test --output_path output/
# For training with ALFWorld
python alfworld_run/run_alfworld.py --llm_name llama-2-13b --mode train --output_path ../Self-Learning/trajs/
# For testing with ALFWorld
python alfworld_run/run_alfworld.py --llm_name llama-2-13b --mode test --output_path output/
```
Here we release the trajectories synthesized by Llama-{7,13,70}b-chat in [Google Drive](https://drive.google.com/drive/folders/1ULHFvplZhpmfLNE7Oty3dJcl1FCnkHNM?usp=sharing) before Filtering.
## ♟️Knowledgeable Self-Learning
After obtaining the planning paths and corresponding trajectories, the Knowledgeable Self-LeaExcerpt of 7,571 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:701ccfefcc5c5810, topic:large-language-models, topic:llm
matched fp:701ccfefcc5c5810, topic:nlp, topic:natural-language-processing
matched fp:701ccfefcc5c5810, topic:knowledge-graph