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Update paper list in README.md and README_zh.md(#440)
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104 changes: 68 additions & 36 deletions README.md
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Expand Up @@ -35,7 +35,6 @@ Start building LLM-empowered multi-agent applications in an easier way.
|----------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------|
| <img src="https://gw.alicdn.com/imgextra/i1/O1CN01hhD1mu1Dd3BWVUvxN_!!6000000000238-2-tps-400-400.png" width="100" height="100"> | <img src="https://img.alicdn.com/imgextra/i2/O1CN01tuJ5971OmAqNg9cOw_!!6000000001747-0-tps-444-460.jpg" width="100" height="100"> |


----

## News
Expand Down Expand Up @@ -187,7 +186,6 @@ the following libraries.
- <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[Conversation with CodeAct Agent](https://github.com/modelscope/agentscope/blob/main/examples/conversation_with_codeact_agent/)
- <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>[Conversation with Router Agent](https://github.com/modelscope/agentscope/blob/main/examples/conversation_with_router_agent/)


- Game
- [Gomoku](https://github.com/modelscope/agentscope/blob/main/examples/game_gomoku)
- [Werewolf](https://github.com/modelscope/agentscope/blob/main/examples/game_werewolf)
Expand Down Expand Up @@ -236,7 +234,6 @@ optional dependencies. Full list of optional dependencies refers to
Taking distribution mode as an example, you can install its dependencies
as follows:


#### On Windows

```bash
Expand All @@ -247,14 +244,14 @@ pip install agentscope[distribute]
```

#### On Mac & Linux

```bash
# From source
pip install -e .\[distribute\]
# From pypi
pip install agentscope\[distribute\]
```


## Quick Start

### Configuration
Expand Down Expand Up @@ -391,35 +388,70 @@ pre-commit install

Please refer to our [Contribution Guide](https://modelscope.github.io/agentscope/en/tutorial/302-contribute.html) for more details.

## References

If you find our work helpful for your research or application, please
cite [our paper](https://arxiv.org/abs/2402.14034):

```
@article{agentscope,
author = {Dawei Gao and
Zitao Li and
Xuchen Pan and
Weirui Kuang and
Zhijian Ma and
Bingchen Qian and
Fei Wei and
Wenhao Zhang and
Yuexiang Xie and
Daoyuan Chen and
Liuyi Yao and
Hongyi Peng and
Ze Yu Zhang and
Lin Zhu and
Chen Cheng and
Hongzhu Shi and
Yaliang Li and
Bolin Ding and
Jingren Zhou},
title = {AgentScope: A Flexible yet Robust Multi-Agent Platform},
journal = {CoRR},
volume = {abs/2402.14034},
year = {2024},
}
```
## Publications

If you find our work helpful for your research or application, please cite our papers.

1. [AgentScope: A Flexible yet Robust Multi-Agent Platform](https://arxiv.org/abs/2402.14034)

```
@article{agentscope,
author = {Dawei Gao and
Zitao Li and
Xuchen Pan and
Weirui Kuang and
Zhijian Ma and
Bingchen Qian and
Fei Wei and
Wenhao Zhang and
Yuexiang Xie and
Daoyuan Chen and
Liuyi Yao and
Hongyi Peng and
Ze Yu Zhang and
Lin Zhu and
Chen Cheng and
Hongzhu Shi and
Yaliang Li and
Bolin Ding and
Jingren Zhou}
title = {AgentScope: A Flexible yet Robust Multi-Agent Platform},
journal = {CoRR},
volume = {abs/2402.14034},
year = {2024},
}
```
2. [On the Design and Analysis of LLM-Based Algorithms](https://arxiv.org/abs/2407.14788)
```
@article{llm_based_algorithms,
author = {Yanxi Chen and
Yaliang Li and
Bolin Ding and
Jingren Zhou},
title = {On the Design and Analysis of LLM-Based Algorithms},
journal = {CoRR},
volume = {abs/2407.14788},
year = {2024},
}
```
3. [Very Large-Scale Multi-Agent Simulation in AgentScope](https://arxiv.org/abs/2407.17789)
```
@article{agentscope_simulation,
author = {Xuchen Pan and
Dawei Gao and
Yuexiang Xie and
Zhewei Wei and
Yaliang Li and
Bolin Ding and
Ji{-}Rong Wen and
Jingren Zhou},
title = {Very Large-Scale Multi-Agent Simulation in AgentScope},
journal = {CoRR},
volume = {abs/2407.17789},
year = {2024},
}
```
102 changes: 68 additions & 34 deletions README_ZH.md
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Expand Up @@ -35,8 +35,6 @@
|---------|----------|
| <img src="https://gw.alicdn.com/imgextra/i1/O1CN01hhD1mu1Dd3BWVUvxN_!!6000000000238-2-tps-400-400.png" width="100" height="100"> | <img src="https://img.alicdn.com/imgextra/i2/O1CN01tuJ5971OmAqNg9cOw_!!6000000001747-0-tps-444-460.jpg" width="100" height="100"> |



----

## 新闻
Expand All @@ -56,7 +54,6 @@
<img src="https://github.com/user-attachments/assets/dfffbd1e-1fe7-49ee-ac11-902415b2b0d6" width="45%" alt="agentscope-logo">
</h5>


- <img src="https://img.alicdn.com/imgextra/i3/O1CN01SFL0Gu26nrQBFKXFR_!!6000000007707-2-tps-500-500.png" alt="new" width="30" height="30"/>**[2024-07-15]** AgentScope 中添加了 Mixture of Agents 算法。使用样例请参考 [MoA 示例](https://github.com/modelscope/agentscope/blob/main/examples/conversation_mixture_of_agents)

- **[2024-06-14]** 新的提示调优(Prompt tuning)模块已经上线 AgentScope,用以帮助开发者生成和优化智能体的 system prompt。更多的细节和使用样例请参考 AgentScope [教程](https://modelscope.github.io/agentscope/en/tutorial/209-prompt_opt.html)
Expand Down Expand Up @@ -232,6 +229,7 @@ pip install agentscope[distribute]
```

#### On Mac & Linux

```bash
# From source
pip install -e .\[distribute\]
Expand Down Expand Up @@ -362,34 +360,70 @@ pre-commit install

请参阅我们的[贡献指南](https://modelscope.github.io/agentscope/zh_CN/tutorial/302-contribute.html)了解更多细节。

## 引用

如果您觉得我们的工作对您的研究或应用有帮助,请引用[我们的论文](https://arxiv.org/abs/2402.14034)

```
@article{agentscope,
author = {Dawei Gao and
Zitao Li and
Xuchen Pan and
Weirui Kuang and
Zhijian Ma and
Bingchen Qian and
Fei Wei and
Wenhao Zhang and
Yuexiang Xie and
Daoyuan Chen and
Liuyi Yao and
Hongyi Peng and
Zeyu Zhang and
Lin Zhu and
Chen Cheng and
Hongzhu Shi and
Yaliang Li and
Bolin Ding and
Jingren Zhou},
title = {AgentScope: A Flexible yet Robust Multi-Agent Platform},
journal = {CoRR},
volume = {abs/2402.14034},
year = {2024},
}
```
## 发表

如果您觉得我们的工作对您的研究或应用有帮助,请引用如下论文

1. [AgentScope: A Flexible yet Robust Multi-Agent Platform](https://arxiv.org/abs/2402.14034)

```
@article{agentscope,
author = {Dawei Gao and
Zitao Li and
Xuchen Pan and
Weirui Kuang and
Zhijian Ma and
Bingchen Qian and
Fei Wei and
Wenhao Zhang and
Yuexiang Xie and
Daoyuan Chen and
Liuyi Yao and
Hongyi Peng and
Ze Yu Zhang and
Lin Zhu and
Chen Cheng and
Hongzhu Shi and
Yaliang Li and
Bolin Ding and
Jingren Zhou}
title = {AgentScope: A Flexible yet Robust Multi-Agent Platform},
journal = {CoRR},
volume = {abs/2402.14034},
year = {2024},
}
```
2. [On the Design and Analysis of LLM-Based Algorithms](https://arxiv.org/abs/2407.14788)
```
@article{llm_based_algorithms,
author = {Yanxi Chen and
Yaliang Li and
Bolin Ding and
Jingren Zhou},
title = {On the Design and Analysis of LLM-Based Algorithms},
journal = {CoRR},
volume = {abs/2407.14788},
year = {2024},
}
```
3. [Very Large-Scale Multi-Agent Simulation in AgentScope](https://arxiv.org/abs/2407.17789)
```
@article{agentscope_simulation,
author = {Xuchen Pan and
Dawei Gao and
Yuexiang Xie and
Zhewei Wei and
Yaliang Li and
Bolin Ding and
Ji{-}Rong Wen and
Jingren Zhou},
title = {Very Large-Scale Multi-Agent Simulation in AgentScope},
journal = {CoRR},
volume = {abs/2407.17789},
year = {2024},
}
```
33 changes: 19 additions & 14 deletions examples/paper_llm_based_algorithm/README.md
Original file line number Diff line number Diff line change
@@ -1,6 +1,5 @@
# LLM-based algorithms


This folder contains the source code for reproducing the experiment results in our arXiv preprint "On the Design and Analysis of LLM-Based Algorithms".

Our work initiates a formal investigation into the design and analysis of LLM-based algorithms,
Expand All @@ -11,7 +10,6 @@ Within this folder, you can find our implementation for the key abstractions,
the LLM-based algorithms in four concrete examples,
and the experiments for validating our analysis in the manuscript.


## Tested Models

The following models have been tested, which are also listed in `model_configs.json`:
Expand All @@ -20,26 +18,25 @@ GPT-3.5 Turbo,
Llama3-8B (with ollama),
Llama3-70B (with vLLM).


## Prerequisites


1. Install AgentScope from source with `pip`, according to the [official instruction](../../README.md).
2. Install matplotlib: `pip install matplotlib`.
3. Change directory: `cd examples/llm_based_algorithm`.
3. Change directory: `cd examples/paper_llm_based_algorithm`.
4. Set up LLM model configs in `model_configs.json`.



## Usage

### Run experiments

To run experiments for a certain task:

```bash
bash ./scripts/exp_{task}.sh
```

or copy a piece of scripts therein, modify the parameters, and run it in the terminal, for example:

```bash
python3 run_exp_single_variable.py \
--task counting \
Expand All @@ -52,6 +49,7 @@ python3 run_exp_single_variable.py \
```

Parameters:

- `task`: name of the task, {"counting", "sorting", "retrieval", "retrieval_no_needle", "rag"}.
- `llm_model`: name of the LLM model, i.e. `config_name` in `model_configs.json`.
- `variable_name`: "n" for problem size, or "m" for sub-task size.
Expand All @@ -60,30 +58,37 @@ Parameters:
- `save_results`: if `True`, experiment results will be saved to `./out`; otherwise, results will be plotted and shown at the end of the experiment, and won't be saved.
- `ntrials`: number of independent trials for each experiment config, i.e. each entry of `lst_variable`.


### Plot results

To plot experiment results that have been saved:

```bash
bash ./scripts/plot_{task}.sh
```

or copy a piece of scripts therein and run it in the terminal, for example:

```bash
python3 plot_exp_results.py \
--folder ./out/counting/exp_counting_vary_n_model_ollama_llama3_8b-2024-06-19-11-11-13-kkwrhc
```

The path to the experiment results need to be replaced with the actual one generated during your own experiment.
The generated figures will be saved to the same folder.


## Reference

For more details, please refer to our arXiv preprint:

```
@article{chen2024llmbasedalgorithms,
title={On the Design and Analysis of LLM-Based Algorithms},
author={Yanxi Chen and Yaliang Li and Bolin Ding and Jingren Zhou},
year={2024},
@article{llm_based_algorithms,
author = {Yanxi Chen and
Yaliang Li and
Bolin Ding and
Jingren Zhou},
title = {On the Design and Analysis of LLM-Based Algorithms},
journal = {CoRR},
volume = {abs/2407.14788},
year = {2024},
}
```

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