Update README.md

README.md

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+---
+# Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant
+
+Repository for the paper [Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant](https://arxiv.org/abs/2504.18373)
+## requirements
+To test the multi-agent frameworks, you need to first install the framework according to the instruction of the framework.
+
+We have tested CamelAI, Langgraph, AgentLite, and AutoGEN.
+
+## 1. start simulated servers
+```
+cd server
+sh run.sh
+```
+## 2. run the test
+Some external servers require API keys. Therefore, to test and send requests to these servers, please make sure to provide the necessary API keys first.
+
+The LLM used in the example code also requires configuration. Please make sure to specify the model name and provide the corresponding API key.
+
+Please also remember to put the data files into ~/data. Or you can modify the data path in test.py.
+
+```
+cd examples/autogen
+sh run.sh
+```
+## 3. run evaluation
+We are using gpt-4 for evaluating. Please set apikey properly. For e.g., export OPENAI_API_KEY="***".
+
+If you want to use other models, please modify eval.py. For instance, if you want to use deepseek-v3 from deepseek API, you can change the model to "deepseek-chat", and change the api to your deepseek api. You also need to change the base_url to "https://api.deepseek.com/v1".
+```
+sh eval.sh
+```
+
+## Citing
+
+If you found the data or code useful, free to cite:
+```bibtex
+@misc{shen2025autoslurpbenchmarkdatasetevaluating,
+      title={Auto-SLURP: A Benchmark Dataset for Evaluating Multi-Agent Frameworks in Smart Personal Assistant}, 
+      author={Lei Shen and Xiaoyu Shen},
+      year={2025},
+      eprint={2504.18373},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2504.18373}, 
+}
+```