# AgentCoord: Visually Exploring Coordination Strategy for LLM-based Multi-Agent Collaboration
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<a ><img src="https://github.com/bopan3/AgentCoord_Backend/assets/21981916/bbad2f66-368f-488a-af36-72e79fdb6805" alt=" AgentCoord: Visually Exploring Coordination Strategy for LLM-based Multi-Agent Collaboration" width="200px"></a>
</p>
AgentCoord is an experimental open-source system to help general users design coordination strategies for multiple LLM-based agents (Research paper forthcoming).

## System Usage
<a href="https://youtu.be/s56rHJx-eqY" target="_blank"><img src="https://github.com/bopan3/AgentCoord_Backend/assets/21981916/0d907e64-2a25-4bdf-977d-e90197ab1aab" 
alt="System Usage Video" width="800" border="5" /></a>

## Installation
### Installation 
```bash
git clone https://github.com/AgentCoord/AgentCoord.git
cd AgentCoord
pip install -r requirements.txt
```
### Configuration
#### LLM configuration
You can set the configuration (i.e. API base, API key, Model name, Max tokens, Response per minute) for default LLM in config\config.yaml. Currently, we only support OpenAI’s LLMs as the default model. We recommend using gpt-4-0125-preview as the default model (WARNING: the execution process of multiple agents may consume a significant number of tokens). 

You can switch to a fast mode that uses the Mistral 8×7B model with hardware acceleration by [Groq](https://groq.com/) for the first time in strategy generation to strike a balance of response quality and efficiency. To achieve this, you need to set the FAST_DESIGN_MODE field in the yaml file as True and fill the GROQ_API_KEY field with the api key of [Groq](https://wow.groq.com/).

#### Agent configuration
Currently, we support config agents by [role-prompting](https://arxiv.org/abs/2305.14688). You can customize your agents by changing the role prompts in AgentRepo\agentBoard_v1.json. We plan to support more methods to customize agents (e.g., supporting RAG, or providing a unified wrapper for customized agents). 

### Launch 
Execute the following command to launch the system:
```bash
python server.py
```
## More Papers & Projects for LLM-based Multi-Agent Collaboration
If you’re interested in LLM-based multi-agent collaboration and want more papers & projects for reference, you may check out the corpus collected by us: