AgentCoord: Visually Exploring Coordination Strategy for LLM-based Multi-Agent Collaboration
System Usage
Installation
Install with Docker (Recommended)
If you have installed docker and docker-compose on your machine, we recommend running AgentCoord in docker：
Step 1: Clone the project:
git clone https://github.com/AgentCoord/AgentCoord.git
cd AgentCoord
Step 2: Config LLM (see [LLM configration](readme.md#LLM configuration)): 链接到 Section 1 and start the servers Step 2: Open http://localhost:8080/ to use the system.
Install on your machine
If you want to install and run AgentCoord on your machine without using docker:
Step 1: Clone the project
git clone https://github.com/AgentCoord/AgentCoord.git
cd AgentCoord
Step 2: Install required packages for the backend and frontend servers (see readme.md in ./frontend and ./backend folders)
Step 3: Run the backend and frontend servers separately (see readme.md in ./frontend and ./backend folders).
Step 4: Open http://localhost:8080/ to use the system.
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 ./backend/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 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.
Agent configuration
Currently, we support config agents by role-prompting. 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).
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: