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@ -39,14 +39,14 @@ Step 3: Run the backend and frontend servers separately (see readme.md in ./fron
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).
### LLM configuration (If use docker)
You can set the configuration (i.e. API base, API key, Model name) 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](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/).
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/).
### LLM configuration (If install on your machine)
You can set the configuration in ./backend/config/config.yaml. See [LLM configuration (If install on your machine)](xxx) for explanations.
### 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).
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) in the future.
## 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: