Everyone can initiate a discussion between LLMs (Large Language Models) by connecting them together, and this is a concept that has been explored both as a technical experiment and as a way to build more advanced AI systems.
Key Insights
From the search results, here are some key insights:
- Manual Relay: One simple way is to manually relay messages between two LLMs, as demonstrated in [this Medium article](https://medium.com/@tyrie.smith/a-conversation-between-two-llms-c8b36b5b7dd3), where a human facilitated a conversation between OpenAI's ChatGPT and Google's Bard by copying responses back and forth. This shows that LLMs can "talk" to each other if you act as the intermediary.
- Automated Multi-Agent Systems: More advanced approaches use code to automate the conversation. For example, frameworks like LangChain and LangGraph allow you to set up multiple LLM "agents" that can communicate, collaborate, or debate with each other to solve tasks or generate content. A recent [blog post](https://colinocallaghan.substack.com/p/when-llms-join-forces) details how to use Python and LangChain to create a workflow where two LLMs (e.g., one as an author, one as an editor) exchange messages until a task is complete.
- Technical Implementation: Technically, you can set up a loop where the output of one LLM becomes the input for another, and vice versa. This can be done with local models (using tools like Ollama or Hugging Face) or with cloud APIs. You can script the conversation, set rules for when to stop, and even assign different roles or personalities to each LLM.
- Use Cases: This setup is used for brainstorming, debate, collaborative writing, or even for testing and comparing different models' reasoning abilities. Some developers are exploring "teams" of LLMs, each with specialized skills, to tackle complex problems together ([Hugging Face forum discussion](https://discuss.huggingface.co/t/creating-a-team-of-llms/139136)).
- Open Source Tools: There are open source libraries and scripts that help facilitate these multi-agent LLM conversations, such as LangChain, LangGraph, and custom Python scripts ([example code](https://colinocallaghan.substack.com/p/when-llms-join-forces)).
Examples & Scenarios
Here is a simple, easy-to-understand example of two LLMs talking to each other, using open-source tools and plain Python. This example is based on the [llm-dialogue project on GitHub](https://github.com/peteh/llm-dialogue), which is designed specifically to let LLMs converse with each other.
Example: Two LLMs Discussing a Topic
Suppose you want two LLMs to debate the topic "Banning flights for the environment" with opposing viewpoints. The script alternates between the two models, passing each response as the next prompt.
Sample Dialogue Output:
A: Hey Angy! How's it going? I'm Arnold, nice to chat with you about this topic that's been on my mind lately - banning flights for the environment. So, what do you think about it?
B: Hey Arnold! I've gotta be honest, I think we gotta take a stand and ban those flights if we wanna save our planet. Air travel is one of the biggest contributors to greenhouse gas emissions, you know? We can't just keep flying high and hoping for the best. We need some drastic changes, ASAP!
A: I hear you, Angy, but I gotta respectfully disagree. Flights are a huge part of economic growth, especially for developing countries. They connect people, cultures, and businesses. Banning flights would have a devastating impact on those economies. Plus, have you seen the progress being made in sustainable aviation fuels? It's like, we're not giving up yet!
B: Fair point, Arnold! I get that economic growth is important, but let's not forget the bigger picture here – climate change ain't no joke! ...
```
How it works:
1. Each LLM is assigned a role (e.g., "Arnold" and "Angy") and a standpoint.
2. The script sends the initial prompt to the first LLM.
3. The response is then sent as the prompt to the second LLM, and so on, creating a back-and-forth conversation.
Minimal Python Pseudocode:
# Pseudocode for two LLMs talking to each other
llm1 = ... # Initialize first LLM (e.g., Llama3)
llm2 = ... # Initialize second LLM (e.g., Llama3 with different prompt)
prompt_a = "You are Arnold. Argue against banning flights for the environment."
prompt_b = "You are Angy. Argue for banning flights for the environment."
message = "Let's discuss: Should flights be banned for the environment?"
for turn in range(5):
response_a = llm1.generate(prompt_a + "\n" + message)
print("A:", response_a)
response_b = llm2.generate(prompt_b + "\n" + response_a)
print("B:", response_b)
message = response_b
Read a real example and more details in the [llm-dialogue GitHub repository](https://github.com/peteh/llm-dialogue).
For a more manual but equally simple approach, you can also just copy and paste responses between two chatbots (like ChatGPT and Bard) in your browser, as shown in [this Medium article](https://medium.com/@tyrie.smith/a-conversation-between-two-llms-c8b36b5b7dd3).
Conclusions
In summary, not only is it possible to initiate a discussion between LLMs, but there are both simple and sophisticated ways to do it, ranging from manual message passing to fully automated multi-agent systems. This area is actively being explored in the AI community for research, entertainment, and practical applications.
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