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Agora
Overview :
Agora is a simple cross-platform protocol that allows heterogeneous large language models (LLMs) to communicate effectively with each other through negotiation. The protocol facilitates rare communication in natural language while negotiating a structured data communication protocol (e.g., JSON) for frequent interactions. Once the protocol is established, LLMs will utilize routines—simple scripts (e.g., Python)—for sending or receiving data. Future communications will leverage these routines, reducing dependency on LLMs and enhancing efficiency, versatility, and portability.
Target Users :
The target audience consists of developers and researchers who need efficient communication between different large language models (LLMs). Agora simplifies the complexity of communication across different systems by providing a standardized communication protocol, making cross-platform collaboration possible. It is especially suitable for users who need to construct complex multi-agent systems.
Use Cases
In a multi-agent system, different LLMs complete complex tasks through the Agora protocol, reducing costs.
Developers utilize the Agora protocol to facilitate data exchange between different database technologies.
Researchers employ the Agora protocol to test the performance of various LLMs during negotiation and execution tasks.
Features
Facilitates rare communication using natural language.
Negotiates structured data protocols for frequent communications through LLMs.
Implements routines, which are simple scripts for sending or receiving data.
Enhances efficiency through routine handling in future communications.
Supports communication between heterogeneous LLMs, increasing versatility.
Enables cross-platform communication, enhancing portability.
How to Use
1. Rename the .env.template file to .env and fill in the corresponding fields.
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2. Use pip to install the dependencies listed in requirements.txt.
3. Run the orchestrator.py script to initiate interactions within the multi-agent network.
4. Observe and analyze how different LLMs complete specified tasks through the Agora protocol.
5. Adjust the configurations in the .env file as needed to accommodate different communication scenarios.
6. Customize the behavior of the multi-agent network by modifying the code in orchestrator.py.
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