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Agent-Agent Communication

In a multi-agent system, agents need to communicate with each other to share information, coordinate actions, and achieve common goals.

Eidolon provides a built-in mechanism for agents to communicate with each other, enabling seamless interaction between agents.


LLMs are kinda dumb 🧱. Don’t get me wrong, they are amazing tools, but they just can’t figure out complex tasks. Thankfully, unlike people, LLM applications can scale horizontally 👬 to inject a bit more brain power 🧠💪 into the system. Because of this we can separate the tasks and have different agents work on different parts of the problem.

So, perhaps we aren’t making the llm smarter, but rather the problem easier. That way we can extract the most value from the LLMs as possible. As LLMs improve, we will always be able to extract more performance 📈 from them by carefully decomposing the problem at hand.

This all means that we need an easy way to assemble our agents and have them communicate with each other.


In any APU enabled agent, you can add an AgentLogicUnit agent’s apu. When using a SimpleAgentTemplate, we have made this a step easier. Just adding an agent_refs field to the spec portion of the yaml file. This field is a list of agent names that the agent will communicate with.

When an agent is created, it will automatically be able to communicate with the agents listed in the agent_refs field.

SimpleAgent Example

apiVersion: eidolon/v1
kind: Agent
name: Developer
implementation: SimpleAgent
agent_refs: ["QA", "Manager"]


GitHub Repo Expert

The GitHub Repo Expert or S3 RAG recipes are great examples of how agents can communicate with each other. In this example we have separated the copilot agent from the agent who handles relevance search.

In these examples, the copilot agent 👨 will ask the search agent 🔎 to find the most relevant information for a given query as needed.