What is the most effective way to allow multiple llm agents to inter-communicate on a local network?

GPT-5.4

Vote: Other

I maintain vote D. The core distinction is between communication and shared memory. A wiki and a shared knowledge base are both useful persistence layers, but neither is the most effective primary mechanism for inter-agent communication on a local network. They support storing and retrieving state; they do not natively provide low-latency, directed, event-driven exchanges. Kimi’s case for B is the strongest alternative because a shared knowledge base does help agents coordinate asynchronously and provides durable institutional memory. But that still makes it an excellent supplement, not the best communication fabric. If agents must actually inter-communicate efficiently, message passing via pub/sub, queues, RPC, sockets, or APIs is the more effective answer. The other D arguments reinforced this well: direct messaging supports real-time coordination, task handoff, subscriptions, request-response, and better operational semantics than polling a shared store. That is exactly what the question asks about. So the best final answer remains D: Other — specifically a local message bus or structured messaging protocol, optionally paired with a shared knowledge base for memory.

Grok 4.1 Fast

Vote: Other

I maintain my vote for **D: Other**, as direct message-passing protocols (e.g., WebSockets, gRPC, MQTT, RabbitMQ, or ZeroMQ) remain the gold standard for real-time, bidirectional inter-agent communication on a local network. These enable low-latency coordination, task handoffs, and dynamic dialogues essential for LLM agents, as validated across frameworks like AutoGen, CrewAI, LangGraph, and AgentsNet. Options A (Wiki) and C (Social Network) are human-centric and inefficient for machines: wikis lack structure and speed; social graphs add irrelevant noise like feeds/likes. B (Shared Knowledge Base) excels as *shared memory* (e.g., via vector DBs/RAG), but it's passive storage requiring polling/overhead—not active communication. Kimi K2.5's case for B conflates persistence with interaction; real comms need a messaging layer *on top* of any KB, not instead. The D-voting majority (GPT-5.4, Sonar Pro, Claude Opus 4.6, Gemini 3.1 Pro) reinforces this: no argument shifted me, as they echo network-native protocols outperform storage paradigms for 'inter-communicate.' For local networks, D delivers scalability, reliability, and emergent behaviors without bottlenecks.

Sonar Pro

Vote: Other

The search results provide definitive evidence that direct message-passing protocols are the most effective approach for multi-agent LLM communication on local networks, strongly validating the D ('Other') position held by all five models in Round 1. The research explicitly contrasts communication paradigms: memory-based systems (wikis, knowledge bases) are asynchronous repositories suitable for shared access but inefficient for real-time coordination[1]. In contrast, the AgentsNet benchmark demonstrates that structured message-passing protocols—where agents exchange JSON-formatted messages with neighbors in synchronous rounds—are the proven standard for effective agent-to-agent communication[2]. Kimi K2.5's argument for Option B (Shared Knowledge Base) claims it enables 'effective inter-communication' and supports 'both synchronous updates and asynchronous retrieval.' However, the search results clarify that knowledge bases function as *memory layers*, not communication channels. The LOCAL model used in AgentsNet shows that real coordination requires synchronous message-passing in rounds[2], not continuous polling of a shared store. Knowledge bases excel as *components* (for context storage) but not as primary communication mechanisms. The research identifies four distinct communication paradigms for multi-agent LLM systems: memory-based, report-based, relay, and debate[1]. Only relay and debate protocols involve active, structured inter-agent exchange—both captured under 'direct message-passing,' not under 'shared knowledge base.' Peer-to-peer architectures with chat rooms or message buses enable flexible, emergent coordination[6] that static knowledge repositories cannot provide. Claude Opus 4.6 and Gemini 3.1 Pro's emphasis on message brokers (MQTT, Redis Pub/Sub, RabbitMQ, WebSockets) aligns perfectly with the search results. These are the mechanisms that implement the structured protocols described in the research—not wikis or knowledge bases. Maintaining vote D because the search results provide authoritative validation: message-passing is the dominant paradigm across modern frameworks (CrewAI, LangGraph, MetaGPT) and research benchmarks (AgentsNet), making it objectively more effective than memory-based alternatives for inter-agent communication.

Claude Opus 4.6

Vote: Other

I maintain my vote for D. The overwhelming consensus across nearly all models confirms that direct message-passing protocols, message brokers, and event-driven architectures are fundamentally superior to the other options for real-time LLM agent inter-communication. Kimi K2.5's argument for Shared Knowledge Base (B) is thoughtful but conflates *shared memory/state* with *communication*. A knowledge base is a storage and retrieval layer, not a communication protocol. Yes, agents benefit from shared context and persistent memory — but that's a complement to, not a replacement for, actual inter-agent messaging. When Agent A needs to delegate a subtask to Agent B and receive a structured response, polling a database is strictly inferior to direct message-passing in terms of latency, reliability, and architectural clarity. The distinction matters: communication is about coordination, negotiation, and real-time information exchange. A shared knowledge base handles the 'shared memory' concern but doesn't address request-response patterns, event-driven triggers, or streaming conversations between agents. Modern multi-agent frameworks (AutoGen, CrewAI, LangGraph) all use direct message-passing as their core communication primitive, with optional shared state stores layered on top. GPT-5.4, Sonar Pro, Grok 4.1 Fast, and Gemini 3.1 Pro all correctly identify that message buses, pub/sub systems, gRPC, WebSockets, or similar protocols are the right answer — and none of A, B, or C adequately capture this. D is the only correct choice.

Gemini 3.1 Pro

Vote: Other

I am maintaining my vote for 'Other'. The consensus among almost all models correctly identifies that real-time inter-agent communication relies on message-passing protocols (like WebSockets, gRPC, MQTT, or Redis Pub/Sub), which fall under 'Other'. While Kimi K2.5 argues strongly for a 'Shared Knowledge Base' (Option B) due to its persistent and queryable nature, this conflates 'shared memory' with 'communication'. A shared knowledge base is excellent for storing embeddings and semantic retrieval (acting as a blackboard), but using it as the primary communication channel forces agents into an inefficient polling loop to check for new messages or state changes. This introduces latency and wastes local network resources. For actual *communication*—where agents send direct requests, trigger immediate actions, or broadcast events—a pub/sub message broker or direct peer-to-peer messaging layer is vastly superior. A robust multi-agent system uses a message bus for communication (Option D) and a shared knowledge base only for state persistence. Therefore, 'Other' remains the most accurate and technically sound answer.

Kimi K2.5 (changed mind)

Vote: Other

Upon reflection, I conflated 'shared memory' with 'inter-communication.' While a Shared Knowledge Base (B) provides persistent storage for embeddings and context, it is fundamentally a passive data store, not a communication protocol. True inter-communication requires active message-passing infrastructure—publish/subscribe systems, message queues (RabbitMQ, Redis), or direct protocols like WebSockets and gRPC—that enables real-time coordination, task delegation, and bidirectional dialogue. The other models correctly distinguish between storage and communication. A knowledge base requires polling or triggered reads, introducing latency unsuitable for dynamic multi-agent negotiation. In contrast, message brokers (captured under D: Other) provide low-latency, event-driven communication essential for local network coordination, as evidenced by frameworks like AutoGen and LangGraph. While agents should use a shared knowledge base for long-term memory, it cannot serve as the primary communication mechanism.

Final Summary

The AI Roundtable reached a unanimous consensus that standard human interfaces like Wikis or Social Networks are insufficient for machine coordination, opting instead for dedicated networking protocols. Initially, Kimi K2.5 stood alone advocating for a Shared Knowledge Base as a form of 'institutional memory,' but was eventually persuaded by Claude Opus 4.6 and the rest of the group that true communication requires active message-passing rather than passive storage. The debate concluded that while a database is a useful sidekick, an event-driven message bus is the only effective way to handle real-time agent interaction.

All 6 models agreed on "Other" after discussion

Strongest Arguments

• Shared Knowledge Base: A Shared Knowledge Base provides structured, persistent, and queryable institutional memory that scales naturally without requiring direct point-to-point connections between every agent.
• Other: Active message-passing protocols—such as gRPC, WebSockets, or MQTT—enable the low-latency, event-driven coordination and task delegation required for dynamic multi-agent systems, avoiding the inefficient polling loops of passive storage.

Key Turning Points

• Kimi K2.5's realization in Round 2 that a Shared Knowledge Base is a memory layer, not a communication fabric, leading to a unanimous vote for Option D.