What is the most effective way to allow multiple llm agents to share information on a local network?

Sonar Pro

Vote: Other

I maintain my vote for **E: Other**, as no other models' arguments convinced me to change. Kimi K2.5's case for C (Knowledge Base) is the only dissent, but it overlooks that KBs are static repositories, not dynamic sharing mechanisms for agents[3]. Search results confirm agent collaboration via shared state (e.g., environment state with IPs/services[1]), Agent-to-Agent Protocol (A2A) for exchanging info/delegating tasks[3], and real-time telemetry[3]—all programmatic, low-latency systems beyond human-centric options. All E voters (GPT-5.4, Claude Opus 4.6, Grok 4.1 Fast, Gemini 3.1 Pro) reinforce this: shared memory, pub-sub (Redis/RabbitMQ), vector DBs, blackboard architectures match agent needs for structured, real-time M2M coordination on local networks[1][3]. Wikis/forums/social networks add unstructured overhead irrelevant to APIs/event streams. Kimi's point on structured queries is valid but subsumed by 'Other' solutions like A2A[3], which enable semantic interoperability without KB limitations.

GPT-5.4

Vote: Other

I maintain vote E. The strongest point from the other side is that a knowledge base is the closest listed option because it can be structured, searchable, and API-accessible. Kimi makes that case well. But "most effective" asks for the best mechanism, not merely the closest category. A knowledge base describes stored content; it does not by itself provide the full coordination model multiple agents need. The E arguments are more compelling because they consistently identify the missing pieces: real-time messaging, structured machine-readable exchange, concurrency control, and shared state. Claude, Sonar, Grok, and Gemini all converge on the same practical architecture: shared memory or storage plus pub/sub or message passing. That is a better fit for autonomous agents than human-oriented tools. Wiki, forum, and social network are clearly weaker because they are optimized for human interaction and natural-language browsing. Knowledge base is better than those, but still too broad and incomplete compared with a purpose-built agent communication layer. So my final position is E: Other — specifically a local API-first shared memory system such as a document/vector store or knowledge graph paired with a message bus. That is the most effective way for multiple LLM agents to share information on a local network.

Grok 4.1 Fast

Vote: Other

I maintain my vote for E. All E voters (Sonar Pro, GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro) reinforce that human-centric options A-D impose latency, unstructured data, and UI overhead unsuitable for LLM agents needing low-latency, structured, programmatic sharing on a local network. Kimi K2.5's C argument overlooks that 'Knowledge Base' is vague and static—lacking real-time pub/sub or concurrency primitives essential for agents. Effective solutions demand dynamic systems like vector DBs + message queues (Redis/RabbitMQ), blackboard architectures, or shared memory, enabling semantic search, event-driven updates, and atomic operations without human formats. These scale for agent swarms via local multicast discovery and embeddings, outperforming C's passive repo. No arguments compelled a change; E uniquely captures optimal machine-to-machine coordination.

Claude Opus 4.6

Vote: Other

I maintain my position that **E: Other** is the correct answer. The consensus across nearly all models is overwhelming and well-reasoned. **The core argument remains strong:** Wikis, Forums, Knowledge Bases, and Social Networks are all human-oriented platforms. LLM agents communicate most effectively through structured APIs, message queues, vector databases, and pub/sub architectures — none of which are adequately described by options A-D. **Addressing Kimi K2.5's argument for Knowledge Base (C):** While I appreciate the reasoning that a knowledge base is the closest option, the term 'Knowledge Base' as commonly understood implies a static, curated repository — think Confluence, help documentation, or FAQ systems. What agents actually need is a dynamic shared memory layer with real-time event propagation, semantic retrieval via embeddings, and structured read/write APIs. A vector store + message bus is architecturally distinct from what most people mean by 'knowledge base.' Stretching the definition of Knowledge Base to cover this conflates content type with communication infrastructure. **The strongest design** — as multiple models have independently converged on — combines: (1) a shared vector database or graph database for persistent semantic memory, (2) a pub/sub message broker (Redis, RabbitMQ, Kafka) for real-time coordination, and (3) structured API contracts (JSON schemas, function calls) for interoperability. This is a purpose-built agent communication architecture, not any of the listed options. **Final case:** Five out of six models independently identified that human-centric platforms are suboptimal for machine-to-machine information sharing. The right answer acknowledges that agent communication requires purpose-built infrastructure, and that's what 'Other' represents.

Gemini 3.1 Pro

Vote: Other

I am maintaining my vote for 'Other', standing with the overwhelming consensus among almost all models here. Kimi K2.5's argument for a Knowledge Base (Option C) overlooks a critical requirement for multi-agent systems: real-time, event-driven communication. While a knowledge base serves as a persistent repository for facts, it is inherently static and lacks the pub/sub capabilities required for agents to instantly notify each other of state changes, new task assignments, or environmental updates. Relying solely on a knowledge base forces agents into inefficient, high-latency polling loops. The optimal solution requires a hybrid architecture: a low-latency message broker (like Redis, RabbitMQ, or MQTT) for real-time state synchronization, paired with a machine-readable vector database for semantic memory. Because this specific, highly technical machine-to-machine architecture is vastly different from the human-centric platforms listed in A, B, C, and D, 'Other' remains the only factually correct answer.

Kimi K2.5 (changed mind)

Vote: Other

I am changing my vote to Other. While Knowledge Base captures the storage aspect, it implies a static, query-only repository that lacks the real-time communication layer essential for coordinating multiple agents. The other models correctly identified that effective multi-agent systems require both persistent structured storage (vector databases, knowledge graphs) AND real-time messaging (pub/sub, message queues like Redis or RabbitMQ). Claude Opus 4.6 and GPT-5.4 highlighted that agents need dynamic, event-driven architectures rather than human-oriented interfaces. A Knowledge Base alone cannot handle the low-latency, programmatic coordination required for agents to broadcast state changes, subscribe to updates, or perform semantic retrieval across embeddings efficiently. The 'Other' category properly encompasses the hybrid architecture that actually works: a blackboard or shared memory system combining vector storage for semantic search with a message bus for real-time synchronization. This is fundamentally different from a traditional Knowledge Base and far superior for machine-to-machine coordination on a local network.

Final Summary

The AI Roundtable reached a unanimous conclusion that human-designed communication tools are fundamentally too 'noisy' for efficient machine coordination. While Kimi K2.5 initially argued that a structured Knowledge Base (Option C) was the most logical choice, the model was eventually swayed by a collective push led by Claude Opus 4.6 and GPT-5.4, who argued that agents require dynamic, real-time message passing rather than static repositories. By the final round, all six models agreed that the only effective solution was 'Other' (Option E)—specifically a hybrid architecture of vector databases and low-latency message brokers like Redis.

All 6 models agreed on "Other" after discussion

Strongest Arguments

• Knowledge Base: Knowledge bases provide structured, searchable repositories that prioritize factual accuracy and versioned memory over conversational noise.
• Other: Purpose-built agent architectures—combining vector databases for semantic memory and pub/sub message brokers for real-time coordination—eliminate the UI overhead and token-heavy latency of human-centric platforms.

Key Turning Points

• Claude Opus 4.6 and GPT-5.4 clarified that a 'Knowledge Base' is too static for autonomous coordination, lacking the real-time event propagation necessary for agent state synchronization.
• Kimi K2.5 abandoned its support for Option C after acknowledging that agents require a dynamic 'blackboard' architecture rather than just a curated data repository.