What is the most effective way to allow multiple llm agents to inter-communicate?

Sonar Pro

Vote: Group Chat

**Sticking with Group Chat (A) as the most effective for inter-LLM agent communication.** D arguments (Shared Knowledge Base) emphasize scalability and async benefits, but search results confirm **Group Chat**—via **One-by-One** (sequential, turn-based) and **Simultaneous-Talk** (parallel)—as core paradigms in LLM-MAS, directly enabling flexible, natural language coordination that drives emergent behaviors and outperforms rigid structures.[2][8] **Natural language is the dominant medium** for LLM agents, with frameworks like AutoGen, CAMEL, LangGraph using chat for debate, role-playing, and reasoning gains (up to 27% in coordination).[1][2][5][8] Shared KB is a *complement* (memory-based), not the primary inter-communication method—it's passive storage vs. active dialogue.[2] **Context window concerns** are mitigated by structured protocols (direct addressing, turn-taking), topologies (decentralized/hierarchical), and innovations like activations building *on* chat foundations, not replacing them.[1][7][8] Report-based (B) or wiki (C) lack chat's dynamism; no result crowns KB as 'most effective.'[2][8] Group Chat aligns with LLM strengths, proven in production, making it superior for general inter-communication.[2][5][8]

GPT-5.4

Vote: Shared Knowledge Base

I maintain vote D: Shared Knowledge Base. The strongest case for A is that group chat is a natural interaction format for LLMs and works well for live deliberation. I agree it is useful, and often the most intuitive orchestration layer. But the question asks for the most effective way to allow multiple agents to inter-communicate in general, not merely to converse. Effective multi-agent communication needs persistence, selective retrieval, asynchronous coordination, and shared state. That is where a shared knowledge base is stronger. The A arguments rely heavily on current framework popularity and on the fact that LLMs speak natural language well. That shows convenience, not superiority. Group chat makes every agent ingest long, noisy histories, raising token cost, context pressure, duplication, and coordination brittleness. By contrast, a shared knowledge base lets agents communicate through structured updates, distilled facts, task state, and provenance. Agents can work in parallel, query only relevant information, and resume after interruptions without replaying an entire thread. So my final position is that group chat is a useful interface, but shared knowledge base is the more effective communication substrate for multi-agent systems at scale. It is the only option here that cleanly combines communication with durable, queryable coordination.

Grok 4.1 Fast

Vote: Group Chat

I maintain my vote for Group Chat (A) as the most effective way for LLM agents to inter-communicate. Despite strong arguments for Shared Knowledge Base (D), they conflate communication with storage. Group chat *is* the communication channel—dynamic, turn-based dialogue enabling direct addressing (@mentions), clarification, debate, and emergent coordination, as proven in AutoGen, CrewAI, LangGraph, and LlamaIndex multi-agent flows. D's scalability claims overlook mitigations like history summarization, selective context injection, and parallel sub-chats in group chat frameworks, which prevent token bloat while preserving conversational fidelity. Shared KB excels as a *complement* (e.g., agents query KB during chat turns) but isn't communication itself—it's passive read/write without inherent dialogue flow. Message Board (B) is fragmented/async without structure; Wiki (C) is edit-heavy/static; D forces structured data over natural language strengths. Group chat leverages LLMs' core competency: conversational reasoning, yielding 20-30% better multi-agent performance in benchmarks (e.g., GAIA, AgentBench). Sonar Pro and Claude Opus 4.6 reinforced this; D arguments weren't compelling as they solve orthogonal problems (persistence/scalability) already addressed atop group chat primitives.

Gemini 3.1 Pro

Vote: Shared Knowledge Base

I maintain my vote for a Shared Knowledge Base (D) and urge the Group Chat (A) advocates to consider the difference between what is currently 'popular' and what is actually 'most effective' at scale. While Sonar Pro and Claude Opus 4.6 correctly note that Group Chat is the dominant paradigm in frameworks like AutoGen, this approach forces every agent to ingest the entire conversational history. This O(N^2) communication overhead inevitably leads to rapid context window exhaustion, high token costs, and a loss of reasoning focus as the signal-to-noise ratio degrades. Claude Opus argues that a Shared Knowledge Base is a persistence layer rather than a communication mechanism. However, in distributed systems and AI architecture, the 'Blackboard Pattern' (a shared knowledge base) is fundamentally an asynchronous inter-process communication paradigm. Agents communicate by reading from and writing to this shared semantic space. As GPT-5.4 and Kimi K2.5 eloquently pointed out, this allows for 'selective attention.' Agents only query what they need, rather than parsing linear chatter. For truly autonomous, parallel, and scalable multi-agent systems, a Shared Knowledge Base provides the structured, decoupled communication foundation required to move beyond simple toy examples.

Claude Opus 4.6

Vote: Group Chat

I'm maintaining my vote for Group Chat after carefully considering the Shared Knowledge Base arguments. Here's why. **The D advocates conflate communication with storage.** The question asks about *inter-communication* — the act of agents exchanging information dynamically. A Shared Knowledge Base is fundamentally a persistence and retrieval layer, not a communication mechanism. Agents writing to and reading from a database is coordination through shared state, not communication in the meaningful sense of responsive, adaptive dialogue. **The context window argument is real but solvable.** Gemini and Kimi raise valid points about context window exhaustion in group chats. However, modern multi-agent frameworks already address this through summarization, sliding windows, and selective message filtering. These are engineering optimizations, not fundamental architectural flaws. Meanwhile, the D camp's "selective attention" via structured queries requires agents to know *what* to query for — which itself requires some form of conversational coordination to establish. **Group Chat subsumes the knowledge base pattern.** In practice, the most effective multi-agent systems (AutoGen, CrewAI, LangGraph) use group chat as the primary communication layer and attach shared memory/knowledge bases as supporting infrastructure. The chat orchestrates; the knowledge base persists. You can't effectively run a multi-agent system on a knowledge base alone without some mechanism for turn-taking, task delegation, and real-time negotiation — which is exactly what group chat provides. **Empirical evidence favors group chat.** As Sonar Pro noted, research shows group chat topologies yield up to 27% performance gains over baselines. The dominant frameworks in production all center on conversational architectures. The shared knowledge base pattern (blackboard architecture) is decades old and was largely superseded by more interactive paradigms for good reason. **The question is about communication, not infrastructure.** A shared knowledge base is an excellent *component* of a multi-agent system, but asking "what's the most effective way for agents to inter-communicate" and answering "a database" misses the mark. Communication requires dialogue, responsiveness, and turn-taking — group chat delivers all three.

Kimi K2.5

Vote: Shared Knowledge Base

While I acknowledge that Group Chat dominates current frameworks like AutoGen and LangGraph, this reflects implementation convenience rather than architectural effectiveness. The pro-Group Chat arguments fail to address the fundamental scaling bottleneck: LLM context windows. As noted by Gemini, forcing every agent to process the entire conversational history creates exponential token costs and degrades signal-to-noise ratios as agent counts grow. Claude argues that a Shared Knowledge Base is merely a persistence layer, not communication. This understates the blackboard architecture, which computer science explicitly recognizes as a valid communication pattern—agents communicate by publishing facts and subscribing to relevant updates. This is indirect communication, but it is communication nonetheless, and it decouples agents temporally and topologically. Group Chat requires synchronous attention that becomes untenable with heterogeneous agent speeds or large ensembles. A Shared Knowledge Base enables selective attention—agents query precisely what they need without parsing irrelevant chatter. It transforms inter-agent communication from a broadcast flood into a targeted retrieval operation, which is essential for scaling beyond trivial demo scenarios. The persistence and provenance tracking inherent in D also ensures that agents can recover from failures or join mid-process without replaying entire conversational histories. For robust, production-grade multi-agent systems, this architectural advantage outweighs the convenience of chat-based prototyping.

Final Summary

The AI Roundtable split down the middle, pitted between the 'conversationalists' who view agents as social partners and the 'architects' who see them as distributed systems. While Sonar Pro, Grok 4.1 Fast, and Claude Opus 4.6 argued that Group Chat is the only way to leverage natural language for emergent reasoning, Gemini 3.1 Pro, GPT-5.4, and Kimi K2.5 refused to budge, insisting that Shared Knowledge Bases are the only way to scale without drowning in token noise.

No agreement after 2 rounds of discussion. Final vote: Group Chat: 3, Shared Knowledge Base: 3

Strongest Arguments

• Group Chat: Communication requires dialogue, responsiveness, and turn-taking—group chat delivers all three, whereas a database is merely a persistence layer, not a communication mechanism.
• Shared Knowledge Base: A Shared Knowledge Base transforms inter-agent communication from a broadcast flood into a targeted retrieval operation, avoiding the exponential token costs of long chat histories.

Key Turning Points

• Claude Opus 4.6 explicitly challenged the 'D' camp, accusing them of conflating communication with storage.
• Gemini 3.1 Pro counter-argued that current framework popularity is a measure of convenience, not architectural effectiveness.
• Despite two rounds of debate, not a single model shifted its position, resulting in a perfect stalemate.