Multi-Agent Topology: Peer Agents and When to Use Them (2026)
Two or more agents collaborate as peers, with no central supervisor. The most common implementations are CrewAI’s role-based crew, AutoGen’s group-chat, and LangGraph’s swarm primitive.
Three named peer patterns
CrewAI role-based crew. Each agent is assigned a named role (researcher, writer, reviewer) with a goal, a backstory, and a set of tools. Tasks pass through the crew sequentially. The CrewAI examples repository github.com/crewAIInc/crewAI-examples is the canonical source for working examples. Access date: 30 April 2026.
AutoGen two-agent and group-chat. Microsoft’s AutoGen frames multi-agent collaboration as a conversation between agent objects. The simplest case is a UserProxyAgent + AssistantAgent pair. The richer case is a GroupChat coordinated by a GroupChatManager that selects the next speaker. The reference is the AutoGen documentation at microsoft.github.io/autogen. Access date: 30 April 2026.
LangGraph swarm. LangGraph documents a swarm primitive in which agents hand control to each other directly via handoff messages, with shared state and no central router. See the LangGraph multi-agent concepts page langchain-ai.github.io/langgraph/concepts/multi_agent. Access date: 30 April 2026.
When peer multi-agent is the right shape
When agents have genuinely separable roles. A researcher agent that retrieves and summarises, a writer agent that drafts, and a reviewer agent that checks against criteria are doing different work that benefits from different system prompts and tool surfaces. A single agent can do all three but at the cost of context-window pressure and prompt complexity.
When parallel work is genuinely possible. If two sub-tasks have no data dependency on each other, two peer agents can run simultaneously. The reduction in wall-clock latency is real. (If the work is not parallelisable, peer multi-agent costs you the coordination overhead without gaining anything; consider the supervisor pattern instead.)
Common failure modes
Agents talking past each other. Without a coordinating supervisor, peer agents tend to generate long monologues directed at the shared message history rather than at each other. The conversation becomes parallel monologue rather than dialogue. AutoGen’s GroupChatManager exists in part to address this.
Cost explosion from unbounded loops. Two agents that each respond to each other can run indefinitely without an explicit termination condition. Every framework that ships a multi-agent primitive ships a max-rounds or max-tokens guard for this reason.
Debugging difficulty. State distributed across multiple agents is harder to trace than state inside a single agent. The remedy is a structured trace (LangSmith, Phoenix, OpenAI’s OpenTelemetry agent traces) and a clear logging convention.
Reference examples
Related on this site
- Single-agent topology: when one agent is enough.
- Supervisor pattern: when peer multi-agent should become orchestrator + workers.
- Hierarchical agents: when sub-teams need their own structure.
For the broader definition of multi-agent systems, see whatisanaiagent.com/multi-agent-systems. For the process-flow view of a multi-agent collaboration, see agenticswimlanes.com.