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Collaborative Circle Architecture
A collaborative multi-agent system where agents contribute to a shared memory and iteratively refine a solution through open, cyclical information sharing.
Basic Sequential
Agents operate in a linear pipeline; each agent’s output is the next agent’s input.
Network (Decentralized) Architecture
Agents communicate directly without a central coordinator for flexible, dynamic collaboration.
Market-Based (Bidding) Architecture
Auctioneer broadcasts tasks; contractor agents bid; best bid wins based on predefined criteria.
Hierarchical Architecture
Multi-level supervision where top-level supervisors manage lower-level supervisors who manage worker agents.
Tool-Augmented Agent Network
Agents are designed to interact with external tools (APIs, databases, code interpreters) to ground reasoning and perform real-world actions.
Critic & Refinement Loop Architecture
Creator agent produces output; Critic reviews and provides feedback; loop continues until approval or iteration limit.
Human-in-the-Loop (HITL) Architecture
Autonomous agents collaborate with humans at critical checkpoints for approval, judgment, or handling sensitive actions.
Foraging Architecture
Decentralized swarm of forager agents explore broadly; discoveries attract others to exploit rich areas.
Group Architecture
Agents organized into teams with rich intra-group communication and structured inter-group coordination via liaisons.
Mixture of Agents
Heterogeneous agents with different specializations collaborate via an orchestrator to solve complex problems.
Concurrent Workflows
Multiple agents work on the same task in parallel, coordinating while processing independently to reduce total time.
Agent Rearrange
Agents dynamically add, remove, or adapt based on changing tasks and performance signals while preserving overall system integrity.
Graph Workflow (DAG)
Agents are nodes and connections are edges; control flow is managed by edges and agents communicate by updating shared graph state.
Interactive Group Chat
An interactive group chat architecture facilitates collaboration among multiple specialized AI agents in a coordinated manner.
Agent Registry
An agent registry is a centralized architecture for managing a collection of agents. It provides functionalities to add, delete, update, and retrieve agents, ensuring that agent definitions are decoupled from their execution.
Spreadsheet Swarm
Spreadsheet Swarm is a multi-agent architecture designed to manage and orchestrate thousands of agents using a CSV file.
Heavy Architecture
A high-performance architecture designed for handling intensive computational workloads by orchestrating multiple agents working on resource-heavy operations—perfect for large-scale data processing and high-throughput task execution.
Router
Priority Rules and LangGraph-style routing: analyzes tasks and routes to optimal agents/architectures based on rules, load, availability, and performance.
Deep Research
Specialized architecture for comprehensive, multi-domain research with iterative planning, cross-validation, and synthesis.
De-Hallucination Architecture
Consensus-based validation to minimize hallucinations: primary agent generates, multiple validators fact-check, consensus decides to accept or refine.
MALT (Multi-Agent Learning Task)
Creator produces an initial solution; Verifiers independently evaluate; Refiners improve based on feedback; iterates to high-quality output.
Majority Voting
Agents independently vote on proposed decisions; the option with the most votes is selected as final.
Round Robin
Tasks are distributed cyclically among agents in a fixed sequence for fair load balancing.
Auto-Builder
Inspects task requirements and automatically composes the necessary agents into a working swarm.
Hybrid Hierarchical Cluster
Top-level coordinator delegates tasks; peer clusters collaborate directly for efficiency.
Election
Agents run a ballot to elect a leader or make collective choices with fault-tolerant coordination.
Dynamic Conversational
An adaptive chat architecture that selects which agents join and how messages are routed based on context, user goals, and prior dialogue—ideal for advanced customer support and context-aware chatbots.
Tree
A strict parent-child hierarchy where each agent has exactly one parent (except the root) and may have multiple children. This structure simplifies delegation, logging, and failure isolation.
Supervisor-Agent Architecture
In a Supervisor-Agent architecture, one agent acts as the central "supervisor" or "orchestrator." This supervisor agent is responsible for receiving a task, breaking it down into smaller sub-tasks, and delegating those sub-tasks to specialized "worker" agents. The supervisor then monitors the progress of the worker agents and synthesizes their outputs to produce the final result. This architecture is easy to start with and is effective for workflows that can be clearly broken down into distinct steps.