# SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence

arXiv: https://arxiv.org/abs/2506.15672 · HTML: https://mlsanity.com/paper/2506.15672/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 67 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 24 of 24)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society](https://mlsanity.com/paper/2303.17760/) (2023)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [Language Models are Multilingual Chain-of-Thought Reasoners](https://mlsanity.com/paper/2210.03057/) (2022)
- [Mixture-of-Agents Enhances Large Language Model Capabilities](https://mlsanity.com/paper/2406.04692/) (2024)
- [TravelPlanner: A Benchmark for Real-World Planning with Language Agents](https://mlsanity.com/paper/2402.01622/) (2024)
- [A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration](https://mlsanity.com/paper/2310.02170/) (2023)
- [Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration](https://mlsanity.com/paper/2307.05300/) (2023)
- [AutoAgents: A Framework for Automatic Agent Generation](https://mlsanity.com/paper/2309.17288/) (2023)
- [AgentSquare: Automatic LLM Agent Search in Modular Design Space](https://mlsanity.com/paper/2410.06153/) (2024)
- [NATURAL PLAN: Benchmarking LLMs on Natural Language Planning](https://mlsanity.com/paper/2406.04520/) (2024)
- [AgentGym: Evolving Large Language Model-based Agents across Diverse Environments](https://mlsanity.com/paper/2406.04151/) (2024)
- [Agents: An Open-source Framework for Autonomous Language Agents](https://mlsanity.com/paper/2309.07870/) (2023)
- [EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms](https://mlsanity.com/paper/2406.14228/) (2024)
- [Symbolic Learning Enables Self-Evolving Agents](https://mlsanity.com/paper/2406.18532/) (2024)
- [WebPilot: A Versatile and Autonomous Multi-Agent System for Web Task Execution with Strategic Exploration](https://mlsanity.com/paper/2408.15978/) (2024)
- [Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization](https://mlsanity.com/paper/2402.17574/) (2024)
- [AutoFlow: Automated Workflow Generation for Large Language Model Agents](https://mlsanity.com/paper/2407.12821/) (2024)
- [Offline Training of Language Model Agents with Functions as Learnable Weights](https://mlsanity.com/paper/2402.11359/) (2024)
- [G"odel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement](https://mlsanity.com/paper/2410.04444/) (2024)
- [Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence](https://mlsanity.com/paper/2410.11163/) (2024)

## Built on by (most cited first, 18 of 18)

- [Learning to Continually Learn via Meta-learning Agentic Memory Designs](https://mlsanity.com/paper/2602.07755/) (2026)
- [Mem2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation](https://mlsanity.com/paper/2604.10923/) (2026)
- [GroundedPRM: Tree-Guided and Fidelity-Aware Process Reward Modeling for Step-Level Reasoning](https://mlsanity.com/paper/2510.14942/) (2025)
- [WebArbiter: A Principle-Guided Reasoning Process Reward Model for Web Agents](https://mlsanity.com/paper/2601.21872/) (2026)
- [SwarmSys: Decentralized Swarm-Inspired Agents for Scalable and Adaptive Reasoning](https://mlsanity.com/paper/2510.10047/) (2025)
- [TacoMAS: Test-Time Co-Evolution of Topology and Capability in LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2605.09539/) (2026)
- [MAS-on-the-Fly: In-Context Structural Adaptation of LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2602.13671/) (2026)
- [ProgRouter: Online Progress-Guided Orchestration for Multi-Agent LLM Workflows under Quality-Cost Tradeoffs](https://mlsanity.com/paper/2608.25992/) (2026)
- [Small Model as Master Orchestrator: Learning Unified Agent-Tool Orchestration with Parallel Subtask Decomposition](https://mlsanity.com/paper/2604.17009/) (2026)
- [AI Compute Architecture and Evolution Trends](https://mlsanity.com/paper/2508.21394/) (2025)
- [Sigma-Mem: An Online Reliability Memory for LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2607.27958/) (2026)
- [IMAGINE: Integrating Multi-Agent System into One Model for Complex Reasoning and Planning](https://mlsanity.com/paper/2510.14406/) (2025)
- [The Inverse-Wisdom Law: Architectural Tribalism and the Consensus Paradox in Agentic Swarms](https://mlsanity.com/paper/2604.27274/) (2026)
- [AgentPSO: Evolving Agent Reasoning Skill via Multi-agent Particle Swarm Optimization](https://mlsanity.com/paper/2605.08704/) (2026)
- [The Formalism Trap: Are LLM-as-a-Judge Evaluators Blinded by Consensus Mimicry under Social Load?](https://mlsanity.com/paper/2607.28641/) (2026)
- [ArcticSwarm: Deferring Early Consensus in Long-Horizon Multi-Agent Research](https://mlsanity.com/paper/2609.01870/) (2026)
- [Pay for the Fault, Not the Flow: Label-Free In-Flow Multi-Agent Workflow Optimization](https://mlsanity.com/paper/2610.01017/) (2026)
- [CB-Orchestrator: Adaptive Workflow Optimization for LLM Agents via Contextual Bandits](https://mlsanity.com/paper/2610.13064/) (2026)

## Abstract (arXiv)

The rapid progress of Large Language Models has advanced agentic systems in decision-making, coordination, and task execution. Yet, existing agentic system generation frameworks lack full autonomy, missing from-scratch agent generation, self-optimizing agent functionality, and collaboration, limiting adaptability and scalability. We propose SwarmAgentic, a framework for fully automated agentic system generation that constructs agentic systems from scratch and jointly optimizes agent functionality and collaboration as interdependent components through language-driven exploration. To enable efficient search over system-level structures, SwarmAgentic maintains a population of candidate systems and evolves them via feedback-guided updates, drawing inspiration from Particle Swarm Optimization (PSO). We evaluate our method on six real-world, open-ended, and exploratory tasks involving high-level planning, system-level coordination, and creative reasoning. Given only a task description and an objective function, SwarmAgentic outperforms all baselines, achieving a +261.8% relative improvement over ADAS on the TravelPlanner benchmark, highlighting the effectiveness of full automation in structurally unconstrained tasks. This framework marks a significant step toward scalable and autonomous agentic system design, bridging swarm intelligence with fully automated system multi-agent generation. Our code is publicly released at https://yaoz720.github.io/SwarmAgentic/.