Language Agents as Optimizable Graphs (2402.16823v3)

Abstract: Various human-designed prompt engineering techniques have been proposed to improve problem solvers based on LLMs, yielding many disparate code bases. We unify these approaches by describing LLM-based agents as computational graphs. The nodes implement functions to process multimodal data or query LLMs, and the edges describe the information flow between operations. Graphs can be recursively combined into larger composite graphs representing hierarchies of inter-agent collaboration (where edges connect operations of different agents). Our novel automatic graph optimizers (1) refine node-level LLM prompts (node optimization) and (2) improve agent orchestration by changing graph connectivity (edge optimization). Experiments demonstrate that our framework can be used to efficiently develop, integrate, and automatically improve various LLM agents. The code can be found at https://github.com/metauto-ai/gptswarm.

• The paper proposes a framework that models language agents as optimizable graphs with nodes for operations and edges for information flow.
• The methodology uses edge and node-level optimizations, applying evolutionary and reinforcement learning techniques to enhance agent collaboration and prompt effectiveness.
• Experiments demonstrate improved adversarial robustness and efficiency in solving complex tasks such as Mini Crosswords and GAIA benchmarks.

Enhancing LLM Agents Through Optimizable Graph Representations

Introduction

The rapid advancements in LLMs have sparked significant interest in leveraging these powerful tools to solve complex problems. One promising direction is the development of autonomous agents that can interact with and manipulate LLMs to perform specific tasks. However, integrating these individual agents into a cohesive and efficient system presents a significant challenge. To address this, we introduce a novel framework that conceptualizes LLM-based agents as optimizable computational graphs. This approach not only enables efficient integration of diverse agent functionalities but also introduces mechanisms for the automatic optimization of agent systems.

Computational Graphs for Language Agents

Our framework proposes a comprehensive representation of language agents and their interactions through directed acyclic graphs (DAGs). In these graphs:

• Nodes represent fundamental operations such as querying LLMs, data processing, or interaction with external tools.
• Edges define the flow of information between operations, outlining the execution structure of an agent or the collaboration pattern among multiple agents.

This graph-based representation provides a modular and flexible foundation for constructing language agents, facilitating both individual improvements and the optimization of agent orchestration.

Optimizing Agent Graphs

The framework introduces two primary avenues for optimization:

1. Edge Optimization: By adjusting the graph's connectivity, we can explore different patterns of agent collaboration and information flow. The goal is to automatically discover optimal orchestration strategies for given tasks or objectives. This process is guided by a utility function that quantifies the effectiveness of a particular graph configuration, employing evolutionary or reinforcement learning techniques for optimization.
2. Node-Level Optimization: Focusing on the prompts used to query LLMs—an essential operation in most nodes—this optimization seeks to refine how each node leverages the LLM's capabilities. This includes adjusting prompt structures, incorporating example-based learning, and fine-tuning node operations based on feedback from their outputs.

Practical Implications and Future Directions

Our experiments across various benchmarks demonstrate the efficacy of the proposed framework in enhancing the performance of language agent systems. Key findings include:

• Adversarial Robustness: In scenarios where agents face adversarial conditions, edge optimization effectively isolates and minimizes the impact of adversarial agents, safeguarding the system's integrity.
• Efficiency in Problem Solving: By optimizing the agent collaboration patterns, our system shows significant improvements in solving complex tasks, such as Mini Crosswords and GAIA benchmarks.
• Self-Improving Agents: The node optimization feature facilitates the continuous improvement of agent operations, leading to better utilization of LLM capabilities over time.

Looking ahead, this framework opens up numerous possibilities for the development of more intelligent and adaptable agent systems. Future work could explore:

• Dynamic Adaptation: Implementing mechanisms for real-time graph optimization, allowing agent systems to dynamically adapt to new tasks or changing environments.
• Scalability: Investigating strategies to efficiently scale the graph-based approach to accommodate larger and more complex agent systems, potentially involving thousands of agents.
• Inter-Agent Communication: Enhancing the framework to support more sophisticated forms of agent communication beyond the current graph-based structure.

Conclusion

By conceptualizing language agents as optimizable graphs, we provide a robust foundation for creating efficient, versatile, and self-improving agent systems. This approach not only simplifies the integration of diverse agent functionalities but also introduces a systematic way to enhance agent performance through optimization. As we continue to push the boundaries of what LLMs and autonomous agents can achieve, frameworks like ours will play a crucial role in harnessing the full potential of these advanced technologies.