66. CoALA (Cognitive Architectures for Language Agents)
Mini-Project: CoALA Taxonomy: Comparing Architectures via CoALA Framework
A structured comparison of three agent architectures (ReAct, Plan-and-Execute, Memory-Augmented) across the CoALA dimensions of memory, action space, and decision-making strategy, printed as a formatted comparison table.
Description
CoALA is a conceptual framework (Sumers et al., 2023) that provides a unified way to describe and compare language agent architectures. It models every language agent as having three core components: (1) memory (working memory, long-term episodic/semantic/procedural), (2) action space (internal reasoning actions + external tool/environment actions), and (3) decision-making (the process that selects actions based on memory state). CoALA gives us a common vocabulary to analyze and design agents.
CoALA is not itself an implementation but a taxonomy. It provides the lens through which we can classify ReAct agents (simple memory + tool actions), Plan-and-Execute agents (richer decision-making), and memory-augmented agents. It unifies all patterns in this guide under a common conceptual framework.
When to Use
- When designing a new agent architecture from scratch
- As an analysis framework for comparing existing agent designs
- When teaching or documenting agent system design
- Architecture reviews and design documents
Core Components
flowchart TD
A[CoALA Agent] --> B[Memory Module]
A --> C[Action Space]
A --> D[Decision Making]
B --> E[Working Memory]
B --> F[Long-Term: Episodic]
B --> G[Long-Term: Semantic]
B --> H[Long-Term: Procedural]
C --> I[Internal Actions: Reasoning, Retrieval]
C --> J[External Actions: Tools, APIs]
D --> K[Propose Actions]
D --> L[Evaluate Actions]
D --> M[Select & Execute]
style A fill:#F44336,color:#fff
style B fill:#2196F3,color:#fff
style C fill:#9C27B0,color:#fff
style D fill:#FF9800,color:#fff