The Cognitive Architectures for Language Agents (CoALA) framework, proposed by Sumers et al. (2023), provides a systematic taxonomy for organizing LLM-based language agents into modular components inspired by cognitive science. Drawing on decades of research in cognitive architectures such as Soar and ACT-R, CoALA formalizes the design space of language agents through memory modules, structured action spaces, and decision-making procedures.

As language model-based agents proliferate — from ReAct to Reflexion to Voyager — the field lacks a unifying framework to compare, categorize, and design them. CoALA addresses this by proposing a modular architecture that retrospectively organizes existing agents and prospectively identifies gaps in the design space. The framework defines an agent as a tuple:

$$A = (M_w, M_{lt}, \mathcal{A}_i, \mathcal{A}_e, D)$$

where $M_w$ is working memory, $M_{lt}$ is long-term memory, $\mathcal{A}_i$ is the internal action space, $\mathcal{A}_e$ is the external action space, and $D$ is the decision procedure.

CoALA divides agent memory into working memory and three types of long-term memory, mirroring distinctions from cognitive psychology:

• Working Memory: A short-term scratchpad holding the agent's current context — recent observations, intermediate reasoning results, and partial plans. Analogous to the limited-capacity buffer in human cognition.
• Episodic Memory: Stores past experiences and events (e.g., “What happened when I tried approach X?”). Enables learning from specific interaction histories.
• Semantic Memory: Holds factual world knowledge (e.g., “Water boils at 100°C at sea level”). Can be stored as text, embeddings, or knowledge graphs.
• Procedural Memory: Encodes skills and procedures — often represented as code snippets, tool definitions, or implicitly within LLM parameters. Defines how to perform actions.

Actions are partitioned into internal and external categories:

• Retrieval: Reading from long-term memory stores
• Reasoning: Updating working memory via LLM inference (chain-of-thought, reflection)
• Learning: Writing new information to long-term memory

• Grounding: Interacting with the outside world — tool use, API calls, web browsing, or robotic control

# Simplified CoALA agent loop
class CoALAAgent:
    def __init__(self, llm, episodic_mem, semantic_mem, procedural_mem):
        self.llm = llm
        self.working_memory = []
        self.episodic = episodic_mem
        self.semantic = semantic_mem
        self.procedural = procedural_mem
 
    def decision_loop(self, observation):
        self.working_memory.append(observation)
        while not self.should_act_externally():
            # Internal actions: retrieve, reason, learn
            retrieved = self.retrieve(self.working_memory)
            reasoning = self.llm.reason(self.working_memory + retrieved)
            self.working_memory.append(reasoning)
        action = self.select_external_action(self.working_memory)
        result = self.execute(action)
        self.episodic.store(observation, action, result)
        return result

CoALA formalizes decision-making as a continuous loop with two stages:

1. Planning Stage: The agent iteratively applies reasoning and retrieval to propose, evaluate, and select actions. This may involve multi-step deliberation or simple reactive mappings.
2. Execution Stage: The selected action is performed (grounding or learning), the environment returns new observations, and the cycle repeats.

This distinguishes agents on a spectrum from purely reactive (single LLM call maps observation to action) to deliberative (multi-step internal planning before acting).

CoALA explicitly builds on classical cognitive architectures:

• Soar: Production rules in long-term memory match working memory contents to trigger actions. CoALA replaces symbolic productions with LLM-based reasoning.
• ACT-R: Distinguishes declarative and procedural memory with activation-based retrieval. CoALA's memory taxonomy mirrors this structure.
• Global Workspace Theory: Working memory serves as a shared workspace where different modules contribute and compete for attention.

The framework positions LLM agents within a 50-year lineage of AI research, arguing that cognitive architectures provide the missing organizational structure for the rapidly expanding space of language agents.

• Sumers et al. "Cognitive Architectures for Language Agents" (2023)
• Shinn et al. "Reflexion: Language Agents with Verbal Reinforcement Learning" (2023)
• Yao et al. "ReAct: Synergizing Reasoning and Acting in Language Models" (2023)

• Tool Learning with Foundation Models
• Self-Refine
• Chain-of-Verification