coala-framework

To build cognitive language agents with memory and planning, use a structured framework integrating modular memory types (working, episodic, semantic, procedural) with a disciplined PLAN, EXECUTE, OBSERVE, and LEARN decision cycle for robust multi-step reasoning.

The four-phase decision cycle for LLM agents is a structured PLAN, EXECUTE, OBSERVE, and LEARN loop. This pattern enables cognitive agents to perform multi-step reasoning, integrate persistent memory, and apply metacognitive enhancements during task automation.

You can add persistent context to customer-support copilots by implementing a memory-driven architecture using working, episodic, semantic, and procedural memory modules to guide agent reasoning and internal or external actions across sustained interactions.

Yes, you can use hierarchical planning for debugging assistants by applying the framework's optional metacognitive enhancements. This structures the agent's workflow, allowing it to reason through complex debugging tasks using the plan-execute-observe-learn loop.

This cognitive architecture requires no external dependencies, but you need an LLM client to instantiate the agent. You wire optional external tools to the agent, enabling it to perform internal and external actions based on its memory and observations.

You should avoid using a memory-driven agent framework for simple, single-step queries that lack a need for persistent context. This architecture is designed for complex, multi-step reasoning tasks like data analysis and research that require disciplined planning.