ReflectiveAgent: An LLM Agent Architecture for Continuous Evolution via Self-Reflection

ReflectiveAgent is an LLM agent system with self-reflection, memory storage, and strategy adaptation capabilities. It solves logical puzzles through a closed-loop architecture, learns and improves from failures, and explores the engineering path from passive text generators to autonomous reasoning agents. The project is maintained by rzadrze and was released on the GitHub platform on May 28, 2026 (link: https://github.com/rzadrzi/ReflectiveAgent).

Current large language models (LLMs) excel in zero-shot and few-shot reasoning, but perform poorly in tasks requiring multi-step logical consistency, long-term planning, or error recovery. ReflectiveAgent is designed to address these limitations, aiming to transform LLMs into autonomous reasoning agents. The core motivation stems from real-world LLM engineering challenges: robustness, evaluability, and continuous improvement capabilities are crucial. Thus, a closed-loop architecture is adopted, enabling continuous evolution through self-reflection and feedback-driven adaptive mechanisms, replacing static prompt engineering.

ReflectiveAgent uses a modular framework with core components including: a base LLM agent (reasoning engine), an evaluation module (analyzes reasoning processes and results), a reflection module (summarizes and stores errors), a memory storage module (maintains episodic and vector memory), and a strategy adaptation module (adjusts prompts and strategies). Optional multi-agent debate layer: multiple sub-agents with different reasoning styles (conservative, exploratory, critical) solve problems independently, and a centralized arbitration selects the final answer to reduce logical inconsistencies.

Self-reflection is the core improvement mechanism, with steps: 1. Result analysis (evaluate correctness); 2. Error identification (locate wrong steps and assumptions); 3. Experience summarization (structured error patterns); 4. Memory update (store experiences). Feedback-driven adaptation: behavior is guided by explicit feedback signals (success/failure indicators, reasoning step efficiency, self-correction frequency, comparison with benchmarks) without end-to-end retraining.

Evaluation metrics include success rate (proportion of correctly solved problems), number of reasoning steps (average steps), self-correction frequency, and improvement trend (long-term performance changes). Comparative experiments use baseline agents without self-improvement or debate mechanisms, evaluating scalability, learning stability, and long-term trends on thousands of puzzle instances, and independently measuring the impact of each component.

Known limitations: 1. Dependence on prompt-level adaptation rather than parameter-level learning; 2. Task-specific rewards limit cross-domain transfer; 3. Multi-agent debate increases computational overhead. Future extensions: Curriculum learning (puzzles with progressive difficulty), formal RL integration (extending feedback into a complete framework), dynamic agent role evolution, cross-domain strategy transfer.

ReflectiveAgent serves as an application of LLM engineering practice, demonstrating practical system design, evaluation methods, and architectural patterns. Application scenarios include automated reasoning systems, decision support tools, autonomous AI agents, educational tutoring systems, and complex problem-solving assistants. The modular design and clear evaluation framework provide a reference implementation for self-improving LLM systems.