LLM-Agent+: A Modular Framework for Intelligent Agents with Reasoning Trace Compression and Tool-Augmented Memory

We present LLM-Agent+, a modular and extensible framework for building intelligent agents powered
by Large Language Models (LLMs). Designed for both research and real-world deployment, LLM-Agent+
integrates natural language understanding (NLU), a dual-layer memory system, a chain-of-thought (CoT)
reasoning engine, and a standardized tool interface—enabling flexible and scalable agent development.
A key innovation is the Reasoning Trace Compression (RTC) mechanism, which dynamically condenses
intermediate reasoning steps to improve memory efficiency, reduce prompt overhead, and enhance
interpretability in long-context tasks. Unlike existing frameworks, RTC adapts to context window
constraints, making LLM-Agent+ particularly effective in multi-step reasoning scenarios. The framework
supports both command-line and web-based interfaces, emphasizing high modularity to facilitate
rapid prototyping of alternative memory and reasoning strategies. We evaluated LLM-Agent+ across a
range of tasks, including software debugging, multi-step planning, and research synthesis. The results
demonstrate competitive performance with a significantly reduced memory footprint, improved task
success rates through enhanced reasoning transparency, and up to 40% reduction in token usage
compared to baseline methods. The dual-layer memory system further contributes to effective longcontext
management. To promote reproducibility and community collaboration, the full source code
has been released under a permissive open-source license. LLM-Agent+ bridges modular reasoning
with efficient context management, offering a unified platform for developing scalable and transparent
LLM-based agents. Its balance of performance, flexibility, and interpretability positions it as a strong
foundation for future research in intelligent agent systems.
Keywords: Intelligent Agents, Reasoning Trace Compression (RTC).