TeLLAgent: A Dual-Agent Framework for Reliable Scientific Discovery with Tool-Enhanced LLMs

Abstract

Large language model agents hold immense promise for automating scientific discovery, yet their real-world application is hindered by an inability to reliably orchestrate tools and execute complex, multi-step plans without encountering hallucinations or logical inconsistencies. Here, we present TeLLAgent, a novel supervisor-executor dual-agent framework that explicitly separates strategic reasoning from precise tool operation to overcome these limitations. The global planning agent, powered by DeepSeek-R1, performs iterative chain-of-thought reasoning to decompose problems and formulate dynamic plans. The local execution agent, leveraging DeepSeek-V3.1, then accurately invokes a curated suite of 30 specialized tools. A critical self-correction loop, mediated by the Model Context Protocol, allows the system to “rethink” and “recover” from failures, significantly enhancing robustness. When rigorously benchmarked on a suite of complex tool-calling tasks, TeLLAgent significantly outperformed GPT-5 and existing agent frameworks, achieving higher success rates in multi-step planning and demonstrating superior scaling with task complexity. Furthermore, TeLLAgent drastically reduced factual hallucinations in knowledge retrieval, as validated by both human experts and LLM judges, underscoring its enhanced reliability. We ultimately demonstrate the power of this approach by deploying TeLLAgent for autonomous discovery in the demanding domain of organic solar cell materials. From a single natural language query, it executed an end-to-end workflow, from molecular design and property prediction to the identification of a high-performance quasi-macromolecular acceptor. This AI-designed molecule was subsequently synthesized and validated, achieving a power conversion efficiency of 16.44%. TeLLAgent establishes a new paradigm for building reliable, autonomous AI systems, proving its potential to accelerate scientific discovery in materials science, drug discovery, and beyond.