astra-langchain4j: Experiences Combining LLMs and Agent Programming
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Given the emergence of Generative AI over the last two years and the increasing focus on Agentic AI as a form of Multi-Agent System it is important to explore both how such technologies can impact the use of traditional Agent Toolkits and how the wealth of experience encapsulated in those toolkits can influence the design of the new agentic platforms. This paper presents an overview of our experience developing a prototype large language model (LLM) integration for the ASTRA programming language. It presents a brief overview of the toolkit, followed by three example implementations, concluding with a discussion of the experiences garnered through the examples.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 4 of its 19 references that are in the map. First posted on arXiv 29 Jan 2026.
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4 of 19 references.
References · 4
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2025The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexitycited by 298
- 2023ChatGPT is not all you need. A State of the Art Review of large Generative AI modelscited by 85
- 2024Evaluating Large Language Models with Grid-Based Game Competitions: An Extensible LLM Benchmark and Leaderboardcited by 22
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