LightAgent: Production-level Open-source Agentic AI Framework

Jinyi Niu; Lingyao Li; Liwen Zhang; Ruiqi Hu; Tenglong Wang; Tong Zhu; Weige Cai; Weining Shen; Xiaowu Dai

REVIEW 4 major objections 5 minor 4 cited by

LightAgent: Production-level Open-source Agentic AI Framework

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read LightAgent claims a 1,000-line Python core can run memory, tool generation, and swarm collaboration without heavy frameworks.

desk verdict A thin wrapper over mem0/DeepSeek-R1 with a 1,000-line pitch and zero benchmarks; the code may be handy, but the paper's production claims are unsupported. read the letter →

arxiv 2509.09292 v1 pith:IE7NEGNS submitted 2025-09-11 cs.AI

Weige Cai , Tong Zhu , Jinyi Niu , Ruiqi Hu , Lingyao Li , Tenglong Wang , Xiaowu Dai , Weining Shen

show 1 more author

Liwen Zhang

This is my paper

classification cs.AI

keywords agenticAIframeworkmulti-agentsystemsmemorymoduletreeofthoughtautomatedtoolgenerationLightSwarmlightweightagentLLMagents

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

LightAgent is an open-source agentic framework that claims to resolve the flexibility-versus-simplicity trade-off found in existing LLM agent platforms. Its core claim is that full agent functionality — persistent per-user memory, custom and auto-generated tools, tree-of-thought planning, and multi-agent collaboration — can be delivered in a 100% Python implementation with a core codebase of roughly 1,000 lines, without depending on heavyweight frameworks such as LangChain or LlamaIndex. If that claim holds, it would let developers deploy self-learning, tool-using multi-agent systems quickly on almost any device, including resource-constrained environments. The paper supports the claim with architecture descriptions, API examples, and small case-study outputs rather than measured performance benchmarks.

What carries the argument

The framework's load-bearing pieces are four integrated modules: (1) the LightAgent core, a ~1,000-line Python object that wraps an LLM API and orchestrates the other components; (2) the mem0-backed memory interface, which stores and retrieves per-user long-term context so agents learn across conversations; (3) the Tool Generator, which takes natural-language or API-documentation descriptions and emits runnable Python tool code into a target directory; and (4) the LightSwarm subsystem, which registers multiple agents, parses task intent, and dynamically delegates subtasks among them. A separate Tree-of-Thought engine, explicitly driven by DeepSeek-R1, structures multi-step reasoning before t

What would settle it

Clone the released repository and count the lines in the core package (excluding examples and third-party stubs); if it substantially exceeds 1,000 lines, the flagship minimalism claim weakens. As a behavioral test, feed a fresh, non-toy API specification to the Tool Generator, execute the generated code against the live endpoint, and run 100 concurrent LightSwarm agents with memory enabled while measuring error rate and end-to-end latency — if tool generation fails on a realistic spec or latency/error rates exceed production tolerance, the central claim collapses.

Watch

Extended reading notes

Core claim

LightAgent's central discovery is architectural: it packages the capabilities usually scattered across heavy agent frameworks — the mem0 memory module for long-term per-user context, a configurable tool system plus an automated tool generator that turns API documentation into Python tool files, a Tree-of-Thought reasoning engine built on DeepSeek-R1 for task decomposition and reflection, and a LightSwarm subsystem for intent-driven multi-agent collaboration — into a minimalist Python core. The framework keeps the core small by delegating heavy lifting to external services (the chosen LLM APIs and the mem0 memory backend) and by exposing a thin, object-oriented interface. LightAgent also adds

Load-bearing premise

The central premise is that a roughly 1,000-line Python core, lean by itself but reliant on external memory and LLM services, can deliver the claimed production-level robustness, low latency, and reliable automatic tool generation under real workloads — asserted in the paper but never demonstrated with measured stress tests or latency benchmarks.

Editorial extensions

If this is right

Developers can install LightAgent with pip, configure a model provider, and run a tool-using, memory-enabled agent in minutes without heavyweight dependencies.
Feeding an API specification to the Tool Generator produces working Python tools (e.g., stock quote and K-line functions), letting agents be extended to new domains in about an hour.
Memory via mem0 gives each user a persistent long-term context, so the agent can personalize responses and avoid repeating mistakes across sessions.
With LightSwarm, multiple specialized agents can be registered and automatically handed tasks, enabling collaboration for complex requests such as HR or meeting-room workflows.
The framework's small footprint makes multi-agent deployment feasible on embedded devices, low-power environments, and real-time interactive applications.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the 1,000-line core is as independent of heavy frameworks as claimed, it suggests that a thin abstraction over LLM APIs — plus a few well-chosen external services — is enough for most production agent workloads, and that much of the bulk of mainstream frameworks is optional.
The Tool Generator's design implies a bootstrap loop: an agent can write its own new tools from documentation, which, if reliable, could let deployments grow capabilities without human coding; the paper does not measure that reliability, so a success-rate study on diverse API specs would be a natural next test.
Routing ToT through a separate, possibly slower reasoning model (DeepSeek-R1) means the framework's planning quality can be upgraded independently of the chat model, but at the cost of extra latency and API calls — a trade-off worth measuring in practice.
Because memory is per-user and stored externally, the framework's behavior across sessions depends on the memory backend's retrieval quality; adversarial or ambiguous memory content could steer later answers, so privacy and retrieval-robustness tests would be valuable before deployment.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: LightAgent is a real open-source artifact—a compact integration of mem0 memory, tool generation, a ToT-style planning hook, and swarm-style multi-agent collaboration. That combination is genuinely convenient for practitioners who want a small codebase to build on. But the paper around it is a release note, not a research paper. The central claims of production-readiness, low latency, and high efficiency are asserted, not measured. The reader's REJECT is right.

Give credit where it's due: the code is open source, the 1,000-line core is a concrete design goal, and the appendix's running examples show the intended API. If the repository is as clean as the paper suggests, it could save someone a day of plumbing. That's real value, just not research value.

The soft spots are the load-bearing ones. Section 4.1 states low latency and high processing efficiency with no numbers. Section 5.4 says developers can generate hundreds of tools in an hour, with no timing, success rate, or correctness check. The only evaluation in the paper is a single toy Sanya travel exchange. There are no baselines, no error bars, no stress tests, no comparison to LangChain, AutoGen, or anything else. That's not a missing discussion; it's the entire empirical basis.

The citation list is another red flag. MetaGPT and XAgent are cited to CSDN blog posts, and there are references to 1984 crystallography and 1989 quasicrystal papers that have nothing to do with agent frameworks. That suggests the scholarly apparatus was not taken seriously.

To be fair, the framework may work fine for simple demos. But 'production-level' is a heavy claim, especially when the actual heavy lifting is done by mem0, DeepSeek-R1, and external LLM APIs. The 1,000-line core is a thin wrapper; its robustness is an empirical question that the paper doesn't touch.

Who's this for? Developers who want a lightweight starting point for agent experiments. Not for readers looking for new algorithms or evidence. I'd desk-reject at a research venue. If there's a software demo track, it might be worth a look after a code review and a basic benchmark—at minimum, latency and success-rate numbers against an existing framework. Until then, peer review would just be referees doing the authors' homework.

Referee Report

4 major / 5 minor

Summary. The paper presents LightAgent, a Python-based agentic framework aimed at lightweight multi-agent development. It integrates memory (via mem0), custom tool import and automatic tool generation, Tree of Thought reasoning (via DeepSeek-R1), and a LightSwarm multi-agent collaboration module. The central claims are that the framework is production-level, resolves the flexibility/simplicity trade-off in existing frameworks, has a core of about 1,000 lines, exhibits low latency and high efficiency, and supports rapid automatic tool generation. The manuscript consists mainly of an architecture overview, code listings, and two small example outputs in the appendix; it contains no quantitative evaluation, no baselines, no benchmarks, and no stress tests.

Significance. If the claimed properties were carefully demonstrated, LightAgent could be a useful open-source contribution, and the public code release is a positive step. However, the significance of the paper as a scientific or engineering contribution cannot currently be assessed. The load-bearing claims — production-readiness, low resource consumption, low latency, robustness, and the ability to generate hundreds of tools per hour — are stated without any supporting measurements or comparisons. The related-work comparisons are also not grounded in primary sources. The framework may work, but the manuscript provides no evidence that would allow a reader to verify the claims or position LightAgent against AutoGen, MetaGPT, or other existing systems.

major comments (4)

[§4.1] The paper claims 'low resource consumption', 'low latency and high processing efficiency', and suitability for 'embedded devices, low-power environments' without reporting any latency, throughput, memory, concurrency, or power measurements. These are empirical claims central to the title's 'production-level' assertion. The '1,000-line core' is also not substantiated by a complexity or dependency analysis, and the framework depends on external services (mem0, DeepSeek-R1, and various LLM APIs), which must be reconciled with the minimalist claim.
[§5.4] The claim that developers can 'generate hundreds of domain-specific tools within one hour' is an empirical, quantitative claim, but no timing, success-rate, correctness, or usability data are provided. The appendix (Listing 8) shows an input description and the API call, but not the generated tool code or an example of using the generated tools. Since automated tool generation is a key differentiator of the framework, this unsupported claim is a central weakness.
[§2.3] The related-work section cites specific quantitative results for MetaGPT (73%), AutoGen (40%), and XAgent (89%) to CSDN blog posts attributed to 'Anonymous', while the actual primary arXiv papers (e.g., MetaGPT, AutoGen, XAgent) also appear elsewhere in the bibliography. These numbers are used to motivate the framework's contribution, but no direct comparison or benchmark is provided. The claim that LightAgent 'effectively resolves the trade-off' is therefore not grounded in a systematic comparison.
[§6 and Appendix] The only functional evidence in the paper consists of two toy conversation outputs (memory retrieval in Listing 7 and a single multi-agent response in Listing 10) that do not demonstrate collaboration, fault tolerance, or robustness. There is no task suite, no ablation of ToT, LightSwarm, or memory, and no failure analysis. §7.3 explicitly states that an agent evaluation tool is future work, confirming that the robustness/efficiency claims are not evaluated. These are not presentation issues but missing evidence for the paper's central claims.

minor comments (5)

[Listing 5] A partial API key ('sk-uXx0H0B***17778F1') appears in the code listing. This should be fully redacted.
[Throughout] There are numerous typos and formatting errors, including 'colnsistency', 'ahents', 'Ligh-tAgent', 'in cl ud ing', and 'a core codebase of just 1,000 line'. These need a careful proofread.
[Listing 3] The comment 'tree_of_thought=False,# Enable Chain of Thought' is confusing: the parameter disables ToT while the comment says to enable it. Clarify the intended semantics.
[References] Reference [8] and [9] duplicate the same Jennings 2001 paper. References [22], [23], and [26] concern crystallography and neutron interferometry and appear unrelated to the text. The bibliography needs to be checked for relevance and consistency.
[Listing 10] The multi-agent output shows only a single response from Agent D; it does not illustrate task switching, information sharing, or collaboration. The authors should show an actual multi-step interaction or explain how the output was produced.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: LightAgent is a framework description with no derivation chain; all claims are assertions, not predictions derived from fitted inputs.

full rationale

The paper contains no equations, no fitted parameters, and no quantitative predictions that could reduce to its own inputs. It is a systems/positioning paper describing the architecture and features of LightAgent. The central claims—'effectively resolving the trade-off between flexibility and simplicity' (Abstract), 'core codebase of only 1,000 lines' (Section 1), 'low latency and high processing efficiency' (Section 4.1), and 'generate hundreds of domain-specific tools within one hour' (Section 5.4)—are unsubstantiated assertions, but they are not derived from any fitted model or self-referential definition. The framework integrates external services (mem0, DeepSeek-R1, LLM APIs) and acknowledges them in the Acknowledgements; these are dependencies, not self-citations. There is no load-bearing citation whose content is equivalent to the paper's own claims. The appendix provides toy running examples but no benchmark; that is an absence-of-evidence concern, which is a correctness risk, not circularity. Under the reviewing rules, a non-finding is appropriate: there is no derivation chain to walk, so no circular step can be exhibited with a quote showing Eq. X = Eq. Y by construction or a fitted parameter renamed as a prediction. Score 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new mathematical axioms or physical entities. Its claims rest on the reliability of the underlying LLM services and the adequacy of a minimal codebase, neither of which is demonstrated. These are domain assumptions about software behavior in production, not formal mathematical premises.

assumptions (5)

domain assumption LLMs can reliably generate correct tool code from API documentation and descriptions.
Section 5.4 and Appendix 'Tools Generators' assume the LLM produces usable code; no validation is provided.
domain assumption External memory module mem0 provides reliable long-term memory without manual intervention.
Section 5.2 and Appendix example rely on mem0's correctness; no integration tests are shown.
domain assumption Tree-of-thought prompting via DeepSeek-R1 improves reasoning over plain generation.
Section 6.2 describes an 8-step generic procedure without implementation details or experiments.
domain assumption A 1,000-line Python core is sufficient to implement all claimed features (memory, tools, ToT, LightSwarm, streaming).
Sections 1 and 4 assert this without a code audit or line-count verification.
domain assumption The framework's LLM-powered error detection and self-correction reduce hallucination and tool failures.
Section 1 claims robustness; no quantitative evidence or ablation supports it.

how reviews work

0 comments

Cite this review

Pith. "Pith review of LightAgent: Production-level Open-source Agentic AI Framework." pith.science (2026). https://pith.science/paper/IE7NEGNS

@misc{pith2026250909292,
  author       = {Pith},
  title        = {Pith review of: LightAgent: Production-level Open-source Agentic AI Framework},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IE7NEGNS}},
  note         = {Machine review of arXiv:2509.09292}
}

read the original abstract

With the rapid advancement of large language models (LLMs), Multi-agent Systems (MAS) have achieved significant progress in various application scenarios. However, substantial challenges remain in designing versatile, robust, and efficient platforms for agent deployment. To address these limitations, we propose \textbf{LightAgent}, a lightweight yet powerful agentic framework, effectively resolving the trade-off between flexibility and simplicity found in existing frameworks. LightAgent integrates core functionalities such as Memory (mem0), Tools, and Tree of Thought (ToT), while maintaining an extremely lightweight structure. As a fully open-source solution, it seamlessly integrates with mainstream chat platforms, enabling developers to easily build self-learning agents. We have released LightAgent at \href{https://github.com/wxai-space/LightAgent}{https://github.com/wxai-space/LightAgent}

Figures

Figures reproduced from arXiv: 2509.09292 by the authors.

**Figure 1.** Framework of LightAgent 4 Lightweight and Adaptable LightAgent is designed to be lightweight, adaptable, and highly efficient. Unlike existing frameworks, it eliminates the need for complex external libraries such as LangChain and LlamaIndex. The framework is implemented entirely in Python, LightAgents ensures broad compatibility with a wide range of large language models (LLMs), including OpenAI (Achiam et al., 202… view at source ↗

Discussion (0). Sign in to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

ShadowMerge: A Novel Poisoning Attack on Graph-Based Agent Memory via Relation-Channel Conflicts
cs.CR 2026-05 unverdicted novelty 8.0 of 10

ShadowMerge poisons graph-based agent memory via relation-channel conflicts using an AIR pipeline, achieving 93.8% average attack success rate on Mem0 and three real-world datasets while bypassing existing defenses.
ShadowMerge: A Novel Poisoning Attack on Graph-Based Agent Memory via Relation-Channel Conflicts
cs.CR 2026-05 unverdicted novelty 8.0 of 10

ShadowMerge poisons graph-based agent memory by creating relation-channel conflicts that get extracted and retrieved, achieving 93.8% attack success rate on Mem0 and datasets like PubMedQA while evading prior defenses.
ShadowMerge: A Novel Poisoning Attack on Graph-Based Agent Memory via Relation-Channel Conflicts
cs.CR 2026-05 unverdicted novelty 8.0 of 10

ShadowMerge exploits relation-channel conflicts to poison graph-based agent memory, achieving 93.8% average attack success rate on Mem0 and real-world datasets while bypassing existing defenses.
MyAG: A Graph-Based Framework for Designing and Analyzing Composable LLM Agent Systems
cs.CL 2026-07 conditional novelty 5.0 of 10

MyAG separates LLM agent systems into component, workflow, and search graphs, making strategies and efficiency tradeoffs easier to compare in one open-source framework.

Reference graph

Works this paper leans on

63 extracted references · 14 linked inside Pith · cited by 2 Pith papers

[1]

Initially, a thor- ough analysis of the problem is required to clarify the key issues and goals, ensuring that subsequent analysis and actions are directed appropriately

Problem Definition:Clearly define the core problem and objectives. Initially, a thor- ough analysis of the problem is required to clarify the key issues and goals, ensuring that subsequent analysis and actions are directed appropriately
[2]

This process involves gathering not only all known data related to the problem but also extending to potential sources that may impact the solution

Information Gathering:Systematically collect relevant data and information. This process involves gathering not only all known data related to the problem but also extending to potential sources that may impact the solution
[3]

This step helps to decompose the large problem into smaller, more manageable parts, allowing for a more detailed analysis

Problem Decomposition:Break down the complex problem into multiple sub-problems or modules. This step helps to decompose the large problem into smaller, more manageable parts, allowing for a more detailed analysis
[4]

This phase ensures that the problem is examined thoroughly from multiple viewpoints to avoid overlooking any critical factors

Multi-Dimensional Analysis:Analyze each sub-problem from various angles and perspec- tives. This phase ensures that the problem is examined thoroughly from multiple viewpoints to avoid overlooking any critical factors
[5]

This step reveals the inherent connections between issues, providing a foundation for integrating and optimizing solutions

Establishing Connections:Identify and analyze the relationships and dependencies be- tween sub-problems. This step reveals the inherent connections between issues, providing a foundation for integrating and optimizing solutions
[6]

This process emphasizes creative thinking to generate multiple response strategies, offering a range of options for decision-making

Solution Generation:Propose potential solutions for each sub-problem. This process emphasizes creative thinking to generate multiple response strategies, offering a range of options for decision-making
[7]

"" 9␣␣␣␣Search␣news␣based␣on␣keywords 10␣␣␣␣

-> str: 8""" 9␣␣␣␣Search␣news␣based␣on␣keywords 10␣␣␣␣""" 11results = f"By␣searching␣for␣{keyword},␣I've␣found␣{max_results}␣ related␣pieces␣of␣information." 12return str(results) 13 14# Custom Tools 15tools = [search_news]# in cl ud ing tool 16 17# I n i t i a l i z e Agent 18# Replace with your model parameters , API key , and base URL 5 19agent = Light...

2024
[8]

qwen-turbo-2024-11-01

Implementation and Feedback:Implement the selected solution and adjust based on feedback. This phase ensures that real-time feedback is utilized to adjust the solution, ensuring the achievement of the desired outcome. 1# Enable Tree of Thought 2agent = LightAgent( 3model="qwen-turbo-2024-11-01", 4api_key="your_api_key", 5base_url="your_base_url", 6tree_of...

2024

Show all 63 references

[9]

This phase focuses on assessing both the practical effectiveness and long-term consequences of each solution

Evaluation and Selection:Evaluate the feasibility, impact, and risks of all proposed solutions, selecting the most appropriate one. This phase focuses on assessing both the practical effectiveness and long-term consequences of each solution
[10]

W., & Karnouskos, S

Leit ao, P., Colombo, A. W., & Karnouskos, S. (2017). Industrial agent: A survey and a vision for future industrial applications.IEEE Transactions on Industrial Informatics,13(4), 1851–1863

2017
[11]

Achiam, J., Adler, S., Agarwal, S., et al. (2023). GPT-4 technical report.arXiv preprint arXiv:2303.08774

2023 arXiv
[13]

Anonymous. (2025). XAgent: Dual-Loop Architecture for Autonomous Task Planning.CSDN Blog. https://blog.csdn.net/m0_56255097/article/details/144421955

2025
[14]

Bai, J., Bai, S., Chu, Y ., et al. (2023). Qwen technical report.arXiv preprint arXiv:2309.16609

2023 arXiv
[15]

Gou, Z., Liu, Z., Wang, S., et al. (2023). XAgent: An autonomous agent for complex task solving. arXiv preprint arXiv:2309.17288

2023 arXiv
[16]

Hong, J., Wang, X., & Chen, Y . (2023). Software Engineering with Multi-Agent Systems.IEEE Transactions on Software Engineering,49(5), 2103–2120

2023
[17]

Hong, S., Zheng, X., Chen, J., et al. (2023). MetaGPT: Meta programming for multi-agent collaborative framework.arXiv preprint arXiv:2308.00352

2023 arXiv
[18]

Jennings, N. R. (2001). An agent-based approach for building complex software systems. Autonomous Agents and Multi-Agent Systems,24(1), 141–174

2001
[19]

Jennings, N. R. (2001). An agent-based approach for building complex software systems. Communications of the ACM,44(4), 35–41

2001
[20]

Ouyang, L., Wu, J., Jiang, X., et al. (2022). Training language models to follow instructions with human feedback.Advances in Neural Information Processing Systems,35, 27730–27744

2022
[21]

Leit ao, P., Karnouskos, S., & Ribeiro, L. (2017). Smart agents in industrial cyber-physical systems.Proceedings of the IEEE,104(5), 1086–1101

2017
[22]

Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval-augmented generation for knowledge- intensive nlp tasks.Advances in Neural Information Processing Systems,33, 9459–9474

2020
[23]

Latsou, C., & Farsi, M. (2021). Digital Twin Integration in Multi-Agent Manufacturing Systems. IFAC-PapersOnLine,54(1), 811–816

2021
[24]

Latsou, C., Kousi, N., & Mourtzis, D. (2021). A multi-agent system approach for adaptive scheduling of manufacturing systems. InProcedia CIRP(V ol. 96, pp. 248–253)

2021
[25]

Liu, A., Feng, B., Xue, B., et al. (2024). Deepseek-v3 technical report.arXiv preprint arXiv:2412.19437

2024 arXiv
[26]

Ma, L., Liu, T., Zhang, X., et al. (2024). A survey on swarm intelligence for deep learning. Swarm and Evolutionary Computation,86, 101340

2024
[27]

Microsoft Research. (2024). AutoGen: LLM-Driven Conversational Agents for Collaborative Tasks.163 Tech Report. https://www.163.com/dy/article/JJON8USO0511805E.html

2024
[28]

J., Oliveira, E., & Jorge, A

Morais, A. J., Oliveira, E., & Jorge, A. M. (2019). A multi-agent recommender system. In Advances in Intelligent Systems(V ol. 151, pp. 281–288)

2019
[29]

Morais, A., Pinto, A., & Oliveira, E. (2019). A multi-agent system for personalized recommen- dation.Expert Systems with Applications,122, 256–271

2019
[30]

Team GLM, Zeng, A., Xu, B., et al. (2024). Chatglm: A family of large language models from glm-130b to glm-4 all tools.arXiv preprint arXiv:2406.12793

2024 arXiv
[31]

S., O’Brien, J

Park, J. S., O’Brien, J. C., Cai, C. J., et al. (2023). Generative Agents: Interactive Simulacra of Human Behavior.Proceedings of the ACM on Human-Computer Interaction,7(CSCW1), 1–24

2023
[32]

Parth’e, E., & Gelato, L. M. (1984). The standardization of inorganic crystal-structure data.Acta Crystallographica Section A,40(C2), 169–183. https://doi.org/10.1107/S0108767384000416

1984 doi
[33]

Pauling, L. (1989). Icosahedral quasicrystals of intermetallic compounds are icosahedral twins of cubic crystals of three kinds, consisting of large (about 5000 atoms) icosahedral complexes in either a cubic body-centered or a cubic face-centered arrangement or smaller (about ...

1989
[34]

Pinto, T., Faia, R., & Corchado, J. M. (2019). Multi-agent-based CBR recommender system for intelligent energy management.IEEE Systems Journal,13(1), 1084–1095

2019
[35]

Pinto, T., Vale, Z., Morais, H., et al. (2019). CBR-based multi-agent system for smart grid management.Energy,172, 118–128

2019
[36]

ur ein Laue-Neutroneninterferometer Teil 1: Dynamische Beugung(Report No. AIAU 74405b). Atominstitut der

Rauch, H., & Petrascheck, D. (1976).Grundlagen f"ur ein Laue-Neutroneninterferometer Teil 1: Dynamische Beugung(Report No. AIAU 74405b). Atominstitut der "Osterreichischen Universit"aten, Austria

1976
[37]

Rawte, V ., Sheth, A., & Das, A. (2023). A survey of hallucination in large language models: Principles, taxonomy, challenges, and open questions.arXiv preprint arXiv:2311.05232

2023 arXiv
[38]

Rawte, V ., Sheth, A., & Das, A. (2023). The Dark Side of Generative AI in Mental Health. Nature Machine Intelligence,5(4), 337–349

2023
[39]

SFI Research Group. (2024). Swarm Intelligence in Multi-Agent Systems.CSDN Technical Report. https://blog.csdn.net/u013709332/article/details/44037085

2024
[40]

Xi, Y ., Liu, M., & Zhao, Q. (2023). Collaborative Problem Solving in Multi-Agent Environments. AI Systems,12(3), 45–67

2023
[41]

Tianfield, H., Unland, R., & Umnai, P. (2013). An autonomic architecture for complex multi- agent systems. InProcedia Computer Science(V ol. 18, pp. 1162–1171)

2013
[42]

Tianfield, H., Yao, X., & Tian, J. (2013). On the architectures of complex multi-agent systems. InProceedings of the IEEE/WIC Conference on Web Intelligence(pp. 195–206)

2013
[43]

Touvron, H., Lavril, T., Izacard, G., et al. (2023). Llama: Open and efficient foundation language models.arXiv preprint arXiv:2302.13971

2023 arXiv
[44]

Touvron, H., Martin, L., Stone, K., et al. (2023). Llama 2: Open foundation and fine-tuned chat models.arXiv preprint arXiv:2307.09288

2023 arXiv
[45]

Wang, Z., Chen, M., Fu, X., et al. (2023). A survey on large language model based autonomous agents.arXiv preprint arXiv:2308.11432

2023 arXiv
[46]

Wang, L., Chen, H., & Zhang, W. (2023). Recent Advances in Multi-Agent Systems.Journal of Artificial Intelligence Research,45, 102–135

2023
[47]

(2009).An introduction to multiagent systems

Wooldridge, M. (2009).An introduction to multiagent systems. John Wiley & Sons

2009
[48]

A., Buch, G., et al

Wu, Q., Bansal, G. A., Buch, G., et al. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.arXiv preprint arXiv:2308.08155

2023 arXiv
[49]

Wu, T., Zhang, L., & Chen, H. (2023). Intelligent Assistant Systems: A Survey.AI Review, 56(2), 123–145

2023
[51]

Xi, Z., Chen, W., Guo, X., et al. (2023). The rise and potential of large language model based agents: A survey.arXiv preprint arXiv:2309.07864

2023 arXiv
[52]

Yao, S., Zhao, J., Yu, D., et al. (2023). ReAct: Synergizing Reasoning and Acting in Language Models.arXiv preprint arXiv:2210.03629

2023 arXiv
[53]

Zhang, J., Gao, J., & Way, A. (2023). Hallucinations in Neural Machine Translation.Machine Translation,37(2), 89–106

2023
[54]

OPENAI_API_KEY

Zhang, Y ., Li, Y ., Cui, L., et al. (2023). Siren’s song in the ai ocean: A survey on hallucination in large language models.arXiv preprint arXiv:2309.01219. 13 Appendix Running Examples of Detachable Fully Automated Memory Module LightAgent supports external extensions of th...

2023 arXiv
[71]

Hawaii␣of␣the␣East,

**Yalong Bay**: Known as the "Hawaii␣of␣the␣East," it has a long beach and clear waters, ideal for swimming, diving, and sunbathing . 8
[92]

Tianya" and

**Tianya Haijiao**: This is a famous cultural landscape, attracting tourists with its magnificent sea view and romantic legends. The giant rocks here are inscribed with the words "Tianya" and " Haijiao," symbolizing eternal love. 10
[113]

Visitors can experience Buddhist culture and visit temples and gardens

**Nanshan Cultural Tourism Zone**: Here there is a 108-meter-tall Nanshan Sea Guanyin statue, the highest sea Guanyin statue in the world. Visitors can experience Buddhist culture and visit temples and gardens. 12
[134]

Visitors can engage in diving, snorkeling, and sea fishing among other activities

**Wuzhizhou Island**: This small island is known for its pristine natural scenery and rich water activities. Visitors can engage in diving, snorkeling, and sea fishing among other activities. 14
[155]

**Dadonghai**: This is a beach located in Sanya city, favored by tourists for its convenient transportation and vibrant nightlife. 16
[176]

This beach is relatively quiet, suitable for visitors who enjoy tranquility

**Sanya Bay**: It is a 22-kilometer long beach and a great place to watch the sunset. This beach is relatively quiet, suitable for visitors who enjoy tranquility. 18
[197]

**Ya Nui National Park**: This is a tropical rainforest park where visitors can experience the natural scenery of tropical rainforests and participate in various adventure activities. 20
[218]

There is also a beautiful legend about a deer

**Luohuitou Park**: A park located on a mountaintop, offering a panoramic view of Sanya city and Sanya Bay. There is also a beautiful legend about a deer. 15 22
[239]

**Xidao**: This is a relatively pristine small island attracting tourists with its serene beaches and rich marine life. 24
[2024]

gpt-4o-mini

and adaptive manufacturing systems (Latsou & Farsi, 2021). 3 Overview 3.1 Basic Concepts in LightAgent 3.1.1 Agent In artificial intelligence, an intelligent agent is an entity equipped with task-specific capabilities powered by LLMs, that perceives its environment, takes acti...

2021
[2510]

Hawaii␣of␣the␣East,

**Sanya Ancient Love**: This is a large cultural theme park showcasing the history and culture of Hainan through performances and exhibitions. 26 27In addition to the above attractions, Sanya has many other places worth exploring, such as tropical botanical gardens and seafood...

2025

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.

[1] [1]

Initially, a thor- ough analysis of the problem is required to clarify the key issues and goals, ensuring that subsequent analysis and actions are directed appropriately

Problem Definition:Clearly define the core problem and objectives. Initially, a thor- ough analysis of the problem is required to clarify the key issues and goals, ensuring that subsequent analysis and actions are directed appropriately

[2] [2]

This process involves gathering not only all known data related to the problem but also extending to potential sources that may impact the solution

Information Gathering:Systematically collect relevant data and information. This process involves gathering not only all known data related to the problem but also extending to potential sources that may impact the solution

[3] [3]

This step helps to decompose the large problem into smaller, more manageable parts, allowing for a more detailed analysis

Problem Decomposition:Break down the complex problem into multiple sub-problems or modules. This step helps to decompose the large problem into smaller, more manageable parts, allowing for a more detailed analysis

[4] [4]

This phase ensures that the problem is examined thoroughly from multiple viewpoints to avoid overlooking any critical factors

Multi-Dimensional Analysis:Analyze each sub-problem from various angles and perspec- tives. This phase ensures that the problem is examined thoroughly from multiple viewpoints to avoid overlooking any critical factors

[5] [5]

This step reveals the inherent connections between issues, providing a foundation for integrating and optimizing solutions

Establishing Connections:Identify and analyze the relationships and dependencies be- tween sub-problems. This step reveals the inherent connections between issues, providing a foundation for integrating and optimizing solutions

[6] [6]

This process emphasizes creative thinking to generate multiple response strategies, offering a range of options for decision-making

Solution Generation:Propose potential solutions for each sub-problem. This process emphasizes creative thinking to generate multiple response strategies, offering a range of options for decision-making

[7] [7]

"" 9␣␣␣␣Search␣news␣based␣on␣keywords 10␣␣␣␣

-> str: 8""" 9␣␣␣␣Search␣news␣based␣on␣keywords 10␣␣␣␣""" 11results = f"By␣searching␣for␣{keyword},␣I've␣found␣{max_results}␣ related␣pieces␣of␣information." 12return str(results) 13 14# Custom Tools 15tools = [search_news]# in cl ud ing tool 16 17# I n i t i a l i z e Agent 18# Replace with your model parameters , API key , and base URL 5 19agent = Light...

2024

[8] [8]

qwen-turbo-2024-11-01

Implementation and Feedback:Implement the selected solution and adjust based on feedback. This phase ensures that real-time feedback is utilized to adjust the solution, ensuring the achievement of the desired outcome. 1# Enable Tree of Thought 2agent = LightAgent( 3model="qwen-turbo-2024-11-01", 4api_key="your_api_key", 5base_url="your_base_url", 6tree_of...

2024