---
title: "Language Agents as Optimizable Graphs — Zhuge 2024"
description: "The authors propose two automatic optimizers: Node optimization rewrites each node's prompt from… 60 references, cited by 89 papers here."
authors: ["Mingchen Zhuge", "Wenyi Wang", "Louis Kirsch", "Francesco Faccio", "Dmitrii Khizbullin", "Jürgen Schmidhuber"]
year: 2024
venue: "arXiv:2402.16823"
source: "https://arxiv.org/abs/2402.16823"
doi: "10.48550/arxiv.2402.16823"
arxiv: "2402.16823"
licence: "CC BY-NC-SA 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-nc-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhuge-2024-language-agents-optimizable-graphs/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/zhuge-2024-language-agents-optimizable-graphs/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/5/) / Language Agents as Optimizable Graphs

# Language Agents as Optimizable Graphs

**Authors:** [Mingchen Zhuge](https://md.research.elamur.ai/free/en/authors/mingchen-zhuge/), Wenyi Wang, Louis Kirsch, Francesco Faccio, [Dmitrii Khizbullin](https://md.research.elamur.ai/free/en/authors/dmitrii-khizbullin/) and [Jürgen Schmidhuber](https://md.research.elamur.ai/free/en/authors/jurgen-schmidhuber/) · **Year:** 2024 · **Venue:** arXiv:2402.16823 · **Licence:** [CC BY-NC-SA 4.0 (arXiv)](http://creativecommons.org/licenses/by-nc-sa/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2402.16823) · [PDF](https://arxiv.org/pdf/2402.16823) · [DOI 10.48550/arxiv.2402.16823](https://doi.org/10.48550/arxiv.2402.16823) · [arXiv 2402.16823](https://arxiv.org/abs/2402.16823) · [OpenAlex](https://openalex.org/W4392270477)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · [89](https://md.research.elamur.ai/free/en/papers/zhuge-2024-language-agents-optimizable-graphs/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Prompting schemes for LLM agents, such as chain of thought, tree of thought, Reflexion and multi-agent role play, each tend to arrive with their own code base and hand-crafted workflow. That makes them hard to combine or improve systematically. GPTSwarm treats any language agent as a directed acyclic computational graph. Nodes are operations like an LLM call, a web search or a tool invocation, and edges pass text between them. Several agent graphs can be merged into a composite "swarm" graph, where edges between agents serve as communication channels.

The authors propose two automatic optimizers:
- **Node optimization** rewrites each node's prompt from its own input/output history, in the spirit of OPRO.
- **Edge optimization** turns the discrete choice of inter-agent links into one probability per candidate edge and tunes these with REINFORCE, sampling only acyclic graphs.

Experiments on MMLU, Mini CrossWords, HumanEval and GAIA indicate that optimized graphs can beat hand-designed configurations. Exact scores are in a section missing from the supplied text. The work matters because it turns agent design from manual engineering into a learnable search problem.

## Key points
- **Formalism:** an agent is a tuple (N, E, F, o) executed in topological order, with the answer taken from output node o. COT, TOT and Self-Consistency all fit this representation.
- **Edge search:** d candidate edges give 2^d configurations. The authors instead maximize expected utility under a distribution D_θ with θ ∈ [0,1]^d. Edges that would create a cycle are skipped during sampling, and REINFORCE is used because the utility is non-differentiable.
- **Comparison with DSPy:** DSPy searches a Cartesian product of per-node candidates. GPTSwarm instead improves nodes one at a time, conditioned on the graph's execution history.
- **Experiments:** benchmarks are MMLU, Mini CrossWords, HumanEval and GAIA, with web access through SearchApi. One demonstration shows Tree of Thought and Reflexion being recombined automatically via edge optimization.
- **Resource:** an open-source framework (gptswarm.org) from KAUST and IDSIA, published at ICML 2024 (PMLR 235).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [King Abdullah University of Science and Technology](https://md.research.elamur.ai/free/en/universities/king-abdullah-university-of-science-and-technology/) — University, Jeddah, Saudi Arabia · 30 works · 1,270 citations
- [Università della Svizzera italiana](https://md.research.elamur.ai/free/en/universities/universita-della-svizzera-italiana/) — University, Lugano, Switzerland · 10 works · 8,004 citations
- [Dalle Molle Institute for Artificial Intelligence Research](https://md.research.elamur.ai/free/en/labs/dalle-molle-institute-for-artificial-intelligence-research/) — Research institute, Lugano, Switzerland · 10 works · 8,825 citations
- [University of Applied Sciences and Arts of Southern Switzerland](https://md.research.elamur.ai/free/en/universities/university-of-applied-sciences-and-arts-of-southern-switzerland/) — University, Manno, Switzerland · 8 works · 7,374 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works

## Cite

Cite as: Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin and Jürgen Schmidhuber. 2024. Language Agents as Optimizable Graphs. arXiv:2402.16823.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{zhuge2024language,
  title = {{Language Agents as Optimizable Graphs}},
  author = {Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin and Jürgen Schmidhuber},
  year = {2024},
  doi = {10.48550/arxiv.2402.16823},
  eprint = {2402.16823},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.16823}
}
```

- [← previous CosyVoice 2: Scalable Streaming Speech Synthesis with Large Language Models](https://md.research.elamur.ai/free/en/papers/du-2024-cosyvoice-2-scalable-streaming-speech/)
- [next → Large Language Models are Inconsistent and Biased Evaluators](https://md.research.elamur.ai/free/en/papers/stureborg-2024-large-language-models-inconsistent-biased/)
- [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/5/)

## References

60 references, 29 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al. [Gpt-4 technical report](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/). arXiv preprint arXiv:2303.08774, 2023.
2. Auer, P., Cesa-Bianchi, N., and Fischer, P. Finite-time analysis of the multiarmed bandit problem. Machine learning, 47:235–256, 2002. [find](https://scholar.google.com/scholar?q=Auer%2C+P%2E%2C+Cesa%2DBianchi%2C+N%2E%2C+and+Fischer%2C+P%2E+Finite%2Dtime+analysis+of+the+multiarmed+bandit+problem%2E+Machine+learning%2C+47%3A235%E2%80%93256%2C+2002%2E)
3. Besta, M., Blach, N., Kubicek, A., Gerstenberger, R., Gianinazzi, L., Gajda, J., Lehmann, T., Podstawski, M., Niewiadomski, H., Nyczyk, P., et al. Graph of thoughts: Solving elaborate problems with large language models. arXiv preprint arXiv:2308.09687, 2023. [find](https://scholar.google.com/scholar?q=Besta%2C+M%2E%2C+Blach%2C+N%2E%2C+Kubicek%2C+A%2E%2C+Gerstenberger%2C+R%2E%2C+Gianinazzi%2C+L%2E%2C+Gajda%2C+J%2E%2C+Lehmann%2C+T%2E%2C+Podstawski%2C+M%2E%2C+Niewiadomski%2C+H%2E%2C+Nyczyk%2C+P%2E%2C+et+al%2E+Graph+of+thoughts%3A+Solving+elaborate+problems+with+la)
4. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. [Language models are few-shot learners](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/). Advances in neural information processing systems, 33: 1877–1901, 2020.
5. Chase, H. LangChain. [https://github.com/hwchase17/langchain](https://github.com/hwchase17/langchain), 2022. [↗](https://github.com/hwchase17/langchain)
6. Chen, G., Dong, S., Shu, Y., Zhang, G., Sesay, J., Karlsson, B. F., Fu, J., and Shi, Y. [Autoagents: A framework for automatic agent generation](https://md.research.elamur.ai/free/en/papers/chen-2023-autoagents-framework-automatic-agent-generation/). arXiv preprint arXiv:2309.17288, 2023a.
7. Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. [Evaluating large language models trained on code](https://md.research.elamur.ai/free/en/papers/chen-2021-evaluating-large-language-models-trained/). arXiv preprint arXiv:2107.03374, 2021.
8. Chen, W., Su, Y., Zuo, J., Yang, C., Yuan, C., Qian, C., Chan, C.-M., Qin, Y., Lu, Y., Xie, R., et al. [Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors](https://md.research.elamur.ai/free/en/papers/chen-2023-agentverse-facilitating-multi-agent/) in agents. arXiv preprint arXiv:2308.10848, 2023b.
9. Deng, M., Wang, J., Hsieh, C.-P., Wang, Y., Guo, H., Shu, T., Song, M., Xing, E. P., and Hu, Z. [Rlprompt: Optimizing discrete text prompts with reinforcement learning](https://md.research.elamur.ai/free/en/papers/deng-2022-rlprompt-optimizing-discrete-text-prompts/). arXiv preprint arXiv:2205.12548, 2022.
10. Du, Y., Li, S., Torralba, A., Tenenbaum, J. B., and Mordatch, I. [Improving factuality and reasoning in language models through multiagent debate](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/). arXiv preprint arXiv:2305.14325, 2023.
11. Elsken, T., Metzen, J. H., and Hutter, F. Neural architecture search: A survey. The Journal of Machine Learning Research, 20(1):1997–2017, 2019. [find](https://scholar.google.com/scholar?q=Elsken%2C+T%2E%2C+Metzen%2C+J%2E+H%2E%2C+and+Hutter%2C+F%2E+Neural+architecture+search%3A+A+survey%2E+The+Journal+of+Machine+Learning+Research%2C+20%281%29%3A1997%E2%80%932017%2C+2019%2E)
12. Fernando, C., Banarse, D., Michalewski, H., Osindero, S., and Rocktäschel, T. [Promptbreeder: Self-referential self-improvement via prompt evolution](https://md.research.elamur.ai/free/en/papers/fernando-2023-promptbreeder-self-referential-self-improvement/). arXiv preprint arXiv:2309.16797, 2023.
13. Gravitas, S. Auto-gpt. GitHub repository, 2023. [find](https://scholar.google.com/scholar?q=Gravitas%2C+S%2E+Auto%2Dgpt%2E+GitHub+repository%2C+2023%2E)
14. Hendrycks, D., Burns, C., Basart, S., Critch, A., Li, J., Song, D., and Steinhardt, J. [Aligning ai with shared human values](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-aligning-ai-shared-human-values/). Proceedings of the International Conference on Learning Representations (ICLR), 2021a.
15. Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J. [Measuring massive multitask language understanding](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/). Proceedings of the International Conference on Learning Representations (ICLR), 2021b.
16. Hong, S., Zheng, X., Chen, J., Cheng, Y., Zhang, C., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., et al. Metagpt: Meta programming for multi-agent collaborative framework. arXiv preprint arXiv:2308.[00352, 2023](https://md.research.elamur.ai/free/en/papers/hong-2023-metagpt-meta-programming-multi/).
17. Irie, K., Schlag, I., Csordás, R., and Schmidhuber, J. A modern self-referential weight matrix that learns to modify itself. In International Conference on Machine Learning, pp. 9660–9677. PMLR, 2022. [find](https://scholar.google.com/scholar?q=Irie%2C+K%2E%2C+Schlag%2C+I%2E%2C+Csorda%CC%81s%2C+R%2E%2C+and+Schmidhuber%2C+J%2E+A+modern+self%2Dreferential+weight+matrix+that+learns+to+modify+itself%2E+In+International+Conference+on+Machine+Learning%2C+pp%2E+9660%E2%80%939677%2E+PMLR%2C+2022)
18. Ivakhnenko, A., Lapa, V., and ENGINEERING., P. U. L. I. S. O. E. Cybernetic Predicting Devices. JPRS 37, 803. Joint Publications Research Service [available from the Clearinghouse for Federal Scientific and Technical Information], 1965. URL [https://books.google.com.sa/books?id=l38DHQAACAAJ](https://books.google.com.sa/books?id=l38DHQAACAAJ). [↗](https://books.google.com.sa/books?id=l38DHQAACAAJ)
19. Ivakhnenko, A. G. The group method of data handling, a rival of the method of stochastic approximation. Soviet Automatic Control, 13(3):43–55, 1968. [find](https://scholar.google.com/scholar?q=Ivakhnenko%2C+A%2E+G%2E+The+group+method+of+data+handling%2C+a+rival+of+the+method+of+stochastic+approximation%2E+Soviet+Automatic+Control%2C+13%283%29%3A43%E2%80%9355%2C+1968%2E)
20. Kennedy, J. Swarm intelligence. In Handbook of nature-inspired and innovative computing: integrating classical models with emerging technologies, pp. 187–219. Springer, 2006. [find](https://scholar.google.com/scholar?q=Kennedy%2C+J%2E+Swarm+intelligence%2E+In+Handbook+of+nature%2Dinspired+and+innovative+computing%3A+integrating+classical+models+with+emerging+technologies%2C+pp%2E+187%E2%80%93219%2E+Springer%2C+2006%2E)
21. Khattab, O., Singhvi, A., Maheshwari, P., Zhang, Z., Santhanam, K., Vardhamanan, S., Haq, S., Sharma, A., Joshi, T. T., Moazam, H., et al. [Dspy: Compiling declarative language model calls into self-improving pipelines](https://md.research.elamur.ai/free/en/papers/khattab-2023-dspy-compiling-declarative-language-model/). arXiv preprint arXiv:2310.03714, 2023.
22. Kingma, D. P. and Ba, J. [Adam: A method for stochastic optimization](https://md.research.elamur.ai/free/en/papers/kingma-2014-adam-method-stochastic-optimization/). arXiv preprint arXiv:1412.6980, 2014.
23. Kirsch, L. and Schmidhuber, J. Meta learning backpropagation and improving it. Advances in Neural Information Processing Systems, 34:14122–14134, 2021. [find](https://scholar.google.com/scholar?q=Kirsch%2C+L%2E+and+Schmidhuber%2C+J%2E+Meta+learning+backpropagation+and+improving+it%2E+Advances+in+Neural+Information+Processing+Systems%2C+34%3A14122%E2%80%9314134%2C+2021%2E)
24. Kirsch, L. and Schmidhuber, J. Eliminating meta optimization through self-referential meta learning. arXiv preprint arXiv:2212.14392 and First Conference on Automated Machine Learning (Workshop), 2022. — [arXiv:2212.14392](https://arxiv.org/abs/2212.14392) [find](https://scholar.google.com/scholar?q=Kirsch%2C+L%2E+and+Schmidhuber%2C+J%2E+Eliminating+meta+optimization+through+self%2Dreferential+meta+learning%2E+arXiv+preprint+arXiv%3A2212%2E14392+and+First+Conference+on+Automated+Machine+Learning+%28Workshop%29%2C+2022)
25. Kirsch, L., Harrison, J., Sohl-Dickstein, J., and Metz, L. General-purpose in-context learning by meta-learning transformers. arXiv preprint arXiv:2212.04458, 2022. — [arXiv:2212.04458](https://arxiv.org/abs/2212.04458) [find](https://scholar.google.com/scholar?q=Kirsch%2C+L%2E%2C+Harrison%2C+J%2E%2C+Sohl%2DDickstein%2C+J%2E%2C+and+Metz%2C+L%2E+General%2Dpurpose+in%2Dcontext+learning+by+meta%2Dlearning+transformers%2E+arXiv+preprint+arXiv%3A2212%2E04458%2C+2022%2E+%E2%80%94+%5BarXiv%3A2212%2E04458%5D%28https%3A%2F%2Farxiv%2E)
26. Kojima, T., Gu, S. S., Reid, M., Matsuo, Y., and Iwasawa, Y. [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/). Advances in neural information processing systems, 35: 22199–22213, 2022. langchain ai. LangGraph. [https://github.com/langchain-ai/langgraph](https://github.com/langchain-ai/langgraph), 2024. [↗](https://github.com/langchain-ai/langgraph)
27. Li, G., Hammoud, H. A. A. K., Itani, H., Khizbullin, D., and Ghanem, B. Camel: Communicative agents for” mind” exploration of large scale language model society. arXiv preprint arXiv:2303.[17760, 2023](https://md.research.elamur.ai/free/en/papers/li-2023-camel-communicative-agents-mind/).
28. Liu, J. LlamaIndex, 11 2022. URL [https://github.com/jerryjliu/llama_index](https://github.com/jerryjliu/llama_index). [↗](https://github.com/jerryjliu/llama_index)
29. Liu, Z., Zhang, Y., Li, P., Liu, Y., and Yang, D. Dynamic llm-agent network: An llm-agent collaboration framework with agent team optimization. arXiv preprint arXiv:2310.[02170, 2023](https://md.research.elamur.ai/free/en/papers/liu-2023-dynamic-llm-powered-agent-network/).
30. Mialon, G., Fourrier, C., Swift, C., Wolf, T., LeCun, Y., and Scialom, T. [Gaia: a benchmark for general ai assistants](https://md.research.elamur.ai/free/en/papers/mialon-2023-gaia-benchmark-general-ai-assistants/). arXiv preprint arXiv:2311.12983, 2023.
31. Minsky, M. Society of mind. Simon and Schuster, 1988. [find](https://scholar.google.com/scholar?q=Minsky%2C+M%2E+Society+of+mind%2E+Simon+and+Schuster%2C+1988%2E)
32. Nakajima, Y. Babyagi. Python. [https://github.com/yoheinakajima/babyagi](https://github.com/yoheinakajima/babyagi), 2023. [↗](https://github.com/yoheinakajima/babyagi)
33. Nepusz, T. and Vicsek, T. Hierarchical self-organization of non-cooperating individuals. Plos one, 8(12):e81449, 2013. [find](https://scholar.google.com/scholar?q=Nepusz%2C+T%2E+and+Vicsek%2C+T%2E+Hierarchical+self%2Dorganization+of+non%2Dcooperating+individuals%2E+Plos+one%2C+8%2812%29%3Ae81449%2C+2013%2E)
34. Park, J. S., O’Brien, J., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology, pp. 1–22, 2023. [find](https://scholar.google.com/scholar?q=Park%2C+J%2E+S%2E%2C+O%E2%80%99Brien%2C+J%2E%2C+Cai%2C+C%2E+J%2E%2C+Morris%2C+M%2E+R%2E%2C+Liang%2C+P%2E%2C+and+Bernstein%2C+M%2E+S%2E+Generative+agents%3A+Interactive+simulacra+of+human+behavior%2E+In+Proceedings+of+the+36th+Annual+ACM+Symposium+on+User)
35. Pryzant, R., Iter, D., Li, J., Lee, Y. T., Zhu, C., and Zeng, M. Automatic prompt optimization with” gradient descent” and beam search. arXiv preprint arXiv:2305.[03495, 2023](https://md.research.elamur.ai/free/en/papers/pryzant-2023-automatic-prompt-optimization-gradient-descent/).
36. Qian, C., Cong, X., Yang, C., Chen, W., Su, Y., Xu, J., Liu, Z., and Sun, M. Communicative agents for software development. arXiv preprint arXiv:2307.[07924, 2023](https://md.research.elamur.ai/free/en/papers/qian-2023-chatdev-communicative-agents-software/).
37. Schmidhuber, J. Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-... hook. PhD thesis, Technische Universität München, 1987. [find](https://scholar.google.com/scholar?q=Schmidhuber%2C+J%2E+Evolutionary+principles+in+self%2Dreferential+learning%2C+or+on+learning+how+to+learn%3A+the+meta%2Dmeta%2D%2E%2E%2E+hook%2E+PhD+thesis%2C+Technische+Universita%CC%88t+Mu%CC%88nchen%2C+1987%2E)
38. Schmidhuber, J. A ‘self-referential’weight matrix. In ICANN’93: Proceedings of the International Conference on Artificial Neural Networks Amsterdam, The Netherlands 13–16 September 1993 3, pp. 446–450. [find](https://scholar.google.com/scholar?q=Schmidhuber%2C+J%2E+A+%E2%80%98self%2Dreferential%E2%80%99weight+matrix%2E+In+ICANN%E2%80%9993%3A+Proceedings+of+the+International+Conference+on+Artificial+Neural+Networks+Amsterdam%2C+The+Netherlands+13%E2%80%9316+September+1993+3%2C+pp%2E+446%E2%80%93450)
39. Schmidhuber, J. Deep learning in neural networks: An overview. Neural networks, 61:85–117, 2015. [find](https://scholar.google.com/scholar?q=Schmidhuber%2C+J%2E+Deep+learning+in+neural+networks%3A+An+overview%2E+Neural+networks%2C+61%3A85%E2%80%93117%2C+2015%2E)
40. Sel, B., Al-Tawaha, A., Khattar, V., Wang, L., Jia, R., and Jin, M. Algorithm of thoughts: Enhancing exploration of ideas in large language models. arXiv preprint arXiv:2308.10379, 2023. [find](https://scholar.google.com/scholar?q=Sel%2C+B%2E%2C+Al%2DTawaha%2C+A%2E%2C+Khattar%2C+V%2E%2C+Wang%2C+L%2E%2C+Jia%2C+R%2E%2C+and+Jin%2C+M%2E+Algorithm+of+thoughts%3A+Enhancing+exploration+of+ideas+in+large+language+models%2E+arXiv+preprint+arXiv%3A2308%2E10379%2C+2023%2E)
41. Shinn, N., Cassano, F., Labash, B., Gopinath, A., Narasimhan, K., and Yao, S. [Reflexion: Language agents with verbal reinforcement learning](https://md.research.elamur.ai/free/en/papers/shinn-2023-reflexion-language-agents-verbal-reinforcement/). arXiv preprint arXiv:2303.11366, 14, 2023.
42. Torantulino et al. Auto-gpt. [https://github.com/Significant-Gravitas/Auto-GPT](https://github.com/Significant-Gravitas/Auto-GPT), 2023. [↗](https://github.com/Significant-Gravitas/Auto-GPT)
43. Vowels, M. J., Camgoz, N. C., and Bowden, R. D’ya like dags? a survey on structure learning and causal discovery. ACM Computing Surveys, 55(4):1–36, 2022. [find](https://scholar.google.com/scholar?q=Vowels%2C+M%2E+J%2E%2C+Camgoz%2C+N%2E+C%2E%2C+and+Bowden%2C+R%2E+D%E2%80%99ya+like+dags%3F+a+survey+on+structure+learning+and+causal+discovery%2E+ACM+Computing+Surveys%2C+55%284%29%3A1%E2%80%9336%2C+2022%2E)
44. Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., et al. [A survey on large language model based autonomous agents](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-large-language-model-based/). arXiv preprint arXiv:2308.11432, 2023.
45. Wang, X., Wei, J., Schuurmans, D., Le, Q., Chi, E., Narang, S., Chowdhery, A., and Zhou, D. [Self-consistency improves chain of thought reasoning in language models](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/). arXiv preprint arXiv:2203.11171, 2022.
46. Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al. [Chain-of-thought prompting elicits reasoning in large language models](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/). Advances in Neural Information Processing Systems, 35: 24824–24837, 2022.
47. Williams, R. J. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine learning, 8:229–256, 1992. [find](https://scholar.google.com/scholar?q=Williams%2C+R%2E+J%2E+Simple+statistical+gradient%2Dfollowing+algorithms+for+connectionist+reinforcement+learning%2E+Machine+learning%2C+8%3A229%E2%80%93256%2C+1992%2E)
48. Wu, Q., Bansal, G., Zhang, J., Wu, Y., Zhang, S., Zhu, E., Li, B., Jiang, L., Zhang, X., and Wang, C. [Autogen: Enabling next-gen llm applications via multi-agent conversation](https://md.research.elamur.ai/free/en/papers/wu-2023-autogen-enabling-next-gen-llm/) framework. arXiv preprint arXiv:2308.08155, 2023.
49. XAgent Team. Xagent: An autonomous agent for complex task solving, 2023. [find](https://scholar.google.com/scholar?q=XAgent+Team%2E+Xagent%3A+An+autonomous+agent+for+complex+task+solving%2C+2023%2E)
50. Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., Zhang, M., Wang, J., Jin, S., Zhou, E., et al. [The rise and potential of large language model based agents: A survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/). arXiv preprint arXiv:2309.07864, 2023.
51. Xie, T., Zhou, F., Cheng, Z., Shi, P., Weng, L., Liu, Y., Hua, T. J., Zhao, J., Liu, Q., Liu, C., et al. [Openagents: An open platform for language agents in the wild](https://md.research.elamur.ai/free/en/papers/xie-2023-openagents-open-platform-language-agents/). arXiv preprint arXiv:2310.10634, 2023.
52. Yan, L. C., Yoshua, B., and Geoffrey, H. Deep learning. nature, 521(7553):436–444, 2015. [find](https://scholar.google.com/scholar?q=Yan%2C+L%2E+C%2E%2C+Yoshua%2C+B%2E%2C+and+Geoffrey%2C+H%2E+Deep+learning%2E+nature%2C+521%287553%29%3A436%E2%80%93444%2C+2015%2E)
53. Yang, C., Wang, X., Lu, Y., Liu, H., Le, Q. V., Zhou, D., and Chen, X. [Large language models as optimizers](https://md.research.elamur.ai/free/en/papers/yang-2023-large-language-models-optimizers/). arXiv preprint arXiv:2309.03409, 2023.
54. Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., and Cao, Y. [React: Synergizing reasoning and acting in language models](https://md.research.elamur.ai/free/en/papers/yao-2022-react-synergizing-reasoning-acting-language/). arXiv preprint arXiv:2210.03629, 2022.
55. Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K. R. Tree of thoughts: Deliberate problem solving with large language models. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. URL [https://openreview.net/forum?id=5Xc1ecxO1h](https://openreview.net/forum?id=5Xc1ecxO1h). [↗](https://openreview.net/forum?id=5Xc1ecxO1h)
56. Yin, Z., Sun, Q., Chang, C., Guo, Q., Dai, J., Huang, X.-J., and Qiu, X. Exchange-of-thought: Enhancing large language model capabilities through cross-model communication. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 15135–15153, 2023. [find](https://scholar.google.com/scholar?q=Yin%2C+Z%2E%2C+Sun%2C+Q%2E%2C+Chang%2C+C%2E%2C+Guo%2C+Q%2E%2C+Dai%2C+J%2E%2C+Huang%2C+X%2E%2DJ%2E%2C+and+Qiu%2C+X%2E+Exchange%2Dof%2Dthought%3A+Enhancing+large+language+model+capabilities+through+cross%2Dmodel+communication%2E+In+Proceedings+of+the+2023+)
57. Zelikman, E., Lorch, E., Mackey, L., and Kalai, A. T. Self-taught optimizer (stop): Recursively self-improving code generation. arXiv preprint arXiv:2310.02304, 2023. — [arXiv:2310.02304](https://arxiv.org/abs/2310.02304) [find](https://scholar.google.com/scholar?q=Zelikman%2C+E%2E%2C+Lorch%2C+E%2E%2C+Mackey%2C+L%2E%2C+and+Kalai%2C+A%2E+T%2E+Self%2Dtaught+optimizer+%28stop%29%3A+Recursively+self%2Dimproving+code+generation%2E+arXiv+preprint+arXiv%3A2310%2E02304%2C+2023%2E+%E2%80%94+%5BarXiv%3A2310%2E02304%5D%28https%3A%2F%2Farxi)
58. Zeng, A., Attarian, M., Ichter, B., Choromanski, K., Wong, A., Welker, S., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., et al. [Socratic models: Composing zero-shot multimodal reasoning with language](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/). arXiv preprint arXiv:2204.00598, 2022.
59. Zhuge, M., Liu, H., Faccio, F., Ashley, D. R., Csordás, R., Gopalakrishnan, A., Hamdi, A., Hammoud, H. A. A. K., Herrmann, V., Irie, K., et al. Mindstorms in natural language-based societies of mind. arXiv preprint arXiv:2305.17066, 2023. — [arXiv:2305.17066](https://arxiv.org/abs/2305.17066) [find](https://scholar.google.com/scholar?q=Zhuge%2C+M%2E%2C+Liu%2C+H%2E%2C+Faccio%2C+F%2E%2C+Ashley%2C+D%2E+R%2E%2C+Csorda%CC%81s%2C+R%2E%2C+Gopalakrishnan%2C+A%2E%2C+Hamdi%2C+A%2E%2C+Hammoud%2C+H%2E+A%2E+A%2E+K%2E%2C+Herrmann%2C+V%2E%2C+Irie%2C+K%2E%2C+et+al%2E+Mindstorms+in+natural+language%2Dbased+societies+of+mind%2E)
60. A. The GPTSwarm Framework A.1. The Vision [find](https://scholar.google.com/scholar?q=A%2E+The+GPTSwarm+Framework+A%2E1%2E+The+Vision)

*Updated 2 November 2026*