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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
List of language agents based on paper "Cognitive Architectures for Language Agents"
| Date | Stars |
|---|---|
| 2026-07-24 | 1247 |
| 2026-07-25 | 1247 |
| 2026-07-28 | 1247 |
| 2026-07-30 | 1247 |
| 2026-07-31 | 1247 |
| 2026-08-06 | 1247 |
Today
— stars today
This week
— stars this week
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Momentum
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growth rate 0.00%/day
# 🐨CoALA: Awesome Language Agents
[](https://awesome.re) [](LICENSE) [](https://github.com/ysymyth/awesome-language-agents/pulls)
A compilation of language agents using the **Cognitive Architectures for Language Agents (🐨CoALA)** framework.
- CoALA Paper (16 pages of main content): https://arxiv.org/abs/2309.02427
- CoALA Tweet (6 threads): https://twitter.com/ShunyuYao12/status/1699396834983362690
- CoALA BibTex file with 300+ related citations: [CoALA.bib](CoALA.bib)
- CoALA BibTex citation if you find our work/resources useful:
```bibtex
@misc{sumers2023cognitive,
title={Cognitive Architectures for Language Agents},
author={Theodore Sumers and Shunyu Yao and Karthik Narasimhan and Thomas L. Griffiths},
year={2023},
eprint={2309.02427},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
## 🐨CoALA Overview
CoALA neatly specifies a language agent starting with its **action space**, which has 2 parts:
* External actions to interact with external environments (**grounding**)
* Internal actions to interact with internal memories (**reasoning**, **retrieval**, **learning**)
* A language agent has a short-term working memory and several (optional) long-term memories (episodic for experience, semantic for knowledge, procedural for code/LLM)
* **Reasoning** = update working memory (with LLM)
* **Retrieval** = read long-term memory
* **Learning** = write long-term memory
Then how does a language agent choose which action to take? Its actions are structured into **decision making** cycles, and each cycle has two stages:
* **Planning**: The agent applies reasoning/retrieval actions to (iteratively) propose and evaluate actions, then select a learning/grounding action.
* **Execution**: The selected learning/grounding action is executed to affect the internal memory or external world.
To understand more, read Section 4 of our [paper](https://arxiv.org/abs/2309.02427).
## Papers
Below is only a subset of papers scraped from [CoALA.bib](CoALA.bib) plus pulled requests, with potentially incorrect action space labels.
Date is based on arxiv v1. They do not represent all language agent work, and we plan to add more work soon (pull requests welcome), and have labels for highly cited work.
* (2021-10) [AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts](http://arxiv.org/abs/2110.01691) (reasoning)
* (2021-10) [SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark](http://arxiv.org/abs/2110.10661) (environment)
* (2022-01) [Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents](http://arxiv.org/abs/2201.07207) (grounding)
* (2022-03) [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](http://arxiv.org/abs/2203.06566) (grounding)
* (2022-03) [ScienceWorld: Is your Agent Smarter than a 5th Grader?](http://arxiv.org/abs/2203.07540) (environment)
* (2022-04) [Do As I Can, Not As I Say: Grounding Language in Robotic Affordances](http://arxiv.org/abs/2204.01691) (grounding)
* (2022-04) [Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language](http://arxiv.org/abs/2204.00598) (grounding)
* (2022-07) [WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](http://arxiv.org/abs/2207.01206) (environment)
* (2022-09) [ProgPrompt: Generating Situated Robot Task Plans using Large Language Models](http://arxiv.org/abs/2209.11302) (grounding)
* (2022-10) [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](http://arxiv.org/abs/2210.02406) (reasoning)
* (2022-10) [Mind's Eye: Grounded Language Model Reasoning through Simulation](http://arxiv.orExcerpt of 14,428 characters
Read on GitHub
Shunyu Yao · Princeton University
48
Yu Gu
1
Ke Tang
1
Qiao Jin · National Institutes of Health · United States
1
Shu
1
Yangjun Ruan · University of Toronto · Canada
1
Zhibin Gou · Tsinghua University · China
1
Han
1
Shibo Hao · UC San Diego · United States
1
1
Shanghua Gao · Harvard university
1
1
1
1
1
Ikko Eltociear Ashimine · Japan
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cc8d7aa82cb46e47, topic:llm, topic:language-model
matched fp:cc8d7aa82cb46e47, topic:natural-language-processing
matched fp:cc8d7aa82cb46e47, topic:awesome-list