awesome-language-agents
VerifiedCurated collection of language agent papers organized via the CoALA framework.
What is awesome-language-agents?
awesome-language-agents is an open-source GitHub repository that gathers papers on language agents and organizes them according to the CoALA framework. The framework breaks down agent design into external grounding actions and internal actions for reasoning, retrieval, and learning, supported by working and long-term memories.
Papers are labeled by their primary action types such as grounding or reasoning, with dates based on arXiv submissions. The list draws from a larger BibTeX collection and welcomes contributions to expand coverage of highly cited work.
It serves researchers and developers who want a structured starting point for exploring how language models can be turned into agents that plan, act, and learn over time.
What you can build with awesome-language-agents
Research Survey
Quickly locate papers on specific agent capabilities like grounding or reasoning without scanning the full literature.
Framework Study
Understand CoALA's action space and planning-execution cycles through the included diagrams and paper references.
Citation Management
Download the provided BibTeX file containing over 300 related citations for academic writing.
Install awesome-language-agents
- 1Visit the GitHub repository at ysymyth/awesome-language-agents
- 2Read the CoALA overview section and linked paper for framework details
- 3Browse the papers list sorted by date and action labels
- 4Download CoALA.bib for citation management in your own work
- 5Submit a pull request to add missing papers or improve labels
awesome-language-agents: pros & cons
Pros
- +Organizes scattered research under a clear cognitive architecture
- +Includes direct links to papers and a comprehensive BibTeX file
- +Open to community contributions for broader coverage
- +Provides visual explanations of action spaces and decision cycles
Cons
- –Only a subset of papers is shown with potentially inaccurate labels
- –No code implementations or runnable examples are included
- –Relies on external arXiv links that may change over time
Frequently asked questions
CoALA defines language agents through their action space of external grounding and internal reasoning, retrieval, and learning operations, plus structured planning and execution cycles.
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