by qianlima-lab · Agent Tool · ★ 279
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TPAMI 2026 | Lifelong Learning of Large Language Model based Agents: A Roadmap Welcome to the repository accompanying our survey paper on Lifelong Learning of Large Language Model based Agents: A Roadmap. This repository collects awesome paper for lifelong learning (also known as, continual learning and incremental learning) of LLM agent. We identify three key modules-Perception, Memory, and Action-that are integral to agent's ability to perform lifelong learning. Please refer to this survey for detailed introduction. Additionally, for other papers, surveys, and resources on lifelong learning (continual learning, incremental learning) of LLMs, you can refer to this repository. A chinese version of this README is provided in this file. 📢 News 2026.01: Our survey paper has been accepted for publication in IEEE TPAMI. An updated version, which includes additional experimental results and more references, will be released soon. 2025.06: We are excited to release the first benchmark LifelongAgentBench for lifelong learning of LLM Agents. The paper, source code, datasets are all available! 2025.01: The interpretation of
| Stars | 279 |
| Forks | 18 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 42.7/100 |
| Last Updated | 2026-02-05 |
| Created | 2024-11-05 |
| Platforms | python |
| Est. Tokens | ~820k |
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awesome-lifelong-llm-agent is TPAMI 2026 | This repository collects awesome survey, resource, and paper for lifelong learning LLM agents. It is categorized as a Agent Tool with 279 GitHub stars.
awesome-lifelong-llm-agent is primarily written in Python. It covers topics such as agent, continual-learning, incremental-learning.
You can find installation instructions and usage details in the awesome-lifelong-llm-agent GitHub repository at github.com/qianlima-lab/awesome-lifelong-llm-agent. The project has 279 stars and 18 forks, indicating an active community.
The top alternatives to awesome-lifelong-llm-agent on Agent Skills Hub include LLM_MultiAgents_Survey_Papers, Awesome-LLM-Papers-Comprehensive-Topics, LLM-Brained-GUI-Agents-Survey. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.