awesome-lifelong-llm-agent — Agent Tool by qianlima-lab

by qianlima-lab · Agent Tool · ★ 279

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About awesome-lifelong-llm-agent

TPAMI 2026 | This repository collects awesome survey, resource, and paper for lifelong learning LLM agents

agentcontinual-learningincremental-learninglifelong-learningllmllm-based-agentsurvey

Quick Facts

Stars279
Forks18
LanguagePython
CategoryAgent Tool
Quality Score42.7/100
Last Updated2026-02-05
Created2024-11-05
Platformspython
Est. Tokens~820k

Compatible Skills

These tools work well together with awesome-lifelong-llm-agent for enhanced workflows:

  • AutoSkill — semantic(0.43)+complementary+rare_topics+same_lang+similar_pop+shared_platform (65%)
  • Noema-Declarative-AI — semantic(0.26)+complementary+same_lang+similar_pop+shared_platform (54%)
  • MetaClaw — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (53%)
  • doc-to-lora — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)
  • SecGPT — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)

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Frequently Asked Questions

What is awesome-lifelong-llm-agent?

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.

What programming language is awesome-lifelong-llm-agent written in?

awesome-lifelong-llm-agent is primarily written in Python. It covers topics such as agent, continual-learning, incremental-learning.

How do I install or use awesome-lifelong-llm-agent?

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.

What are the best alternatives to awesome-lifelong-llm-agent?

The top alternatives to awesome-lifelong-llm-agent on Agent Skills Hub include LLM_MultiAgents_Survey_Papers, SkillClaw, Awesome-LLM-Papers-Comprehensive-Topics. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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