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

by qianlima-lab · Agent Tool · ★ 341

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 58/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentcontinual-learningincremental-learninglifelong-learningllmllm-based-agentsurvey

Quick Facts

Stars341
Forks26
LanguagePython
CategoryAgent Tool
Quality Score58/100
Last Updated2026-11-06
Created2024-11-05
Platformspython
Est. Tokens~16k

About awesome-lifelong-llm-agent

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.

Compatible Skills

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

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  • 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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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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