KnowledgeMay 14, 2026·2 min read

Awesome-AI-Memory — Papers & Projects for LLM Memory

Curated knowledge base for LLM/agent memory systems (399 papers, 104 projects): long-term memory design, retrieval, eval; verified 862★, pushed 2026-05-14.

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Agent ready

This asset can be read and installed directly by agents

TokRepo exposes a universal CLI command, install contract, metadata JSON, adapter-aware plan, and raw content links so agents can judge fit, risk, and next actions.

Native · 94/100Policy: allow
Agent surface
Any MCP/CLI agent
Kind
Memory
Install
Git|Web
Trust
Trust: Established
Entrypoint
git clone https://github.com/IAAR-Shanghai/Awesome-AI-Memory
Universal CLI install command
npx tokrepo install fc08ef33-c297-5943-84a6-12c73cb615e8
install contractmetadata JSONadapter planraw content
Intro

Curated knowledge base for LLM/agent memory systems (399 papers, 104 projects): long-term memory design, retrieval, eval; verified 862★, pushed 2026-05-14.

Best for: Researchers/engineers building long-term memory for agents who want a single curated map of the field

Works with: GitHub browsing or cloning; paper/project lists linked from README (badges show 399 papers + 104 projects)

Setup time: 5-15 minutes

Key facts (verified)

  • GitHub: 862 stars · 78 forks · pushed 2026-05-14.
  • License: Apache-2.0 · owner avatar + repo URL verified via GitHub API.
  • README-backed entrypoint: git clone https://github.com/IAAR-Shanghai/Awesome-AI-Memory.

Main

  • Use it as a reading queue: start from the scope/goal section, then jump to the linked Papers and Projects lists (README badges show counts).

  • When designing memory, keep components explicit: storage (vector/graph/sql), write policy, retrieval policy, and compression/forgetting.

  • Track evaluation early: use the repo’s benchmark/evaluation sections to choose measurable tasks (long-context, personalization, multi-session).

  • Keep your own “memory design doc” next to your codebase and cite entries from this repo as references for tradeoffs.

Source-backed notes

  • README includes badges with quantitative counts: 399 papers and 104 open source projects.
  • README describes the motivation (context window limits) and positions memory systems as external/persistent structures for agents.
  • README links to separate Chinese README and outlines scope and exclusions.

FAQ

  • Is this an implementation?: No — it’s a curated list of papers/projects; use it to choose architectures and references.
  • How do I keep it actionable?: Pick one memory pattern, one benchmark, and one open-source project to replicate as a baseline.
  • Where are the numbers from?: The counts (papers/projects) are shown in README badges at the top of the repo.
🙏

Source & Thanks

Source: https://github.com/iaar-shanghai/awesome-ai-memory > License: Apache-2.0 > GitHub stars: 862 · forks: 78

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