# AGI-Edgerunners/LLM-Agents-Papers

A repo lists papers related to LLM based agent

Repository: https://github.com/AGI-Edgerunners/LLM-Agents-Papers
Canonical: https://ross.abutalabs.com/products/llm-agents-papers
Language: Python
License Family: other
Topics: agents, large-language-models, llm-agent, paper-list
Last push: 2025-07-12T07:39:24+00:00

## Health v2 (maintenance only)
Score: 43/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 31, release rhythm 35, longevity 85
- inputs: {"age_days": 1190, "days_push": 417, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2340, forks 155 (observed 2026-08-28T04:06:38.788240+00:00)

## What it is
A curated list of research papers on LLM-based agents, organized by topics such as planning, memory, multi-agent systems, applications, training, and safety. It is a reading resource rather than executable software, with a small Python component and regular updates.

## Use cases
- find research papers on llm agents
- survey of llm-based agent techniques
- reading list for multi-agent systems
- papers on agent planning and memory
- llm agent safety and hallucination papers
- benchmark and evaluation papers for llm agents

## When to choose
- you want a broad, categorized collection of LLM agent research papers
- you need pointers to surveys, benchmarks, and application papers across many domains
- you want a regularly updated paper list with links to related lists

## When to avoid
- you need runnable agent framework code or libraries
- you want production tooling rather than a reading list
- you require a licensed or formally maintained software project

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: artificial-intelligence, large-language-models, awesome-lists, tutorials
- platform: -
- tags: paper-list, llm-agents, awesome-list, research-papers, survey, ai-agents, web-server

## Member repositories
- AGI-Edgerunners/LLM-Agents-Papers (main) score 43

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:38.788240+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:37:24.181947+00:00, confidence not recorded.
  - readme: https://github.com/AGI-Edgerunners/LLM-Agents-Papers (fetched 2026-08-28T04:06:38.788240+00:00, sha d7c8fa463436)
- Data as of 2026-08-30T08:39:29.467469+00:00.