# FoundationAgents/awesome-foundation-agents

About Awesome things towards foundation agents. Papers / Repos / Blogs / ...

Repository: https://github.com/FoundationAgents/awesome-foundation-agents
Canonical: https://ross.abutalabs.com/products/awesome-foundation-agents
License: MIT
License Family: permissive
Last push: 2025-07-28T13:31:18+00:00

## Health v2 (maintenance only)
Score: 35/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 34, release rhythm 35, longevity 37
- inputs: {"age_days": 528, "days_push": 401, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2210, forks 222 (observed 2026-08-28T04:06:26.282261+00:00)

## What it is
A curated awesome-list of papers, repositories, and blogs on the path toward Foundation Agents, accompanying the 'Advances and Challenges in Foundation Agents' survey paper. It organizes research by agent components such as cognition, memory, perception, world models, action, reward, and safety.

## Use cases
- find research papers on LLM-based agents
- survey the foundation agents research landscape
- learn about agent memory and world models
- discover open-source agent code repositories
- prepare a literature review on intelligent agents
- find papers on agent self-improvement and safety

## When to choose
- you need a curated reading list on AI agents
- you are researching LLM agent architectures
- you want paper-to-code links for agent research

## When to avoid
- you need runnable agent software rather than a reading list
- you need production agent tooling or frameworks
- you need a maintained codebase rather than references

## Facets
- artifact type: learning-resource
- maturity: active
- function: agent-framework, llm-training, prompt-engineering
- domain: artificial-intelligence, large-language-models, awesome-lists, tutorials
- platform: -
- tags: awesome-list, curated-papers, foundation-agents, research-survey, llm-agents, ai-agents, web-server

## Member repositories
- FoundationAgents/awesome-foundation-agents (main) score 35

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:26.282261+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 2026-08-30T02:46:06.404220+00:00, confidence not recorded.
  - readme: https://github.com/FoundationAgents/awesome-foundation-agents (fetched 2026-08-28T04:06:26.282261+00:00, sha 83cf9dc36bd2)
- Data as of 2026-08-30T08:39:29.467469+00:00.