# CharlesQ9/Self-Evolving-Agents

Repository: https://github.com/CharlesQ9/Self-Evolving-Agents
Canonical: https://ross.abutalabs.com/products/self-evolving-agents
License: Apache-2.0
License Family: permissive
Last push: 2025-10-15T08:17:14+00:00

## Health v2 (maintenance only)
Score: 39/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 47, release rhythm 35, longevity 29
- inputs: {"age_days": 408, "days_push": 322, "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 1302, forks 107 (observed 2026-08-28T04:04:18.010455+00:00)

## What it is
A curated survey repository cataloging research papers on self-evolving AI agents, organized by what, when, how, and where agents evolve. It accompanies an academic survey paper on the path toward artificial super intelligence.

## Use cases
- find papers on self-evolving LLM agents
- survey memory evolution and prompt optimization research
- learn about reward-based and evolutionary agent methods
- research multi-agent system optimization
- find literature on test-time self-improvement of agents
- explore evaluation methods for lifelong learning agents

## When to choose
- you need a structured reading list on agent self-evolution
- you are writing a literature review on adaptive AI agents
- you want a taxonomy of agent improvement methods

## When to avoid
- you need runnable agent code or a framework
- you want production tooling rather than research papers

## Facets
- artifact type: learning-resource
- maturity: active
- function: agent-framework, llm-training, prompt-engineering, machine-learning
- domain: artificial-intelligence, large-language-models, machine-learning, tutorials, awesome-lists
- platform: -
- tags: survey, paper-collection, self-evolving-agents, lifelong-learning, research, ai-agents, web-server

## Member repositories
- CharlesQ9/Self-Evolving-Agents (main) score 39

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:18.010455+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 2026-08-30T04:51:14.885894+00:00, confidence not recorded.
  - readme: https://github.com/CharlesQ9/Self-Evolving-Agents (fetched 2026-08-28T04:04:18.010455+00:00, sha 3ff520e021ba)
- Data as of 2026-08-30T08:39:29.467469+00:00.