hermes-agent-self-evolution README

The project's README from github.com/NousResearch/hermes-agent-self-evolution. As one markdown file: README.md.

🧬 Hermes Agent Self-Evolution

Evolutionary self-improvement for Hermes Agent.

Hermes Agent Self-Evolution uses DSPy + GEPA (Genetic-Pareto Prompt Evolution) to automatically evolve and optimize Hermes Agent's skills, tool descriptions, system prompts, and code — producing measurably better versions through reflective evolutionary search.

No GPU training required. Everything operates via API calls — mutating text, evaluating results, and selecting the best variants. ~$2-10 per optimization run.

How It Works

Read current skill/prompt/tool ──► Generate eval dataset
                                        │
                                        ▼
                                   GEPA Optimizer ◄── Execution traces
                                        │                    ▲
                                        ▼                    │
                                   Candidate variants ──► Evaluate
                                        │
                                   Constraint gates (tests, size limits, benchmarks)
                                        │
                                        ▼
                                   Best variant ──► PR against hermes-agent

GEPA reads execution traces to understand why things fail (not just that they failed), then proposes targeted improvements. ICLR 2026 Oral, MIT licensed.

Quick Start

# Install
git clone https://github.com/NousResearch/hermes-agent-self-evolution.git
cd hermes-agent-self-evolution
pip install -e ".[dev]"

# Point at your hermes-agent repo
export HERMES_AGENT_REPO=~/.hermes/hermes-agent

# Evolve a skill (synthetic eval data)
python -m evolution.skills.evolve_skill \
    --skill github-code-review \
    --iterations 10 \
    --eval-source synthetic

# Or use real session history from Claude Code, Copilot, and Hermes
python -m evolution.skills.evolve_skill \
    --skill github-code-review \
    --iterations 10 \
    --eval-source sessiondb

What It Optimizes

Phase Target Engine Status
Phase 1 Skill files (SKILL.md) DSPy + GEPA ✅ Implemented
Phase 2 Tool descriptions DSPy + GEPA 🔲 Planned
Phase 3 System prompt sections DSPy + GEPA 🔲 Planned
Phase 4 Tool implementation code Darwinian Evolver 🔲 Planned
Phase 5 Continuous improvement loop Automated pipeline 🔲 Planned

Engines

Engine What It Does License
DSPy + GEPA Reflective prompt evolution — reads execution traces, proposes targeted mutations MIT
Darwinian Evolver Code evolution with Git-based organisms AGPL v3 (external CLI only)

Guardrails

Every evolved variant must pass:

  1. Full test suite — pytest tests/ -q must pass 100%
  2. Size limits — Skills ≤15KB, tool descriptions ≤500 chars
  3. Caching compatibility — No mid-conversation changes
  4. Semantic preservation — Must not drift from original purpose
  5. PR review — All changes go through human review, never direct commit

Full Plan

See PLAN.md for the complete architecture, evaluation data strategy, constraints, benchmarks integration, and phased timeline.

License

MIT — © 2026 Nous Research

hermes-agent-self-evolution (NousResearch/hermes-agent-self-evolution)

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Hermes-agent-self-evolution
这是什么？这是一个面向 Hermes Agent 的进化式自我改进系统，它使用 DSPy 和 GEPA（Genetic-Pareto Prompt Evolution，遗传帕累托提示进化）自动优化智能体的技能、工具描述、系统提示词和代码，并通过反思式进化搜索生成经过可衡量验证的更优版本。
这是一个通用的优化库，还是专门为 Hermes Agent 打造的？它是专门为 Hermes Agent 打造的：它直接处理 Hermes Agent 实际的 SKILL.md 文件、工具描述和提示词，而不是一个可以随意指向任何项目的通用优化器。
我该如何运行一次优化？README 中包含一个快速开始部分，列出了设置命令；之后，流程会通过 API 调用自动变异文本、评估结果，并选出最佳版本。
目前实际可用的功能有哪些？目前只实现了第一阶段，即使用 DSPy + GEPA 进化技能文件（SKILL.md）；优化工具描述、系统提示词片段、工具代码以及持续改进循环仍在计划中。
GEPA 与随机重试提示词有什么不同？GEPA 会读取执行轨迹，以理解某个变体为何失败，而不仅仅知道它失败了；然后它提出有针对性的改进，而不是随机变异。
有哪些防护措施可以阻止有缺陷的进化变体被发布？每一项进化后的改动都必须通过完整的 pytest 测试套件（通过率 100%），满足大小限制（技能文件小于 15KB，工具描述少于 500 个字符），保持缓存兼容性，保留原有的语义用途，并且在合并前要经过人工 PR 审查。
这为什么与 ICLR 2026 有关？优化引擎所基于的 GEPA 方法已被 ICLR 2026 接收为 Oral 论文，这为反思式进化搜索方法提供了学术层面的认可。
在哪里可以查看完整路线图？代码仓库中的 PLAN.md 涵盖了 README 之外的完整架构、评估数据策略、约束条件、基准测试集成以及分阶段时间表。
What are similar projects?imp, rrsi, Proteus, darwin-skill, SkillOpt, wikiskill, awesome-rsi, Raven
Which topics is it in?nous-research, self-improvement, recursive-self-improvement, self-evolving-agents, hermes-agent, agentic, optimization, autonomous-agents, evaluation, prompt-engineering, agent-skills, agent
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NousResearch
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Updated
CommitJun 17
Age
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Commits · 90 Days