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Hermes Agent Self Evolution

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Description

⚒ Evolutionary self-improvement for Hermes Agent — optimize skills, prompts, and code using DSPy + GEPA

Installation

This entry records only its repository, not the path inside it, so there is no exact command to give. Open the source below and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

README

🧬 Hermes Agent Self-Evolution

**Evolutionary self-improvement for [Hermes Agent](https://github.com/NousResearch/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 \
    --it