🧬 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
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GEPA Optimizer ◄── Execution traces
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Candidate variants ──► Evaluate
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Constraint gates (tests, size limits, benchmarks)
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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 | | | Tool descriptions | DSPy + GEPA | 🔲 Planned | | | System prompt sections | DSPy + GEPA | 🔲 Planned | | | Tool implementation code | Darwinian Evolver | 🔲 Planned | | | Continuous improvement loop | Automated pipeline | 🔲 Planned |