🧬 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

Engines

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