by NousResearch
⚒ Evolutionary self-improvement for Hermes Agent — optimize skills, prompts, and code using DSPy + GEPA
# Add to your Claude Code skills
git clone https://github.com/NousResearch/hermes-agent-self-evolutionGuides for using ai agents skills like hermes-agent-self-evolution.
Last scanned: 8/21/2026
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"status": "PASSED",
"scannedAt": "2026-08-21T04:36:36.872Z",
"npmAuditRan": true,
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}hermes-agent-self-evolution is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by NousResearch. ⚒ Evolutionary self-improvement for Hermes Agent — optimize skills, prompts, and code using DSPy + GEPA. It has 5,090 GitHub stars.
Yes. hermes-agent-self-evolution passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/NousResearch/hermes-agent-self-evolution" and add it to your Claude Code skills directory (see the Installation section above).
hermes-agent-self-evolution is primarily written in Python. It is open-source under NousResearch on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh hermes-agent-self-evolution against similar tools.
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⚠️ Third-Party Software Notice
This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.
Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.
Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.
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.
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.
# 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
| 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 |
| 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) |
Every evolved variant must pass:
pytest tests/ -q must pass 100%See PLAN.md for the complete architecture, evaluation data strategy, constraints, benchmarks integration, and phased timeline.
MIT — © 2026 Nous Research