hermes-agent-self-evolution

You can automate agent prompt optimization using evolutionary search with DSPy and GEPA. This pipeline mutates and evaluates skill artifacts against datasets to iteratively improve agent prompts and tool descriptions without requiring GPU training or manual re-implementation.

Evolutionary search for agent skills is an automated, data-driven improvement process using DSPy and GEPA. It applies mutation and crossover to system prompts and tool descriptions, evaluating them against synthetic or real sessions to continuously enhance agent capabilities.

Yes, you can use DSPy and GEPA to evolve tool descriptions and system prompts. The pipeline applies mutation and crossover operations to these artifacts, evaluating their performance against session datasets to drive automated, trace-driven improvements.

You integrate optimized agent prompts back into your repository using PR-based integration. The evolution pipeline generates improvements through mutation and evaluation, then submits these optimized skill artifacts via pull requests for seamless integration into Hermes Agent repos.

No, the Hermes Agent evolution pipeline does not require manual code re-implementation. It applies evolutionary search to automatically mutate, evaluate, and improve tool descriptions, system prompts, and code using trace-driven feedback and guardrails.

A limitation of using evolutionary search for agent optimization is the reliance on evaluation datasets, requiring synthetic or real sessions for accurate trace-driven feedback. The pipeline also depends on PR-based integration to merge improvements into Hermes Agent repos.