EvoSkill, Sentient's open-source framework for automatically evolving coding agents, just shipped v1.3.0 with Fireworks AI as a fully supported inference provider. That means you can now run your entire agent self-improvement loop, both the evolution harness and the LLM scorer that judges results, entirely on fast, cheap open-source models. No Claude API required.
What EvoSkill actually does
EvoSkill is an open-source framework that automatically generates structured skills for AI agents by analyzing their failure cases, enabling continuous self-improvement. The core idea is to treat your agent's configuration, its system prompt and learned skills, as something that can be evolved rather than hand-tuned.
The self-improvement loop runs five stages on repeat:
- Base Agent attempts benchmark questions using the current best program.
- Proposer analyzes failures and suggests targeted skill or prompt changes.
- Generator writes the actual new skill files or rewrites the system prompt.
- Evaluator scores the new variant on a held-out validation set.
- Frontier tracks the top-N performing programs as git branches; the best survive to the next iteration.
EvoSkill significantly extends the feedback-driven idea of GEPA from single-file optimization to complete agent evolution. Instead of only revising one prompt in place, EvoSkill proposes multiple skill and prompt mutations jointly, evaluates new variants on held-out data, and has each iteration produce an entirely new agent program.
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