@kernel.chat/prompt-evolver

@kernel.chat/prompt-evolver

GEPA-style prompt self-optimization for AI agents. Prompts evolve from execution traces — auto-mutate, measure, rollback bad changes. Zero LLM calls.

Part of the kernel.chat open-source stack. Used by @kernel.chat/kbot to let specialist prompts improve in place without a human in the loop.

Why this exists

Prompt iteration is usually a human-in-the-loop process: read failures, hypothesize a fix, edit the prompt, retest. That loop is slow and doesn't scale across dozens of specialists.

This package codifies an alternative: feed execution traces in, let the evolver propose small mutations, measure them in production, and roll back any mutation whose success rate regresses past a threshold. The whole loop is deterministic — no model call ever — so it costs nothing per evolution step.

The pattern is loosely modeled on GEPA (Genetic-Evolutionary Prompt Augmentation) but simplified for production use.

Install

npm install @kernel.chat/prompt-evolver

Usage

import { PromptEvolver } from '@kernel.chat/prompt-evolver'

const evolver = new PromptEvolver({
  // Optional: see EvolverConfig type for thresholds, rollback windows, etc.
})

// Feed in execution traces — successes and failures both.
evolver.recordTrace({
  agent: 'researcher',
  promptHash: 'sha256:...',
  outcome: 'success',
  // ...additional fields per Trace interface
})

// Ask the evolver to propose a mutation for an agent's prompt.
const mutation = evolver.evolve('researcher')
if (mutation) {
  // Apply mutation.amendment to the live prompt and start measuring.
}

// Periodically check whether any mutation should be rolled back.
const rollback = evolver.checkRollback('researcher')
if (rollback) {
  // Revert and record the failure.
}

// Get the current prompt amendment for an agent (the active set of mutations).
const amendment = evolver.getAmendment('researcher')

// Persist state across runs.
evolver.save('./evolver-state.json')
evolver.load('./evolver-state.json')

// Inspect what's happening.
console.log(evolver.summary())

Public API

Export	Shape
PromptEvolver	Main class — recordTrace, evolve, checkRollback, getActiveMutations, getAmendment, getGeneration, toJSON/fromJSON, save/load, summary
Trace	Interface — what you feed into recordTrace
Mutation	Interface — what evolve() and checkRollback() return
EvolverConfig	Interface — constructor config
EvolverState	Interface — serialized state shape

Status

v1.0.x — production use inside @kernel.chat/kbot; light external test coverage. The core algorithm has been running in production behind kbot since early 2026. The public API is stable. External test suite is being expanded in v1.1.

If you're using this outside kernel.chat and hit edge cases, file an issue at github.com/isaacsight/kernel.

Related packages

Package	Discipline
@kernel.chat/kbot	The agent itself
@kernel.chat/memory-tiers	Three-tier memory (observations → reflections → identity)
@kernel.chat/skill-router	Bayesian routing across specialists
@kernel.chat/tool-forge	Runtime tool creation

See docs/agentic-engineering.md for the field map this package sits inside.

License

MIT.