nasfai 0.20.1

Natural Selection for AI — a generalized agent-optimization framework that evolves LLM agents against explicit fitness functions.

nasfai — Natural Selection for AI

Optimize AI agents by natural selection. Give nasfai a declarative agent blueprint, an explicit fitness function, and an eval dataset; it generates a population of agent variants, measures each against the objective, selects and recombines the best, and returns a champion agent — plus a fully reproducible record of how it got there.

The optimization machinery is the product; the agents it produces are the output.

blueprint ─▶ seed population ─▶ ┌─ evaluate fitness ─▶ select ─▶ vary ─┐ ─▶ champion + lineage
                                └──────────────── repeat ──────────────┘

• Explicit objectives. The fitness function is a first-class, swappable plugin, logged verbatim in every run — never implicit. Cost (tokens/$) is always a component. Score on classification metrics, sandboxed pass@1, or a user-defined multi-criteria LLM judge.
• A real search, not a random walk. Fitness-aware LLM prompt mutation, multi-objective Pareto selection, tournament/rank/roulette strategies, variance-aware (repeat + LCB) scoring, adaptive operator rates, successive-halving early elimination, and cross-run warm-start — all opt-in and determinism-preserving.
• Reproducible & auditable. Every genome is content-addressed; every run pins its inputs in a manifest; every champion traces through a lineage DAG back to the seed.
• Observable by default. Instrumented with OpenTelemetry GenAI semantic conventions; swap the exporter (console → jsonl → OTLP) with one config field.
• Provider-neutral. A deterministic mock provider runs the whole loop — and CI — with zero API spend. Real runs go through the Claude Agent SDK or the Anthropic API.

Install

uv sync                      # dev: creates the venv, installs everything
# or, as a dependency:
pip install nasfai           # core; add extras: nasfai[claude,data,otlp]

Quickstart

Evolve and optimize a Claude Agent SDK coding agent on the deterministic mock provider — no API key, no network, fully reproducible:

uv run nasfai evolve --run examples/claude_sdk_coding_agent/run.mock.yaml

You'll watch pass@1 climb across generations and get a champion plus a report at .nasfai/runs/<id>/report.md. Then inspect what happened:

uv run nasfai report  <run-id>
uv run nasfai lineage --genome <champion-id> --run <run-id>
uv run nasfai blueprint list

To run it for real (opt-in, needs credentials + budget), see examples/claude_sdk_coding_agent/.

Dashboard

A dependency-free local dashboard to browse, trace, and compare runs — fitness curves, champion + lineage, per-task cost and wall-clock, the OpenTelemetry span timeline, and side-by-side run comparison:

uv run nasfai serve              # → http://127.0.0.1:8000

Light, dense, and built on the same run store the CLI writes — point it at any .nasfai/runs directory.

Library API

from nasfai import evolve
from nasfai.config import load_run_config

config = load_run_config("examples/claude_sdk_coding_agent/run.mock.yaml")
run = evolve("claude-sdk-coder", config)
champion = run.champion
print(champion.genome.id, champion.fitness.score, champion.fitness.components)

Use it from another project — the Agent Skill

nasfai ships as a portable Agent Skill in skills/nasfai/, so an AI coding agent (Claude Code, etc.) can optimize your agents without you copying any framework code. Drop it in and ask your agent to "optimize my agent against <metric> on <data>":

mkdir -p ~/.claude/skills && cp -r skills/nasfai ~/.claude/skills/nasfai

It bundles the workflow, the exact blueprint/fitness/run‑config schemas, and a scaffolder that generates a ready‑to‑run experiment. See skills/nasfai/README.md.

The vocabulary

A small evolutionary vocabulary runs through the whole system: genome (a serializable agent), phenotype (its behavior when run), population, generation, fitness, selection, variation (mutation + recombination, bounded by the blueprint's search space), lineage, and run. Full definitions in docs/03-concepts.md.

Development

just            # lint + test
just test-cov   # tests with coverage gate
just lint       # ruff + ruff format --check + mypy
just fix        # auto-fix lint + format

Documentation

Full design docs, architecture, and ADRs are in docs/. The build plan and milestones are in docs/13-implementation-plan.md; the backlog is in stories/.

License

Apache-2.0.