Examples

Start with the offline quickstart. It defines cases, an agent, a judge, a starting prompt, and a custom candidate generator in one file.

Evaluation And Improvement

Goal	Example	Requirements
Improve with a custom SurfaceProposer	selfimprove-quickstart	Offline
Wrap an existing agent	foreign-agent-quickstart	Offline or an OpenAI-compatible endpoint
Compare official GEPA and SkillOpt	compare-optimization-methods	Python optimizer packages and an LLM endpoint
Evaluate several attempts per case	multi-shot-optimization	Offline
Apply a release rule without search	held-out-gate	Offline
Load folder-based cases	eval-fixtures-quickstart	Offline

Run an offline example from the repository root:

pnpm tsx examples/selfimprove-quickstart/index.ts

Run one official optimizer:

OPTIMIZERS=gepa \
LLM_API_KEY="$OPENAI_API_KEY" \
GEPA_PRICE_IN_PER_M=0.4 \
GEPA_PRICE_OUT_PER_M=1.6 \
pnpm tsx examples/compare-optimization-methods/index.ts

Replace the example rates with the exact endpoint rates. Use OPTIMIZERS=skillopt for SkillOpt or OPTIMIZERS=gepa,skillopt for a shared comparison. Read the optimizer install instructions first.

Existing Data

Goal	Example
Analyze human approvals and rejections	customer-feedback-loop
Analyze OpenTelemetry spans	customer-otel-traces
Record and compare scores over time	scorecard
Reuse file-based cases and cached results	eval-fixtures-quickstart

Benchmarks And Training

Goal	Example
Run public benchmark adapters	benchmarks
Export supervised and preference rows	publish-rl-dataset
Fine-tune through Prime Intellect	fine-tune-with-prime-rl

Execution

Goal	Example
Coordinate workers across processes	distributed-driver
Evaluate a multi-turn simulated user	user-simulation-driver
Run setup, execution, and scoring in one work directory	same-sandbox-harness
Receive optional hosted events	hosted-ingest-server

_shared/ contains fixtures reused by multiple examples. It is not a standalone example.