Setup, once per example directory (installs the example's harness and its declared dependencies, including reef-client and, where used, reef-eval):

pip install -e .

Then ./run.sh — it starts Reef (the example's stack YAML) and runs the loop (run.py).

The catalog below groups recipes by the task type they serve and by what they evolve, model weights or the agent harness. Weight recipes need the GPU training stack, while harness recipes need only a model endpoint. Reefine ships with reef-infra and every other recipe here is a cookbook package. Basic is the record-only starting stack and stays outside the catalog, and beta recipes join it once they publish learning results. The root README and the recipes guide show the same catalog.

One hard problem with a measurable objective, where the recipe makes repeated attempts and trains on those attempts at test time.

TTT-Discover separates a normal, service-agnostic rollout harness from its Reef adapter. It demonstrates grouped discovery rollouts, continuous evaluation, exact inference-to-report references, and paper-faithful PUCT state reuse. Its README keeps the formal circle-packing runs and an Erdős run, with the stored W&B history.

Guidance-TTT trains a summary-only Qwen guidance policy while a frozen external execution model writes verifier-scored programs. It demonstrates how to attach an execution model without adding it to Reef's training or inference-token capture path.

A stream of independent tasks that a verifier scores one by one, so the recipe learns from each score before the next task arrives.

SAO is the functional smoke for the cookbook SAO recipe, the smallest weight-updating loop. Three IMOAnswerBench problems run in order by run.py, each driving six scored rollouts through Reef with a verifiable binary reward, and every scored rollout is one training step.

GEPA rebuilds reflective prompt evolution as a method package on the same mechanism: propose is one GEPA iteration - Pareto sample a parent from the method's own archive, reflect on one component with a stronger model over the served composition's failing traffic, and accept the child only if it beats its parent on the minibatch - and selection publishes only on a strict mean improvement over the full validation set. Nothing in it imports the upstream package. Its AIME example is the validation: the driver embeds the Reef service, runs the quickstart's 45 training problems through it three at a time, and seals the two 150-problem test passes against the retained official record (26.67% to 38.67% on AIME 2025, seed 0); the method's own seed-0 run reflected from the same parents on the same problems and reached 46.67%, and its seed-1 run gained the official 12 points.

Meta-Harness searches complete harness compositions using all retained candidates and scores. It selects strict mean-score improvements and commits the population with Reef's serving state. See the Terminal-Bench example, results, and selected harness.

Real interaction where no one reports a score or the feedback arrives late, so the recipe reads the signal out of the traffic it already serves.

OpenClaw-RL runs the paper's personal-agent experiment as a reef-eval task stream: a simulated student brings 72 GSM8K homework problems to a Hermes agent whose model calls go through reef, and the metric is the number of sessions before the agent's answers match the student's taste. The method (session correlation, PRM judging, the hint-conditioned teacher) is the openclawrl cookbook package, so the example contains only the harness side.

SkillClaw rebuilds the SkillClaw reproduction as a method package on the same mechanism: propose is the sealed night (one decision per skill group plus the no-skill bucket) mapped to one composite mutation sequence, selection: always publishes every non skip night as the paper's ungated regime does, and the method ships its own delivery - a recipe surface that injects the served pool's catalog into every proxied request. The campaign driver embeds the Reef service, runs the frozen 60-task WildClawBench day in docker, pulls the published pool from GET /reef/harness, and seals rounds for the preregistered gain criterion carried verbatim from the sealed campaign. Its harbor/ is one WildClawBench task vendored in the standard Harbor format (self-contained image, the benchmark's own programmatic grader), and run.py solve is the one-episode reef-eval smoke over it.

Reefine is the built-in recipe that turns a plain-language request into a harness update. The served model proposes the change and the gate scores it, and code extensions wait for a promote before they run. reef serve --recipe reefine starts its profile without a checkout, and the Reefine tutorial records a bug-fix flow, a research loop, and which requests won the gate.

Basic is everything on the core, record-only recipe — the deployment that learns nothing, and the smallest complete loop around it. Its two stack files are where a deployment starts before it picks a method, and what the quickstart serves:

• external-provider.yaml — no GPU, no local model: one Reef process proxying to an HTTP provider (reef serve -c recipes/basic/external-provider.yaml).
• local-sglang.yaml — local inference: an SGLang server plus Reef, no training.

Each is complete and runnable: a flat reef: section (translated into the frozen ServiceConfig by reef/service/deploy/service_config.py) plus a services: list the orchestrator starts in dependency order, with ${VAR} environment and ${dotted.path} config interpolation. ${REEF_PYTHON} defaults to the interpreter running reef serve, so Python services that use it share Reef's environment without changing the meaning of literal python commands. Copy one and adapt it; reef serve -c <stack> --<section.field> <value> overrides the matching YAML setting, for example --inference.model-path /models/demo or --reef.port 9000. The recipe they bind is the base contract in reef/recipe/base.py; a stack that binds a method lives with that method (recipes/<method>/examples/<example>/serve.yaml; the smallest weight-training one is recipes/sao/examples/sao/serve.yaml). Two contracts hold the set honest: test_training_server.py boots the internal service from every cookbook stack, and docs/site/scripts/check-doc-contracts.mjs derives the documented port and health route from local-sglang.yaml.

Around those stacks, the loop on the Harbor task standard: a Harbor task (harbor/), a Harbor agent harness that records its model call through Reef and reports the verifier reward back at trial end (harness/), the loop written out (run.py — reef-eval's Lab.run, one episode), and a launcher (run.sh) that starts Reef from external-provider.yaml with local overrides and runs it.

CORAL TTT and its coral_demo example are beta. They live under recipes/beta/coral/ until complete, reproducible learning results are published. Integration and smoke tests validate the wiring but do not establish learning performance. See the recipe's validation instructions.

CORAL TTT runs a CORAL discovery task — parallel coding agents in git worktrees, graded attempts on one problem — with every agent call served and attributed through Reef. CORAL's gateway traffic carries Reef receipts into an append-only call journal; a watcher reports each finalized attempt exactly once with its exact inference references, and sibling attempts of one parent commit train as one grouped relative-reward step (reusing the TTT-Discover preparer and loss family). Its example is a real CORAL task driven by CORAL's own runtime, plus a no-GPU smoke lane that runs the whole loop against the production Reef service with a canned model.