// agents · Human-Agent-Society/reef

reef

reef ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von Human-Agent-Society (Human-Agent-Society/reef auf GitHub). Es hat 7.504 Sterne auf GitHub und 698 Forks, wird unter der Lizenz Apache-2.0 veröffentlicht und wurde zuletzt am 8. Oktober 2026 auf GitHub aktualisiert.

Reef — infrastructure for continually self-improving agents: weight-training and harness-evolving deployments.

★ 7,5 Tsd. Sterne⑂ 698 ForksApache-2.0aktualisiert Okt 2026
▦ Visual — das README dieses Projekts enthält keinen einbettbaren Screenshot,
darum hier ein Platzhalter. Die Anleitung unten ist trotzdem vollständig.
// projektdaten

Projektdaten

GitHub-Sterne
7.504
Forks
698
Lizenz
Apache-2.0
Zuletzt auf GitHub aktualisiert
8. Oktober 2026
Kategorie
KI-Agenten
Eintragstyp
Vollständige Installationsanleitung
Offizielle Website
Official site
// so funktioniert es

So funktioniert es

  1. Clarify your goal

    Choose your improvement path — weight training, harness evolution, or both — and identify which agent traffic and feedback signals will drive it.

  2. Get the project

    Clone Human-Agent-Society/reef from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.

  3. Install and configure

    Install reef-infra from PyPI for use, or clone and install from source for development and the full recipes.

  4. Run your first task

    Stand up a minimal deployment, send a provider-compatible inference request through it, then send an outcome report and confirm the loop records it.

// funktionen

Warum es so beliebt ist

Two improvement paths

Supports weight-training deployments and harness-evolving deployments, so improvement can target the model or its scaffolding.

Provider-compatible inference

Your agent sends provider-compatible inference requests through Reef, keeping the integration surface familiar.

Outcome reporting loop

Clients send reports about inference outcomes back to Reef, closing the loop that continual improvement needs.

Fits your existing stack

Designed to slot into an agent stack you already run, rather than replacing your agent framework.

Recipes and examples

Ships recipes and examples for the documented deployment patterns.

PyPI or source install

Install reef-infra from PyPI, or from source with uv, including an optional slime extra for the training path.

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// selbst ausführen

Führe es selbst aus

  1. Clone the repository

    Get the source from the official GitHub repository for reef.

    git clone https://github.com/Human-Agent-Society/reef.git
    cd reef
  2. Install from PyPI

    Install the published package as the README's installation section shows.

    uv pip install reef-infra
  3. Or install from source

    Clone the repository and install it in editable mode (with the training extra if you need it).

    git clone https://github.com/Human-Agent-Society/reef.git
    cd reef
    uv pip install -e .
// anwendungsfälle

Was du damit bauen kannst

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// häufige fragen

Häufige Fragen

Who maintains reef?

Human-Agent-Society, through the Human-Agent-Society/reef repository, with the site at reefinfra.ai.

Is reef free to use?

Yes — the project is open source under the Apache-2.0 license as recorded for this listing. See the license terms for any conditions on redistribution.

Does reef need a paid API key?

Reef's inference path is provider-compatible, so model calls use your configured provider and keys; Reef itself is free and Apache-2.0 licensed.

How does Reef compare to a standard agent framework?

Frameworks help you build and run an agent once. Reef is infrastructure for what happens next — collecting inference outcomes and continually improving weights or harness — so it complements a framework rather than competing with it.

How do I get started with reef?

Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — stand up a minimal deployment, send a provider-compatible inference request through it, then send an outcome report and confirm the loop records it.

// weiter entdecken

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// alternativen

Alternativen im Bereich KI-Agenten

Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls reef nicht das Richtige ist.

// mehr in agents

Mehr in KI-Agenten

Verwandte Projekte der Kategorie KI-Agenten im Katalog, die reef bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.

Hol dir reef

Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.