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Dataset env Photos & media AGPL-3.0 capture-browser-scenario

PhotoPrismEnv-REA

PhotoPrismEnv-REA is an AgentEnv replay environment for PhotoPrism: Photo library and media management. Built with REA, published as a Hugging Face dataset, practiced via MCP tools — this page is a teaching mirror on rea.run, not the runtime.

Numbers and fields are cited from the Hugging Face note / catalog snapshot.

Open on Hugging Face

Datasets and large HAR/parquet stay on HF. This page is a teaching mirror only — no runtime here.

Open HF dataset ↗

Recorded target

Product: PhotoPrism

Version: 261007

License: AGPL-3.0

Hosting: self-hosted on synthetic data

Capture: REA's capture-browser-scenario

Updated: Oct 19, 2026 (Asia/Shanghai)

AgentEnv metrics

MCP tools: 13

Retrieval tasks: 10

Downloads (HF snapshot): 62

Likes: 0

id: earakely-scale/PhotoPrismEnv-REA

What this is

PhotoPrismEnv-REA is an AgentEnv replay environment for PhotoPrism. Scenario: Photo library and media management. Teaching mirror on rea.run — practice keywords: AgentEnv, REA, replay environment, MCP tools.

  • Product: PhotoPrism
  • Scenario: Photo library and media management
  • Category: Photos & media

What REA captured

  • Capture form: REA's capture-browser-scenario (note: Captured by REA's capture-browser-scenario).
  • Target product / version: PhotoPrism 261007.
  • License (HF note): AGPL-3.0.
  • Hosting / data shape: self-hosted on synthetic data.
  • Large HAR/parquet files live in the Hugging Face dataset — not on these Pages.

What you practice in AgentEnv

This env exposes about 13 MCP tools and 10 retrieval tasks (HF note snapshot). Retrieval tasks: the agent uses MCP tools to find or act on information inside the replay environment; the baseline table shows published model scores. How to read baselines: scores look like a/b — a = tasks correct, b = task count; cross-check with the task total above. Practice runs on AgentEnv / HF, not on rea.run.

Metric Value (HF note)
MCP tools 13
Retrieval tasks 10

Model baselines

Scores cited from the HF note snapshot (retrieval-task scores). How to read: numerator = tasks correct, denominator = task count.

Model Score
gpt-oss-120b 10/10
GLM-5.3-Flash 10/10

Relation to REA

Pipeline: REA captures an authorized browser/API session → published as a Hugging Face dataset → replayed as MCP tools on Scale AgentEnv. rea.run provides the categorized catalog and teaching copy — it is not the runtime and does not host capture files.

  1. 1

    Authorize the target (own / OSS / written permission / approved lab).

  2. 2

    Capture the browser session with REA (HAR or capture-browser-scenario).

  3. 3

    Replay as AgentEnv MCP tools on Scale AgentEnv / the HF space — not on rea.run.

License & compliance

License (cited from HF note): AGPL-3.0. Authorized use only: own systems, OSS, written permission, or an approved lab. Do not record or replay against unauthorized production targets. This site does not provide cracking, bypass, or unauthorized-access guidance.

Compliance · Legal boundary

Next steps

Install AgentEnv / REA

To record sessions locally with REA, install rea-agents from official sources. AgentEnv replay and eval run on Scale / HF — not on this site.

HF note (cited)

Snapshot text from the catalog note — not a verbatim dump of a long HF card.

Photos & media · PhotoPrism 261007 (AGPL-3.0), self-hosted on synthetic data. Photo library and media management. 13 tools, 10 retrieval tasks; gpt-oss-120b 10/10, GLM-5.3-Flash 10/10. Captured by REA's capture-browser-scenario.

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