README.md

metadata

tags:
  - rl-environment
  - agentenv
license: apache-2.0
configs:
  - config_name: bookstack_tasks
    data_files:
      - split: train
        path: tasks/bookstack.parquet
    default: true
  - config_name: bookstack_episodes
    data_files:
      - split: train
        path: episodes/bookstack.parquet

bookstack

bookstack

An AgentEnv bundle with its recorded runs, published with agentenv-hf.

Tables

• bookstack_tasks: one row per task of the bundle, with its prompt, envs, verifiers and steps.
• bookstack_episodes: one row per recorded run, with its status, model, reward and scores, the prompt and response, and the agent's transcript as chat messages when its trajectory format is one agentenv-hf reads.

raw/bookstack.jsonl holds each run's record and native trajectory, and bundles/bookstack/ the bundle itself.

Run it

pip install agentenv-framework huggingface_hub
hf download earakely-scale/BookStackEnv-REA --repo-type dataset --local-dir agentenv-dataset
agent-env run agentenv-dataset/bundles/bookstack

BookStack on AgentEnv, from a REA capture

What this is. A replay environment of BookStack 26.09.1 (MIT), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (31 tools), and rea environment-export wrote the bundle you can run from this dataset. Every tool call replays a retained record from the capture, cites the record and the Evidence it came from, and nothing executes BookStack. Tasks are graded deterministically, with no judge.

What's in it

• 31 tools (27 GET, 2 POST, 1 DELETE, 1 PUT) over 65 recorded exchanges on http://127.0.0.1:3219; no recorded group excluded.
• Path parameters: 14 numeric. REA's four format rules (numeric, uuid, hex, iso-date) applied unconditionally; the three opt-in rules (after-uid-literal, opaque-token, sibling-variance) were requested and are labelled per parameter. With the four format rules alone the manifest has 31 tools.
• Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
• 12 retrieval tasks (evals/bookstack-retrieval.toml): each asks one factual question about a seeded object, with a distinct answer that the app does not ship by default, answerable through a recorded exchange; reward 1 if the response contains the recorded value, else 0. Plus smoke, which deploys the environment and checks replay through the MCP endpoint.
• 24 recorded episodes by openai/gpt-oss-120b:groq, zai-org/GLM-5.3-Flash:zai-org through the Hugging Face router, with their chat transcripts.
• Provenance: capture ev_07a98c12a171833f8ca00f5f4769a165b358a5e2cca784718af469fc35ee5b77 (scoped HAR sha256 1f807d4d2d2ef490cdc5cac50ffcbf573abfc967351038256b92e18f4ebe5366, 127,666 bytes), manifest view ev_47deffa8044f6f1c6835193c59746931187576db6b1c0cc367b7c4380714c322, confidence inferred, authority historical-reference. The REA build is a prerelease of the environment commands proposed upstream in morluto/rea#4954, #4955, #4975 and #4999; the bundle installs it from the tarball it ships, so the environment runs without waiting for an npm release.

What replay means, and does not

A tool call returns the recorded response whose path and query values best match the arguments, with replay.exact_match telling you whether it matched exactly or fell back to the closest record. Reads of seeded objects behave like the application. Writes return the recorded answer of the one write the session made, and the environment has no state: a created object does not appear in a later list. This is what makes retrieval tasks hard-gradable and write tasks weak; the manifest's unknowns say so.

Tasks and grading

task	tool	arguments	field	answer
qa-01-get_pages_by_id	get_pages_by_id	id=17	name	January Tide Readings
qa-02-get_content_permissions_page_by_id	get_content_permissions_page_by_id	id=21	owner.slug	mina-okafor
qa-03-get_content_permissions_book_by_id	get_content_permissions_book_by_id	id=3	owner.name	Rui Castellanos
qa-04-get_books_by_id	get_books_by_id	id=2	shelves[0].slug	lighthouse-rota-handbooks
qa-05-get_search_chapter_by_id	get_search_chapter_by_id	id=12, query=terrace	data[0].name	Terrace Seven Tally
qa-06-get_chapters_by_id	get_chapters_by_id	id=11	owned_by.slug	ada-finch
qa-07-get_shelves_by_id	get_shelves_by_id	id=7	books[0].slug	quarry-audit-manual
qa-08-get_attachments_by_id	get_attachments_by_id	id=2	name	Terrace Seven Survey Sheet
qa-09-get_users_by_id	get_users_by_id	id=3	email	ada.finch@example.test
qa-10-get_search_book_by_id	get_search_book_by_id	id=1, query=wick	data[0].name	Wick and Lens Care
qa-11-get_roles_by_id	get_roles_by_id	id=5	display_name	Tide Clerks
qa-12-get_shelves	get_shelves	no arguments	data[0].name	Lighthouse Rota Handbooks

Episodes

model	tasks	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	12	100%	1.0	6,712
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	8,567

Every episode is in the episodes config with its transcript; rewards are the deterministic verifier scores.

Every recorded episode passed.

Run it yourself

uv tool install agentenv-framework --with 'agentenv-hf @ git+https://github.com/earakely-scale/agentenv-hf-plugin@v0.3.0'
agent-env hf run earakely-scale/BookStackEnv-REA --bundle bookstack --task smoke          # Docker, no model
agent-env hf run earakely-scale/BookStackEnv-REA --bundle bookstack --eval bookstack-retrieval --model openai/gpt-oss-120b:groq

The retrieval eval needs a model endpoint: set [model] base_url and api_key in .agentenv/config.toml (the Hugging Face router works with any hf_ token). The bundle's agents/replay-qa is a small OpenAI-compatible tool-calling agent; any A2A agent can replace it.

How it was made

1. BookStack ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (2 roles (Tide Clerks, Quarry Auditors), 3 users besides the renamed admin (Ada Finch, Rui Castellanos, Mina Okafor), 3 shelves, 5 books, 5 chapters and 8 pages with tags, 2 link attachments, 2 page comments, and 9 items handed to other owners); people and projects are invented.
2. Playwright drove a login, the main pages and a scripted read, write, read sequence through the app's JSON API; the HAR was scoped to the API, the login exchange dropped, secret-looking body fields scrubbed, headers and cookies removed.
3. rea inspect-web-network-capture retained the exchanges as Evidence; rea inspect-analysis-view projected the environment-manifest view; rea environment-export wrote this bundle; gen_tasks.py derived the retrieval tasks from the manifest and the recorded bodies; agent-env run recorded the episodes; agent-env hf publish wrote this dataset. Scripts: https://github.com/earakely-scale/rea-environments.

Compared with PortSimEnv on AgentEnv

	PortSimEnv-AgentEnv	BookStack (this dataset)
Environment	A simulation: a container quay, ships, cranes, weather, with A2A ship agents	A replay of a real application's API, captured from one session
Observation	Tool results computed by the simulator as the week unfolds	Recorded responses, cited to Evidence
Action space	Planning tools (check_plan, confirm_berths, advance, message_agent)	31 tools mirroring the application's endpoints
State	Full: a virtual clock, triggers, frozen windows, agents that answer	None: every call is independent; writes replay one recorded answer
Reward	Deterministic, against a CP-SAT optimum or a re-planner anchor; continuous 0 to 1	Deterministic, recorded-value match; 0 or 1 per task
What it measures	Planning under uncertainty and negotiation	Tool discovery, parameter inference and faithful retrieval in a real API
Data provenance	Real 2024 port calls and weather; simulated workloads	Synthetic seed data on a self-hosted BookStack; nothing real
How it was built	Hand-written plugin, months of work	Generated from a capture in one scripted pass
Where it is strong	Long-horizon decisions with hard rewards	Breadth: any app you can capture becomes an environment

The two are complementary: PortSimEnv shows what a hand-built simulation can grade; this dataset shows how far a capture alone gets you, and the manifest's unknowns list exactly what a stateful version would still have to specify.

Licence and credits

Bundle code and tasks: Apache-2.0. Recorded content: responses generated by a self-hosted BookStack instance over synthetic data; BookStack is MIT software by Dan Brown and the BookStack project contributors and is not redistributed here beyond the recorded JSON responses. REA is MIT software by morluto; AgentEnv Framework is Apache-2.0 by Scale AI.