| --- |
| 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](https://www.agentenvframework.com) bundle with its recorded runs, published with |
| [agentenv-hf](https://github.com/earakely-scale/agentenv-hf-plugin). |
|
|
| ## 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 |
|
|
| ```bash |
| 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 |
| ``` |
|
|
| <!-- agentenv-hf:bookstack --> |
| # BookStack on AgentEnv, from a REA capture |
|
|
| <!-- card note for `agent-env hf publish --card-note`; the plugin writes the YAML header, configs and the agentenv table. --> |
|
|
| **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](https://github.com/morluto/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 |
|
|
| ```bash |
| 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. |
| <!-- /agentenv-hf:bookstack --> |
|
|
