earakely-scale/HomeboxEnv-REA

homebox

homebox

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

Tables

• homebox_tasks: one row per task of the bundle, with its prompt, envs, verifiers and steps.
• homebox_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/homebox.jsonl holds each run's record and native trajectory, and bundles/homebox/ the bundle itself.

Run it

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

Homebox on AgentEnv, from a REA capture

What this is. An environment of Homebox 0.27.0 (AGPL-3.0), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (26 tools) and an environment state, the entities the capture's reads showed, and rea environment-export wrote the bundle you can run from this dataset. Reads the state can render are answered from it, write tools whose effect the capture confirmed change it, and data/reset restores it. Nothing executes Homebox. Tasks are graded deterministically, with no judge.

v0.2.0 rebuilds this environment from a new capture of an instance seeded the same way, recorded so each write's effect is shown: two scratch objects per write target, every write read back at once. A REA build that serves a state, confirmed writes and list queries projects it; the tag v0.1.0 keeps the earlier replay-only version.

What's in it

• 26 tools (23 GET, 1 POST, 1 DELETE, 1 PUT) over 101 recorded exchanges on http://127.0.0.1:3208; no recorded group excluded. 24 are offered; the other 2 are write tools the environment cannot apply (post_entities, put_entities_by_uuid), left out rather than faked.
• Path parameters: 7 uuid. REA's four format rules (numeric, uuid, hex, iso-date) applied unconditionally; the four opt-in rules (after-uid-literal, opaque-token, sibling-variance, prefix-variance) were requested and are labelled per parameter. With the four format rules alone the manifest has 26 tools.
• State: 207 entities of 12 types. 21 read tools answer from it and 2 replay their recorded responses. 1 list tool also answers filters, search, sorting or paging from it. Served write tools: delete_entities_by_uuid (delete).
• Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
• 10 retrieval tasks (evals/homebox-retrieval.toml): each asks one factual question about a seeded object, with a distinct answer that the app does not ship by default; reward 1 if the response contains the recorded value, else 0. Plus smoke, which deploys the environment and checks it through the MCP endpoint.
• 2 write tasks (evals/homebox-writes.toml): each asks for a change in plain language and is graded from the environment's state through data/get, not from the agent's reply; reward 1 only if every check passes. Their grade is in scores.state; reward holds the retrieval verifier's score, so it is empty for them, as for smoke.
• 20 retrieval episodes and 4 write episodes, plus the smoke run, 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_44a3e4e37d30fba929dfd579393f0d6ef366a98432356ebe5a21a96b04e6d6cf (scoped HAR sha256 c3002e43f68d5cc3126398291b7b969eb48f404c8064fca4c6f74e7432fc2ab8, 339,993 bytes), manifest view ev_4a75654532ee3dd65b62ff18667fd2325f3a6b15ede5b6d894d5d2740c88c8b4, confidence inferred, authority historical-reference. The REA build is rea-agents 6.7.0 from main 3220e17b with open pull requests merged: morluto/rea#5600 at 45a5c8b9 (which carries #5351 and #5352), #5787 at 27dd6062, #5800 at 1b699306, #6136 at 755dfca4 and #6138 at 58185484. The bundle installs it from the tarball it ships, so the environment runs without waiting for an npm release.

What the state means, and does not

Reads the state can render, item, list and document views of the entities the capture's responses showed, answer from it. List reads filtered, searched, sorted or paged answer from it too wherever the capture's recorded calls explain what each query parameter does, including values no call recorded; a parameter the recordings do not explain, or a flag's unrecorded value, replays. Other reads replay the recorded response whose path and query values best match the arguments, with replay.exact_match saying whether it matched exactly; a replayed read does not change after a write. A served write was confirmed by the capture itself: replaying the session against the extracted state, a later recorded read reproduced its effect and none contradicted it. A create generates ids and clock values deterministically, so the same calls after a reset give the same state; an update changes the members the call sends; a delete removes the entity. Each takes only the body members the recorded call showed and refuses others with a message that names the accepted ones. Its effect shows in the reads the state renders, lists included wherever a list lists its whole collection or a learned query answers the call; a list call nothing explains keeps its recorded members, dropping a deleted entity but not adding a new one. The data plane at /agentenv takes data/reset (restore the extracted state), data/add and data/get (return it), which is how write tasks are graded.

Not modelled: side effects a write has beyond its own entity (counters, copies in other collections, related entities), preconditions the application enforces before a write, list query parameters the recorded calls do not explain, paging members such as page counts and next links when a call asks for another page size or page, and anything the capture never showed. The manifest's unknowns list what a capture cannot establish.

Tasks and grading

Retrieval

task	tool	arguments	field	answer
qa-01-get_templates_by_uuid	get_templates_by_uuid	uuid=274afa65-d928-43aa-aa14-9b549be088d8	description	Template for parts kept for the lamp room.
qa-02-get_entities_by_uuid	get_entities_by_uuid	uuid=5929f60b-6470-417f-aa1f-fdaa5f6a35bf	children[0].description	Locked stone room under the depot where the audit ledgers are kept.
qa-03-get_entities_maintenance_by_uuid	get_entities_maintenance_by_uuid	uuid=66b1e135-56a5-4eae-a0d8-28d9f43bebb3, status=scheduled	[0].name	Recalibrate tide gauge
qa-04-get_templates	get_templates	no arguments	[0].name	Lighthouse Spare Part
qa-05-get_maintenance	get_maintenance	status=scheduled	[0].description	Zero against the Granite Point benchmark before the spring census.
qa-06-get_entities	get_entities	filterChildren=false, isLocation=true	items[0].entityType.name	Storage Room
qa-07-get_groups	get_groups	no arguments	name	Copperfield Annex Stores
qa-08-get_templates_by_uuid	get_templates_by_uuid	uuid=274afa65-d928-43aa-aa14-9b549be088d8	defaultTags[0].name	Spare Parts
qa-09-get_entities_maintenance_by_uuid	get_entities_maintenance_by_uuid	uuid=66b1e135-56a5-4eae-a0d8-28d9f43bebb3, status=scheduled	[0].itemName	Tide Gauge Logger
qa-10-get_entities	get_entities	filterChildren=false, isLocation=true	items[0].description	Cold pantry next to the pass; brassware lives on the top shelf.

Writes

Graded from the environment's state through data/get (scores.state in the episodes table; reward 1 only if every check passes).

task	prompt	checks
write-delete	In this Homebox inventory, delete the item "Quarry Tally Counter".	the Quarry Tally Counter is gone; its location, the Copperfield Annex Workshop, is still there; every other location and item is still there
write-delete-lookup	In this Homebox inventory, one item is stored in the Blue Heron Kitchen Pantry. Find it and delete it, but keep the pantry itself.	the Copper Stockpot 40 L, the pantry's only item, is gone; the Blue Heron Kitchen Pantry is still there; every other location and item is still there

Episodes

model	retrieval episodes	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	10	10	100%	1.0	11,066
zai-org/GLM-5.3-Flash:zai-org	10	10	100%	1.0	12,796

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

model	write episodes	passed
openai/gpt-oss-120b:groq	2	1
zai-org/GLM-5.3-Flash:zai-org	2	2

gpt-oss-120b:groq missed write-delete: each entity-list call it made (q=Quarry Tally Counter, q=Quarry, no arguments) replayed the web app's recorded location list, because the entity list's recorded lines do not all render from the state, so it never saw the item and deleted nothing.

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/HomeboxEnv-REA --bundle homebox --task smoke          # Docker, no model
agent-env hf run earakely-scale/HomeboxEnv-REA --bundle homebox --eval homebox-retrieval --model openai/gpt-oss-120b:groq
agent-env hf run earakely-scale/HomeboxEnv-REA --bundle homebox --eval homebox-writes --model openai/gpt-oss-120b:groq

The evals need 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. Homebox ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (user Rea Admin in the collection Copperfield Annex Stores (EUR); default tags and locations removed; 2 custom entity types (Storage Room, Spare Part); 5 tags (Lighthouse Rota, Calibration Due, Kitchen Brass, Spare Parts with nested Lens Spares); 4 locations (Saltmarsh Depot with nested Granite Ledger Vault, Copperfield Annex Workshop, Blue Heron Kitchen Pantry); 6 items with manufacturer, model, serial, price, notes and custom fields; 3 maintenance entries; 1 item template); people and projects are invented.
2. Playwright drove a login, the main pages and a scripted read sequence through the app's JSON API, then wrote two scratch objects per write target (created, then updated and deleted where the app allows), reading each write back at once; the HAR was scoped to the API, the login exchange and the web app's own view-settings calls dropped, secret-looking string 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 and the state it serves; gen_tasks.py derived the retrieval tasks from the manifest and the recorded bodies; the write tasks were written by hand against the served write tools and the seeded names; 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	Homebox (this dataset)
Environment	A simulation: a container quay, ships, cranes, weather, with A2A ship agents	An application's API rebuilt from one captured session
Observation	Tool results computed by the simulator as the week unfolds	Answers rendered from the extracted state, or recorded responses, cited to Evidence
Action space	Planning tools (check_plan, confirm_berths, advance, message_agent)	24 tools mirroring the application's endpoints
State	Full: a virtual clock, triggers, frozen windows, agents that answer	A state extracted from the capture (207 entities); served writes change it; data/reset restores it
Reward	Deterministic, against a CP-SAT optimum or a re-planner anchor; continuous 0 to 1	Deterministic: recorded-value match for retrieval, state checks for writes; 0 or 1 per task
What it measures	Planning under uncertainty and negotiation	Tool discovery, parameter inference and faithful retrieval in a real API, and changing its state
Data provenance	Real 2024 port calls and weather; simulated workloads	Synthetic seed data on a self-hosted Homebox; 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 and the state's limits above say what it still leaves out.

Licence and credits

Bundle code and tasks: Apache-2.0. Recorded content: responses generated by a self-hosted Homebox instance over synthetic data; Homebox is AGPL-3.0 software by the Homebox contributors (sysadminsmedia) 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.