earakely-scale/GrocyEnv-REA

grocy

grocy

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

Tables

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

Run it

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

Grocy on AgentEnv, from a REA capture

What this is. A replay environment of Grocy 4.7.1 (MIT), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (45 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 Grocy. Tasks are graded deterministically, with no judge.

What's in it

• 45 tools (40 GET, 3 PUT, 1 POST, 1 DELETE) over 75 recorded exchanges on http://127.0.0.1:3222; no recorded group excluded.
• Path parameters: 22 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 45 tools.
• Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
• 12 retrieval tasks (evals/grocy-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_853ed4f6409f2a62f73da27f0a83010aff0ba3f0365d71788c364cfade127838 (scoped HAR sha256 4e673f0a954204d036082a7901bc0bdf4b116cea8b849a92ceaa88a87174662a, 144,824 bytes), manifest view ev_cd77ac50b20a04df27506d51dc91c8e252065c659fbbde30e497181ddbd95b1a, 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_objects_batteries_by_id	get_objects_batteries_by_id	id=2	name	Tide Gauge Pack
qa-02-get_objects_quantity_units_by_id	get_objects_quantity_units_by_id	id=4	description	Wooden crate of twelve, stamped with the depot mark.
qa-03-get_stock_locations_entries_by_id	get_stock_locations_entries_by_id	id=3	[0].note	Batch SD-118 from the east pans
qa-04-get_objects_shopping_locations_by_id	get_objects_shopping_locations_by_id	id=1	name	Blue Heron Kitchen Market
qa-05-get_objects_locations_by_id	get_objects_locations_by_id	id=4	name	Granite Ledger Lockers
qa-06-get_objects_products_by_id	get_objects_products_by_id	id=1	name	Smoked Kelp Broth
qa-07-get_stock_products_locations_by_id	get_stock_products_locations_by_id	id=3	[0].location_name	Copperfield Annex Larder
qa-08-get_objects_product_groups_by_id	get_objects_product_groups_by_id	id=2	name	Lamp Supplies
qa-09-get_recipes_fulfillment_by_id	get_recipes_fulfillment_by_id	id=1	product_names_comma_separated	Smoked Kelp Broth,Granite Salt Flakes
qa-10-get_objects_shopping_lists_by_id	get_objects_shopping_lists_by_id	id=2	name	Lighthouse Rota Night Shift
qa-11-get_objects_equipment_by_id	get_objects_equipment_by_id	id=1	name	Copperfield Annex Dehydrator
qa-12-get_objects_shopping_list_by_id	get_objects_shopping_list_by_id	id=3	note	Two crates before the winter shift plan starts

Episodes

model	tasks	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	11	92%	1.0	8,960
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	11,667

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

openai/gpt-oss-120b:groq missed qa-03-get_stock_locations_entries_by_id. Miss by gpt-oss-120b:groq on qa-03-get_stock_locations_entries_by_id: the model's answer did not contain the expected value.

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/GrocyEnv-REA --bundle grocy --task smoke          # Docker, no model
agent-env hf run earakely-scale/GrocyEnv-REA --bundle grocy --eval grocy-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. Grocy ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (the default admin renamed to rea-admin plus two members (Ada Finch, Rui Castellanos); 3 locations such as Saltmarsh Depot Cold Room; Crate and Tin quantity units; product groups Tide Rations and Lamp Supplies; stores Blue Heron Kitchen Market and Lighthouse Rota Co-op; 5 products with stock (Smoked Kelp Broth, Granite Salt Flakes, Heron Rye Crackers, Lamp Wick Bundle, Copperfield Lamp Oil); 2 shopping lists with 4 items; 3 chores; 2 batteries; 2 task categories and 2 tasks; 2 pieces of equipment; the recipe Tide-Table Kelp Stew); 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	Grocy (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)	45 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 Grocy; 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 Grocy instance over synthetic data; Grocy is MIT software by Bernd Bestel and the Grocy 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.