earakely-scale/GristEnv-REA

grist

grist

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

Tables

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

Run it

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

Grist on AgentEnv, from a REA capture

What this is. A replay environment of Grist 1.7.21 (Apache-2.0), 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 Grist. Tasks are graded deterministically, with no judge.

What's in it

• 31 tools (25 GET, 3 POST, 2 PATCH, 1 DELETE) over 65 recorded exchanges on http://127.0.0.1:3220; no recorded group excluded.
• Path parameters: 7 numeric, 4 sibling-variance. 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 43 tools.
• Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
• 12 retrieval tasks (evals/grist-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_f74ce8e1b2435b5ff5ce9cf930d3ece7b866503615fbd15e54657be3a268d1a1 (scoped HAR sha256 0ba1ce8653bb74e217cf221c92cb213dddcfaf807aa2fc13687c03295532d2c9, 111,326 bytes), manifest view ev_017809f63eed60ddfc1f97299fe7f7e58e3d111585cd49939499d18341fdbb99, 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_o_docs_docs_by_doc_id	get_o_docs_docs_by_doc_id	doc_id=uSqDsYEz5xor	workspace.name	Lighthouse Rota
qa-02-get_o_docs_docs_sql_by_doc_id	get_o_docs_docs_sql_by_doc_id	doc_id=uSqDsYEz5xornkGvKczKKC, q=select Name, Station from Keepers order by Name	records[0].fields.Name	Ada Finch
qa-03-get_o_docs_workspaces_by_id	get_o_docs_workspaces_by_id	id=4	name	Granite Ledger
qa-04-get_o_docs_docs_usqdsyez5xornkgvkczkkc_t	get_o_docs_docs_usqdsyez5xornkgvkczkkc_tables_keepers_record	no arguments	records[0].fields.Email	ada.finch@example.test
qa-05-get_o_docs_docs_x3dzespgnxte9r8dujjwto_t	get_o_docs_docs_x3dzespgnxte9r8dujjwto_tables_carts_records	no arguments	records[0].fields.Label	Cart 7
qa-06-get_o_docs_docs_kmaxkkxtuiemmqmnqbzkeu_t	get_o_docs_docs_kmaxkkxtuiemmqmnqbzkeu_tables_dishes_records	no arguments	records[0].fields.Description	Kelp goes in after twenty minutes of simmering
qa-07-get_o_docs_docs_usqdsyez5xornkgvkczkkc_t	get_o_docs_docs_usqdsyez5xornkgvkczkkc_tables_supplies_recor	no arguments	records[0].fields.Item	Lamp oil
qa-08-get_o_docs_docs_usqdsyez5xornkgvkczkkc_t	get_o_docs_docs_usqdsyez5xornkgvkczkkc_tables_shifts_records	no arguments	records[0].fields.Title	Shift Three
qa-09-get_o_docs_docs_x3dzespgnxte9r8dujjwto_t	get_o_docs_docs_x3dzespgnxte9r8dujjwto_tables_terraces_recor	no arguments	records[0].fields.Name	Terrace Seven
qa-10-get_o_docs_docs_by_doc_id	get_o_docs_docs_by_doc_id	doc_id=uSqDsYEz5xor	name	Night Shift Planner
qa-11-get_o_docs_docs_sql_by_doc_id	get_o_docs_docs_sql_by_doc_id	doc_id=uSqDsYEz5xornkGvKczKKC, q=select Name, Station from Keepers order by Name	records[0].fields.Station	Lamp Room
qa-12-get_o_docs_workspaces_by_id	get_o_docs_workspaces_by_id	id=4	docs[0].name	Quarry Audit 2026

Episodes

model	tasks	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	11	92%	0.9	8,912
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	10,586

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

openai/gpt-oss-120b:groq missed qa-06-get_o_docs_docs_kmaxkkxtuiemmqmnqbzkeu_t. Miss by gpt-oss-120b:groq on qa-06-get_o_docs_docs_kmaxkkxtuiemmqmnqbzkeu_t: the model provider rejected a malformed tool call the model generated (HTTP 400), so the episode ended without an answer.

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/GristEnv-REA --bundle grist --task smoke          # Docker, no model
agent-env hf run earakely-scale/GristEnv-REA --bundle grist --eval grist-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. Grist ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (2 workspaces besides Home (Lighthouse Rota, Granite Ledger), 3 documents (Night Shift Planner, Blue Heron Kitchen Menu, Quarry Audit 2026), 6 tables with typed columns (one formula column, one reference column) and 17 records, two documents shared with three invented people, the default user renamed to Rea Admin); 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	Grist (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 Grist; 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 Grist instance over synthetic data; Grist is Apache-2.0 software by Grist Labs and the grist-core 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.