earakely-scale/DirectusEnv-REA

directus

directus

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

Tables

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

Run it

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

Directus on AgentEnv, from a REA capture

What this is. An environment of Directus 11.17.4 (BSL-1.1), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (40 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 Directus. 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

• 40 tools (38 GET, 1 POST, 1 PATCH) over 96 recorded exchanges on http://127.0.0.1:3223; 2 recorded groups excluded (2 as non-JSON replies such as bodiless 204 writes). 40 are offered.
• Path parameters: 9 uuid, 4 numeric. 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 40 tools.
• State: 17 entities of 12 types. 31 read tools answer from it and 7 replay their recorded responses. 5 list tools also answer filters, search, sorting or paging from it. Served write tools: post_items_provisions (create), patch_items_provisions_by_id (update).
• Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
• 12 retrieval tasks (evals/directus-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/directus-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.
• 24 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_c5b6fd36acdc9d4a03b8785ae617dff618fb66b03fbc51bc30de105efa0e6a03 (scoped HAR sha256 b72f2c447695c9ce90cd01aa2a36cfaf81698f473e85cf81f6497bb8a4d66e9d, 234,020 bytes), manifest view ev_078f1b8a2f8a7246add6366fe956c83241b1d7fe1599b832d95968b14b777342, 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_items_crew_rota_by_id	get_items_crew_rota_by_id	id=1	data.name	Ada Finch
qa-02-get_presets_by_id	get_presets_by_id	id=1	data.bookmark	Night shift crew
qa-03-get_dashboards_by_uuid	get_dashboards_by_uuid	uuid=b9c4734b-06ec-44bc-a263-b2a1f1d93c33	data.name	Depot Overview
qa-04-get_files_by_uuid	get_files_by_uuid	uuid=00e6fb70-f6ae-4f7f-9143-7dc8e7131353	data.title	Granite Ledger tally GL-2291
qa-05-get_users_by_uuid	get_users_by_uuid	uuid=09d0b64a-a70f-4bfa-beae-5ddaafc8eebb	data.email	ada.finch@example.com
qa-06-get_flows_by_uuid	get_flows_by_uuid	uuid=1657b0c4-a642-4b6f-9b99-e3e418a1c560	data.name	Announce new provisions
qa-07-get_items_provisions_by_id	get_items_provisions_by_id	id=2	data.title	Pale Meridian Tea
qa-08-get_policies_by_uuid	get_policies_by_uuid	uuid=a5234d2f-f895-456b-a6ed-97e760d5f5fa	data.description	Create and update crew rota entries and read provisions.
qa-09-get_roles_by_uuid	get_roles_by_uuid	uuid=1d0e26ce-8f7a-4cb0-b243-6569c182c507	data.description	Keepers of the crew rota for the Lighthouse Rota.
qa-10-get_folders_by_uuid	get_folders_by_uuid	uuid=994a6f6d-a316-41ec-9535-e57b9a6e5218	data.name	Granite Ledger Scans
qa-11-get_comments_by_uuid	get_comments_by_uuid	uuid=ae445e61-b33a-4b11-8838-22a5b6cf4185	data.comment	Ada swaps the January night watch with Tomas Vey.
qa-12-get_panels_by_uuid	get_panels_by_uuid	uuid=6fa9d6b8-446f-4631-8cc5-8a76b2ec90ed	data.name	Provisions on hand

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-create	In this Directus project, add a provision titled "Copperfield Lamp Wicks" in the Lamp category: 24 packs, stored in Bay B2, with the supplier note "Spare wicks for the North Lamp Gallery."	a provision titled Copperfield Lamp Wicks exists; it is 24 packs in the Lamp category; it is stored in Bay B2; its supplier note is the one asked for; the provisions the state started with are still there, and nothing else was added
write-update	In this Directus project, Pale Meridian Tea was restocked: set its quantity to 20 and its supplier note to "Restocked after the October storm."	its quantity is 20; its supplier note is the one asked for; it is the same provision, still in Bay A1; no provision was added or removed

Episodes

model	retrieval episodes	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	12	100%	1.0	16,018
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	18,919

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

The capture leaves out the Data Studio's provision row click: its item page reads the provision with fields=title, which returns the item without its id, and that read would leave REA unable to tell which field identifies a provision. In write-create, openai/gpt-oss-120b:groq stored the bay as B2 rather than Bay B2, the spelling every provision uses, so its storage check failed. The state holds the two provisions the capture read by id (Pale Meridian Tea and Northern Quill Ink). The provisions list replays its recording, so it shows the five seeded provisions and never a created one, and a write to one of the other three seeded provisions is refused rather than faked.

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/DirectusEnv-REA --bundle directus --task smoke          # Docker, no model
agent-env hf run earakely-scale/DirectusEnv-REA --bundle directus --eval directus-retrieval --model openai/gpt-oss-120b:groq
agent-env hf run earakely-scale/DirectusEnv-REA --bundle directus --eval directus-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. Directus ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (two user collections with notes and field notes, crew_rota (Ada Finch, Rui Castellanos, Mina Okafor, Tomas Vey, Marisol Okonkwo-Reyes with role titles, shifts, watch stations) and provisions (Saffron Tide Biscuits, Pale Meridian Tea, Ember Compass Matches, Silver Anvil Rivets, Northern Quill Ink with category, quantity, bay, supplier note); folders Lighthouse Rota Archive and Granite Ledger Scans with one uploaded text file (Granite Ledger tally GL-2291); policy Rota Editing with 4 permissions; roles Rota Editors and Depot Viewers; users Ada Finch and Rui Castellanos; the admin's profile;...); 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 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	Directus (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)	40 tools mirroring the application's endpoints
State	Full: a virtual clock, triggers, frozen windows, agents that answer	A state extracted from the capture (17 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 Directus; 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 Directus instance over synthetic data; Directus is BSL-1.1 software by Monospace, Inc. and the Directus 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.