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. A replay 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 (43 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 Directus. Tasks are graded deterministically, with no judge.

What's in it

• 43 tools (41 GET, 1 POST, 1 PATCH) over 90 recorded exchanges on http://127.0.0.1:3223; 2 recorded groups excluded (2 as non-JSON replies such as bodiless 204 writes).
• Path parameters: 9 uuid, 4 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 43 tools.
• 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, 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_726c10bf55a915e584548f44ddfcacfabb7b72780f9ce24a1a1d18d093836af2 (scoped HAR sha256 1667a554741c257c7831f80302833dfff820050f35866ac9f0fd6bc1d6e5a8c9, 219,187 bytes), manifest view ev_df3fc9049d7dd8e54cbefab23250d29be75af6e6ba7bbc9c3cc092e64c134c58, 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_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=cdeb9180-bf6a-449c-949e-3ff680c7671e	data.name	Depot Overview
qa-04-get_files_by_uuid	get_files_by_uuid	uuid=4361c27c-a326-498a-9efc-7ca24be30c45	data.title	Granite Ledger tally GL-2291
qa-05-get_users_by_uuid	get_users_by_uuid	uuid=d59a4f7e-7dfd-49dd-ba21-e1e93722ec2f	data.email	ada.finch@example.com
qa-06-get_flows_by_uuid	get_flows_by_uuid	uuid=5252bcd1-9b0d-41ae-ae7f-0c38e076ce61	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=17175c8d-62f4-4ccd-b166-944a386a4648	data.description	Create and update crew rota entries and read provisions.
qa-09-get_roles_by_uuid	get_roles_by_uuid	uuid=767e203a-01d9-464e-a0d8-99d0a40e7243	data.description	Keepers of the crew rota for the Lighthouse Rota.
qa-10-get_folders_by_uuid	get_folders_by_uuid	uuid=ed349e7c-28e2-4418-afac-abf5c08b575a	data.name	Granite Ledger Scans
qa-11-get_comments_by_uuid	get_comments_by_uuid	uuid=b093107c-baf2-4360-8fe9-682384626d09	data.comment	Ada swaps the January night watch with Tomas Vey.
qa-12-get_panels_by_uuid	get_panels_by_uuid	uuid=457bea94-3099-497a-a804-4a9dd779414a	data.name	Provisions on hand

Episodes

model	tasks	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	12	100%	1.0	11,391
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	15,077

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

Every recorded episode passed.

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

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. 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, 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	Directus (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)	43 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 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 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 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.