earakely-scale/VikunjaEnv-REA

vikunja

vikunja

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

Tables

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

Run it

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

Vikunja on AgentEnv, from a REA capture

What this is. An environment of Vikunja 2.7 (AGPL-3.0-or-later), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (28 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, no write tool is offered because the capture confirmed none, and data/reset restores it. Nothing executes Vikunja. Tasks are graded deterministically, with no judge.

v0.2.0 rebuilds this environment from the same capture with a REA build that serves a state, confirmed writes and list queries; the tag v0.1.0 keeps the earlier replay-only version.

What's in it

• 28 tools (24 GET, 1 POST, 1 DELETE, 1 PATCH, 1 PUT) over 102 recorded exchanges on http://127.0.0.1:3211; no recorded group excluded. 24 are offered; the other 4 are write tools the environment cannot apply (post_projects_tasks_by_id, delete_tasks_by_id, patch_tasks_by_id, put_tasks_read_by_id), left out rather than faked.
• Path parameters: 25 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 28 tools.
• State: 40 entities of 15 types. 21 read tools answer from it and 3 replay their recorded responses. 1 list tool also answers filters, search, sorting or paging from it. No write tool is served.
• Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
• 12 retrieval tasks (evals/vikunja-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.
• 24 retrieval 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_6172766db5dacc12fa38511878e7a2db573f50122947cad181f09d52b850cceb (scoped HAR sha256 10843162cbd00ebf87245969fcda682fa750ff07d0640cae18c1f9985883b314, 378,047 bytes), manifest view ev_3c59d880be64c05d8f6f524636e238b902c1c4849dcdda2e426daa8c2c5e0ad9, confidence inferred, authority historical-reference. The REA build is rea-agents 6.7.0 from main 335c042d with open pull requests merged: morluto/rea#5600 at fe8ab584 (which carries #5351 and #5352), #5664 at 03b66df9, #5787 at edc09dba, #5800 at 6db41d29 and #5699 at ac3ca8a3. 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. No write tool is offered: its task create, update and delete write to task collections the capture never read as a type of their own, so the capture confirmed no write's effect and the environment is read-only. 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_projects_by_id	get_projects_by_id	id=7	description	Quarterly stone counts for the north quarry and the tidal yard.
qa-02-get_teams_by_id	get_teams_by_id	id=2	name	Quarry Auditors
qa-03-get_projects_users_by_id	get_projects_users_by_id	id=8	items[0].name	Mina Okafor
qa-04-get_tasks_comments_by_id_id_2	get_tasks_comments_by_id_id_2	id=6, id_2=2	author.username	mina-okafor
qa-05-get_tasks_comments_by_id	get_tasks_comments_by_id	id=4	items[0].comment	Counted 97 blocks on the fourteenth; two are cracked.
qa-06-get_projects_views_buckets_tasks_by_id_i	get_projects_views_buckets_tasks_by_id_id_2	id=8, id_2=48, filter_include_nulls=false, filter_timezone=UTC, per_page=25, expand=is_unread	items[0].tasks[0].title	Print the tide-table menu cards
qa-07-get_tasks_by_id	get_tasks_by_id	id=4	description	97 blocks expected after the October storm; ledger page 2291.
qa-08-get_filters_by_id	get_filters_by_id	id=5	title	Tide-critical open work
qa-09-get_projects_tasks_by_index_by_id_id_2	get_projects_tasks_by_index_by_id_id_2	id=7, id_2=1	related_tasks.related[0].description	The axle on cart number 7 is bent; move its load to cart 9.
qa-10-get_projects_views_tasks_by_id_id_2	get_projects_views_tasks_by_id_id_2	id=7, id_2=41	items[0].title	Count the blocks in the tidal yard
qa-11-get_teams_by_id	get_teams_by_id	id=2	description	Stone counters for the Granite Ledger.
qa-12-get_tasks_comments_by_id_id_2	get_tasks_comments_by_id_id_2	id=6, id_2=2	comment	The depot confirmed it can deliver on Thursday.

Episodes

model	retrieval episodes	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	12	100%	1.0	12,429
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	14,800

Every episode is in the episodes config with its transcript; scores 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/VikunjaEnv-REA --bundle vikunja --task smoke          # Docker, no model
agent-env hf run earakely-scale/VikunjaEnv-REA --bundle vikunja --eval vikunja-retrieval --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. Vikunja ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (4 users (rea-admin and three crew members), 5 projects (one child, one archived), 3 labels, 7 tasks with priorities, due dates, labels, assignees and a relation, 2 comments, 2 teams, project shares to the teams and one user, and 1 saved filter); 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 and the state it serves; 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	Vikunja (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 (40 entities); read-only; 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; 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 Vikunja; 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 Vikunja instance over synthetic data; Vikunja is AGPL-3.0-or-later software by the Vikunja authors 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.