earakely-scale/NavidromeEnv-REA

navidrome

navidrome

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

Tables

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

Run it

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

Navidrome on AgentEnv, from a REA capture

What this is. A replay environment of Navidrome 0.64.2 (GPL-3.0), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (25 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 Navidrome. Tasks are graded deterministically, with no judge.

What's in it

• 25 tools (22 GET, 1 POST, 1 DELETE, 1 PUT) over 71 recorded exchanges on http://127.0.0.1:3233; no recorded group excluded.
• Path parameters: 8 sibling-variance, 1 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 46 tools.
• Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
• 12 retrieval tasks (evals/navidrome-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_3995896845c068943afaf1c5bb032f2e6f303a279f4c24c26fae001311ec22b7 (scoped HAR sha256 b516aa95b4651db7489d229659785af72dc85c906793b52919cd759f191dedf5, 222,808 bytes), manifest view ev_e37526a3edbe955837e258ccc596d67628ca77f09f9b2a6eb2b27a967ab57f67, 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_playlist_tracks_by_playlist_id	get_playlist_tracks_by_playlist_id	playlist_id=63eo9PCPWN1OwlFMFtB9oR, _end=100, _order=ASC, _sort=id, _start=0	[0].title	Twenty-Two Hundred Hours
qa-02-get_song_by_song_id	get_song_by_song_id	song_id=0MymNExkLS0yPTDV73lFx8	title	Audit on the Fourteenth
qa-03-get_share_by_share_id	get_share_by_share_id	share_id=g6lktGVJbd	description	Quarry Hymns for the Granite Ledger auditors
qa-04-get_playlist_by_playlist_id	get_playlist_by_playlist_id	playlist_id=63eo9PCPWN1OwlFMFtB9oR	name	Lighthouse Rota Night Mix
qa-05-get_radio_by_radio_id	get_radio_by_radio_id	radio_id=7gBzDu3laalpsTj3lbXbtV	name	Copperfield Annex Radio
qa-06-get_playlist	get_playlist	_end=0, _sort=name, _start=-100	[0].name	Blue Heron Kitchen Prep
qa-07-get_user	get_user	_end=0, _order=ASC, _sort=name, _start=0	[0].userName	mina.okafor
qa-08-get_song	get_song	_end=0, _order=ASC, _sort=album, _start=0, album_id=6ONaUA21fRfkx2SpAhNjCl	[0].title	Spring Tide at Granite Point
qa-09-get_album	get_album	_end=18, _order=DESC, _sort=recently_added, _start=0, seed=0.08665565834980982-0	[0].orderAlbumName	quarry hymns
qa-10-get_share	get_share	_end=15, _order=DESC, _sort=createdAt, _start=0	[0].description	Night Mix for the relief crew at Granite Point
qa-11-get_playlist_tracks_by_playlist_id	get_playlist_tracks_by_playlist_id	playlist_id=63eo9PCPWN1OwlFMFtB9oR, _end=100, _order=ASC, _sort=id, _start=0	[0].comment	Opens every night shift of the Lighthouse Rota.
qa-12-get_song_by_song_id	get_song_by_song_id	song_id=0MymNExkLS0yPTDV73lFx8	comment	Sung by the auditors before the ledger closes.

Episodes

model	tasks	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	12	100%	1.0	9,096
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	11,692

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/NavidromeEnv-REA --bundle navidrome --task smoke          # Docker, no model
agent-env hf run earakely-scale/NavidromeEnv-REA --bundle navidrome --eval navidrome-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. Navidrome ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (a generated library of 10 silent MP3 tracks with ID3 tags on 3 albums (Tide Tables by The Saltmarsh Lanterns, Night Shift at the Lamp Room by Ada Finch Quartet, Quarry Hymns by Granite Ledger Choir), composers Mina Okafor, Ada Finch and Rui Castellanos; library renamed Saltmarsh Depot Archive; admin Rea Admin and user Mina Okafor; 2 playlists (Lighthouse Rota Night Mix, Blue Heron Kitchen Prep); 2 internet radio stations on example.test; 2 shares (album, playlist); stars and ratings); 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	Navidrome (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)	25 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 Navidrome; 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 Navidrome instance over synthetic data; Navidrome is GPL-3.0 software by Deluan Quintao and the Navidrome 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.