earakely-scale/LinkdingEnv-REA

linkding

linkding

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

Tables

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

Run it

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

linkding on AgentEnv, from a REA capture

What this is. A replay environment of linkding 1.47.0 (MIT), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (23 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 linkding. Tasks are graded deterministically, with no judge.

What's in it

• 23 tools (12 GET, 5 POST, 3 DELETE, 2 PATCH, 1 PUT) over 64 recorded exchanges on http://127.0.0.1:3203; no recorded group excluded.
• Path parameters: 14 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 23 tools.
• Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
• 12 retrieval tasks (evals/linkding-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_07064b4e06c39fb3e0b3c5331f4786510cf63f2f2f798640f74bbec660a08b7e (scoped HAR sha256 255d191acf58773d0d7376a2d048dc6296f9c7baa6e7ae675053506734ff2b40, 97,323 bytes), manifest view ev_90f90a6a50da524179de380eec1153fd19b59eccb38018c84f882897c1a52994, 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_bundles_by_id	get_bundles_by_id	id=2	name	Blue Heron reading list
qa-02-get_bookmarks_assets_by_id	get_bookmarks_assets_by_id	id=1	results[0].display_name	granite-ledger-gl-2291-tally.txt
qa-03-get_bookmarks_by_id	get_bookmarks_by_id	id=1	title	Granite Ledger quarterly stone count
qa-04-get_bundles	get_bundles	no arguments	results[0].name	Quarry and depot audits
qa-05-get_bookmarks_check	get_bookmarks_check	url=https://saltmarsh-depot.example.test/gate-log	bookmark.title	Saltmarsh Depot gate log
qa-06-get_bookmarks	get_bookmarks	no arguments	results[0].description	Shift three runs 22:00 to 06:00 through January.
qa-07-get_bookmarks_archived	get_bookmarks_archived	no arguments	results[0].description	Spring tides on the 3rd and the 18th.
qa-08-get_bookmarks_by_id	get_bookmarks_by_id	id=1	description	412 blocks counted in the north quarry and 97 in the tidal yard.
qa-09-get_bundles	get_bundles	no arguments	results[0].any_tags	granite-ledger saltmarsh-depot
qa-10-get_bookmarks_check	get_bookmarks_check	url=https://saltmarsh-depot.example.test/gate-log	bookmark.description	Open-time log for gates 1 to 4 at the depot.
qa-11-get_bookmarks	get_bookmarks	no arguments	results[0].title	Lighthouse Rota winter shift plan
qa-12-get_bookmarks_archived	get_bookmarks_archived	no arguments	results[0].title	Tide tables for January

Episodes

model	tasks	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	11	92%	0.9	6,342
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	7,758

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

openai/gpt-oss-120b:groq missed qa-11-get_bookmarks. Miss by gpt-oss-120b:groq on qa-11-get_bookmarks: 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/LinkdingEnv-REA --bundle linkding --task smoke          # Docker, no model
agent-env hf run earakely-scale/LinkdingEnv-REA --bundle linkding --eval linkding-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. linkding ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (superuser rea-admin; 6 bookmarks on invented example.test URLs with titles, descriptions and notes (Granite Ledger quarterly stone count, Blue Heron Kitchen tide-table menu, Saltmarsh Depot gate log, Copperfield Annex cart register, Lighthouse Rota winter shift plan, Tide tables for January; 2 archived, 1 unread); 8 tags; bundles Quarry and depot audits and Blue Heron reading list; a text asset granite-ledger-gl-2291-tally.txt on the Granite Ledger bookmark); 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	linkding (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)	23 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 linkding; 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 linkding instance over synthetic data; linkding is MIT software by Sascha Ißbrücker and the linkding 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.