earakely-scale/LycheeEnv-REA

lychee

lychee

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

Tables

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

Run it

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

Lychee on AgentEnv, from a REA capture

What this is. An environment of Lychee 6.10.4 (MIT), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (15 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 REA could confirm none, and data/reset restores it. Nothing executes Lychee. 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

• 15 tools (11 GET, 2 POST, 1 DELETE, 1 PATCH) over 134 recorded exchanges on http://127.0.0.1:3402; 66 recorded groups excluded (66 as UI pages or assets). 11 are offered; the other 4 are write tools the environment cannot apply (delete_album, patch_album, post_album, post_metrics_photo), left out rather than faked.
• Path parameters: 1 sibling-variance. 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 27 tools.
• State: 31 entities of 4 types. 7 read tools answer from it and 4 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/lychee-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_4c93356ec80d86dddda8881ba10b66ea5bb7bb3dc22787cff2d986fe12319669 (a capture_browser_scenario session REA recorded itself; capture file sha256 420c6f740ee3b72b6d7217abde543a280ad51b66dac844786b87729db1473dd9, 2,395,283 bytes), manifest view ev_cfbba28831dabfbeac63ea28324a6272a6bc8959188da6b7b89f687124dc0d37, confidence inferred, authority controlled-replay. 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. No write tool is offered: Lychee names the album a write changes in query and body parameters (album_id, album_ids) rather than in its path, while REA models writes only on collections a path names, which leaves the album create, edit and delete with no state type to change, so REA 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_by_v2_id	get_by_v2_id	v2_id=Gallery::Init	title	Lychee v6
qa-02-get_albums	get_albums	no arguments	pinned_albums[0].title	Lighthouse Rota
qa-03-get_timeline	get_timeline	no arguments	photos[0].title	Annex facade bands, option C
qa-04-get_users	get_users	no arguments	[0].username	ada.finch
qa-05-get_tag	get_tag	tag_id=6	name	lamp room
qa-06-get_album	get_album	album_id=7essfu5uxVTOtu3jvbCzG-we, page=1	resource.photos[0].title	Ballast wagon stripes
qa-07-get_search	get_search	terms=bGFtcA==, page=1	albums[0].description	Night frames from signal box nine during the lamp check rota kept by Tomas Vey.
qa-08-get_flow	get_flow	page=1	albums[0].title	Copperfield Annex
qa-09-get_tags	get_tags	no arguments	tags[0].name	brass rail
qa-10-get_albums	get_albums	no arguments	pinned_albums[0].description	Lamp-room studies from the winter rota, one frame per shift, filed by Ada Finch.
qa-11-get_tag	get_tag	tag_id=6	photos[0].title	Fresnel rings, shift three
qa-12-get_album	get_album	album_id=7essfu5uxVTOtu3jvbCzG-we, page=1	resource.title	Saltmarsh Depot

Episodes

model	retrieval episodes	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	11	92%	0.9	7,876
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	8,800

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

gpt-oss-120b:groq's qa-03-get_timeline episode ended without an answer: the model provider rejected a malformed tool call the model generated (HTTP 400).

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/LycheeEnv-REA --bundle lychee --task smoke          # Docker, no model
agent-env hf run earakely-scale/LycheeEnv-REA --bundle lychee --eval lychee-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. Lychee ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (6 albums, two of them nested; 14 generated photos with titles, descriptions, tags and dates, 3 starred; 11 tags; 2 extra users); people and projects are invented.
2. REA's capture-browser-scenario launched Chromium in a seeded session and drove the app's own UI: every album, a photo in each nested album, tags, timeline, flow, sharing and search, then one scratch album created, renamed and described, and deleted through its forms. It recorded every network transaction with credential headers and declared secrets redacted, and the environment-manifest view read that capture directly, with no HAR or scrubbing step.
3. rea inspect-analysis-view projected the environment-manifest view straight from the scenario Evidence; 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	Lychee (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)	11 tools mirroring the application's endpoints
State	Full: a virtual clock, triggers, frozen windows, agents that answer	A state extracted from the capture (31 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 Lychee; 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 Lychee instance over synthetic data; Lychee is MIT software by LycheeOrg 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.