earakely-scale/GhostEnv-REA

ghost

ghost

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

Tables

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

Run it

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

Ghost on AgentEnv, from a REA capture

What this is. A replay environment of Ghost 5.130 (MIT), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (36 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 Ghost. Tasks are graded deterministically, with no judge.

What's in it

• 36 tools (33 GET, 2 POST, 1 PUT) over 76 recorded exchanges on http://127.0.0.1:3105; no recorded group excluded.
• Path parameters: 4 opaque-token. 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 37 tools.
• Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
• 12 retrieval tasks (evals/ghost-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_f03cc058b2bdb78972c98464162d3063dba21e2e154c3a2c6df6f7d5aa94a437 (scoped HAR sha256 e094258cb20cd36f7a877e0f19825fa0d12191c5aea91f33ad51a08a10ce15e3, 356,087 bytes), manifest view ev_1eb4590fb23ad38e07ec1085a2320dc95d8763c98defeaacc5b929f9efc6fd0f, 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_ghost_admin_posts_by_uid	get_ghost_admin_posts_by_uid	uid=6ad47b5c1023b500010873e3, formats=mobiledoc,lexical, include=tags,authors,authors.roles,email,tiers,newsletter,count.clicks,post_revisions,post_revisions.author	posts[0].slug	granite-ledger-quarterly-stone-count
qa-02-get_ghost_admin_members_by_uid	get_ghost_admin_members_by_uid	uid=6ad47b5d1023b500010873f8	members[0].email	ada.finch@example.test
qa-03-get_ghost_admin_labels	get_ghost_admin_labels	limit=100, page=1	labels[0].name	Rota Crew
qa-04-get_ghost_admin_stats_referrers	get_ghost_admin_stats_referrers	no arguments	stats[0].source	Created manually
qa-05-get_ghost_admin_posts	get_ghost_admin_posts	formats=mobiledoc,lexical, limit=5, filter=status:[published,sent], order=published_at desc	posts[0].title	Granite Ledger: Quarterly Stone Count
qa-06-get_ghost_admin_integrations	get_ghost_admin_integrations	include=api_keys,webhooks, limit=50	integrations[0].description	Built-in Zapier integration
qa-07-get_ghost_admin_tags	get_ghost_admin_tags	limit=100, page=1, order=name asc, include=count.posts, filter=visibility:public	tags[0].description	Menus that follow the tides.
qa-08-get_ghost_admin_members	get_ghost_admin_members	order=id, limit=1, page=1, include=newsletters,labels	members[0].name	Ada Finch
qa-09-get_ghost_admin_posts_slug_blue_heron_ki	get_ghost_admin_posts_slug_blue_heron_kitchen_tide_table_men	no arguments	posts[0].slug	blue-heron-kitchen-tide-table-menu
qa-10-get_ghost_admin_posts_by_uid	get_ghost_admin_posts_by_uid	uid=6ad47b5c1023b500010873e3, formats=mobiledoc,lexical, include=tags,authors,authors.roles,email,tiers,newsletter,count.clicks,post_revisions,post_revisions.author	posts[0].custom_excerpt	412 blocks counted, audited by Rui Castellanos.
qa-11-get_ghost_admin_members_by_uid	get_ghost_admin_members_by_uid	uid=6ad47b5d1023b500010873f8	members[0].labels[0].slug	rota-crew
qa-12-get_ghost_admin_members	get_ghost_admin_members	order=id, limit=1, page=1, include=newsletters,labels	members[0].note	Keeps the tide tables for the winter rota.

Episodes

model	tasks	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	12	100%	1.0	10,316
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	12,805

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/GhostEnv-REA --bundle ghost --task smoke          # Docker, no model
agent-env hf run earakely-scale/GhostEnv-REA --bundle ghost --eval ghost-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. Ghost ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (owner: Rea Admin rea-admin@example.test (bio + location updated); blog title: Lighthouse Rota; tags: Granite Ledger, Blue Heron Kitchen; posts published: Granite Ledger: Quarterly Stone Count, Blue Heron Kitchen opens its tide-table menu; posts draft: Lighthouse Rota: winter shift plan (draft); pages: About the Lighthouse Rota; members: Ada Finch ada.finch@example.test (label Rota Crew); newsletter: default, untouched; scratch during capture: post Rea scratch post created, edited (PUT), read, deleted (204), re-read (404)); 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	Ghost (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)	36 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 Ghost; 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 Ghost instance over synthetic data; Ghost is MIT software by the Ghost Foundation 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.