earakely-scale/KeycloakEnv-REA

keycloak

keycloak

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

Tables

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

Run it

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

Keycloak on AgentEnv, from a REA capture

What this is. A replay environment of Keycloak 26.8.1 (Apache-2.0), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (40 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 Keycloak. Tasks are graded deterministically, with no judge.

What's in it

• 40 tools (39 GET, 1 POST) over 73 recorded exchanges on http://127.0.0.1:3210; 2 recorded groups excluded (2 as non-JSON replies such as bodiless 204 writes).
• Path parameters: 18 uuid. 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 40 tools.
• Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
• 12 retrieval tasks (evals/keycloak-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_21d8808f42337b36a19f943f7bca1058d9f2c73b660d5bce1f10b05539ca525d (scoped HAR sha256 7c78d99263c9fb922b3fbf714619a3d378f08781b2f677d6a473bd28d5b85b3b, 197,162 bytes), manifest view ev_b1030270ddcec481e452055bb5db7711cf3539cb97b7d86b9733f1023452fafa, 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_admin_realms_lighthouse_rota_groups_	get_admin_realms_lighthouse_rota_groups_members_by_uuid	uuid=0945b85e-a22c-426f-bb35-689473f6806b	[0].username	rui.castellanos
qa-02-get_admin_realms_lighthouse_rota_clients	get_admin_realms_lighthouse_rota_clients_by_uuid	uuid=29ace5d9-4d8b-41ad-b822-e0bf67507028	name	Blue Heron Kitchen Till
qa-03-get_admin_realms_lighthouse_rota_users_b	get_admin_realms_lighthouse_rota_users_by_uuid	uuid=41c1c89c-91ef-45a8-9862-9f8f146205ac	email	rui.castellanos@example.test
qa-04-get_admin_realms_lighthouse_rota_users_g	get_admin_realms_lighthouse_rota_users_groups_by_uuid	uuid=22b61766-08f3-4ad1-86b5-c5703ebad11f	[0].name	Lamp Room Crew
qa-05-get_admin_realms_lighthouse_rota_organiz	get_admin_realms_lighthouse_rota_organizations_by_uuid	uuid=7261538a-e446-48ff-af0a-342936cd8d2e	name	Saltmarsh Depot
qa-06-get_admin_realms_lighthouse_rota_clients	get_admin_realms_lighthouse_rota_clients_roles_till_operator	uuid=29ace5d9-4d8b-41ad-b822-e0bf67507028	description	Opens and closes the till for each sitting.
qa-07-get_admin_realms_lighthouse_rota_users_r	get_admin_realms_lighthouse_rota_users_role_mappings_clients	uuid=19cac069-3d62-4ca9-abc7-86f5db125cb6, uuid_2=29ace5d9-4d8b-41ad-b822-e0bf67507028	[0].name	till-operator
qa-08-get_admin_realms_lighthouse_rota_clients	get_admin_realms_lighthouse_rota_clients_roles_by_uuid	uuid=62ccc535-35be-4053-9829-d46c9aa9a60f	[0].description	Reads the quarterly stone counts.
qa-09-get_admin_realms_lighthouse_rota_authent	get_admin_realms_lighthouse_rota_authentication_flows_by_uui	uuid=d4eb267b-e73b-4a25-bd38-dbd542065622	description	Browser login used by the Granite Ledger kiosks in the quarry office.
qa-10-get_admin_realms_lighthouse_rota_roles_b	get_admin_realms_lighthouse_rota_roles_by_id_by_uuid	uuid=237dfaf6-63ef-4f99-945d-e53f00912633	name	quarry-auditor
qa-11-get_admin_realms_lighthouse_rota_groups_	get_admin_realms_lighthouse_rota_groups_by_uuid	uuid=0945b85e-a22c-426f-bb35-689473f6806b	name	Quarry Crew
qa-12-get_admin_realms_lighthouse_rota_organiz	get_admin_realms_lighthouse_rota_organizations_members_by_uu	uuid=7261538a-e446-48ff-af0a-342936cd8d2e	[0].email	mina.okafor@example.test

Episodes

model	tasks	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	12	100%	1.0	11,135
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	13,974

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/KeycloakEnv-REA --bundle keycloak --task smoke          # Docker, no model
agent-env hf run earakely-scale/KeycloakEnv-REA --bundle keycloak --eval keycloak-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. Keycloak ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (a realm lighthouse-rota with 4 users, 4 groups (one nested), 3 realm roles, 2 public clients with 3 client roles, 1 client scope, 2 organizations and 1 copied browser login flow); 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	Keycloak (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)	40 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 Keycloak; 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 Keycloak instance over synthetic data; Keycloak is Apache-2.0 software by the Keycloak authors (a CNCF project) 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.