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. An 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 (42 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, write tools whose effect the capture confirmed change it, and data/reset restores it. Nothing executes Keycloak. Tasks are graded deterministically, with no judge.

v0.2.0 rebuilds this environment from a new capture of an instance seeded the same way, recorded so each write's effect is shown: two scratch objects per write target, every write read back at once. A REA build that serves a state, confirmed writes and list queries projects it; the tag v0.1.0 keeps the earlier replay-only version.

What's in it

• 42 tools (39 GET, 1 POST, 1 DELETE, 1 PUT) over 94 recorded exchanges on http://127.0.0.1:3210; no recorded group excluded. 40 are offered; the other 2 are write tools the environment cannot apply (post_admin_realms_lighthouse_rota_users, put_admin_realms_lighthouse_rota_users_by_uuid), left out rather than faked.
• Path parameters: 20 uuid. 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 42 tools.
• State: 91 entities of 21 types. 34 read tools answer from it and 5 replay their recorded responses. 7 list tools also answer filters, search, sorting or paging from it. Served write tools: delete_admin_realms_lighthouse_rota_users_by_uuid (delete).
• 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; reward 1 if the response contains the recorded value, else 0. Plus smoke, which deploys the environment and checks it through the MCP endpoint.
• 1 write tasks (evals/keycloak-writes.toml): each asks for a change in plain language and is graded from the environment's state through data/get, not from the agent's reply; reward 1 only if every check passes. Their grade is in scores.state; reward holds the retrieval verifier's score, so it is empty for them, as for smoke.
• 24 retrieval episodes and 2 write 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_3e28bcdff9589b6212f6881faae7a43f673631181687095e1ab79c7cbb75d7d1 (scoped HAR sha256 66797f954e40e0d775e954a8bb53db3d61709e4ccecd0048bf9733e7d0e49c36, 242,157 bytes), manifest view ev_daed0948799c347da43e6c547b67e8118719fa5a08d9b209047ad1cb1774a95d, confidence inferred, authority historical-reference. The REA build is rea-agents 6.7.0 from main 7091be29 with open pull requests merged: morluto/rea#6385 at 28c3ae97. 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. A served write was confirmed by the capture itself: replaying the session against the extracted state, a later recorded read reproduced its effect and none contradicted it. A create generates ids and clock values deterministically, so the same calls after a reset give the same state; an update changes the members the call sends; a delete removes the entity. Each takes only the body members the recorded call showed and refuses others with a message that names the accepted ones. Its effect shows in the reads the state renders, lists included wherever a list lists its whole collection or a learned query answers the call; a list call nothing explains keeps its recorded members, dropping a deleted entity but not adding a new one. 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_admin_realms_lighthouse_rota_clients	get_admin_realms_lighthouse_rota_clients_roles_till_operator	uuid=74916294-263b-4ece-896b-a8f74a35bb9b	description	Opens and closes the till for each sitting.
qa-02-get_admin_realms_lighthouse_rota_users_g	get_admin_realms_lighthouse_rota_users_groups_by_uuid	uuid=6783c3ac-851a-436e-ba25-bc9b3fdf77f1	[0].name	Lamp Room Crew
qa-03-get_admin_realms_lighthouse_rota_roles_b	get_admin_realms_lighthouse_rota_roles_by_id_by_uuid	uuid=537bbd50-814b-474f-a97e-5d79825f5aab	name	quarry-auditor
qa-04-get_admin_realms_lighthouse_rota_groups_	get_admin_realms_lighthouse_rota_groups_by_uuid	uuid=f17645d7-903d-4335-a55e-bf7ad5a479f6	name	Quarry Crew
qa-05-get_admin_realms_lighthouse_rota_users_r	get_admin_realms_lighthouse_rota_users_role_mappings_clients	uuid=60f89479-69f5-4802-9ee1-2e243954cce2, uuid_2=74916294-263b-4ece-896b-a8f74a35bb9b	[0].name	till-operator
qa-06-get_admin_realms_lighthouse_rota_clients	get_admin_realms_lighthouse_rota_clients_roles_by_uuid	uuid=58221e2c-52fa-420e-bfc8-c2a0c9c476e8	[0].name	ledger-editor
qa-07-get_admin_realms_lighthouse_rota_organiz	get_admin_realms_lighthouse_rota_organizations_by_uuid	uuid=907aa230-6673-414b-b2e2-8c9e0d0cc598	name	Saltmarsh Depot
qa-08-get_admin_realms_lighthouse_rota_authent	get_admin_realms_lighthouse_rota_authentication_flows_by_uui	uuid=8952130c-9d16-47e2-8eb5-b4265428a455	description	Browser login used by the Granite Ledger kiosks in the quarry office.
qa-09-get_admin_realms_lighthouse_rota_clients	get_admin_realms_lighthouse_rota_clients_by_uuid	uuid=74916294-263b-4ece-896b-a8f74a35bb9b	name	Blue Heron Kitchen Till
qa-10-get_admin_realms_lighthouse_rota_client_	get_admin_realms_lighthouse_rota_client_scopes_by_uuid	uuid=efae60cf-4353-469d-ac6c-4899ae2177a5	description	Grants read access to the published tide tables.
qa-11-get_admin_realms_lighthouse_rota_groups_	get_admin_realms_lighthouse_rota_groups_children_by_uuid	uuid=9dc0457c-2c62-4a75-8a5f-9c8e9df81df7	[0].description	Trims wicks and polishes the Fresnel lens before dusk.
qa-12-get_admin_realms_lighthouse_rota_organiz	get_admin_realms_lighthouse_rota_organizations_2	first=0, max=1	[0].name	Copperfield Annex

Writes

Graded from the environment's state through data/get (scores.state in the episodes table; reward 1 only if every check passes).

task	prompt	checks
write-delete	In this Keycloak admin console, Tomas Vey has left the lighthouse rota for good: delete his user account from the lighthouse-rota realm, and leave every other user as it is.	Tomas Vey's user (tomas.vey) no longer exists; the other three users are still there, and nothing else was added

Episodes

model	retrieval episodes	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	12	100%	1.0	19,385
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	22,349

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

model	write episodes	passed
openai/gpt-oss-120b:groq	1	1
zai-org/GLM-5.3-Flash:zai-org	1	1

The user create and update are left out rather than faked. Keycloak's users list shows each user's access as {"manage": true}, while a user read shows the full permission map, and REA gives a created user the map from its read, so the list read after the create disagrees with it; the update's later checks go through the same lists and stay unconfirmed.

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
agent-env hf run earakely-scale/KeycloakEnv-REA --bundle keycloak --eval keycloak-writes --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. 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 sequence through the app's JSON API, then wrote two scratch objects per write target (created, then updated and deleted where the app allows), reading each write back at once; the HAR was scoped to the API, the login exchange dropped, secret-looking string 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 and the state it serves; gen_tasks.py derived the retrieval tasks from the manifest and the recorded bodies; the write tasks were written by hand against the served write tools and the seeded names; 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	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)	40 tools mirroring the application's endpoints
State	Full: a virtual clock, triggers, frozen windows, agents that answer	A state extracted from the capture (91 entities); served writes change it; 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, state checks for writes; 0 or 1 per task
What it measures	Planning under uncertainty and negotiation	Tool discovery, parameter inference and faithful retrieval in a real API, and changing its state
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 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 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.