earakely-scale/MemosEnv-REA

memos

memos

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

Tables

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

Run it

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

Memos on AgentEnv, from a REA capture

What this is. An environment of Memos 0.31.0 (MIT), built from one recorded browser session against a self-hosted instance seeded with synthetic data. REA projected the capture into an environment manifest (26 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 Memos. 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

• 26 tools (23 GET, 1 POST, 1 DELETE, 1 PATCH) over 80 recorded exchanges on http://127.0.0.1:3205; no recorded group excluded. 23 are offered; the other 3 are write tools the environment cannot apply (post_memos, delete_memos_by_memo_id, patch_memos_by_memo_id), left out rather than faked.
• Path parameters: 7 sibling-variance, 7 prefix-variance, 7 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 59 tools.
• State: 20 entities of 10 types. 18 read tools answer from it and 5 replay their recorded responses. 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/memos-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_9a022f8f7a608eee340226341c8e7796132f16117f5d43bbc39bbfe752fc4908 (scoped HAR sha256 82239f712d1b7317db9c0ee77efe70fc1811ec94825e8b52ee313ab2594f206c, 176,934 bytes), manifest view ev_486af99b40d69775898935d923ff0f77f34656b9bace73158b38ab31935f8fdf, confidence inferred, authority historical-reference. The REA build is rea-agents 6.7.0 from main 3dbe314a with open pull requests merged: morluto/rea#5600 at 325c128f (which carries #5351 and #5352), #5787 at 9b868c07, #5800 at 35f3f2b5, #6136 at a477b21d, #6138 at 04b24a3e and #6169 at 733833ef. 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: its memo create, update and delete write to memos, which REA could not model as a type, because a memo read answers with name set to memos/<id> while its path carries only <id>, so the read proves no identity, and one filtered memo list orders a memo's relations differently from the other reads, 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_users_views_by_user_id_uuid	get_users_views_by_user_id_uuid	user_id=rea-admin, uuid=5faa57d0-5cf1-4f18-a0ba-9c45002c3ad6	title	Granite Ledger notes
qa-02-get_memos_comments_by_memo_id	get_memos_comments_by_memo_id	memo_id=GKhCLz3QgmJimUdPCvmiUk	memos[0].content	I will bring the January tide tables to the handover.
qa-03-get_users_views_by_user_id	get_users_views_by_user_id	user_id=rea-admin	memoViews[0].filter	"granite-ledger" in tags
qa-04-get_users_by_user_id	get_users_by_user_id	user_id=ada-finch	displayName	Ada Finch
qa-05-get_users_spaceinvitations_by_user_id	get_users_spaceinvitations_by_user_id	user_id=rea-admin	spaceInvitations[0].space.description	Night-shift supper rota for the Heron Flats keepers.
qa-06-get_spaces_invitations_by_uuid	get_spaces_invitations_by_uuid	uuid=7d05f1ce-0674-4140-98d9-b103a0e57477	spaceInvitations[0].space.title	Saltmarsh Depot Crew
qa-07-get_memos_by_memo_id	get_memos_by_memo_id	memo_id=Q9Tq4hkBRqjNnuFjyefFad	content	#blue-heron-kitchen Blue Heron Kitchen serves 36 covers per sitting.
qa-08-get_spaces_by_uuid	get_spaces_by_uuid	uuid=7d05f1ce-0674-4140-98d9-b103a0e57477	description	Keepers and quartermasters of the Saltmarsh Depot.
qa-09-get_users	get_users	no arguments	users[0].description	Relief keeper on Blue Heron Spit.
qa-10-get_memos	get_memos	pageSize=20	memos[0].content	#saltmarsh-depot Crew meeting at the Copperfield Annex boathouse, 18:30.
qa-11-get_instance_profile	get_instance_profile	no arguments	admin.description	Keeper of the Lighthouse Rota since the spring census.
qa-12-get_users_by_user_id	get_users_by_user_id	user_id=ada-finch	email	ada.finch@example.test

Episodes

model	retrieval episodes	passed	pass rate	mean tool calls	mean input tokens
openai/gpt-oss-120b:groq	12	12	100%	1.0	10,676
zai-org/GLM-5.3-Flash:zai-org	12	12	100%	1.0	12,499

Every episode is in the episodes config with its transcript; scores 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/MemosEnv-REA --bundle memos --task smoke          # Docker, no model
agent-env hf run earakely-scale/MemosEnv-REA --bundle memos --eval memos-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. Memos ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (users: rea-admin (Rea Admin, ADMIN, first account), ada-finch (Ada Finch), rui-castellanos (Rui Castellanos), mina-okafor (Mina Okafor); memos: rea-admin: winter rota (PROTECTED, pinned, #lighthouse-rota), stone count GL-2291 (PUBLIC, #granite-ledger), Blue Heron Kitchen 36 covers (PUBLIC, location 'Blue Heron Spit boathouse'), lamp-oil order SO-4471 (PRIVATE, #saltmarsh-depot), Granite Ledger checklist (task list, references the stone count), Copperfield Annex beacon (ARCHIVED), crew meeting (SPACE visibility, in space Saltmarsh Depot Crew); ada-finch: January low water 04:12 (PROTECTED);...); 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; 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	Memos (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)	23 tools mirroring the application's endpoints
State	Full: a virtual clock, triggers, frozen windows, agents that answer	A state extracted from the capture (20 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 Memos; 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 Memos instance over synthetic data; Memos is MIT software by the Memos 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.