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metadata 
tags:
  - rl-environment
  - agentenv
license: apache-2.0
configs:
  - config_name: netbox_tasks
    data_files:
      - split: train
        path: tasks/netbox.parquet
    default: true
  - config_name: netbox_episodes
    data_files:
      - split: train
        path: episodes/netbox.parquet

netbox

netbox

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

Tables

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

Run it 

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

NetBox on AgentEnv, from a REA capture

What this is. A replay environment of NetBox 4.7 (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 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 NetBox. Tasks are graded deterministically, with no judge.

What's in it

  • 42 tools (39 GET, 1 POST, 1 DELETE, 1 PATCH) over 100 recorded exchanges on http://127.0.0.1:3503; no recorded group excluded.
  • Path parameters: 21 numeric. 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 42 tools.
  • Unknowns the manifest states explicitly: authorization model, response semantics beyond recorded exchanges, state transitions between tools.
  • 12 retrieval tasks (evals/netbox-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_f0ea4345138715ae98fd1b523d129cc054375c34188204296a93efbd974b99ea (scoped HAR sha256 4a3deb631550090b3921305e28dfd787c562458db4b33a359f8b13f069126cd7, 301,066 bytes), manifest view ev_1f404e4754499ab52eb407480151cb26628a215a819093208368aed203104027, confidence inferred, authority historical-reference. The REA build is rea-agents 6.7.0 from main at commit 3fca2e0c, which merged the environment commands (morluto/rea#4954, #4955, #4975, #4999 and #5182); 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_dcim_sites_by_id get_dcim_sites_by_id id=1 region.name Lantern Coast
qa-02-get_circuits_providers_by_id get_circuits_providers_by_id id=1 description Invented coastal carrier that lays seabed fiber
qa-03-get_ipam_prefixes_by_id get_ipam_prefixes_by_id id=1 vrf.name Ledger Vault
qa-04-get_dcim_regions_by_id get_dcim_regions_by_id id=1 description Coastal sites strung along the invented lantern shoreline
qa-05-get_tenancy_contacts_by_id get_tenancy_contacts_by_id id=1 title Tide Table Keeper
qa-06-get_dcim_locations_by_id get_dcim_locations_by_id id=1 name North Cage
qa-07-get_ipam_vlans_by_id get_ipam_vlans_by_id id=1 name Lamp Telemetry
qa-08-get_dcim_device_roles_by_id get_dcim_device_roles_by_id id=1 slug core-router
qa-09-get_dcim_interfaces_by_id get_dcim_interfaces_by_id id=1 device.name lr-core-aurora-01
qa-10-get_circuits_circuit_types_by_id get_circuits_circuit_types_by_id id=1 description Leased strands on the coastal seabed route
qa-11-get_dcim_platforms_by_id get_dcim_platforms_by_id id=1 description Fictional switch firmware train from Tidewright
qa-12-get_dcim_devices_by_id get_dcim_devices_by_id id=1 device_type.description Core router that carries the lamp telemetry ring

Episodes

model tasks passed pass rate mean tool calls mean input tokens
openai/gpt-oss-120b:groq 12 12 100% 1.0 12,074
zai-org/GLM-5.3-Flash:zai-org 12 12 100% 1.0 15,192

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/NetBoxEnv-REA --bundle netbox --task smoke          # Docker, no model
agent-env hf run earakely-scale/NetBoxEnv-REA --bundle netbox --eval netbox-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. NetBox ran in Docker on a throwaway VM, bound to loopback, seeded through its own API with synthetic records (tenants: Granite Ledger Cooperative, Blue Heron Kitchen Collective, Saltmarsh Depot Logistics; contacts: Ada Finch (Tide Table Keeper), Rui Castellanos (Quarry Ledger Auditor), Mina Okafor (Cold Chain Lead), Tomas Vey (Annex Night Operator); regions: Lantern Coast, Copper Valley; sites: Lighthouse Rota Exchange (Lamp Room LR-7), Copperfield Annex (Annex Hall CA-3), Saltmarsh Depot Edge (Cold Room SD-12), Blue Heron Kitchen Closet (Pantry Closet BH-2); locations: North Cage, Mezzanine Row; racks: Aurora Stack 01, Aurora Stack 02, Smelter Frame 11, Brine Cabinet 3; manufacturers: Tidewright...); 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 NetBox (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) 42 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 NetBox; 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 NetBox instance over synthetic data; NetBox is Apache-2.0 software by DigitalOcean, LLC and the NetBox community 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.