World Model Harness

wmh is an open-source project for running and building continuously improving agents. It includes a flexible agent runtime, a world model that simulates tool calls, and an optimizer that builds task-specific harnesses for stronger performance at lower cost.

🌐 Platform | 📚 Docs | Discord

Getting started

Local setup

Install WMH, choose the model provider for the built-in runtime agent, and start a local run:

pip install world-model-harness
wmh providers set
wmh run --task "Inspect this repository and explain it"

Build a named world model from collected traces:

wmh build --file traces.jsonl --name my-environment

Then optimize an agent harness against that model and a set of tasks:

wmh optimize my-agent my-environment --tasks tasks.jsonl

Hosted platform

Create an account at platform.experientiallabs.ai, then authenticate the CLI:

wmh login

Copy an agent ID from the platform and run its current champion harness:

wmh run <agent-id>

E2B backend

Hosted agents already run in platform-managed E2B sandboxes. To evaluate a local optimization in E2B, install the extra and provide an E2B key:

pip install "world-model-harness[e2b]"
export E2B_API_KEY=...
wmh optimize my-agent my-environment --tasks tasks.jsonl --backend e2b

Use a world model as an API

from wmh import Action, ActionKind
from wmh.config.store import WorldModelStore
from wmh.engine.loader import load_world_model

model_dir = WorldModelStore(".wmh").resolve("airline")
wm, _provider = load_world_model(model_dir)

session = wm.new_session(task="check out the cart")
obs = wm.step(session.id, Action(kind=ActionKind.TOOL_CALL, name="add_to_cart",
                                 arguments={"sku": "A1"}))
print(obs.content)

Or over HTTP (same code path), namespaced by model name: GET /world_models, then POST /world_models/{name}/sessions and POST /world_models/{name}/sessions/{id}/step.

Run after platform login

After wmh login, the same wmh run command can open a hosted world model or run an agent's current champion harness in E2B. The platform manages model and sandbox credentials, so hosted runs do not need local API keys.

wmh login
wmh run <world-model-or-agent-id>
wmh run <agent-id> -u . --task "fix the failing tests"

Workspace upload is opt-in with -u: WMH live-syncs changes and preserves concurrent local edits. Long-running agents can detach, continue in the platform, and be messaged or reattached later.

wmh run <agent-id> -u . --detach
wmh run --send "Now run the full test suite"
wmh run --attach
wmh run --end

Runtime agents and optimizers in E2B sandboxes

WMH can run the real pi worker inside isolated E2B sandboxes while the world model supplies the environment. Optimization and evaluation rollouts run in parallel, and model credentials stay outside the sandbox.

wmh optimize my-agent my-environment --tasks tasks.jsonl --backend e2b
wmh eval tasks.jsonl --mode closed-loop --harness my-agent --harness-backend e2b

The optimizer can change prompts, tools, policies, skills, and runtime code. Every candidate is measured against the same simulated tasks, and only changes that pass the evaluation gates become the new versioned champion harness.

Development

Managed with uv; linting/formatting with ruff; type checking with ty. Conventions live in AGENTS.md.

uv sync --extra dev      # env + dev tools
uv run ruff check .      # lint
uv run ruff format .     # format
uv run ty check          # type check
uv run pytest -q         # tests

Usage telemetry

wmh uses anonymous usage telemetry to track the volume of usage. Telemetry is strictly metadata. It never includes prompts, traces, actions, observations, file paths, model names, provider credentials, or raw user content.

Telemetry is enabled by default. To opt out for a project:

uv run wmh config telemetry disable

This writes .wmh/settings.toml. You can re-enable it with uv run wmh config telemetry enable, check the current setting with uv run wmh config telemetry status, or disable it for a process with DO_NOT_TRACK=1 or WMH_TELEMETRY=0.