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wmo turns agent traces you already collect into continuous improvement. Start with a model endpoint at frontier quality with 40%+ lower cost. Keep improving it with world model simulations, meta-harness optimization, and model distillation.

🌐 Platform | 📚 Docs | Discord

Getting started

1. Register your providers.

pip install world-model-optimizer wmo providers set

1. Tune a router on your OTel traces.

wmo build --file traces.jsonl --name my-endpoint

Score every registered model on held-out tasks from your traces

wmo optimize route sweep my-endpoint --traces traces.otel.jsonl

Turn those measurements into a routing policy

wmo optimize route fit matrix.json --kind knn \ --out .wmo/models/my-endpoint/policy.json

1. Serve it.

wmo serve --name my-endpoint

See what it bought you against the model you were using before:

wmo optimize route report matrix.json .wmo/models/my-endpoint/policy.json \ --baseline gpt-5.5

Distill your own small model into the pool with wmo optimize model, serve a single model with no routing via wmo optimize route pin, or build an optimized harness for your agent with wmo optimize harness.

Hosted platform

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

wmo login

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

wmo run

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-optimizer[e2b]" export E2B_API_KEY=... wmo optimize harness my-agent my-environment --tasks tasks.jsonl --backend e2b

Use a world model as an API

world-model-optimizer includes world models that can be used to simulate your agent environment for testing and optimization.

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

model_dir = WorldModelStore(".wmo").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 wmo login, the same wmo 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.

wmo login wmo run wmo run -u . --task "fix the failing tests"

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

wmo run -u . --detach wmo run --send "Now run the full test suite" wmo run --attach wmo run --end

Runtime agents and optimizers in E2B sandboxes

WMO 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.

wmo optimize harness my-agent my-environment --tasks tasks.jsonl --backend e2b wmo 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

wmo 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 wmo config telemetry disable

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

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