# LLMs as 5x Faster Sandboxes > Source: > Published: 2026-06-30 16:30:08+00:00 Docker as an LLM.Simulate an agent environment from traces instead of standing up a sandbox. A frontier LLM acts as the environment your agent steps against, reconstructed from OpenTelemetry traces. The harness ingests recorded `(state, action) -> observation` steps, builds a retrieval index, evolves the base environment prompt with GEPA, and serves the resulting world model locally. **Build** from OTel traces: ingest, normalize, split train/held-out, index the replay buffer, and optimize the environment prompt.**Serve or play** the built model: agents call`WorldModel.step(action)` in-process or through the local HTTP backend.**Evaluate** reconstruction fidelity with`wmh eval` against trace files. ``` uv sync wmh providers verify wmh build --name airline --file examples/tau-bench/traces.otel.jsonl wmh list wmh eval examples/tau-bench/traces.otel.jsonl wmh eval list wmh eval run tau-bench wmh eval results wmh examples list wmh examples run tau-bench -- --trace 0 wmh serve wmh demo --name airline wmh play --name airline ``` `wmh build` with no flags launches a guided creation wizard on an interactive terminal. Pass `--file` and related flags, or `--no-interactive` , for scriptable runs. World models are named and stored under `.wmh/models//` . `wmh list` , `wmh serve` , `wmh demo` , and `wmh play` only use models built locally in that directory. | Command | What it does | |---|---| `wmh build` | Builds a named world model from OTel traces or a vendor trace pull. It ingests traces, normalizes them, splits train/held-out data, builds the retrieval index, runs GEPA prompt optimization, and writes the artifact to `.wmh/models//` . With no required inputs on a TTY, it opens the guided wizard. | `wmh list` | Lists world models found under the selected root's `models/` directory, including provider, held-out score, rollout count, and frontier size when those metrics exist. By default, the selected root is `.wmh/` , so plain `wmh list` does not read committed example artifacts. | `wmh eval ` | Scores reconstruction fidelity on one or more OTel trace files. It performs a deterministic train/held-out split, replays held-out steps through the base or supplied prompt, grades predicted observations against recorded observations, and prints per-file plus overall fidelity. | `wmh eval list` | Lists named eval suites from `examples//evals/*.toml` . Suites are example-local definitions for repeatable reconstruction-fidelity runs. | `wmh eval run ` | Runs a named eval suite, using its configured trace files and split/scoring settings. Results are written as local JSON under `.wmh/evals///` unless `--out` is supplied. The default suite for an example can be selected by task name, e.g. `wmh eval run tau-bench` . | `wmh eval results [suite]` | Summarizes locally saved named eval results from `.wmh/evals/` . These are generated artifacts and should not be committed. | `wmh serve` | Starts the local FastAPI backend on `127.0.0.1:8000` by default. It serves all locally built models, or only the repeated `--name` selections, through `/world_models/...` HTTP routes. | `wmh demo` | Runs a short demo against a built model. A throwaway LLM agent proposes an action from sampled trace examples, the world model predicts the environment observation, and the CLI prints the action, environment prompt, and observation. | `wmh play` | Opens an interactive REPL for a built model. You type tool calls or free-text actions, and the world model returns observations while maintaining session state and history. | `wmh providers verify` | Checks provider connectivity for locally built models. It verifies configured completion providers and any provider-backed embedder paths, skipping the offline hashing embedder. | `wmh examples list` | Lists self-contained task examples under `examples//` that include a `traces.otel.jsonl` corpus or `run.sh` launcher. | `wmh examples run -- ` | Runs the selected example's local `run.sh` launcher and forwards all arguments after `--` . This is the standard entrypoint for dataset-specific example helpers. | Dataset-specific logic lives only under `examples/` . Each task folder is self-contained: `examples/swe-bench/traces.otel.jsonl` `examples/tau-bench/traces.otel.jsonl` `examples/terminal-tasks/traces.otel.jsonl` Each example folder may include task-local capture or launch helpers. Launch them through `wmh examples run -- ` . Reusable harness behavior belongs in `wmh/` and should be exposed through the `wmh` CLI. Repeatable eval suite definitions live under `examples//evals/*.toml` . They point at example-local trace files and configure replay options such as train split, sampling, RAG, and judge. Generated eval results stay local under `.wmh/evals/` . Example-local prebuilt artifacts live under `examples//models//` ; pass `--root examples/` to `wmh list` , `wmh demo` , `wmh play` , or `wmh serve` to use one without copying it into `.wmh/` . ``` python 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) ``` Over HTTP, use `GET /world_models` , then `POST /world_models/{name}/sessions` and `POST /world_models/{name}/sessions/{id}/step` . Credentials are read from the environment. | Provider | Default model family | Env vars | |---|---|---| | Anthropic | Claude Opus | `ANTHROPIC_API_KEY` | | AWS Bedrock | Claude Opus | `AWS_REGION` , `AWS_ACCESS_KEY_ID` , `AWS_SECRET_ACCESS_KEY` | | Azure OpenAI | GPT | `AZURE_OPENAI_API_KEY` , `AZURE_OPENAI_ENDPOINT` | | OpenAI | GPT | `OPENAI_API_KEY` | ``` uv sync --extra dev uv run ruff check . uv run ruff format . uv run ty check uv run pytest -q ``` Conventions live in `AGENTS.md` . Tests are inline next to the code they cover (`foo.py` -> `foo_test.py` ) under `wmh/` .