> For the complete documentation index, see [llms.txt](https://docs.algenta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.algenta.ai/sdks/python/simulations-and-queries.md).

# Simulations & queries

Python SDK reference for the simulation and query methods — simulate, resolve, query, query\_batch, verify, recommend, score, compare, and plan\_decision.

These methods run Monte Carlo simulations and governed queries against the Algenta engine. They are available on both `AlgentaClient` and `AsyncAlgentaClient` (await the async calls). Simulation and recommendation methods take keyword arguments; the query methods take a request dict and, where noted, merge extra keyword arguments into the body. See [API overview](/http-api/overview.md) for the underlying HTTP contract and [Simulation models](/concepts/simulation-models.md) for the distributions.

## Method reference

| Method                                        | Purpose                                                                                                                            |
| --------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------- |
| `simulate(**kwargs)`                          | Run a Monte Carlo simulation and return a `DecisionEnvelope`. Posts to `/v1/simulate`.                                             |
| `resolve(request=None, **kwargs)`             | Resolve a partial or natural-language hint into a typed, executable plan (`ResolveResult`). Posts to `/v1/resolve`.                |
| `query(request=None, **kwargs)`               | Run a governed exact query and return matching rows (`QueryResult`). Posts to `/v1/query`.                                         |
| `query_with_metadata(request=None, **kwargs)` | Like `query`, plus execution metadata (request id, timing) and response headers (`QueryWithMetadataResult`). Posts to `/v1/query`. |
| `query_batch(request)`                        | Run several governed queries in one round trip (`QueryBatchResult`). Posts to `/v1/query/batch`.                                   |
| `query_sql_report(request)`                   | Run a read-only SQL-style report over a dataset (`QuerySqlReportResult`). Posts to `/v1/query/sql-report`.                         |
| `plan_decision(request)`                      | Turn a goal and context into a structured decision plan (`DecisionPlanResult`). Posts to `/v1/decisions/plan`.                     |
| `verify(request=None, **kwargs)`              | Check a claim against governed data and return a verdict (`VerifyResult`). Posts to `/v1/verify`.                                  |
| `explain(request=None, **kwargs)`             | Run a query and annotate the resolved plan so you can see how the answer was produced (`ExplainResult`).                           |
| `recommend(actions, **kwargs)`                | Rank candidate actions against an objective. Posts to `/v1/recommend`.                                                             |
| `score(request, scoring_weights=None)`        | Score a scenario or action set, optionally with custom weights. Posts to `/v1/score`.                                              |
| `batch(items)`                                | Run a list of simulation requests together. Posts to `/v1/batch`.                                                                  |
| `compare(scenarios, **kwargs)`                | Compare multiple scenarios side by side. Posts to `/v1/compare`.                                                                   |

{% hint style="info" %}
`plan_decision` lives on this surface (it turns intent into a plan) while the decision-memory methods that record and replay decisions live in [Decisions and products](/sdks/python/decisions-and-products.md).
{% endhint %}

## Example: simulate a scenario

```python
import os
from decision_engine import AlgentaClient

client = AlgentaClient(api_key=os.environ["ALGENTA_API_KEY"])

envelope = client.simulate(
    mode="auto",
    simulation_model="monte_carlo",
    scenario={
        "name": "Product launch decision",
        "variables": {
            "revenue": {"low": 80000, "high": 200000},
            "cost": {"low": 40000, "high": 90000},
        },
        "objective": "maximize_net_value",
    },
    runs=10000,
)

print(envelope.recommended_action, envelope.confidence)
print(envelope.metrics.expected_value)
```

## Example: resolve, query, and batch

```python
import os
from decision_engine import AlgentaClient

client = AlgentaClient(api_key=os.environ["ALGENTA_API_KEY"])

# Resolve a hint into a typed plan, then run a query with metadata.
plan = client.resolve({"dataset_id": "$DATASET_ID", "metric": {"hint": "revenue"}})

result = client.query_with_metadata(
    {
        "dataset_id": "$DATASET_ID",
        "metric": {"hint": "revenue"},
        "aggregation": "sum",
    }
)
print(result.metadata.request_id, result.data)

# Run several governed queries in one round trip.
batch = client.query_batch(
    {
        "queries": [
            {"dataset_id": "$DATASET_ID", "metric": {"hint": "revenue"}, "aggregation": "sum"},
            {"dataset_id": "$DATASET_ID", "metric": {"hint": "orders"}, "aggregation": "count"},
        ]
    }
)
print(batch.results)
```

## Related pages

{% content-ref url="/pages/xo3kyzyPZXFb0ETopz2h" %}
[Simulate a scenario](/guides/simulate.md)
{% endcontent-ref %}

{% content-ref url="/pages/1hWq4uaSOQ9pNlD9Ox0f" %}
[Query governed data](/guides/governed-query.md)
{% endcontent-ref %}

{% content-ref url="/pages/azHBRVU9OvmCqePoXOAt" %}
[Decisions & products](/sdks/python/decisions-and-products.md)
{% endcontent-ref %}


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