> 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 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 %} --- # Agent Instructions This documentation is published with GitBook. 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