Integration coverage
Choose an integration by its verified behavior, package version, and remaining limits.
The documented SDK versions are Python 1.3.2 and Node.js 1.2.9. Coverage is method-specific. A framework name in this site does not establish automatic enforcement of its tools, outputs, or every model client.
Verified paths
Reviewed on 16 October 2026 against the installed SDK packages and an isolated local PostgreSQL/API deployment.
| Path | Executed evidence | Limits |
|---|---|---|
| Python direct evaluation | Installed package → authenticated API → persisted metadata-only trace | Verify your policies, quota, TLS, and deployment. |
| Node direct evaluation | Installed package → authenticated API; response and error contracts | Other runtimes and provider combinations need their own acceptance tests. |
| Python OpenAI input enforcement | Real Chat Completions request and a blocked request with zero provider calls | Specific extracted text and patched methods; generated-token blocking is separate. |
| Node OpenAI enforcement | Real streaming call plus wrapper/stream lifecycle regressions | Application owns telemetry setup and stream cleanup. |
| Python LangGraph + ChatOpenAI | Real tool-calling graph: allowed request, warning disclosure, blocked tool result, and input blocked before any provider request | Explicit application checks gate tool results and final output. The tool in the test was read-only and synthetic. |
| Python instrumentation | Actual exporter receipts, model spans, token counts, and durable metadata-only traces | Every instrumentor/framework version requires its own content and lifecycle verification. |
| Generator/stream tracing | Sync/async lifecycle, early close, cancellation, and context-isolation regressions | Trace completion is not an output enforcement guarantee. |
Integration recipes
The remaining integration guides show where to place explicit evaluation around a provider or framework call. They are implementation recipes, not a completed live compatibility certification for each vendor. Use versions approved for your application, lock dependencies, and test the exact method you ship.
Python automatic input interception has implementations for specific OpenAI, Anthropic, and LangChain entry points. Node's enforce() wraps supported OpenAI client methods. See enforcement scope. Other clients can use direct evaluation without depending on interception.
Accept an integration
- Enable a named policy with the intended agent scope.
- Verify a safe request and a synthetic unsafe request.
- Count actual model or tool operations. A pre-call block must leave that count at zero.
- Switch warning to blocking, disable cached decisions for the retest, and repeat.
- Make the evaluator unavailable and confirm the configured failure behavior.
- Flush tracing, inspect backend receipt, and check that content capture matches workspace settings.
- For streaming, test normal completion, early close, cancellation, and output handling.
Detector and deployment scope
There are 16 built-in policy types. Model-backed checks depend on installed dependencies, model availability, or configured judge endpoints. A healthy lightweight Secrets check does not establish that every ML detector is available. Use readiness and diagnostics.
Local and controlled-delay measurements do not establish latency for a deployed cloud database. No universal latency or accuracy guarantee is made here. Measure representative workload, concurrency, cold starts, and network placement in your deployment.