> For the complete documentation index, see [llms.txt](https://docs.layerlens.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.layerlens.ai/2.-get-started/samples-overview.md). # Samples (70+ runnable) Stratix Python SDK samples — categorized index of the runnable samples that ship with the SDK. The LayerLens Python SDK ships with **70+ runnable samples** covering every public resource — from a single trace evaluation to multi-agent evaluation patterns and CI/CD quality gates. All samples live in [`stratix-python/samples/`](https://github.com/layerlens/stratix-python/tree/main/samples) and run directly after installing the SDK. ## Quick start ```bash pip install layerlens --extra-index-url https://sdk.layerlens.ai/package export LAYERLENS_STRATIX_API_KEY=your-api-key python samples/core/quickstart.py ``` [`quickstart.py`](https://github.com/layerlens/stratix-python/blob/main/samples/core/quickstart.py) walks through the complete workflow end-to-end: upload a trace, create a judge, run an evaluation, and retrieve results. ## Samples by category Each category groups samples around a specific Stratix capability or integration surface. Pick the category closest to what you're building. | Category | What's in it | Count | | ------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------- | | [Core SDK operations](#core-sdk-operations) | Every public resource — traces, judges, evaluations, results, models, benchmarks — used individually and together, including async patterns and pagination | 18 | | [Industry solutions](#industry-solutions) | Domain-specific evaluation scenarios with judges tuned for regulated and high-stakes verticals (healthcare, financial services, legal, government, insurance, retail) | 10 | | [Multi-agent evaluation](#multi-agent-evaluation) | Patterns for evaluating multi-agent collaboration where each agent's output needs independent quality assessment | 5 | | [Content-type evaluations](#content-type-evaluations) | Specialized judges applied to different content types — text responses, brand assets, structured documents | 3 | | [CI/CD integration](#cicd-integration) | Quality gates that embed evaluation into build and deployment pipelines so regressions never reach production | 2 + workflow | | [LLM provider integrations](#llm-provider-integrations) | Trace and evaluate outputs from OpenAI and Anthropic — both manual trace upload and auto-instrumentation paths | 4 | | [OpenClaw agent evaluation](#openclaw-agent-evaluation) | Trace, evaluate, and monitor [OpenClaw](https://openclaw.ai/) autonomous AI agents — cage-match tournaments, code gating, drift detection, content auditing | 10 + skill | | [MCP server](#mcp-server) | Expose Stratix capabilities as tools for Claude, Cursor, and any MCP-compatible AI assistant | 1 | | [CopilotKit integration](#copilotkit-integration) | Full-stack pattern with LangGraph CoAgents + generative-UI cards for human-in-the-loop evaluation workflows | 2 agents + UI | | [Claude Code skills](#claude-code-skills) | Slash commands that bring Stratix workflows into the Claude Code CLI | 6 | | [Sample data](#sample-data) | Pre-built trace files and 16 industry-specific evaluation datasets so every sample runs without sourcing your own data | n/a | *** ## Core SDK operations Located in [`samples/core/`](https://github.com/layerlens/stratix-python/tree/main/samples/core). Start here to learn how every public Stratix resource works, individually and combined. Key samples: * `quickstart.py` — first evaluation in under 30 lines * `trace_evaluation.py` — full trace-evaluation lifecycle * `judge_optimization.py` — optimize a judge via automated prompt evolution (GEPA) * `evaluation_pipeline.py` — chain judges, traces, and results into an automated pipeline * `async_workflow.py` — concurrent operations with `AsyncStratix` See the [Core SDK README](https://github.com/layerlens/stratix-python/blob/main/samples/core/README.md). Maps to gitbook: [Tutorials](/2.-get-started/all-tutorials.md) · [Concept: Evaluations](https://github.com/LayerLens/gitbook-full/blob/main/13-reference/sdk-python/evaluations.md). ## Industry solutions Located in [`samples/industry/`](https://github.com/layerlens/stratix-python/tree/main/samples/industry). Domain-specific scenarios with judges and scoring configurations tuned for regulated and high-stakes verticals. Key samples: * `healthcare_clinical.py` — clinical decision support evaluation * `financial_trading.py` — SOX-aligned trading compliance * `legal_contracts.py` — contract review quality assessment See the [Industry Solutions README](https://github.com/layerlens/stratix-python/blob/main/samples/industry/README.md). Maps to gitbook: [Industry patterns](/6.-build-wire-your-code/sdk-python.md). ## Multi-agent evaluation Located in [`samples/cowork/`](https://github.com/layerlens/stratix-python/tree/main/samples/cowork). Patterns for evaluating multi-agent collaboration — applicable to any framework where multiple agents produce intermediate outputs that need independent quality assessment. Key samples: * `multi_agent_eval.py` — Generator-Evaluator pattern * `code_review.py` — Instrumentor-Reviewer pattern * `rag_assessment.py` — RAG-quality evaluation See the [Multi-Agent README](https://github.com/layerlens/stratix-python/blob/main/samples/cowork/README.md). Maps to gitbook: [Concept: Agentic evaluation](/8.-evaluate-score-the-outputs/agentic-evaluation.md). ## Content-type evaluations Located in [`samples/modalities/`](https://github.com/layerlens/stratix-python/tree/main/samples/modalities). Specialized judges applied to different content types. * `text_evaluation.py` — score text across safety, relevance, and compliance * `brand_evaluation.py` — enforce brand-voice consistency * `document_evaluation.py` — validate document-extraction accuracy ## CI/CD integration Located in [`samples/cicd/`](https://github.com/layerlens/stratix-python/tree/main/samples/cicd). Embed evaluation quality gates into build and deployment pipelines. * `quality_gate.py` — gate deployments on evaluation pass rates * `pre_commit_hook.py` — catch regressions at commit time * `github_actions_gate.yml` — drop-in GitHub Actions workflow See the [CI/CD README](https://github.com/layerlens/stratix-python/blob/main/samples/cicd/README.md). Maps to gitbook: [Tutorial 3: CI/CD gates](/6.-build-wire-your-code/03-cicd-gates.md) · [Cookbook: containerized CI](/6.-build-wire-your-code/containerized-ci.md). ## LLM provider integrations Located in [`samples/integrations/`](https://github.com/layerlens/stratix-python/tree/main/samples/integrations). Two paths: manual trace upload for full control, and auto-instrumentation via `layerlens.instrument` for zero-code observability. * `openai_traced.py` — manual trace upload around an OpenAI completion * `anthropic_traced.py` — capture multi-turn Claude conversations (manual trace upload) * `openai_instrumented.py` — auto-instrument OpenAI with `instrument_openai()`, `@trace`, `span()` * `langchain_instrumented.py` — auto-capture LangChain chain execution with `LangChainCallbackHandler` Maps to gitbook: [Integrations](/6.-build-wire-your-code/sdk-python.md). ## OpenClaw agent evaluation Located in [`samples/openclaw/`](https://github.com/layerlens/stratix-python/tree/main/samples/openclaw). Trace, evaluate, and monitor [OpenClaw](https://openclaw.ai/) autonomous AI agents — including cage-match model tournaments, code gating, drift detection, content auditing, honeypot-skill auditing, and adversarial red-teaming. See the [OpenClaw README](https://github.com/layerlens/stratix-python/blob/main/samples/openclaw/README.md) for the full list and advanced evaluation patterns. ## MCP server Located in [`samples/mcp/`](https://github.com/layerlens/stratix-python/tree/main/samples/mcp). Expose Stratix capabilities as tools for Claude, Cursor, and any MCP-compatible AI assistant. * `layerlens_server.py` — MCP server exposing trace management, judge creation, and evaluation execution See the [MCP README](https://github.com/layerlens/stratix-python/blob/main/samples/mcp/README.md) for setup. ## CopilotKit integration Located in [`samples/copilotkit/`](https://github.com/layerlens/stratix-python/tree/main/samples/copilotkit). Full-stack pattern combining LangGraph CoAgents (Python) with generative-UI card components (React) for human-in-the-loop evaluation workflows. * `agents/evaluator_agent.py` — LangGraph CoAgent for evaluation workflows (uses `interrupt()` for human-in-the-loop judge confirmation) * `agents/investigator_agent.py` — LangGraph CoAgent for trace investigation * `components/*.tsx` — React card components for rendering results (`EvaluationCard`, `JudgeVerdictCard`, `TraceCard`, `MetricCard`, `ComplianceCard`) * `hooks/*.ts` — CopilotKit hooks for wiring Stratix actions (`useLayerLensActions`, `useLayerLensContext`) > **Checkpointer note:** any LangGraph CoAgent that calls `interrupt()` (such as `evaluator_agent.py`) **must** be compiled with a checkpointer. Without one, the AG-UI stream ends without emitting `RUN_FINISHED` and CopilotKit blocks all subsequent messages. The sample ships with `InMemorySaver` for a zero-setup local run; the [README](https://github.com/layerlens/stratix-python/blob/main/samples/copilotkit/README.md) documents Postgres / SQLite / Redis / LangGraph Platform alternatives for production. See the [CopilotKit README](https://github.com/layerlens/stratix-python/blob/main/samples/copilotkit/README.md) for setup, full file list, and the human-in-the-loop walkthrough. ## Claude Code skills Located in [`samples/claude-code/`](https://github.com/layerlens/stratix-python/tree/main/samples/claude-code). Slash commands that bring Stratix workflows directly into the Claude Code CLI — manage traces, judges, evaluations, optimizations, benchmarks, and investigations without leaving your terminal. `/trace`, `/judge`, `/evaluate`, `/optimize`, `/benchmark`, `/investigate`. See the [Claude Code Skills README](https://github.com/layerlens/stratix-python/blob/main/samples/claude-code/README.md). ## Sample data Located in [`samples/data/`](https://github.com/layerlens/stratix-python/tree/main/samples/data). Pre-built trace files, test datasets, and 16 industry-specific evaluation datasets so every sample runs without you sourcing your own data first. See the [Sample Data README](https://github.com/layerlens/stratix-python/blob/main/samples/data/README.md) for contents. *** ## Full sample reference For the complete table of every sample with descriptions, see the [samples README in the SDK repo](https://github.com/layerlens/stratix-python/blob/main/samples/README.md). ## Where to next * [SDK README](/6.-build-wire-your-code/sdk-python.md) — full resource list * [Cookbook](/2.-get-started/all-cookbook-recipes.md) — recipes drawn from these samples * [Integrations](/6.-build-wire-your-code/sdk-python.md) — full adapter list * [Tutorials](/2.-get-started/all-tutorials.md) — guided walkthroughs anchored to these samples