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TypeScript/JavaScript SDK. Interact with the full LangWatch API and use the LangWatch OpenTelemetry SDK to instrument your application. For more information, see https://docs.langwatch.ai/integration/typescript/guide","maintainers":[{"name":"rchaves","email":"rogeriocfj+npm@gmail.com"},{"name":"drew.langwatch","email":"andrew@langwatch.ai"},{"name":"richhuth2","email":"richard@langwatch.ai"}],"readme":"# LangWatch TypeScript SDK\n\n<p align=\"center\">\n  <img src=\"https://github.com/langwatch/langwatch/blob/main/assets/logo-header.webp?raw=true\" alt=\"LangWatch Logo\" />\n</p>\n\n<p align=\"center\">\n  <b>Observability, Prompt Management, and Evaluation for JS LLM/GenAI Apps</b><br/>\n  <a href=\"https://langwatch.ai\">langwatch.ai</a> &nbsp;|&nbsp; <a href=\"https://github.com/langwatch/langwatch\">GitHub</a>\n</p>\n\n<p align=\"center\">\n  <a href=\"https://www.npmjs.com/package/langwatch\"><img src=\"https://img.shields.io/npm/v/langwatch.svg?style=flat-square\" alt=\"npm version\"></a>\n  <a href=\"https://opensource.org/licenses/MIT\"><img src=\"https://img.shields.io/badge/License-MIT-yellow.svg?style=flat-square\" alt=\"License\"></a>\n</p>\n\n---\n\nLangWatch helps you monitor, debug, and optimize your LLM/GenAI applications. This TypeScript SDK provides:\n\n- **OpenTelemetry-based tracing** for LLM, RAG, tool, and workflow spans\n- **Prompt management** with versioning and variable interpolation\n- **Automated and custom evaluation** of model outputs\n\n---\n\n## Features\n\n- 📊 **Observability**: Trace LLM, RAG, tool, and workflow operations with rich context\n- 📝 **Prompt Management**: Fetch, version, and interpolate prompts with variables\n- 🧪 **Evaluation**: Run and record evaluations, with results linked to traces\n- 🔌 **OpenTelemetry**: Integrates with your existing observability stack\n- 🦾 **TypeScript-first**: Full type safety and modern API design\n\n---\n\n## Installation\n\n```bash\nnpm install langwatch\n```\n\n---\n\n## 🚀 Getting Started\n\nHere's the fastest way to get LangWatch working in your app:\n\n```ts\nimport { setupObservability } from \"langwatch/observability/node\";\nimport { getLangWatchTracer } from \"langwatch\";\n\n// 1. Initialize LangWatch (Node.js example)\nawait setupObservability(); // By default this will read the LANGWATCH_API_KEY environment variable to set the API key.\n\n// 2. Create a tracer and span\nconst tracer = getLangWatchTracer(\"my-app\");\nconst span = tracer.startSpan(\"my-operation\");\nspan.setInput(\"User prompt\");\nspan.setOutput(\"Model response\");\nspan.end();\n```\n\n> **Tip:** For use in the browser, use `import { setupObservability } from \"langwatch/browser\"` instead.\n\n---\n\n## Quick Start: Core Features\n\n### 1. Tracing & Observability\n\n- **Get a tracer:**\n  ```ts\n  const tracer = getLangWatchTracer(\"my-app\");\n  ```\n- **Start a span and record input/output:**\n  ```ts\n  const span = tracer.startSpan(\"call-llm\");\n  span.setType(\"llm\");\n  span.setInput(\"User prompt\"); // Main way to record input\n  span.setOutput(\"Model response\"); // Main way to record output\n  span.end();\n  ```\n  > **Note:** `setInput` and `setOutput` are the primary methods to record input/output. Use `setInputString`/`setOutputString` for plain text, or pass any serializable value.\n\n- **Use withActiveSpan for automatic error handling:**\n  ```ts\n  await tracer.withActiveSpan(\"my-operation\", async (span) => {\n    span.setType(\"llm\");\n    span.setInput(\"User prompt\");\n    // ... your code ...\n    span.setOutput(\"Model response\");\n  });\n  ```\n\n- **Record an evaluation directly on a span:**\n  ```ts\n  span.recordEvaluation({ name: \"My Eval\", passed: true, score: 1.0 });\n  ```\n  > **Note:** This associates evaluation results with a specific span (operation or model call).\n\n- **(Optional) Add GenAI message events:**\n  ```ts\n  span.addGenAISystemMessageEvent({ content: \"You are a helpful assistant.\" });\n  span.addGenAIUserMessageEvent({ content: \"Hello!\" });\n  span.addGenAIAssistantMessageEvent({ content: \"Hi! How can I help you?\" });\n  span.addGenAIToolMessageEvent({ content: \"Tool result\", id: \"tool-1\" });\n  span.addGenAIChoiceEvent({ finish_reason: \"stop\", index: 0, message: { content: \"Response\" } });\n  ```\n  > **Advanced:** The `addGenAI...` methods are optional and mainly for advanced/manual instrumentation. Most users do not need these unless you want fine-grained message event logs.\n\n- **RAG context, metrics, and model information:**\n  ```ts\n  span.setRAGContexts([{ document_id: \"doc1\", chunk_id: \"c1\", content: \"...\" }]);\n  span.setMetrics({ promptTokens: 10, completionTokens: 20, cost: 0.002 });\n  span.setRequestModel(\"gpt-4\");\n  span.setResponseModel(\"gpt-4\");\n  ```\n\n### 2. Prompt Management\n\n- **Fetch and format a prompt:**\n  ```ts\n  import { getPrompt } from \"langwatch/prompt\";\n  const prompt = await getPrompt(\"prompt-id\");\n  const compiledPrompt = await getPrompt(\"prompt-id\", { user: \"Alice\" });\n  ```\n- **Fetch a specific prompt version:**\n  ```ts\n  import { getPromptVersion } from \"langwatch/prompt\";\n  const compiledPrompt = await getPromptVersion(\"prompt-id\", \"version-id\", {\n    user: \"Alice\",\n  });\n  ```\n\n  > **Note:** The prompt APIs (`getPrompt`, `getPromptVersion`) automatically create spans and add useful tracing information.\n\n### 3. Evaluation\n\n- **Run an evaluation:**\n  ```ts\n  import { runEvaluation } from \"langwatch/evaluation\";\n  const result = await runEvaluation({\n    slug: \"helpfulness\",\n    data: { input: \"Hi\", output: \"Hello!\" }\n  });\n  ```\n- **Record a custom evaluation:**\n  ```ts\n  import { recordEvaluation } from \"langwatch/evaluation\";\n  recordEvaluation({\n    name: \"Manual Eval\",\n    passed: true,\n    score: 0.9,\n    details: \"Looks good!\"\n  });\n  ```\n  > **Note:** The evaluation APIs (`runEvaluation`, `recordEvaluation`) also create spans and add tracing/evaluation info automatically.\n\n### 4. LangChain Integration\n\n- **Use with LangChain:**\n  ```ts\n  import { LangWatchCallbackHandler } from \"langwatch/observability/instrumentation/langchain\";\n\n  const chatModel = new ChatOpenAI({\n    callbacks: [new LangWatchCallbackHandler()],\n  });\n  ```\n\n## Advanced\n\n### Filtering Spans\n\nControl which spans are sent to LangWatch using the built-in filter DSL. By default, HTTP request spans are excluded to reduce framework noise.\n\n#### Using Presets\n\n```ts\nimport { LangWatchTraceExporter } from \"langwatch\";\n\n// Keep only Vercel AI SDK spans\nconst exporter = new LangWatchTraceExporter({\n  filters: [{ preset: \"vercelAIOnly\" }],\n});\n\n// No filtering (send all spans)\nconst exporter = new LangWatchTraceExporter({\n  filters: null, // or filters: []\n});\n```\n\n#### Custom Filters\n\n```ts\n// Include only specific scopes\nconst exporter = new LangWatchTraceExporter({\n  filters: [\n    { include: { instrumentationScopeName: [{ equals: \"ai\" }] } },\n  ],\n});\n\n// Exclude spans by name pattern\nconst exporter = new LangWatchTraceExporter({\n  filters: [\n    { exclude: { name: [{ startsWith: \"internal.\" }] } },\n  ],\n});\n\n// Combine filters (AND pipeline)\nconst exporter = new LangWatchTraceExporter({\n  filters: [\n    { include: { instrumentationScopeName: [{ equals: \"ai\" }] } },\n    { preset: \"excludeHttpRequests\" },\n  ],\n});\n```\n\n**Learn more:** See the [Filtering Spans Tutorial](https://docs.langwatch.ai/integration/typescript/tutorials/filtering-spans) for comprehensive examples and best practices.\n\n### Custom OpenTelemetry Integration\n```ts\nimport { FilterableBatchSpanProcessor, LangWatchExporter } from \"langwatch\";\n\nconst processor = new FilterableBatchSpanProcessor(\n  new LangWatchExporter({\n    apiKey: \"your-api-key\",\n    endpoint: \"https://custom.langwatch.com\",\n  }),\n  excludeRules\n);\n```\n\n### Span Processing Rules\n```ts\nconst excludeRules: SpanProcessingExcludeRule[] = [\n  { attribute: \"http.url\", value: \"/health\" },\n  { attribute: \"span.type\", value: \"health\" },\n];\n```\n\n### Manual Instrumentation\n```ts\nimport { semconv } from \"langwatch/observability\";\n\nspan.setAttributes({\n  [semconv.ATTR_LANGWATCH_THREAD_ID]: threadId,\n});\n```\n\n## Testing\n\n## Unit and Integration Testing\n\nThis will run the unit and integration tests. You will need to make sure the values in the `.env` file are set correctly, but you can omit the `E2E_` prefixed variables for these tests.\n\n```bash\npnpm test\n```\n\n## E2E Testing\n\nFor E2E tests, you will need to set the `E2E_` prefixed variables in the `.env` file. You will also need to have run build before.\n\n```bash\npnpm build\npnpm test:e2e\n```\n\n---\n\n## Community & Support\n\n- [LangWatch Website](https://langwatch.ai)\n- [Documentation](https://docs.langwatch.ai)\n- [GitHub Issues](https://github.com/langwatch/langwatch/issues)\n- [Discord Community](https://discord.gg/langwatch)\n\n---\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.\n","readmeFilename":"README.md"}