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integration for LangChain.js","maintainers":[{"name":"lc-oss-admin","email":"oss-admin@langchain.dev"},{"name":"langchain-security","email":"infra@langchain.dev"}],"readme":"# @langchain/xai\n\nThis package contains the LangChain.js integrations for xAI.\n\n## Installation\n\n```bash npm2yarn\nnpm install @langchain/xai @langchain/core\n```\n\n## Chat models\n\nThis package adds support for xAI chat model inference.\n\nSet the necessary environment variable (or pass it in via the constructor):\n\n```bash\nexport XAI_API_KEY=\n```\n\n```typescript\nimport { ChatXAI } from \"@langchain/xai\";\nimport { HumanMessage } from \"@langchain/core/messages\";\n\nconst model = new ChatXAI({\n  apiKey: process.env.XAI_API_KEY, // Default value.\n});\n\nconst message = new HumanMessage(\"What color is the sky?\");\n\nconst res = await model.invoke([message]);\n```\n\n## Server Tool Calling (Live Search)\n\nxAI supports server-side tools that are executed by the API rather than requiring client-side execution. The `live_search` tool enables the model to search the web for real-time information.\n\n### Using the built-in live_search tool\n\n```typescript\nimport { ChatXAI, tools } from \"@langchain/xai\";\n\nconst model = new ChatXAI({\n  model: \"grok-3-fast\",\n});\n\n// Create the built-in live_search tool with optional parameters\nconst searchTool = tools.xaiLiveSearch({\n  maxSearchResults: 5,\n  returnCitations: true,\n});\n\n// Bind the live_search tool to the model\nconst modelWithSearch = model.bindTools([searchTool]);\n\n// The model will search the web for real-time information\nconst result = await modelWithSearch.invoke(\n  \"What happened in tech news today?\"\n);\nconsole.log(result.content);\n```\n\n### Using searchParameters for more control\n\n```typescript\nimport { ChatXAI } from \"@langchain/xai\";\n\nconst model = new ChatXAI({\n  model: \"grok-3-fast\",\n  searchParameters: {\n    mode: \"auto\", // \"auto\" | \"on\" | \"off\"\n    max_search_results: 5,\n    from_date: \"2024-01-01\", // ISO date string\n    return_citations: true,\n  },\n});\n\nconst result = await model.invoke(\"What are the latest AI developments?\");\n```\n\n### Override search parameters per request\n\n```typescript\nconst result = await model.invoke(\"Find recent news about SpaceX\", {\n  searchParameters: {\n    mode: \"on\",\n    max_search_results: 10,\n    sources: [\n      {\n        type: \"web\",\n        allowed_websites: [\"spacex.com\", \"nasa.gov\"],\n      },\n    ],\n  },\n});\n```\n\n### Configuring data sources with `sources`\n\nYou can configure which data sources Live Search should use via the `sources` field\nin `searchParameters`. Each entry corresponds to one of the sources described in the\nofficial xAI Live Search docs (`web`, `news`, `x`, `rss`).\n\n```typescript\nconst result = await model.invoke(\n  \"What are the latest updates from xAI and related news?\",\n  {\n    searchParameters: {\n      mode: \"on\",\n      sources: [\n        {\n          type: \"web\",\n          // Only search on these websites\n          allowed_websites: [\"x.ai\"],\n        },\n        {\n          type: \"news\",\n          // Exclude specific news websites\n          excluded_websites: [\"bbc.co.uk\"],\n        },\n        {\n          type: \"x\",\n          // Focus on specific X handles\n          included_x_handles: [\"xai\"],\n        },\n      ],\n    },\n  }\n);\n```\n\nYou can also use RSS feeds as a data source:\n\n```typescript\nconst result = await model.invoke(\"Summarize the latest posts from this feed\", {\n  searchParameters: {\n    mode: \"on\",\n    sources: [\n      {\n        type: \"rss\",\n        links: [\"https://example.com/feed.rss\"],\n      },\n    ],\n  },\n});\n```\n\n> Notes:\n>\n> - The `xaiLiveSearch` tool options use **camelCase** field names in TypeScript\n>   (for example `maxSearchResults`, `fromDate`, `returnCitations`,\n>   `allowedWebsites`, `excludedWebsites`, `includedXHandles`). These are\n>   automatically mapped to the underlying JSON API's `search_parameters`\n>   object, which uses `snake_case` field names as documented in the official\n>   xAI Live Search docs.\n\n### Combining live_search with custom tools\n\n```typescript\nimport { ChatXAI, tools } from \"@langchain/xai\";\n\nconst model = new ChatXAI({ model: \"grok-3-fast\" });\n\nconst modelWithTools = model.bindTools([\n  tools.xaiLiveSearch(), // Built-in server tool\n  {\n    // Custom function tool\n    type: \"function\",\n    function: {\n      name: \"get_stock_price\",\n      description: \"Get the current stock price\",\n      parameters: {\n        type: \"object\",\n        properties: {\n          symbol: { type: \"string\" },\n        },\n        required: [\"symbol\"],\n      },\n    },\n  },\n]);\n```\n\n## Development\n\nTo develop the `@langchain/xai` package, you'll need to follow these instructions:\n\n### Install dependencies\n\n```bash\npnpm install\n```\n\n### Build the package\n\n```bash\npnpm build\n```\n\nOr from the repo root:\n\n```bash\npnpm build --filter @langchain/xai\n```\n\n### Run tests\n\nTest files should live within a `tests/` file in the `src/` folder. Unit tests should end in `.test.ts` and integration tests should\nend in `.int.test.ts`:\n\n```bash\n$ pnpm test\n$ pnpm test:int\n```\n\n### Lint & Format\n\nRun the linter & formatter to ensure your code is up to standard:\n\n```bash\npnpm lint && pnpm format\n```\n\n### Adding new entrypoints\n\nIf you add a new file to be exported, either import & re-export from `src/index.ts`, or add it to the `exports` field in the `package.json` file and run `pnpm build` to generate the new entrypoint.\n","readmeFilename":"README.md"}