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Vitarana"},"license":"Apache-2.0","keywords":["modelrelay","llm","sdk","typescript"],"description":"TypeScript SDK for the ModelRelay API","maintainers":[{"name":"shanev","email":"shanev@gmail.com"}],"readme":"# ModelRelay TypeScript SDK\n\n```bash\nbun add @modelrelay/sdk\n```\n\n## Convenience API\n\nThe simplest way to get started. Three methods cover the most common use cases:\n\n### Ask — Get a Quick Answer\n\n```typescript\nimport { ModelRelay } from \"@modelrelay/sdk\";\n\nconst mr = ModelRelay.fromSecretKey(process.env.MODELRELAY_API_KEY!);\n\nconst answer = await mr.ask(\"claude-sonnet-4-5\", \"What is 2 + 2?\");\nconsole.log(answer); // \"4\"\n```\n\n### Chat — Full Response with Metadata\n\n```typescript\nconst response = await mr.chat(\"claude-sonnet-4-5\", \"Explain quantum computing\", {\n system: \"You are a physics professor\",\n});\n\nconsole.log(response.output);\nconsole.log(\"Tokens:\", response.usage.totalTokens);\n```\n\n### Agent — Agentic Tool Loops\n\nRun an agent that automatically executes tools until completion:\n\n```typescript\nimport { z } from \"zod\";\n\nconst tools = mr\n .tools()\n .add(\n \"read_file\",\n \"Read a file from the filesystem\",\n z.object({ path: z.string().describe(\"File path to read\") }),\n async (args) => {\n const content = await fs.readFile(args.path, \"utf-8\");\n return content;\n }\n );\n\nconst result = await mr.agent(\"claude-sonnet-4-5\", {\n tools,\n prompt: \"Read config.json and summarize it\",\n system: \"You are a helpful file assistant\",\n});\n\nconsole.log(result.output);\nconsole.log(\"Tool calls:\", result.usage.toolCalls);\n```\n\n### User Interaction — `user.ask`\n\nUse the built-in `user.ask` tool to request human input in a workflow run:\n\n```typescript\nimport { ModelRelay, ToolRegistry, ToolRunner, createUserAskTool } from \"@modelrelay/sdk\";\n\nconst mr = ModelRelay.fromSecretKey(process.env.MODELRELAY_API_KEY!);\n\nconst tools = [createUserAskTool()];\nconst registry = new ToolRegistry();\n\nconst runner = new ToolRunner({\n registry,\n runsClient: mr.runs,\n onUserAsk: async (_pending, args) => {\n const answer = await promptUser(args.question); // your UI/input here\n return { answer, is_freeform: true };\n },\n});\n\nconst run = await mr.runs.create(spec);\n\nfor await (const event of mr.runs.events(run.run_id)) {\n if (event.type === \"node_waiting\") {\n await runner.handleNodeWaiting(run.run_id, event.node_id, event.waiting);\n }\n}\n```\n\n## Token Providers (Automatic Bearer Auth)\n\nUse token providers when you want the SDK to automatically obtain/refresh **bearer tokens** for data-plane calls like `/responses` and `/runs`.\n\n### Secret key → customer bearer token (mint)\n\n```ts\nimport { CustomerTokenProvider, ModelRelay } from \"@modelrelay/sdk\";\n\nconst tokenProvider = new CustomerTokenProvider({\n secretKey: process.env.MODELRELAY_API_KEY!,\n request: { customerId: \"customer_...\" },\n});\n\nconst mr = new ModelRelay({ tokenProvider });\n```\n\n## Streaming Responses\n\n```ts\nimport { ModelRelay } from \"@modelrelay/sdk\";\n\nconst mr = ModelRelay.fromSecretKey(\"mr_sk_...\");\n\nconst req = mr.responses\n .new()\n .model(\"claude-sonnet-4-5\")\n .user(\"Hello\")\n .build();\n\nconst stream = await mr.responses.stream(req);\n\nfor await (const event of stream) {\n if (event.type === \"message_delta\" && event.textDelta) {\n process.stdout.write(event.textDelta);\n }\n}\n```\n\n## Customer-Scoped Convenience\n\n```ts\nimport { ModelRelay } from \"@modelrelay/sdk\";\n\nconst mr = ModelRelay.fromSecretKey(\"mr_sk_...\");\nconst customer = mr.forCustomer(\"customer_abc123\");\n\nconst text = await customer.responses.text(\n \"You are a helpful assistant.\",\n \"Summarize Q4 results\",\n);\n```\n\nYou can also stream structured JSON for a specific customer:\n\n```ts\nimport { z } from \"zod\";\nimport { ModelRelay, outputFormatFromZod } from \"@modelrelay/sdk\";\n\nconst mr = ModelRelay.fromSecretKey(\"mr_sk_...\");\nconst customer = mr.forCustomer(\"customer_abc123\");\n\nconst schema = z.object({\n summary: z.string(),\n highlights: z.array(z.string()),\n});\n\nconst req = customer.responses\n .new()\n .outputFormat(outputFormatFromZod(schema))\n .system(\"You are a helpful assistant.\")\n .user(\"Summarize Q4 results\")\n .build();\n\nconst stream = await customer.responses.streamJSON>(req);\nfor await (const event of stream) {\n if (event.type === \"completion\") {\n console.log(event.payload);\n }\n}\n```\n\nYou can also pass a single object to `textForCustomer`:\n\n```ts\nconst text = await mr.responses.textForCustomer({\n customerId: \"customer_abc123\",\n system: \"You are a helpful assistant.\",\n user: \"Summarize Q4 results\",\n});\n```\n\n## Workflows\n\nBuild multi-step AI pipelines with the workflow helpers.\n\n### Sequential Chain\n\n```ts\nimport { chain, llm } from \"@modelrelay/sdk\";\n\nconst spec = chain([\n llm(\"summarize\", (n) => n.system(\"Summarize.\").user(\"{{task}}\")),\n llm(\"translate\", (n) => n.system(\"Translate to French.\").user(\"{{summarize}}\")),\n], { name: \"summarize-translate\", model: \"claude-sonnet-4-5\" })\n .output(\"result\", \"translate\")\n .build();\n\nconst { run_id } = await mr.runs.create(spec);\n```\n\n### Parallel with Aggregation\n\n```ts\nimport { parallel, llm } from \"@modelrelay/sdk\";\n\nconst spec = parallel([\n llm(\"agent_a\", (n) => n.user(\"Write 3 ideas for {{task}}.\")),\n llm(\"agent_b\", (n) => n.user(\"Write 3 objections for {{task}}.\")),\n], { name: \"multi-agent\", model: \"claude-sonnet-4-5\" })\n .llm(\"aggregate\", (n) => n.system(\"Synthesize.\").user(\"{{join}}\"))\n .edge(\"join\", \"aggregate\")\n .output(\"result\", \"aggregate\")\n .build();\n```\n\n### Precompiled Workflows\n\nFor workflows that run repeatedly, compile once and reuse:\n\n```ts\n// Compile once\nconst { plan_hash } = await mr.workflows.compile(spec);\n\n// Run multiple times with different inputs\nfor (const task of tasks) {\n const run = await mr.runs.createFromPlan(plan_hash, {\n input: { task },\n });\n}\n```\n\n### Plugins (Workflows)\n\nLoad GitHub-hosted plugins (markdown commands + agents), convert to workflows via `/responses`, then run them with `/runs`:\n\n```ts\nimport { ModelRelay, OrchestrationModes } from \"@modelrelay/sdk\";\nimport { createLocalFSTools } from \"@modelrelay/sdk/node\";\n\nconst mr = ModelRelay.fromSecretKey(process.env.MODELRELAY_API_KEY!);\nconst tools = createLocalFSTools({ root: process.cwd() });\n\nconst plugin = await mr.plugins.load(\"github.com/your-org/your-plugin\");\nconst result = await mr.plugins.run(plugin, \"run\", {\n userTask: \"Summarize the repo and suggest next steps.\",\n orchestrationMode: OrchestrationModes.Dynamic,\n toolRegistry: tools,\n});\n\nconsole.log(result.outputs?.result);\n```\n\n## Chat-Like Text Helpers\n\nFor the most common path (**system + user → assistant text**):\n\n```ts\nconst text = await mr.responses.text(\n \"claude-sonnet-4-5\",\n \"Answer concisely.\",\n \"Say hi.\",\n);\nconsole.log(text);\n```\n\nFor customer-attributed requests where the backend selects the model:\n\n```ts\nconst text = await mr.responses.textForCustomer(\n \"customer-123\",\n \"Answer concisely.\",\n \"Say hi.\",\n);\n```\n\nTo stream only message text deltas:\n\n```ts\nconst deltas = await mr.responses.streamTextDeltas(\n \"claude-sonnet-4-5\",\n \"Answer concisely.\",\n \"Say hi.\",\n);\nfor await (const delta of deltas) {\n process.stdout.write(delta);\n}\n```\n\n## Structured Outputs with Zod\n\nThe simplest way to get typed structured output:\n\n```ts\nimport { ModelRelay } from \"@modelrelay/sdk\";\nimport { z } from \"zod\";\n\nconst mr = ModelRelay.fromSecretKey(\"mr_sk_...\");\n\nconst Person = z.object({\n name: z.string(),\n age: z.number(),\n});\n\n// Simple one-call API (recommended)\nconst person = await mr.responses.object>({\n model: \"claude-sonnet-4-5\",\n schema: Person,\n prompt: \"Extract: John Doe is 30 years old\",\n});\n\nconsole.log(person.name); // \"John Doe\"\nconsole.log(person.age); // 30\n```\n\nFor parallel structured output calls:\n\n```ts\nconst [security, performance] = await Promise.all([\n mr.responses.object({\n model: \"claude-sonnet-4-5\",\n schema: SecuritySchema,\n system: \"You are a security expert.\",\n prompt: code,\n }),\n mr.responses.object({\n model: \"claude-sonnet-4-5\",\n schema: PerformanceSchema,\n system: \"You are a performance expert.\",\n prompt: code,\n }),\n]);\n```\n\nFor more control (retries, custom handlers, metadata):\n\n```ts\nconst result = await mr.responses.structured(\n Person,\n mr.responses.new().model(\"claude-sonnet-4-5\").user(\"Extract: John Doe is 30\").build(),\n { maxRetries: 2 },\n);\n\nconsole.log(result.value); // { name: \"John Doe\", age: 30 }\nconsole.log(result.attempts); // 1\n```\n\n## Streaming Structured Outputs\n\nBuild progressive UIs that render fields as they complete:\n\n```ts\nimport { ModelRelay, parseSecretKey } from \"@modelrelay/sdk\";\nimport { z } from \"zod\";\n\nconst mr = new ModelRelay({ key: parseSecretKey(\"mr_sk_...\") });\n\nconst Article = z.object({\n title: z.string(),\n summary: z.string(),\n body: z.string(),\n});\n\nconst stream = await mr.responses.streamStructured(\n Article,\n mr.responses.new().model(\"claude-sonnet-4-5\").user(\"Write an article about TypeScript\").build(),\n);\n\nfor await (const event of stream) {\n // Render fields as soon as they're complete\n if (event.completeFields.has(\"title\")) {\n renderTitle(event.payload.title); // Safe to display\n }\n if (event.completeFields.has(\"summary\")) {\n renderSummary(event.payload.summary);\n }\n\n // Show streaming preview of incomplete fields\n if (!event.completeFields.has(\"body\")) {\n renderBodyPreview(event.payload.body + \"▋\");\n }\n}\n```\n\n## Customer-Attributed Requests\n\nFor metered billing, use `customerId()` — the customer's subscription tier determines the model and `model` can be omitted:\n\n```ts\nconst req = mr.responses\n .new()\n .customerId(\"customer-123\")\n .user(\"Hello\")\n .build();\n\nconst stream = await mr.responses.stream(req);\n```\n\n## Customer Management (Backend)\n\n```ts\n// Create/update customer\nconst customer = await mr.customers.upsert({\n external_id: \"your-user-id\",\n email: \"user@example.com\",\n});\n\n// Create checkout session for subscription billing\nconst session = await mr.customers.subscribe(customer.customer.id, {\n tier_id: \"tier-uuid\",\n success_url: \"https://myapp.com/success\",\n cancel_url: \"https://myapp.com/cancel\",\n});\n\n// Check subscription status\nconst status = await mr.customers.getSubscription(customer.customer.id);\n```\n\n## Error Handling\n\nErrors are typed so callers can branch cleanly:\n\n```ts\nimport {\n ModelRelay,\n APIError,\n TransportError,\n StreamTimeoutError,\n ConfigError,\n} from \"@modelrelay/sdk\";\n\ntry {\n const response = await mr.responses.text(\n \"claude-sonnet-4-5\",\n \"You are helpful.\",\n \"Hello!\"\n );\n} catch (error) {\n if (error instanceof APIError) {\n console.log(\"Status:\", error.status);\n console.log(\"Code:\", error.code);\n console.log(\"Message:\", error.message);\n\n if (error.isRateLimit()) {\n // Back off and retry\n } else if (error.isUnauthorized()) {\n // Re-authenticate\n }\n } else if (error instanceof TransportError) {\n console.log(\"Network error:\", error.message);\n } else if (error instanceof StreamTimeoutError) {\n console.log(\"Stream timeout:\", error.kind); // \"ttft\" | \"idle\" | \"total\"\n }\n}\n```\n\n## Configuration\n\n```ts\nconst mr = new ModelRelay({\n key: parseSecretKey(\"mr_sk_...\"),\n environment: \"production\", // or \"staging\", \"sandbox\"\n timeoutMs: 30_000,\n retry: { maxAttempts: 3 },\n});\n```\n\n## Documentation\n\nFor detailed guides and API reference, visit [docs.modelrelay.ai](https://docs.modelrelay.ai):\n\n- [First Request](https://docs.modelrelay.ai/getting-started/first-request) — Make your first API call\n- [Streaming](https://docs.modelrelay.ai/guides/streaming) — Real-time response streaming\n- [Structured Output](https://docs.modelrelay.ai/guides/structured-output) — Get typed JSON responses\n- [Tool Use](https://docs.modelrelay.ai/guides/tools) — Let models call functions\n- [Error Handling](https://docs.modelrelay.ai/guides/error-handling) — Handle errors gracefully\n- [Workflows](https://docs.modelrelay.ai/guides/workflows) — Multi-step AI pipelines\n","readmeFilename":"README.md"}