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Agent Observability — Track OpenAI, LangChain, LangGraph, Bedrock, Vercel AI, Anthropic","maintainers":[{"name":"alondo11","email":"Alon121211@gmail.com"}],"readme":"<div align=\"center\">\n\n# visibe.ai SDK for Node.js\n\n<img src=\"https://i.imgur.com/PR6W6Mn.jpeg\" alt=\"Visibe Analytics\" width=\"100%\" />\n\n**Observability for AI agents.** Track costs, performance, and errors across your entire AI stack — whether you're using LangChain, LangGraph, Vercel AI, Anthropic, AWS Bedrock, or direct OpenAI calls.\n\n[![npm version](https://img.shields.io/npm/v/@visibe.ai/node.svg)](https://www.npmjs.com/package/@visibe.ai/node)\n![Node](https://img.shields.io/badge/node-18+-green.svg)\n![TypeScript](https://img.shields.io/badge/TypeScript-ready-blue.svg)\n\n</div>\n\n---\n\n## 🚀 Quick Start\n\n```bash\nnpm install @visibe.ai/node\n```\n\nGet your API key at [app.visibe.ai](https://app.visibe.ai) → Settings → API Keys, then add one line to your app:\n\n```typescript\n// CJS Syntax\nconst { init } = require('@visibe.ai/node')\n// ESM Syntax\nimport { init } from '@visibe.ai/node'\n\ninit({ apiKey: 'sk_live_your_key_here' })\n```\n\nThat's it. Every OpenAI, Anthropic, LangChain, LangGraph, Vercel AI, and Bedrock call is automatically traced from this point on — no wrappers, no config changes.\n\n---\n\n## 🧩 Supported Frameworks\n\n- [OpenAI](https://github.com/openai/openai-node)\n- [Anthropic](https://github.com/anthropics/anthropic-sdk-node)\n- [LangChain](https://github.com/langchain-ai/langchainjs)\n- [LangGraph](https://github.com/langchain-ai/langgraphjs)\n- [Vercel AI](https://github.com/vercel/ai)\n- [AWS Bedrock](https://github.com/aws/aws-sdk-js-v3)\n\nAlso works with OpenAI-compatible providers: Azure OpenAI, Groq, Together.ai, DeepSeek, and others.\n\n---\n\n## ⚙️ Configuration\n\n| Option | Type | Default | Description |\n|--------|------|---------|-------------|\n| `apiKey` | `string` | — | Your Visibe API key. Falls back to `VISIBE_API_KEY` env var |\n| `redactContent` | `boolean` | `false` | Omit prompt and completion text from all traces. Only metadata is sent (tokens, cost, duration, model, errors). |\n| `sessionId` | `string` | — | Tag all traces with a session ID |\n| `frameworks` | `string[]` | All detected | Limit auto-instrumentation to specific frameworks |\n\n---\n\n## 📊 What Gets Tracked\n\n| Metric | Sent when `redactContent: true` |\n|--------|:---:|\n| Cost, tokens, duration | ✅ |\n| Model & provider | ✅ |\n| Tool calls (name, duration, success/failure) | ✅ |\n| Errors (type, message, HTTP status) | ✅ |\n| Full execution timeline (spans) | ✅ |\n| Prompt text | ❌ omitted |\n| Completion text | ❌ omitted |\n\nWhen `redactContent: true`, prompt and completion text never leave your codebase. You retain full observability over costs, performance, and errors.\n\n---\n\n## 📖 API Reference\n\n### `init()`\n\nCall once at the top of your app, before creating any clients. Returns a `Visibe` instance.\n\n```typescript\nimport { init } from '@visibe.ai/node'\n\nconst visibe = init({ apiKey: 'sk_live_abc123' })\n```\n\n### `track()`\n\nGroups multiple LLM calls into a single named trace.\n\n```typescript\nawait visibe.track(client, 'my-conversation', async () => {\n  await client.chat.completions.create({ model: 'gpt-4o-mini', messages: [...] })\n  await client.chat.completions.create({ model: 'gpt-4o-mini', messages: [...] })\n})\n// Both calls appear as spans under one trace.\n```\n\n### `runWithSession()`\n\nLike `track()`, but works across all already-instrumented clients without specifying one.\n\n```typescript\nawait visibe.runWithSession('research-task', async () => {\n  // Any instrumented client used here is grouped into one trace.\n})\n```\n\n### `startSession()`\n\nOpens a persistent trace that accumulates spans across **multiple async calls** (e.g., a multi-turn chat conversation spanning many HTTP requests). Use this when `runWithSession()` isn't enough because the trace needs to outlive a single function call.\n\n```typescript\n// Open the session once (e.g., on first message)\nconst session = await visibe.startSession('Chat Session', { sessionId: userId })\n\n// Each turn: wrap your agent call in session.run()\nconst reply = await session.run(() => runAgent(message))\n\n// When the conversation ends, close the session\nawait session.end()\n```\n\n**`session.run(fn)`** — Executes `fn` inside the session's trace context. Every instrumented LLM/tool call inside `fn` is recorded as a span under the same trace, regardless of how many HTTP requests it spans.\n\n**`session.end()`** — Closes the trace and sends the final aggregated metrics (total cost, token counts, duration, LLM call count) to the dashboard. Safe to call multiple times — subsequent calls are no-ops.\n\n#### Example: Express chatbot with per-conversation traces\n\n```typescript\nimport { init } from '@visibe.ai/node'\n\nconst visibe = init({ apiKey: process.env.VISIBE_API_KEY })\nvisibe.instrument(openaiClient)\n\nconst sessions = new Map()  // sessionId → VisibeSession\n\napp.post('/api/chat', async (req, res) => {\n  const { message, sessionId } = req.body\n\n  let session = sessions.get(sessionId)\n  if (!session) {\n    session = await visibe.startSession('Chat Session', { sessionId })\n    sessions.set(sessionId, session)\n  }\n\n  const reply = await session.run(() => runAgent(message))\n\n  // Close the trace when the conversation is done\n  if (isConversationOver(message)) {\n    await session.end()\n    sessions.delete(sessionId)\n    res.json({ reply, closeSession: true })\n  } else {\n    res.json({ reply })\n  }\n})\n```\n\n### `middleware()`\n\nCreates one trace per HTTP request. Works with Express, Fastify, and any `(req, res, next)` compatible framework.\n\n```typescript\nimport express from 'express'\n\nconst app = express()\napp.use(visibe.middleware())\n\n// Custom trace name:\napp.use(visibe.middleware({ name: (req) => `${req.method} ${req.url}` }))\n```\n\nConcurrent requests are fully isolated via `AsyncLocalStorage`.\n\n### `instrument()` / `uninstrument()`\n\nManually instrument a specific client instance instead of relying on auto-instrumentation.\n\n```typescript\nvisibe.instrument(client, { name: 'my-agent' })\nvisibe.uninstrument(client)\n```\n\n### `shutdown()`\n\nFlushes buffered spans before process exit. Not needed for typical servers — the SDK handles `SIGTERM` / `SIGINT` automatically. Only needed for short-lived scripts or test suites.\n\n```typescript\nimport { shutdown } from '@visibe.ai/node'\nawait shutdown()\n```\n\n---\n\n\n## 🛡️ Safety Guarantees\n\n- **No crashes** — every SDK operation is wrapped in try/catch\n- **No latency** — all backend calls are fire-and-forget\n- **No leaks** — internal timer is `unref()`'d, won't block process exit\n- **No key, no problem** — SDK is silently a no-op when no API key is set\n\nNo data is sold or shared with third parties. Content is used solely to display traces in your dashboard.\n\n---\n\n## 🔗 Resources\n\n- [visibe.ai](https://visibe.ai) — Product website\n- [app.visibe.ai](https://app.visibe.ai) — Dashboard\n- [npm Package](https://www.npmjs.com/package/@visibe.ai/node)\n\n---\n\n## 📃 License\n\nMIT — see [LICENSE](LICENSE) for details.\n","readmeFilename":"README.md"}