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By default it prototype-patches OpenAI, Anthropic, Groq, LangChain, Gemini, and Mistral (check `patched` / `patchedAsync` for what applied). Vercel AI, Cohere, Bedrock, Mastra, and LlamaIndex still need the returned `wrap*` factories.\n\nThe SDK mirrors the surface of Eden's Python SDK (`eden-sdk` / `eden.trace`) so existing agent code can be ported by changing the import.\n\n## Install\n\n```bash\nbun add @eden-ai/sdk\n# or\nnpm install @eden-ai/sdk\n# or\npnpm add @eden-ai/sdk\n```\n\n## Configure\n\nThe SDK reads the following env vars by default. In production set `orgId` (or `EDEN_ORG_ID`); without it the client stays disabled and emits are no-ops (counted on `droppedTotal`, not sent):\n\n| Variable | Default | Description |\n| --- | --- | --- |\n| `EDEN_ORG_ID` | — | Required for production. Sent in URL path + `X-Org-Id` header. |\n| `EDEN_ORG_API_KEY` | — | API key sent as `X-Org-Api-Key`. |\n| `EDEN_BASE_URL` | `https://api.edenobservability.com` | Gateway base URL. |\n| `EDEN_PROJECT_ID` | `default` | Sent as `X-Project-Id`. |\n| `EDEN_REALM` | `production` | Realm tag. |\n| `EDEN_DEBUG` | `0` | Set to `1` to log every batch flush. |\n\nOr pass the same values explicitly:\n\n```ts\nimport { configure } from \"@eden-ai/sdk\";\n\nconfigure({\n  orgId: \"org_abc\",\n  apiKey: \"sk_...\",\n  baseUrl: \"https://api.edenobservability.com\",\n  tags: [\"prod\", \"checkout-agent\"],\n  debug: false,\n});\n```\n\n## Auto-instrument OpenAI\n\n```ts\nimport OpenAI from \"openai\";\nimport { configure, wrapOpenAI, getDefaultClient } from \"@eden-ai/sdk\";\n\nconfigure({ orgId: \"org_abc\", apiKey: \"sk_...\" });\n\nconst raw = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });\nconst openai = wrapOpenAI(raw, { tags: [\"checkout\"] });\n\n// Every call through this Proxy emits telemetry to\n// POST /orgs/{orgId}/ingest/openai.\nconst reply = await openai.chat.completions.create({\n  model: \"gpt-4o-mini\",\n  messages: [{ role: \"user\", content: \"Hello!\" }],\n});\n\n// Streaming:\nconst stream = await openai.chat.completions.create({\n  model: \"gpt-4o-mini\",\n  stream: true,\n  messages: [{ role: \"user\", content: \"Stream a haiku.\" }],\n});\nfor await (const chunk of stream) {\n  process.stdout.write(chunk.choices[0]?.delta?.content ?? \"\");\n}\n```\n\nFor streaming calls the SDK emits one `llm_completion_stream` event after the iterator exhausts, with:\n\n- `ttft_ms` — time-to-first-token\n- `itl_ms_avg` — average inter-token latency\n- `tpot_ms` — time-per-output-token\n- `completion_duration_ms` — total wall-clock\n- `output_tokens` — token estimate (chunk-based; SDK's reported usage wins when present)\n\nThe SDK also buffers each chunk and flushes them to `POST /orgs/{orgId}/ingest/chunks` for per-token replay (mirrors the Python SDK's `_flush_stream_chunks`).\n\n## Auto-instrument Anthropic\n\n```ts\nimport Anthropic from \"@anthropic-ai/sdk\";\nimport { wrapAnthropic } from \"@eden-ai/sdk\";\n\nconst raw = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });\nconst anthropic = wrapAnthropic(raw, { tags: [\"checkout\"] });\n\nconst reply = await anthropic.messages.create({\n  model: \"claude-3-5-sonnet-20241022\",\n  max_tokens: 256,\n  messages: [{ role: \"user\", content: \"Hello!\" }],\n});\n```\n\n## Auto-instrument Vercel AI\n\n```ts\nimport * as ai from \"ai\";\nimport { openai } from \"@ai-sdk/openai\";\nimport { wrapVercelAI } from \"@eden-ai/sdk\";\n\nconst wrapped = wrapVercelAI(ai);\n\nconst result = await wrapped.generateText({\n  model: openai(\"gpt-4o-mini\"),\n  prompt: \"Write a haiku.\",\n});\n\nconst stream = await wrapped.streamText({\n  model: openai(\"gpt-4o-mini\"),\n  prompt: \"Stream a haiku.\",\n});\nfor await (const chunk of stream.textStream) process.stdout.write(chunk);\n```\n\n## Auto-instrument LangChain\n\n```ts\nimport { ChatOpenAI } from \"@langchain/openai\";\nimport { HumanMessage } from \"@langchain/core/messages\";\nimport { wrapLangChain } from \"@eden-ai/sdk\";\n\nconst raw = new ChatOpenAI({ modelName: \"gpt-4o-mini\" });\nconst chat = wrapLangChain(raw, { tags: [\"checkout\"] });\n\nconst result = await chat.invoke([new HumanMessage(\"Hello!\")]);\n```\n\n`wrapLangChain` is a Proxy that instruments `invoke`, `call`, `stream`, and `batch` methods on any chat-model instance.\n\n## Auto-instrument LlamaIndex.TS\n\n```ts\nimport { VectorStoreIndex } from \"llamaindex\";\nimport { wrapLlamaIndex } from \"@eden-ai/sdk\";\n\nconst index = await VectorStoreIndex.fromDocuments(documents);\nconst queryEngine = wrapLlamaIndex(index.asQueryEngine(), { tags: [\"docs-qa\"] });\n\nconst response = await queryEngine.query({ query: \"What did the author do in college?\" });\n\n// Streaming is supported the same way — the wrapper detects the\n// AsyncIterable response and buffers per-chunk timing to\n// /ingest/chunks, same as the OpenAI/Anthropic/LangChain streams.\nconst stream = await queryEngine.query({ query: \"Summarize it.\", stream: true });\nfor await (const chunk of stream) process.stdout.write(chunk.response);\n```\n\n`wrapLlamaIndex` also wraps `chat()` (chat engines from `index.asChatEngine()`) and `retrieve()` (retrievers from `index.asRetriever()`). Each call emits one `agent.run` event (`sourceFormat: \"llamaindex\"`) with duration, outcome, and `sourceNodes` count — LlamaIndex.TS has no public span-handler API to hook globally (unlike the Python `llama-index-core` package), so this instruments at the query/chat/retrieve call boundary instead, same as `wrapMastra` / `wrapLangChain`.\n\n## Instrumentation parity matrix\n\n| Framework | Auto-patch (`instrument()`) | Manual wrap |\n|---|---|---|\n| OpenAI | Yes (`Chat.Completions.prototype.create`) | `wrapOpenAI` |\n| xAI (Grok) | Yes (same OpenAI class, `baseURL` detection) | `wrapXai` |\n| Anthropic | Yes (`Messages.prototype.create`) | `wrapAnthropic` |\n| Groq | Yes (`Chat.Completions.prototype.create`) | `wrapGroq` |\n| LangChain | Yes (`BaseChatModel` invoke/call/stream/batch) | `wrapLangChain` |\n| Gemini / Vertex | Yes (`GenerativeModel` / `@google/genai` Models) | `wrapGemini` |\n| Mistral | Yes (`Chat.complete` / `.stream`) when installed | `wrapMistral` |\n| Vercel AI | **No** — ESM namespace exports are immutable; use `wrapVercelAI` | `wrapVercelAI` |\n| Cohere | No (dual client shapes) | `wrapCohere` |\n| Bedrock | No (command `send` pattern) | `wrapBedrock` |\n| Mastra | No (instance-level API) | `wrapMastra` |\n| LlamaIndex.TS | No (no global span handler) | `wrapLlamaIndex` |\n| CrewAI / AutoGen / Pydantic AI | Python-only — use `eden-sdk` | — |\n\n### Agent sessions (F23)\n\n```ts\nimport { begin, identify } from \"@eden-ai/sdk\";\n\nconst session = await begin({ agentId: \"bot\", userId: \"u1\" });\nawait identify(\"u1\");\nawait session.trace(\"turn-1\", async () => { /* ... */ });\nawait session.completeSession(true);\n// POSTs to /orgs/{orgId}/ingest/agent-sessions with X-Org-Api-Key\n```\n\n### Agent identity (`agentId`)\n\nSet a stable `agentId` on every trace — not only in `begin(...)`. Eden uses it for per-agent analytics breakdowns, issue extraction, and segmentation; those features only key off it when the field is populated.\n\n```ts\nimport { configure } from \"@eden-ai/sdk\";\n\n// Process-wide default (also via EDEN_AGENT_ID)\nconfigure({ orgId: \"org_abc\", apiKey: \"sk_...\", agentId: \"support-bot\" });\n```\n\nWhen omitted on an individual event, the configured `agentId` is stamped on the payload and sent as `X-Eden-Agent-Id`.\n\n### Stumbles (F28)\n\n```ts\nimport { recordStumble } from \"@eden-ai/sdk\";\nawait recordStumble(\"tool returned empty\", { traceId, kind: \"tool_error\" });\n```\n\n### Sampling + offline\n\n| Env | Default | Purpose |\n|---|---|---|\n| `EDEN_SAMPLING_PROFILE` | `full` | `full` / `balanced` (~15%) / `aggressive` (~5%); keep-errors default on. **Recommended: `balanced` in production** once you've reviewed a few days of `full` traffic. |\n| `EDEN_OFFLINE_QUEUE` | off | Spill failed batches to `~/.eden/offline_queue.jsonl` |\n| `EDEN_CAPTURE_MODE` | `bounded` | `bounded` (head+tail truncate long `setInput`/`setOutput` strings) / `full` / `metrics_only`. Auto-instrumentation is unaffected — it already ships metrics-only. |\n| `EDEN_CAPTURE_MAX_STRING_BYTES` | `8192` | Per-string-field cap in bytes for `bounded` mode. |\n\nOTel bridge uses `BatchSpanProcessor` → `/ingest/otel`.\n\n## Talking to the Eden gateway (OpenAI-compatible)\n\nEden ships an OpenAI-compatible proxy at `https://api.edenobservability.com/gateway/v1`\nthat exposes `/chat/completions`, `/completions`, `/embeddings`, and\n`/models`. Point any OpenAI-compatible client at it and Eden applies\nPII redaction, semantic caching, per-tenant budgets, circuit\nbreakers, and cost-aware routing in the path.\n\nThe SDK ships a typed filter-config surface so callers can configure\nPII (and any future gateway filters) without hand-rolling headers:\n\n```ts\nimport {\n  EdenSDK,\n  FilterConfig,\n  PIIConfig,\n  openaiClient,\n} from \"@eden-ai/sdk\";\n\nconst sdk = new EdenSDK({\n  orgId: \"org_abc\",\n  apiKey: \"sk_...\",\n  filters: new FilterConfig({\n    pii: new PIIConfig({ mode: \"fail_closed\", disabledCategories: [\"api_key\"] }),\n  }),\n});\n\n// Built-in helper that wires the right URL + headers in one call:\nconst client = await openaiClient(sdk, {\n  // Optional per-call upstream override — route this specific call\n  // through your own OpenAI-compatible server instead of the\n  // Eden-default upstream.\n  upstreamBaseUrl: \"https://my-internal-llm.example.com/v1\",\n  upstreamApiKey: \"<your-internal-key>\",\n});\n\nconst reply = await client.chat.completions.create({\n  model: \"gpt-5\",\n  messages: [{ role: \"user\", content: \"hello\" }],\n});\n```\n\nYou can also call `sdk.gatewayHeaders()` to get the headers as a\nrecord and pass them to any HTTP client:\n\n```ts\nconst headers = sdk.gatewayHeaders({\n  piiBypass: true,        // skip PII for this one call\n  filters: new FilterConfig({ pii: new PIIConfig({ mode: \"off\" }) }),\n});\nawait fetch(`${sdk.resolvedGatewayUrl()}/chat/completions`, {\n  method: \"POST\",\n  headers,\n  body: JSON.stringify({ model: \"gpt-5\", messages: [...] }),\n});\n```\n\n`resolvedGatewayUrl()` returns `${baseUrl}/gateway/v1` by default and honours\nan explicit `gatewayUrl` option when one is set. The gateway strips\n`X-Upstream-Api-Key`, `X-Org-Api-Key`, and `X-Org-Id` before\nforwarding to the upstream so Eden credentials never leak to a\nthird-party LLM provider.\n\nThe Python SDK has the same surface (`eden_sdk.filters.FilterConfig`,\n`EdenConfig.filters`, `openai_client()`, `chat_completion()`); see\n`sdk/python/README.md` for the Python equivalent.\n\n## Manual tracing\n\nIf you want to trace arbitrary functions (not just LLM calls), use `trace()` or `@traceAgent()`.\n\n```ts\nimport { trace, traceAgent } from \"@eden-ai/sdk\";\n\nclass MyAgent {\n  @traceAgent({ tags: [\"prod\"], environmentId: \"env_abc\" })\n  async run(query: string) {\n    return await fetch(\"/api/echo\", { body: query });\n  }\n}\n\n// or imperative:\nconst result = await trace(\"myAgent\", async (ctx) => {\n  // ctx.traceId / ctx.spanId / ctx.startMs\n  return await openai.chat.completions.create({ ... });\n});\n```\n\n## Auto-instrument everything\n\n```ts\nimport { configure, instrument } from \"@eden-ai/sdk\";\nimport OpenAI from \"openai\";\n\nconfigure({ orgId: \"org_abc\", apiKey: \"sk_...\" });\n\n// One-liner: class-level patches for supported frameworks.\nconst { patched, patchedAsync, wrapVercelAI } = instrument({ tags: [\"prod\"] });\n// patched: { openai, anthropic, groq, langchain, gemini, mistral, vercelAi }\n// vercelAi is always false — use wrapVercelAI(ai) for Vercel AI SDK.\n\nconst openai = new OpenAI(); // auto-traced when patched.openai === true\nawait patchedAsync; // await before first ESM-imported client if needed\n\nimport * as ai from \"ai\";\nconst wrappedAi = wrapVercelAI(ai); // required for Vercel AI\n```\n\n`instrument({ autoPatch: false })` opts out of class-level patching; `wrap*`\nfactories remain on the return value. Detection-only: `detectInstalledFrameworks()`.\n\nMissing modules return `false` (the SDK never hard-imports them). Detection checks for `@aws-sdk/client-bedrock-runtime` (Bedrock), `@google/generative-ai` or `@google/genai` (Gemini), and `llamaindex` (LlamaIndex.TS) — mirroring the Python SDK's `boto3`/`botocore` and `google.generativeai`/`google.genai` checks.\n\n## Batching & flush\n\nThe SDK is fire-and-forget:\n\n- Events are queued and flushed in batches of `maxBatchSize` (default **50**) or every `flushIntervalMs` (default **250 ms**), whichever comes first.\n- Stream chunks share the same cap and are POSTed separately to `/ingest/chunks`.\n- `await getDefaultClient().dispose()` flushes pending work — call this from `beforeExit` or a graceful-shutdown handler.\n\n## Telemetry payload shape\n\nEvery emitted event matches the gateway's expected `telemetry_events` row (see `src/eden/db_migrations/versions/009_telemetry_events.py`):\n\n```json\n{\n  \"eventId\": \"lzx...24\",\n  \"tenantId\": \"org_abc\",\n  \"projectId\": \"default\",\n  \"traceId\": \"lzx...24\",\n  \"realm\": \"production\",\n  \"eventType\": \"llm_completion\",\n  \"timestamp\": \"2026-06-08T12:00:00.000Z\",\n  \"sourceFormat\": \"openai\",\n  \"payload\": {\n    \"provider\": \"openai\",\n    \"model\": \"gpt-4o-mini\",\n    \"duration_ms\": 234,\n    \"status\": \"success\",\n    \"error_type\": null\n  },\n  \"metrics\": { \"duration_ms\": 234 },\n  \"tags\": [\"prod\", \"auto_instrument\"]\n}\n```\n\n`sourceFormat` is one of `openai | anthropic | vercel-ai | langchain | mastra | bedrock | gemini | llamaindex | groq | cohere | mistral | eden-trace-agent-ts | eden-auto-instrument-ts` — the portal uses it to filter events by framework.\n\nFor LLM-completion events, the SDK also populates:\n\n| Field | Source | Description |\n| --- | --- | --- |\n| `metrics.input_tokens` | provider `usage` block | prompt tokens (or `promptTokens` / `prompt_tokens`) |\n| `metrics.output_tokens` | provider `usage` block | completion tokens (or `completionTokens` / `completion_tokens`) |\n| `metrics.cache_read_input_tokens` | Anthropic / OpenAI `prompt_tokens_details.cached_tokens` | cached input tokens (Anthropic gives ~90% discount) |\n| `metrics.cache_creation_input_tokens` | Anthropic `cache_creation_input_tokens` | cache write tokens (billed at 25% premium) |\n| `modelNormalized` | `normalizeModelName(model)` | dated-suffix-stripped alias: `gpt-4o-2024-08-06` → `gpt-4o`, `claude-3-5-sonnet-20241022` → `claude-3-5-sonnet` |\n\nMissing token counts are reported as `null` (never 0) so billing\nmath doesn't accidentally bill free input.\n\n## Token extraction & model normalization\n\nThe same helpers used by every instrumentation are also exported as\na public API for users who want to pre-screen their own payloads:\n\n```ts\nimport { extractUsage, normalizeModelName } from \"@eden-ai/sdk\";\n\nextractUsage({\n  prompt_tokens: 100,\n  completion_tokens: 50,\n  prompt_tokens_details: { cached_tokens: 25 },\n});\n// => { inputTokens: 100, outputTokens: 50,\n//      cacheReadTokens: 25, cacheCreationTokens: null }\n\nnormalizeModelName(\"gpt-4o-2024-08-06\");           // => \"gpt-4o\"\nnormalizeModelName(\"claude-3-5-sonnet-20241022\"); // => \"claude-3-5-sonnet\"\nnormalizeModelName(\"claude-3-5-sonnet-latest\");   // => \"claude-3-5-sonnet\"\n```\n\n## Why is it fire-and-forget?\n\nTracing must never block the user's call. Every emit path is wrapped in a try/catch; network failures spill to the durable offline queue (or count toward `droppedTotal` when the queue is full). Batches that hit HTTP 413 are bisected and retried; a single event that still cannot fit is counted as dropped rather than wiped silently. To opt into strict mode (throw on misconfiguration):\n\n```ts\nconfigure({ strict: true, orgId: \"...\" });\n```\n\n## Testing\n\n```bash\ncd sdk/ts-ai\nbun install\nbun test            # run unit tests\nbun run typecheck   # tsc --noEmit\n```\n\nThe unit tests stub `global.fetch` and assert on the captured POST bodies — no real network or gateway required.\n\n## End-to-end\n\nWith the Eden backend running locally:\n\n```bash\ncd sdk/ts-ai\nbun install\nOPENAI_API_KEY=sk-... bun run examples/basic-chat.ts\n```\n\nVerify a row was written to `telemetry_events` with `framework=\"openai\"` and the expected payload fields.\n\n## License\n\nMIT\n\n## What's new in 0.3.5\n\n- **`wrapLlamaIndex` — native LlamaIndex.TS instrumentation.** Wraps `query()` / `chat()` / `retrieve()` on any query engine, chat engine, or retriever (`index.asQueryEngine()` / `asChatEngine()` / `asRetriever()`), emitting one `agent.run` event per call (duration, outcome, `sourceNodes` count), with streaming responses buffered to `/ingest/chunks` for per-chunk timing (parity with OpenAI/Anthropic/LangChain). Auto-detected when `llamaindex` is installed.\n- **Bedrock + Gemini are now auto-detected.** `wrapBedrock` / `wrapGemini` shipped full usage-extraction in 0.3.3 but were never wired into `instrument()` / `detectInstalledFrameworks()` — callers had to already know to import them manually. `detectInstalledFrameworks()` now reports `bedrock` (`@aws-sdk/client-bedrock-runtime`) and `gemini` (`@google/generative-ai` or `@google/genai`), and `instrument()` returns `wrapBedrock` / `wrapGemini` / `wrapLlamaIndex` alongside the existing wrap helpers.\n- CrewAI / AutoGen / Pydantic AI remain Python-only frameworks with no official TS/JS port — clarified in the parity matrix that this is not a TS gap (there's no upstream package to wrap).\n\n## What's new in 0.3.2\n\n- **Bedrock + Gemini usage extraction.** `wrapBedrock` / `wrapGemini` now extract input/output/cache/total token counts (Converse API, InvokeModel body shapes for Anthropic/Titan/Llama, and Gemini `usageMetadata`) so both providers report cost data like OpenAI/Anthropic.\n- **`redactOnClient` now defaults to `false`.** The gateway remains the authoritative PII layer; restore the old behaviour with `redactOnClient: true` or `EDEN_REDACT_ON_CLIENT=1`.\n- **`instrument()` no longer enables HTTP capture by default** (avoids double-emit with `wrap*`); pass `httpCapture: true` to opt in.\n- Streaming chunks omit `deltaText` unless `captureChunkText: true` (or `EDEN_CAPTURE_CHUNK_TEXT=1`); ingest POSTs use gzip; multi-format flush is concurrent.\n- See `sdk/ts-ai/CHANGELOG.md` for the full 0.3.x notes.\n\n## What's new in 0.2.0\n\n- **Context-manager `trace()`.** `import { TraceController } from \"@eden-ai/sdk\"` and build the span imperatively with `setInput / setOutput / setTag / setStatus / recordError / end`. The legacy `await trace(\"name\", async (ctx) => ...)` form is preserved.\n- **Retry with backoff.** `EdenSDK.postJson` retries transient gateway failures (3 attempts, honours `Retry-After`, never retries 4xx other than 408/429).\n- **Config parity with Python.** New `maxQueueSize` (default 4096, oldest-event drop), `timeoutS` (default 5), `redactOnClient` (default `true`), and `disabled` flags.\n- **Client-side PII safety net.** `redactPayload()` masks obvious emails / phones / SSNs / credit cards in every outgoing payload. Disable via `new EdenSDK({ redactOnClient: false })` when the gateway is your trusted redaction point.\n- **Detection helpers.** `detectInstalledFrameworks()` reports which LLM SDKs are installed (detection-only; `autoInstrument()` is a deprecated alias); same shape as the Python SDK.\n- **CHANGELOG.md** ships with the package; see `sdk/ts-ai/CHANGELOG.md` for the full 0.2.0 notes.\n","readmeFilename":"README.md"}