Struct agent observability SDK for TypeScript/Node.js. Auto-instruments AI agent frameworks — the Anthropic SDK and LangChain.js — and emits OpenTelemetry traces + logs to struct.ai with zero config.

This is the TypeScript port of struct-sdk (Python). Span names, attribute keys, and log event shapes are identical across the two SDKs so the server processes both uniformly.

Install

npm install @struct-ai/sdk
# optional — the SDK auto-instruments these if present
npm install @anthropic-ai/sdk @langchain/core @langchain/langgraph

Requires Node 18+.

Quickstart

Get an ingest key from app.struct.ai/settings?tab=ingest-keys, then:

import { struct } from "@struct-ai/sdk";
// Initialize once, as early as possible in your process
struct.init({
  ingestKey: process.env.STRUCT_INGEST_KEY!, // or pass the string directly
  serviceName: "my-agent",
  environment: "production",
});

// Use your agent code as normal — spans + log events are emitted automatically.
import Anthropic from "@anthropic-ai/sdk";
const client = new Anthropic();

await struct.agent({ name: "checkout" }, async () => {
  const msg = await client.messages.create({
    model: "claude-3-5-sonnet-20241022",
    max_tokens: 1024,
    messages: [{ role: "user", content: "plan my checkout flow" }],
  });

  // tool_call_id is auto-filled from the preceding Anthropic response
  await struct.tool({ name: "search" }, async () => {
    return await search(msg);
  });
});

What gets instrumented

Library	Hook	Span type	Notes
`@anthropic-ai/sdk`	`Messages.prototype.create`, `.stream`	`chat {model}`	Cache-token accounting, streaming with tool-use reconstruction
`@anthropic-ai/bedrock-sdk`, `@anthropic-ai/vertex-sdk`	`Messages.prototype.*`	`chat {model}`	Best-effort, if installed
`@langchain/core` `BaseChatModel`	`.invoke`, `.stream`	`chat {model}`	Skipped when a provider-direct instrumentor is active (e.g. ChatAnthropic + Anthropic patch → single span)
`@langchain/core` `StructuredTool`	`.invoke`	`execute_tool {name}`	Extracts `tool_call_id` from LangChain ToolCall input or pending queue
`@langchain/core` `BaseRetriever`	`.invoke`	`retrieval {name}`
`@langchain/langgraph` `Pregel`	`.invoke`, `.stream`	`invoke_agent {name}`	Covers `createReactAgent`, custom graphs. `thread_id` → `gen_ai.conversation.id`

Framework integration

struct.init() takes the same options regardless of which framework you're instrumenting. Required: ingestKey (get one at app.struct.ai/settings?tab=ingest-keys). Recommended: serviceName, environment.

What you need to do beyond init() depends on whether you're using an agent framework (which has built-in concepts of agents and tools) or an LLM SDK directly (which only knows about chat completions). The SDK auto-instruments both, but only agent frameworks get full agent + tool spans for free — when you call an LLM SDK directly, you have to tell the SDK where the agent and tool boundaries are.

Call init() once, as early as possible, before the instrumented libraries are imported, so their prototypes are patched before any instance is constructed.

Agent frameworks — fully auto-instrumented

For these, calling struct.init() is the only setup. Agent, tool, chat, and retrieval spans all emit automatically.

LangChain / LangGraph (with an agent or graph)

import { struct } from "@struct-ai/sdk";
struct.init({ ingestKey: "pk-...", serviceName: "my-graph" });

import { createReactAgent } from "@langchain/langgraph/prebuilt";
// Pregel invocations get invoke_agent spans. BaseChatModel calls get
// chat spans. StructuredTool.invoke gets execute_tool spans.
// BaseRetriever.invoke gets retrieval spans.

LLM SDKs used directly — manual agent + tool scopes required

When you call an LLM SDK directly (no agent framework wrapping it), only chat spans emit automatically. You need to wrap your agent loop in struct.agent() and each tool execution in struct.tool() so the SDK knows where to put the agent and tool boundaries — otherwise you'll see free-floating chat spans with no agent or tool context around them.

Anthropic SDK (raw)

import { struct } from "@struct-ai/sdk";
struct.init({ ingestKey: "pk-...", serviceName: "checkout-agent" });

import Anthropic from "@anthropic-ai/sdk";
const client = new Anthropic();

// Required: wrap the agent loop yourself.
await struct.agent({ name: "checkout" }, async () => {
  const msg = await client.messages.create({
    model: "claude-3-5-sonnet-20241022",
    max_tokens: 1024,
    messages: [...],
  });

  // Required: wrap each tool execution.
  // tool_call_id is auto-filled from the preceding Anthropic response.
  await struct.tool({ name: "search" }, async () => {
    return await search(...);
  });
});

@anthropic-ai/sdk, @anthropic-ai/bedrock-sdk, and @anthropic-ai/vertex-sdk are all auto-instrumented for chat spans.

LangChain `BaseChatModel` (no agent/graph)

If you call ChatAnthropic.invoke(...) (or any other BaseChatModel) without wrapping it in AgentExecutor or a LangGraph graph, only the chat span emits automatically. Same rule as raw Anthropic — wrap your agent loop in struct.agent() and tool execution in struct.tool().

import { struct } from "@struct-ai/sdk";
struct.init({ ingestKey: "pk-...", serviceName: "my-agent" });

import { ChatAnthropic } from "@langchain/anthropic";
const llm = new ChatAnthropic({ model: "claude-3-5-sonnet-20241022" });

await struct.agent({ name: "my-agent" }, async () => {
  const response = await llm.invoke([["user", "..."]]);
  await struct.tool({ name: "search" }, async () => {
    // ...
  });
});

When you do use ChatAnthropic and have @anthropic-ai/sdk installed, the chat span comes from the Anthropic patch (single span); the LangChain layer suppresses its duplicate.

Content capture

The SDK supports four capture modes controlling how prompt/response content is emitted.

import { struct, ContentCaptureMode } from "@struct-ai/sdk";
struct.init({
  ingestKey: ...,
  contentCapture: ContentCaptureMode.EventOnly, // default
  // or ContentCaptureMode.None, SpanOnly, SpanAndEvent
});

EventOnly (default): per-message content lands on OTel log records (gen_ai.{user,assistant,system,tool}.message, gen_ai.choice). Spans carry metadata only.
SpanOnly: content on span attributes (gen_ai.input.messages, gen_ai.output.messages).
SpanAndEvent: both.
None: no content captured. Token counts, tool call IDs, finish reasons, and other metadata still flow.

Set captureContent: false for the legacy bool API (equivalent to ContentCaptureMode.None).

Manual scopes

struct.agent() and struct.tool() create invoke_agent and execute_tool spans. These are optional — LangChain's Pregel patch creates agent spans automatically when your graph has a thread_id in the config.

await struct.agent(
  { name: "onboarding", sessionId: conversationId, metadata: { tenant: "acme" } },
  async () => {
    await struct.tool({ name: "fetch-profile" }, async () => {
      return fetchProfile();
    });
  }
);

Nested agents set struct.agent.parent_session_id on the inner span, linking subagents back to the parent.

Semantic conventions

Emits attributes per the OTel GenAI semantic conventions:

gen_ai.operation.name — chat, execute_tool, invoke_agent, retrieval
gen_ai.provider.name — anthropic, openai, langchain, struct, …
gen_ai.request.{model, max_tokens, temperature, top_p, top_k, stop_sequences}
gen_ai.response.{model, id, finish_reasons}
gen_ai.usage.{input_tokens, output_tokens, cache_read.input_tokens, cache_creation.input_tokens}
gen_ai.conversation.id
gen_ai.tool.{name, call.id, call.arguments, call.result}
error.type + StatusCode.ERROR on failures

Note: gen_ai.usage.input_tokens for Anthropic is the TRUE total — we add back cache_read_input_tokens + cache_creation_input_tokens (which Anthropic's raw response excludes). Matches the Python SDK.

Troubleshooting

Spans missing after instrumenting: Import @struct-ai/sdk (or struct.init()) before the instrumented libraries, so their class prototypes are patched before any instance is constructed. In most Node setups this is automatic, but some bundlers tree-shake aggressively.
No logs appearing: LogRecords only emit when sdk.emitEvents is true (EventOnly or SpanAndEvent capture mode, which is the default). If you set captureContent: false you disable them.
Duplicate chat spans: The LangChain integration suppresses its own chat span when a provider-direct patch is active (e.g. ChatAnthropic calls through to @anthropic-ai/sdk, which emits its own chat span). If you see doubles, confirm both integrations are auto-instrumenting (check struct.initialized).
Subagent in a different trace / missing from parent's "Subagents" list: If you invoke a nested agent (subagent.invoke(...)) from inside a tool body, define the outer tool with tool(func, { name, description, schema }) from @langchain/core/tools — not new DynamicTool({...}). The tool() factory wraps your function in AsyncLocalStorageProviderSingleton.runWithConfig(...), which is what lets the nested invoke inherit the tool's callback chain and share the parent's trace_id. DynamicTool skips that wrap, so the subagent starts a new trace and parent↔subagent linkage breaks. The struct.agent.parent_session_id attribute is still set, so "Spawned by" on the child side still renders — but the parent's forward link to the subagent won't appear.