Sessions (TypeScript)

Track multi-turn conversations by grouping traces with session IDs. Use withSpan directly from @arizeai/openinference-core - no wrappers or custom utilities needed.

Core Concept

Session Pattern:

1. Generate a unique session.id once at application startup
2. Export SESSION_ID, import withSpan where needed
3. Use withSpan to create a parent CHAIN span with session.id for each interaction
4. All child spans (LLM, TOOL, AGENT, etc.) automatically group under the parent
5. Query traces by session.id in Phoenix to see all interactions

Implementation (Best Practice)

1. Setup (instrumentation.ts)

import { register } from "@arizeai/phoenix-otel";
import { randomUUID } from "node:crypto";

// Initialize Phoenix
register({
  projectName: "your-app",
  url: process.env.PHOENIX_COLLECTOR_ENDPOINT || "http://localhost:6006",
  apiKey: process.env.PHOENIX_API_KEY,
  batch: true,
});

// Generate and export session ID
export const SESSION_ID = randomUUID();

2. Usage (app code)

import { withSpan } from "@arizeai/openinference-core";
import { SESSION_ID } from "./instrumentation";

// Use withSpan directly - no wrapper needed
const handleInteraction = withSpan(
  async () => {
    const result = await agent.generate({ prompt: userInput });
    return result;
  },
  {
    name: "cli.interaction",
    kind: "CHAIN",
    attributes: { "session.id": SESSION_ID },
  }
);

// Call it
const result = await handleInteraction();

With Input Parameters

const processQuery = withSpan(
  async (query: string) => {
    return await agent.generate({ prompt: query });
  },
  {
    name: "process.query",
    kind: "CHAIN",
    attributes: { "session.id": SESSION_ID },
  }
);

await processQuery("What is 2+2?");

Key Points

Session ID Scope

• CLI/Desktop Apps: Generate once at process startup
• Web Servers: Generate per-user session (e.g., on login, store in session storage)
• Stateless APIs: Accept session.id as a parameter from client

Span Hierarchy

cli.interaction (CHAIN) ← session.id here
├── ai.generateText (AGENT)
│   ├── ai.generateText.doGenerate (LLM)
│   └── ai.toolCall (TOOL)
└── ai.generateText.doGenerate (LLM)

The session.id is only set on the root span. Child spans are automatically grouped by the trace hierarchy.

Querying Sessions

# Get all traces for a session
npx @arizeai/phoenix-cli traces \
  --endpoint http://localhost:6006 \
  --project your-app \
  --format raw \
  --no-progress | \
  jq '.[] | select(.spans[0].attributes["session.id"] == "YOUR-SESSION-ID")'

Dependencies

{
  "dependencies": {
    "@arizeai/openinference-core": "^2.0.5",
    "@arizeai/phoenix-otel": "^0.4.1"
  }
}

Note: @opentelemetry/api is NOT needed - it's only for manual span management.

Why This Pattern?

1. Simple: Just export SESSION_ID, use withSpan directly - no wrappers
2. Built-in: withSpan from @arizeai/openinference-core handles everything
3. Type-safe: Preserves function signatures and type information
4. Automatic lifecycle: Handles span creation, error tracking, and cleanup
5. Framework-agnostic: Works with any LLM framework (AI SDK, LangChain, etc.)
6. No extra deps: Don't need @opentelemetry/api or custom utilities

Adding More Attributes

import { withSpan } from "@arizeai/openinference-core";
import { SESSION_ID } from "./instrumentation";

const handleWithContext = withSpan(
  async (userInput: string) => {
    return await agent.generate({ prompt: userInput });
  },
  {
    name: "cli.interaction",
    kind: "CHAIN",
    attributes: {
      "session.id": SESSION_ID,
      "user.id": userId,              // Track user
      "metadata.environment": "prod",  // Custom metadata
    },
  }
);

Anti-Pattern: Don't Create Wrappers

❌ Don't do this:

// Unnecessary wrapper
export function withSessionTracking(fn) {
  return withSpan(fn, { attributes: { "session.id": SESSION_ID } });
}

✅ Do this instead:

// Use withSpan directly
import { withSpan } from "@arizeai/openinference-core";
import { SESSION_ID } from "./instrumentation";

const handler = withSpan(fn, {
  attributes: { "session.id": SESSION_ID }
});

Alternative: Context API Pattern

For web servers or complex async flows where you need to propagate session IDs through middleware, you can use the Context API:

import { context } from "@opentelemetry/api";
import { setSession } from "@arizeai/openinference-core";

await context.with(
  setSession(context.active(), { sessionId: "user_123_conv_456" }),
  async () => {
    const response = await llm.invoke(prompt);
  }
);

Use Context API when:

• Building web servers with middleware chains
• Session ID needs to flow through many async boundaries
• You don't control the call stack (e.g., framework-provided handlers)

Use withSpan when:

• Building CLI apps or scripts
• You control the function call points
• Simpler, more explicit code is preferred

Related

• fundamentals-universal-attributes.md - Other universal attributes (user.id, metadata)
• span-chain.md - CHAIN span specification
• sessions-python.md - Python session tracking patterns