# Source: https://docs.helicone.ai/features/sessions.md

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.helicone.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Sessions

When building AI agents or complex workflows, your application often makes multiple LLM calls, vector database queries, and tool calls to complete a single task. Sessions group these related requests together, letting you trace the entire agent flow from initial user input to final response in one unified view.

## Why use Sessions

* **Debug AI agent flows**: See the entire agent workflow in one view, from initial request to final response
* **Track multi-step conversations**: Reconstruct the complete flow of chatbot interactions and complex tasks
* **Analyze performance**: Measure outcomes across entire interaction sequences, not just individual requests

<Frame caption="Example: A Session creating an outline for a book about space">
  <img src="https://mintcdn.com/helicone/NVRJN0bOLLl-z8a0/images/sessions/helicone-session-ui.webp?fit=max&auto=format&n=NVRJN0bOLLl-z8a0&q=85&s=b084d6b8351f7c5e442486a840d5c42b" alt="Helicone example of a session template for monitoring and managing inputs from requests sent to your AI applications." data-og-width="5324" width="5324" data-og-height="3128" height="3128" data-path="images/sessions/helicone-session-ui.webp" data-optimize="true" data-opv="3" srcset="https://mintcdn.com/helicone/NVRJN0bOLLl-z8a0/images/sessions/helicone-session-ui.webp?w=280&fit=max&auto=format&n=NVRJN0bOLLl-z8a0&q=85&s=0898b67f7a8ce4521404df46d0de6938 280w, https://mintcdn.com/helicone/NVRJN0bOLLl-z8a0/images/sessions/helicone-session-ui.webp?w=560&fit=max&auto=format&n=NVRJN0bOLLl-z8a0&q=85&s=e3e409c797a435a77b07159928dec6af 560w, https://mintcdn.com/helicone/NVRJN0bOLLl-z8a0/images/sessions/helicone-session-ui.webp?w=840&fit=max&auto=format&n=NVRJN0bOLLl-z8a0&q=85&s=8f00dc1c5520f7fdd9f7d0ea7d95ccb1 840w, https://mintcdn.com/helicone/NVRJN0bOLLl-z8a0/images/sessions/helicone-session-ui.webp?w=1100&fit=max&auto=format&n=NVRJN0bOLLl-z8a0&q=85&s=0358bd1fad3b2a3efc3d6bf7286b0830 1100w, https://mintcdn.com/helicone/NVRJN0bOLLl-z8a0/images/sessions/helicone-session-ui.webp?w=1650&fit=max&auto=format&n=NVRJN0bOLLl-z8a0&q=85&s=ea64868858362af78ce6813b7ef698ab 1650w, https://mintcdn.com/helicone/NVRJN0bOLLl-z8a0/images/sessions/helicone-session-ui.webp?w=2500&fit=max&auto=format&n=NVRJN0bOLLl-z8a0&q=85&s=d42de142d864f649a99afc15821e6b95 2500w" />
</Frame>

## Quick Start

<Steps>
  <Step title="Add Session Headers">
    Include three required headers in your LLM requests:

    ```typescript  theme={null}
    {
      "Helicone-Session-Id": "unique-session-id",
      "Helicone-Session-Path": "/trace-path", 
      "Helicone-Session-Name": "Session Name"
    }
    ```
  </Step>

  <Step title="Structure Your Paths">
    Use path syntax to represent parent-child relationships:

    ```typescript  theme={null}
    "/abstract"                    // Top-level trace
    "/abstract/outline"           // Child trace
    "/abstract/outline/lesson-1"  // Grandchild trace
    ```
  </Step>

  <Step title="Make Your Request">
    Execute your LLM request with the session headers included:

    ```typescript  theme={null}
    const response = await client.chat.completions.create(
      { 
        messages: [{ role: "user", content: "Hello" }], 
        model: "gpt-4o-mini" 
      },
      {
        headers: {
          "Helicone-Session-Id": sessionId,
          "Helicone-Session-Path": "/greeting",
          "Helicone-Session-Name": "User Conversation"
        }
      }
    );
    ```
  </Step>
</Steps>

## Understanding Sessions

### What Sessions Can Track

Sessions can group together all types of requests in your AI workflow:

* **LLM calls** - OpenAI, Anthropic, and other model requests
* **[Vector database queries](/integrations/vectordb/logger-sdk)** - Embeddings, similarity searches, and retrievals
* **[Tool calls](/integrations/tools/logger-sdk)** - Function executions, API calls, and custom tools
* **Any logged request** - Anything sent through Helicone's logging

This gives you a complete view of your AI agent's behavior, not just the LLM interactions.

### Session IDs

The session ID is a unique identifier that groups all related requests together. Think of it as a conversation thread ID.

**What to use:**

* **UUIDs** (recommended): `550e8400-e29b-41d4-a716-446655440000`
* **Unique strings**: `user_123_conversation_456`

**Why it matters:**

* Same ID = requests get grouped together in the dashboard
* Different IDs = separate sessions, even if they're related
* Reusing IDs across different workflows will mix unrelated requests

```typescript  theme={null}
// ✅ Good - unique per conversation
const sessionId = randomUUID(); // Different for each user conversation

// ❌ Bad - reuses same ID
const sessionId = "chat_session"; // All users get mixed together
```

### Session Paths

Paths create the hierarchy within your session, showing how requests relate to each other.

**Path Naming Philosophy:**

Think of session paths as **conceptual groupings** rather than chronological order. Requests with the same path represent the same "type" of work, even if they happen at different times.

*Example: In a code review agent, all "security check" requests get the same path (`/review/security`) whether they happen early or late in the analysis. This lets you see patterns in the duration distribution chart - all security checks will be colored the same, showing you when they typically occur and how long they take.*

**Path Structure Rules:**

* Start with `/` (forward slash)
* Use `/` to separate levels: `/parent/child/grandchild`
* Keep names descriptive: `/analyze_request/fetch_data/process_results`
* **Group by function, not by time** - same conceptual work = same path

**How Hierarchy Works:**

```typescript  theme={null}
"/conversation"                    // Root level
"/conversation/initial_question"   // Child of conversation  
"/conversation/followup"          // Another child of conversation
"/conversation/followup/clarify"  // Child of followup
```

**Path Design Patterns:**

```typescript  theme={null}
// Workflow pattern - good for AI agents
"/task"
"/task/research" 
"/task/research/web_search"
"/task/generate"

// Conversation pattern - good for chatbots
"/session"
"/session/question_1"
"/session/answer_1" 
"/session/question_2"

// Pipeline pattern - good for data processing
"/process"
"/process/extract"
"/process/transform"
"/process/load"
```

### Session Names

The session name is a high-level grouping that makes it easy to filter and organize similar types of sessions in the dashboard.

**Good session names:**

* `"Customer Support"` - All support sessions use this name
* `"Content Generation"` - All content creation sessions use this name
* `"Trip Planning Agent"` - All trip planning workflows use this name

**Purpose:**

* **Quick filtering** - Filter dashboard to show only "Customer Support" sessions
* **High-level organization** - Group alike sessions for easy comparison
* **Performance analysis** - Compare metrics across the same session type

## Configuration Reference

### Required Headers

<ParamField header="Helicone-Session-Id" type="string" required>
  Unique identifier for the session. Use UUIDs to avoid conflicts.

  Example: `"550e8400-e29b-41d4-a716-446655440000"`
</ParamField>

<ParamField header="Helicone-Session-Path" type="string" required>
  Path representing the trace hierarchy using `/` syntax. Shows parent-child relationships.

  Example: `"/abstract"` or `"/parent/child"`
</ParamField>

<ParamField header="Helicone-Session-Name" type="string" required>
  Human-readable name for the session type. Groups similar workflows together.

  Example: `"Course Plan"` or `"Customer Support"`
</ParamField>

## Common Patterns

<Tabs>
  <Tab title="Code Generation Assistant">
    Track a complete code generation workflow with clarifications and refinements:

    <CodeGroup>
      ```typescript Node.js theme={null}
      import { randomUUID } from "crypto";
      import { OpenAI } from "openai";

      const client = new OpenAI({
        baseURL: "https://ai-gateway.helicone.ai",
        apiKey: process.env.HELICONE_API_KEY,
      });

      const sessionId = randomUUID();

      // Initial feature request
      const response1 = await client.chat.completions.create(
        {
          messages: [{ role: "user", content: "Create a React component for user authentication with email and password" }],
          model: "gpt-4o-mini",
        },
        {
          headers: {
            "Helicone-Session-Id": sessionId,
            "Helicone-Session-Path": "/request",
            "Helicone-Session-Name": "Code Generation Assistant",
          },
        }
      );

      // User asks for clarification
      const response2 = await client.chat.completions.create(
        {
          messages: [
            { role: "user", content: "Create a React component for user authentication with email and password" },
            { role: "assistant", content: response1.choices[0].message.content },
            { role: "user", content: "Can you add form validation and error handling?" }
          ],
          model: "gpt-4o-mini",
        },
        {
          headers: {
            "Helicone-Session-Id": sessionId,
            "Helicone-Session-Path": "/request/validation",
            "Helicone-Session-Name": "Code Generation Assistant",
          },
        }
      );

      // User requests TypeScript version
      const response3 = await client.chat.completions.create(
        {
          messages: [
            { role: "user", content: "Convert this to TypeScript with proper interfaces" }
          ],
          model: "gpt-4o-mini",
        },
        {
          headers: {
            "Helicone-Session-Id": sessionId,
            "Helicone-Session-Path": "/request/validation/typescript",
            "Helicone-Session-Name": "Code Generation Assistant",
          },
        }
      );
      ```

      ```python Python theme={null}
      import uuid
      import os
      from openai import OpenAI

      client = OpenAI(
          base_url="https://ai-gateway.helicone.ai",
          api_key=os.environ.get("HELICONE_API_KEY"),
      )

      session_id = str(uuid.uuid4())

      # Initial feature request
      response1 = client.chat.completions.create(
          model="gpt-4o-mini",
          messages=[{"role": "user", "content": "Create a React component for user authentication with email and password"}],
          extra_headers={
              "Helicone-Session-Id": session_id,
              "Helicone-Session-Path": "/request",
              "Helicone-Session-Name": "Code Generation Assistant",
          }
      )

      # User asks for clarification
      response2 = client.chat.completions.create(
          model="gpt-4o-mini",
          messages=[
              {"role": "user", "content": "Create a React component for user authentication with email and password"},
              {"role": "assistant", "content": response1.choices[0].message.content},
              {"role": "user", "content": "Can you add form validation and error handling?"}
          ],
          extra_headers={
              "Helicone-Session-Id": session_id,
              "Helicone-Session-Path": "/request/validation",
              "Helicone-Session-Name": "Code Generation Assistant",
          }
      )

      # User requests TypeScript version
      response3 = client.chat.completions.create(
          model="gpt-4o-mini",
          messages=[{"role": "user", "content": "Convert this to TypeScript with proper interfaces"}],
          extra_headers={
              "Helicone-Session-Id": session_id,
              "Helicone-Session-Path": "/request/validation/typescript",
              "Helicone-Session-Name": "Code Generation Assistant",
          }
      )
      ```
    </CodeGroup>
  </Tab>

  <Tab title="Pull Request Review">
    Track an automated PR review workflow with multiple analysis steps:

    ```typescript  theme={null}
    const sessionId = randomUUID();

    // Initial PR analysis
    const analysis = await client.chat.completions.create(
      { 
        messages: [{ role: "user", content: "Analyze this pull request for code quality and potential issues: [PR diff content]" }],
        model: "gpt-4o-mini" 
      },
      {
        headers: {
          "Helicone-Session-Id": sessionId,
          "Helicone-Session-Path": "/analysis",
          "Helicone-Session-Name": "PR Review Bot",
        },
      }
    );

    // Security check
    const securityCheck = await client.chat.completions.create(
      { 
        messages: [{ role: "user", content: "Check for security vulnerabilities: SQL injection, XSS, authentication issues" }],
        model: "gpt-4o-mini" 
      },
      {
        headers: {
          "Helicone-Session-Id": sessionId,
          "Helicone-Session-Path": "/analysis/security",
          "Helicone-Session-Name": "PR Review Bot",
        },
      }
    );

    // Generate review comments
    const reviewComments = await client.chat.completions.create(
      { 
        messages: [{ role: "user", content: "Generate constructive review comments based on analysis" }],
        model: "gpt-4o-mini" 
      },
      {
        headers: {
          "Helicone-Session-Id": sessionId,
          "Helicone-Session-Path": "/analysis/security/comments",
          "Helicone-Session-Name": "PR Review Bot",
        },
      }
    );
    ```
  </Tab>

  <Tab title="API Documentation Generator">
    Track a multi-step API documentation generation workflow:

    ```typescript  theme={null}
    const sessionId = randomUUID();

    // Analyze API endpoints
    const endpoints = await client.chat.completions.create(
      {
        messages: [{ role: "user", content: "Analyze these API routes and extract endpoint information: [code content]" }],
        model: "gpt-4o-mini",
      },
      {
        headers: {
          "Helicone-Session-Id": sessionId,
          "Helicone-Session-Path": "/analyze",
          "Helicone-Session-Name": "API Documentation Generator",
        },
      }
    );

    // Generate OpenAPI spec
    const openApiSpec = await client.chat.completions.create(
      {
        messages: [
          { role: "user", content: "Generate OpenAPI 3.0 specification based on these endpoints" }
        ],
        model: "gpt-4o-mini",
      },
      {
        headers: {
          "Helicone-Session-Id": sessionId,
          "Helicone-Session-Path": "/analyze/openapi",
          "Helicone-Session-Name": "API Documentation Generator",
        },
      }
    );

    // Create usage examples
    const examples = await client.chat.completions.create(
      {
        messages: [{ role: "user", content: "Create code examples for each endpoint in multiple languages" }],
        model: "gpt-4o-mini",
      },
      {
        headers: {
          "Helicone-Session-Id": sessionId,
          "Helicone-Session-Path": "/analyze/openapi/examples",
          "Helicone-Session-Name": "API Documentation Generator",
        },
      }
    );
    ```
  </Tab>
</Tabs>

<Card title="Complete Session Example" icon="js" iconType="duotone" color="#FF" href="https://github.com/Helicone/helicone/blob/main/examples/session_example/index.ts">
  Full JavaScript implementation showing session hierarchy and tracking
</Card>

## Related Features

<CardGroup cols={2}>
  <Card title="Vector Database Logging" icon="database" href="/integrations/vectordb/logger-sdk">
    Track vector database queries and embeddings alongside LLM calls
  </Card>

  <Card title="Tool Call Logging" icon="wrench" href="/integrations/tools/logger-sdk">
    Monitor tool calls and function executions within your agent workflows
  </Card>

  <Card title="Custom Properties" icon="tag" href="/features/advanced-usage/custom-properties">
    Add metadata to individual requests within sessions
  </Card>

  <Card title="User Metrics" icon="chart-line" href="/features/advanced-usage/user-metrics">
    Track user behavior patterns across multiple sessions
  </Card>
</CardGroup>

***

<Accordion title="Need more help?">
  Additional questions or feedback? Reach out to
  [help@helicone.ai](mailto:help@helicone.ai) or [schedule a
  call](https://cal.com/team/helicone/helicone-discovery) with us.
</Accordion>