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

# Large Language Model Session Token Counter

<a href="/release-notes/4.92"><Badge className="version-badge" color="blue">Updated in 4.92</Badge></a>

In Cognigy.AI, you can use the `api.getLLMTokenUsageForSession()` API call in a [Code Node](/ai/for-developers/code/overview) or an [Extension Node](/ai/for-developers/extensions) to fetch the Large Language Model (LLM) total token usage in a conversation or chat session. This way, you can track your [LLM providers](/ai/agents/develop/gen-ai-and-llms/providers/all-providers) costs.

The LLM Session Token Counter retrieves the token usage information via LLM provider-specific API calls to return the exact number of tokens the LLM uses. If the information isn't available by the provider, Cognigy.AI estimates the token usage.

## Prerequisite

* You have added a Node that uses an LLM to your Flow, for example, an [AI Agent Node](/ai/agents/develop/node-reference/ai/ai-agent), [LLM Prompt Node](/ai/agents/develop/node-reference/service/llm-prompt), [Search Extract Output Node](/ai/agents/develop/node-reference/other-nodes/knowledge-search), or [Agent Copilot Nodes](/ai/agents/develop/node-reference/agent-copilot/overview).

## Limitations

* The LLM Session Token Counter doesn't support embedding models from Aleph Alpha.

## Fetching Session-Wide Token Usage

1. Add a [Code Node](/ai/for-developers/code/overview) after the Node that uses an LLM, for example, after an [AI Agent Node](/ai/agents/develop/node-reference/ai/ai-agent).
2. Add the following code to the Code Node:

   ```js theme={null}
   const tokens = api.getLLMTokenUsageForSession();
   ```

This Code Node fetches the total token usage in the session and stores it in the `token` variable.

<Accordion title="Returned JSON Object">
  The following JSON example shows an object returned by `api.getLLMTokenUsageForSession()`:

  ```json theme={null}
  {
      "1c945b38-5dbb-4fcf-9fdd-112bd8177f9c": {
          "IlmDisplayName": "openAI - text- embedding-3-large - 1735891122507",
          "providerType": "openAI",
          "modelType" : "text-embedding-3-large",
          "usage": {
              "inputTokens": 6,
              "outputTokens": 0
          }
      },
      "ce190e1f-eff7-4f6b-9074-9f705375a3d3": {
          "IlmDisplayName": "openAI - gpt-40 - 1735891179896",
          "providerType": "openAI",
          "modelType": "gpt-40",
          "usage": {
              "inputTokens": 441,
              "outputTokens": 106
          }
      }
  }
  ```

  | Key            | Type   | Description                                                                                                                                           | Example                                |
  | -------------- | ------ | ----------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------- |
  | llmReferenceId | String | The LLM reference ID.                                                                                                                                 | `ce190e1f-eff7-4f6b-9074-9f705375a3d3` |
  | llmDisplayName | String | The name of the LLM.                                                                                                                                  | `openAI - gpt-40 - 1735891179896`      |
  | providerType   | String | The LLM provider.                                                                                                                                     | `openAI`                               |
  | modelType      | String | The LLM model.                                                                                                                                        | `gpt-40`                               |
  | usage          | String | The object containing information about the session total token usage.                                                                                | -                                      |
  | inputTokens    | Number | The number of input tokens.                                                                                                                           | 441                                    |
  | outputTokens   | Number | The number output tokens. The output tokens count for embedding models is always 0 since embedding models output embedding vectors instead of tokens. | 106                                    |

  For testing in the Interaction Panel, you can output the JSON object by adding `api.say()` at the end of your [Code Node](/ai/for-developers/code/api-functions).
</Accordion>

## More Information

* [Overview](/ai/agents/develop/gen-ai-and-llms/llms)
* [LLM Providers](/ai/agents/develop/gen-ai-and-llms/providers/all-providers)
* [Model Support by Feature](/ai/agents/develop/gen-ai-and-llms/model-support-by-feature)