LiteLLM integration

​

Prerequisites

• A TCC API key set as TCC_API_KEY environment variable
• The contextcompany package installed

​

Setup

​

Step 1: Install dependencies

pip install contextcompany[litellm]

poetry add contextcompany[litellm]

​

Step 2: Initialize the callback

import litellm
from contextcompany.litellm import TCCCallback

litellm.callbacks = [TCCCallback()]

​

Step 3: Create a run with LiteLLM steps

import contextcompany as tcc
import litellm
from contextcompany.litellm import TCCCallback

# Initialize the callback
litellm.callbacks = [TCCCallback()]

r = tcc.run()
r.prompt("What is the weather in London?")  # required

# --- Your agent loop ---
response = litellm.completion(
    ...,
    metadata={"tcc.runId": r.run_id},  # required
)
# --- End of agent loop ---

r.response(response.choices[0].message.content)  # optional
r.end()  # required

​

Custom metadata

response = litellm.completion(
    ...,
    metadata={
        "tcc.runId": r.run_id,  # required
        "userId": "550e8400-e29b-41d4-a716-446655440000",  # you can pass any metadata here
    },
)

​

What gets captured

• Model: Both the requested and used model
• Messages: Input and output messages
• Token usage: Prompt and completion tokens
• Finish reason: Why the LLM stopped generating
• Tool calls: Any tool calls made by the model