LiteLLM

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Setup

uv add braintrust litellm

pip install braintrust litellm

BRAINTRUST_API_KEY=<your-braintrust-api-key>
OPENAI_API_KEY=<your-openai-api-key>

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Auto-instrumentation

import os

import braintrust

braintrust.auto_instrument()
braintrust.init_logger(
    api_key=os.environ["BRAINTRUST_API_KEY"],
    project="litellm-example",  # Replace with your project name
)

import litellm

response = litellm.completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
)
print(response.choices[0].message.content)

from braintrust.integrations.litellm import patch_litellm

patch_litellm()

import litellm
from braintrust import init_logger

# Initialize Braintrust
logger = init_logger(project="litellm-example")

# Use LiteLLM as normal - all calls are automatically traced
response = litellm.completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What is the capital of France?"}]
)

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Manual instrumentation

import os

import litellm
from braintrust import init_logger
from braintrust.integrations.litellm import wrap_litellm

init_logger(
    api_key=os.environ["BRAINTRUST_API_KEY"],
    project="litellm-example",  # Replace with your project name
)

wrap_litellm(litellm)

response = litellm.completion(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "What is the capital of France?"}],
)
print(response.choices[0].message.content)

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What Braintrust traces

• Completion spans (Completion) for litellm.completion, litellm.acompletion, litellm.text_completion, and litellm.atext_completion, with messages or prompt, model, and request parameters; response choices, token usage, and time-to-first-token for streaming.
• Responses API spans (Response) for litellm.responses / litellm.aresponses, with input and request parameters; response output, token usage, and time-to-first-token for streaming.
• Image generation spans (Image Generation) for litellm.image_generation / litellm.aimage_generation, with prompt and request parameters; output capturing per-image data (attachment for base64 responses or URL reference for URL responses) plus metadata like output format, size, quality, and image count, plus timing and token usage when reported.
• Embedding spans (Embedding) for litellm.embedding / litellm.aembedding, with input text and request parameters; output summarized as the embedding vector dimension (length of the first embedding), plus token usage.
• Moderation spans (Moderation) for litellm.moderation / litellm.amoderation, with input and request parameters; classification results and token usage when reported.
• Speech spans (Speech) for litellm.speech / litellm.aspeech, with text input and request parameters; generated audio captured as an attachment, plus timing.
• Transcription spans (Transcription) for litellm.transcription / litellm.atranscription, with the input audio captured as an attachment plus model and request parameters; transcribed text and token usage.
• Rerank spans (Rerank) for litellm.rerank / litellm.arerank, with query, documents, and request parameters (plus auto-derived document_count); results as a list of {index, relevance_score} items (capped at 100, with documents intentionally dropped); token metrics (prompt, completion, total), plus Cohere-style billed-unit metrics (search units, classifications) when the response includes them.
• Token usage metrics (prompt, completion, total, plus cached and reasoning tokens when the provider reports them).
• Errors captured on every call.

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Resources

• LiteLLM documentation
• LiteLLM supported providers
• DSPy integration, which combines LiteLLM tracing with DSPy-specific callbacks