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Auto-Instrumentation

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Supported providers

• LLMs: openai, anthropic, google_generativeai, cohere, bedrock, vertexai, ollama, mistralai, together, replicate
• Vector DBs: chromadb, pinecone, qdrant
• Frameworks: langchain, llamaindex, haystack

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Installation

# One provider
pip install "basalt-sdk[openai]"

# A whole category
pip install "basalt-sdk[llm-all]"

# Everything
pip install "basalt-sdk[all]"

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Enable instrumentation

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Enable all installed providers

from basalt import Basalt

basalt = Basalt(api_key="your-api-key")

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Enable only specific providers

from basalt import Basalt

basalt = Basalt(
    api_key="your-api-key",
    enabled_instruments=["openai", "chromadb"],
)

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Disable specific providers

from basalt import Basalt

basalt = Basalt(
    api_key="your-api-key",
    disabled_instruments=["langchain"],
)

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What gets captured

• Operation name and duration
• Provider/model identifiers (for LLMs)
• Token usage and errors (when the underlying SDK exposes them)

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Example (OpenAI)

from basalt import Basalt
import openai

basalt = Basalt(api_key="...", enabled_instruments=["openai"])
openai_client = openai.OpenAI(api_key="...")

@start_observe(feature_slug="my-feature", name="Process Request")
def main():
    response = openai_client.chat.completions.create(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}],
)

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Context propagation

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Best practices

• Enable only the providers you use (enabled_instruments) to keep overhead low.
• Don’t double-instrument: if a call is auto-instrumented, avoid wrapping the same call in a manual observe span.
• Call basalt.shutdown() when your process exits to flush traces.

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Next steps

• Patterns
• Workflows
• API Reference