Supported frameworks and LLM providers for Atla Insights in Python
Atla Insights supports a wide range of LLM providers and AI agent frameworks. All instrumentation methods share a common interface for easy integration.
from atla_insights import configure, instrument_openaifrom openai import OpenAIconfigure(token="<MY_ATLA_INSIGHTS_TOKEN>")instrument_openai()client = OpenAI()# All OpenAI calls are now instrumented
By default, instrumented LLM calls will be treated independently from one another. To logically group LLM calls into a trace, use the @instrument decorator.
For Agno, BAML, OpenAI Agents and Smolagents in Python, you will need to instrument both the framework and the underlying LLM provider(s).
from atla_insights import configure, instrument_agnoconfigure(token="<MY_ATLA_INSIGHTS_TOKEN>")# If you are using a single LLM provider (e.g., via `OpenAIChat`)instrument_agno("openai")# If you are using multiple LLM providers (e.g., `OpenAIChat` and `Claude`)instrument_agno(["anthropic", "openai"])
If you are using a framework or LLM provider without native support, you can manually record LLM generations via our lower-level SDK.
from atla_insights.span import start_as_current_spanwith start_as_current_span("my-llm-generation") as span: # Run my LLM generation via an unsupported framework. input_messages = [{"role": "user", "content": "What is the capital of France?"}] tools = [ { "type": "function", "function": { "name": "get_capital", "parameters": {"type": "object", "properties": {"country": {"type": "string"}}}, }, } ] result = my_client.chat.completions.create(messages=input_messages, tools=tools) # Manually record LLM generation. span.record_generation( input_messages=input_messages, output_messages=[choice.message for choice in result.choices], tools=tools, )
Note that all arguments are expected to be in an OpenAI-compatible format.See the relevant OpenAI documentation for more details:
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