Agno is a Python agent framework for building lean LLM applications with model abstraction, tool use, memory, and reasoning. Arize AX captures every Agno agent run — the agent’s reasoning steps, tool invocations, and LLM calls — via theDocumentation Index
Fetch the complete documentation index at: https://arize-ax.mintlify.dev/docs/llms.txt
Use this file to discover all available pages before exploring further.
openinference-instrumentation-agno package.
Prerequisites
- Python 3.10+
- An Arize AX account (sign up)
- An
OPENAI_API_KEYfrom the OpenAI Platform
Launch Arize AX
- Sign in to your Arize AX account.
- From Space Settings, copy your Space ID and API Key. You will set them as
ARIZE_SPACE_IDandARIZE_API_KEYbelow.
Install
Configure credentials
Setup tracing
Run Agno
Expected output
Verify in Arize AX
- Open your Arize AX space and select project
agno-tracing-example. - You should see a new trace within ~30 seconds containing an
Agent.runparent span wrapping anOpenAIChatLLM child span with the prompt, response, and token usage attached. - If no traces appear, see Troubleshooting.
Troubleshooting
- No traces in Arize AX. Confirm
ARIZE_SPACE_IDandARIZE_API_KEYare set in the same shell that runsexample.py. Enable OpenTelemetry debug logs withexport OTEL_LOG_LEVEL=debugand re-run. - Agno spans missing but other spans present.
AgnoInstrumentor().instrument(...)must run before anyfrom agno import .... Make sureinstrumentation.pyis the first import in your entry point. 401from OpenAI. VerifyOPENAI_API_KEYis set and has access togpt-5. Swap for a model your key can call.- Other LLM providers. Agno supports many models —
from agno.models.anthropic import Claude,from agno.models.groq import Groq, etc. The sameAgnoInstrumentorcovers them.