Trace Google ADK
Agent Frameworks
Auto-instrument Google ADK with traceAI in under 3 minutes. Every LLM call, tool use, retrieval, and chain step becomes an OpenTelemetry span you can search, replay, and debug.
Recipes for Google ADK
Prerequisites
Before you start
- · A working Google ADK app — local or already in production.
- · A free Future AGI account with
FI_API_KEYandFI_SECRET_KEY. - · Python 3.9+ / Node 18+ / Java 17+ depending on which SDK you're installing.
Install
pip install traceAI-google-adkTrace recipe
from fi_instrumentation import register
from fi_instrumentation.fi_types import ProjectType
from traceai_google_adk import GoogleADKInstrumentor
trace_provider = register(
project_type=ProjectType.OBSERVE,
project_name="GOOGLE_ADK_APP",
)
GoogleADKInstrumentor().instrument(tracer_provider=trace_provider)
# Your existing Google ADK code runs unchanged from here.
# Every call is now an OpenTelemetry span in Future AGI.What Future AGI captures
Trace fields you'll see in the dashboard
-
Spans for every Google ADK call: input, output, latency, tokens, cost, model name, errors
-
Trace tree across LLM, tool, retrieval, embedding, and chain spans
-
Custom attributes via `using_attributes` (session_id, user_id, prompt_template, tags, custom dicts)
-
Streaming-safe — partial chunks aggregated into a single span
Common gotchas
Read these before you ship
- 01
Set `FI_API_KEY` and `FI_SECRET_KEY` in env before calling `register()` — silent fallback otherwise.
- 02
Async frameworks: instantiate the instrumentor *before* you create the client, not after.
- 03
Streaming responses are aggregated into a single span only when you use the official SDK iterator.
Next: chain it with the other recipes
Trace is the first step. Most teams add an evaluator the same week, and start optimising or simulating once they have a baseline. Each recipe takes minutes to wire up.
Adjacent integrations
More integrations like Google ADK
LangChain
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LangChain4j
Java-native chains, agents, and RAG with LangChain4j.
DSPy
Declarative LLM programs with optimisable signatures and modules.
CrewAI
Role-based multi-agent crews with task plans and tool use.