Google ADK (Python) 2.13.0: our test log

Google ADK (Python) 2.13.0 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 1.7 s.

Result at a glance

What we found

• 3 of 3 task trials passed: the model's tool call reached the framework, the function ran with 17 and 25, and the final answer contained 42.
• Cold install took 1.7 s with uv and 13.2 s with pip, and left 71 MB in 48 packages (uv).
• Used the LiteLlm wrapper to reach a non-Google model. That path needs the google-adk[extensions] extra, which grew the environment from 71 MB to 652 MB and took 19.6 s more with uv; mcp was also missing and installed separately (1.0 s).

What we ran, step by step

# Install with uv (cold, no cache)
uv venv -q -p 3.13 work/py/google-adk && VIRTUAL_ENV=work/py/google-adk uv pip install --no-cache -q google-adk

# Install with pip (cold, no cache)
uv venv -q --seed -p 3.13 work/pip/google-adk && work/pip/google-adk/bin/pip install --no-cache-dir -q google-adk

# Install google-adk[extensions] on top
VIRTUAL_ENV=work/py/google-adk uv pip install --no-cache -q 'google-adk[extensions]'

# Install mcp on top
VIRTUAL_ENV=work/py/google-adk uv pip install --no-cache -q mcp

# Task command (all trials)
OTEL_SDK_DISABLED=true work/py/google-adk/bin/python tasks/google_adk_agent.py

Raw output (excerpts)

from common import *
import asyncio
from google.adk.agents import Agent
from google.adk.models.lite_llm import LiteLlm
from google.adk.runners import InMemoryRunner
from google.genai import types
agent = Agent(name="calc", model=LiteLlm(model=f"openai/{MODEL}", api_base=BASE, api_key=KEY), instruction="Use tools.", tools=[add_numbers])
async def main():
    runner = InMemoryRunner(agent=agent, app_name="t")
    s = await runner.session_service.create_session(app_name="t", user_id="u")
    out = ""
    async for ev in runner.run_async(user_id="u", session_id=s.id, new_message=types.Content(role="user", parts=[types.Part(text=TASK)])):
        if ev.is_final_response() and ev.content and ev.content.parts: out = ev.content.parts[0].text
    return out
report(asyncio.run(main()))

import json, os
BASE = "http://127.0.0.1:11434/v1"
MODEL = os.environ.get("MODEL", "qwen2.5-3b")
KEY = "ollama"
TASK = "Use the add_numbers tool to compute 17 + 25, then tell me the result."
CALLS = []
def add_numbers(a: int, b: int) -> int:
    """Add two integers and return the sum."""
    CALLS.append((a, b))
    return int(a) + int(b)
def report(final):
    ok = any({int(x), int(y)} == {17, 25} for x, y in CALLS) and "42" in str(final)
    print("RESULT_JSON " + json.dumps({"calls": CALLS, "final": str(final)[:300], "ok": ok}))

RESULT_JSON {"calls": [[17, 25]], "final": "The result of adding 17 and 25 is 42.", "ok": true}

Gotchas and things that broke

• The bare google-adk install cannot talk to a local OpenAI-compatible model: you need the extensions extra, and import errors name a missing mcp package until you install it.

What we did not test

• Any vendor-hosted model: everything ran on a local 3B model, so tool-call reliability with frontier models is not measured here.
• Multi-agent flows, memory, long conversations, streaming, tracing and cloud deployment.
• Anything beyond one tool and one question; three trials is a smoke test, not a benchmark.

Reproduce it

Versions change. These commands install whatever is latest on the day you run them unless a version is pinned, so expect different results later. The version we measured is in the table above.

# 1. Serve any local model that supports tool calling behind an OpenAI-compatible endpoint, registered as qwen2.5-3b:
#    http://127.0.0.1:11434/v1  (we used Ollama with a Qwen2.5-3B-Instruct Q4_K_M GGUF)
uv venv -p 3.13 .venv && VIRTUAL_ENV=.venv uv pip install google-adk
# 2. Save common.py (above) and the task script next to each other, then:
.venv/bin/python google_adk_agent.py

How to read this test

Every number on this page was measured on this one machine, which also ran other jobs, so timings are indicative and vary run to run. A pass means our small task worked once or as stated, a fail means it did not work as we ran it; neither says how the tool behaves with a vendor's frontier model, on a GPU, or at scale. Where a tool could not run without an account or a GPU, we say so instead of guessing.

See the testing rig for the hardware, the local model and the rules we follow, and the test hub for every other test.

Related tests: agent frameworks and sdks

• Haystack: Haystack 3.4.1 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 2.3 s.
• Instructor: Instructor 1.17.0 returned a validated Pydantic object from a local 3B model in 3 of 3 trials (structured extraction, not a tool call); cold uv install 1.4 s.
• LangChain: LangChain 1.4.7 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 0.8 s.
• LangChain.js: LangChain.js 1.5.19 passed our one-tool task 3 of 3 on Node 20 and 3 of 3 on Node 24; about 101 MB in 36 packages.
• LangGraph: LangGraph 1.2.17 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 2.5 s.
• LiteLLM: LiteLLM 1.106.4 passed our one-tool task on a local 3B model: 3 of 3 trials; cold uv install 2.3 s.

All test logs · How we test

FAQ

Sources

• adk-python repository (fetched 2026-10-25)

Related

• All test logs
• Methodology and testing rig
• Study: install size and first-task results across 16 frameworks
• Google ADK facts and sources