flyteplugins-agents-google 2.5.11

Run Google ADK (Agent Development Kit) agents on Flyte.

flyteplugins-agents-google

Run Google ADK (Agent Development Kit) agents on Flyte. You keep writing ADK agents; Flyte is the runtime underneath.

pip install flyteplugins-agents-google

import flyte
from flyteplugins.agents.google import tool, run_agent

env = flyte.TaskEnvironment(
    "google-agent",
    secrets=[flyte.Secret(key="google_api_key", as_env_var="GOOGLE_API_KEY")],
)

@tool
@env.task(cache="auto", retries=3)
async def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"The weather in {city} is sunny, 22°C."

@env.task(report=True, retries=3)
async def city_agent(question: str) -> str:
    return await run_agent(question, tools=[get_weather], model="gemini-2.0-flash")

How it maps to Flyte

• The SDK owns the loop — we don't reimplement it. ADK's Runner drives the agent loop (model + tools, yielding Events); run_agent builds an LlmAgent, runs Runner.run_async inside your @env.task, and returns the final answer.
• Tools as durable child actions. tool wraps an @env.task as the Python function ADK calls; its body dispatches to task.aio(), so each tool call runs as a durable Flyte child action. ADK derives the tool declaration from the task signature.
• Durable, replayable model turns. With durable=True, the agent's model is wrapped (FlyteLlm) so each turn through BaseLlm.generate_content_async — the seam below the loop — is recorded via flyte.trace. On a crash/retry, completed turns replay from their recorded LlmResponse and tools are cache hits. (Same idea as swapping OpenAI's ModelProvider: trace the model-call seam, not the loop.)
• Observability: the turns and tool calls render into the task report.

The API key is read from the environment (e.g. GOOGLE_API_KEY for Gemini, or your Vertex AI config), so it can't leak into task inputs — wire it as a Flyte secret.

Memory

Pass memory_key (a user/thread id) for cross-run memory — the agent continues the same conversation across separate runs:

await run_agent(message, model="gemini-2.0-flash", memory_key="user-alice")

ADK keeps the conversation as a list of Events on the session; we persist those to a durable, keyed MemoryStore and restore them into a fresh session on the next run with the same key.

Examples

See examples/:

• google_durable_agent.py — a single durable agent: tools as Flyte tasks, traced model turns, agent timeline in the report.
• google_multi_agent.py — multi-agent orchestration: a planner agent decomposes a topic, researcher agents fan out in parallel, an editor agent synthesizes — each agent its own durable action.
• google_crash_resume.py — crash & resume: the task crashes on its first attempt; on retry the completed model turns replay from their flyte.trace records and the tool calls are cache hits. Run on a backend.
• google_memory.py — cross-run memory: two separate runs share a memory_key; the agent learns a fact in run 1 and recalls it in run 2.
• google_handoffs.py — native agent transfer: a triage agent transfers to a billing or technical-support sub-agent, the whole agent tree durable on Flyte. The specialist can pause on a Flyte condition (flyte.new_condition) to have a human share details mid-conversation, then resume with them.

Conformance

This adapter passes the shared flyteplugins.agents.core.testing.assert_adapter_conforms check — the same one every adapter runs — so it follows the common format (tool + run_agent, tool tasks wired to the resolver), shared with the OpenAI, Claude and Mistral adapters.