Parallel tool calls with LiteLLM + OpenAI: some function calls missing in sequential agent flow #187

model=LiteLlm(model="openai/gpt-4o-mini")

model="gemini-2.0-flash"

from google.adk.agents import LlmAgent, SequentialAgent
from google.adk.models.lite_llm import LiteLlm
from google.adk.tools import FunctionTool
from google.adk.sessions import InMemorySessionService
from google.adk.runners import Runner
from google.genai import types

# Tool that echoes the keyword back with some metadata
def keyword_info(keyword: str) -> dict:
    return {"keyword": keyword, "length": len(keyword)}

tool = FunctionTool(func=keyword_info)

# Agent 1: extracts simple keywords (simulated)
extract_agent = LlmAgent(
    name="ExtractAgent",
    model=LiteLlm(model="openai/gpt-4o-mini"),  # <- works fine here
    instruction="Extract keywords from the user input. Output only the list of keywords.",
    output_key="keywords"
)

# Agent 2: invokes a function once per keyword
info_agent = LlmAgent(
    name="InfoAgent",
    model=LiteLlm(model="openai/gpt-4o-mini"),  # <- problem happens here, try with model="gemini-2.0-flash"
    instruction="For each keyword in 'keywords', call the 'keyword_info' tool to get more information.",
    tools=[tool],
    output_key="keyword_infos"
)

# Agent 3: summarizes
summarizer_agent = LlmAgent(
    name="SummaryAgent",
    model=LiteLlm(model="openai/gpt-4o-mini"), # <- works fine here
    instruction="Summarize the keyword info in a user-friendly way.",
    output_key="final_output"
)

# Chain them
pipeline = SequentialAgent(
    name="PipelineAgent",
    sub_agents=[extract_agent, info_agent, summarizer_agent]
)

# Session
APP_NAME = "demo_pipeline"
USER_ID = "test_user"
SESSION_ID = "test_session"

session_service = InMemorySessionService()
session_service.create_session(app_name=APP_NAME, user_id=USER_ID, session_id=SESSION_ID)
runner = Runner(agent=pipeline, app_name=APP_NAME, session_service=session_service)

# Trigger agent
input_text = "Cats, space exploration, artificial intelligence"
content = types.Content(role="user", parts=[types.Part(text=input_text)])
events = runner.run(user_id=USER_ID, session_id=SESSION_ID, new_message=content)

for event in events:
    if event.is_final_response():
        print("Final Output:", event.content.parts[0].text)