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Force an agent to return a typed Pydantic object instead of free-form text. Pass output_schema to LlmAgent. 
from pydantic import BaseModel, Field
from langchain_adk import LlmAgent
from langchain_adk.events.event import Event, EventType

class CompanyAnalysis(BaseModel):
    name: str = Field(description="Company name")
    industry: str = Field(description="Primary industry")
    strengths: list[str] = Field(description="Key strengths")
    risks: list[str] = Field(description="Key risks")
    recommendation: str = Field(description="Buy, Hold, or Sell")
    confidence: float = Field(description="Confidence score 0-1")

agent = LlmAgent(
    name="AnalystAgent",
    llm=llm,
    tools=[get_financials, get_news_sentiment],
    output_schema=CompanyAnalysis,
    instructions="You are a financial analyst.",
)
The parsed object is available on event.data when it’s the final response: 
async for event in agent.astream("Analyze Apple", ctx=ctx):
    if event.is_final_response():
        analysis = event.data  # CompanyAnalysis instance
        print(f"{analysis.name}: {analysis.recommendation} ({analysis.confidence:.0%})")

​
How it works

  1. PydanticOutputParser.get_format_instructions() is appended to the system prompt
  2. When the LLM responds, PydanticOutputParser.parse() extracts and validates JSON
  3. If direct parsing fails, with_structured_output() is used as a fallback
  4. Works with streaming and multi-agent compositions