Implement Agentic Query Planning

Building upon your LlamaIndex data pipeline, implement an agentic query engine capable of handling complex queries. The agent should be able to break down a high-level query like 'Analyze the strategic implications of the Musk vs. OpenAI lawsuit by summarizing key legal points and market reactions' into sub-queries. Utilize `QueryEngineTool` and `LlamaPack` or a custom `RouterQueryEngine` to orchestrate this process with GPT-4o. Show how the agent routes questions to specific sub-query engines or tools. Include Python code.

```python
from llama_index.core import VectorStoreIndex, Document
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.agent import AgentRunner
from llama_index.llms.openai import OpenAI
from llama_index.vector_stores.pinecone import PineconeVectorStore
# ... other necessary imports

# Assume 'legal_index' and 'news_index' are already created VectorStoreIndex instances
# backed by PineconeVectorStore

legal_query_engine = legal_index.as_query_engine(similarity_top_k=3)
news_query_engine = news_index.as_query_engine(similarity_top_k=5)

legal_tool = QueryEngineTool(query_engine=legal_query_engine, metadata=ToolMetadata(name='legal_analyzer', description='Provides summaries and context from legal documents and filings.'))
news_tool = QueryEngineTool(query_engine=news_query_engine, metadata=ToolMetadata(name='market_news_analyzer', description='Provides insights and sentiment from market news articles and reports.'))

llm = OpenAI(model='gpt-4o', api_key='YOUR_OPENAI_API_KEY')

# Your task: Initialize an agent (e.g., FunctionCallingAgentWorker or ReActAgent) with these tools
# and demonstrate how it handles a complex query.
# For example, using AgentRunner with a custom agent worker:
# agent = AgentRunner(your_agent_worker)
# response = agent.chat('Analyze the strategic implications of the Musk vs. OpenAI lawsuit...')
```