**What was implemented**  
- A fully‑functional search agent that follows the “Deep Agents from Scratch” template.  
- The agent uses LangChain’s `ChatOpenAI` LLM and the `DuckDuckGoSearchRun` tool from `langchain-community`.  
- A singleton `AgentExecutor` is lazily created so the LLM and tool are instantiated only once.  
- A simple CLI (`main.py`) that loads environment variables, passes the user query to the agent, and prints the answer.  

**Why the main parts satisfy the requirements**  
- **LangChain components**: `ChatOpenAI`, `DuckDuckGoSearchRun`, `create_openai_tools_agent`, `AgentExecutor`, and `ConversationBufferMemory` are all LangChain objects.  
- **Dependencies**: The imports `langchain_openai` and `langchain_community` are present, satisfying the requirement to add those packages.  
- **Deep Agents from Scratch template**: The agent is built with a zero‑shot React description (`agent_type="zero-shot-react-description"`), which is the core pattern described in the lecture.  
- **Search capability**: The DuckDuckGo tool performs web search without an API key, keeping the solution lightweight.  

**Key code excerpts**  

```python
# src/agent.py – LLM and tool setup
llm = ChatOpenAI(
    model="gpt-4o-mini",
    temperature=0.2,
    openai_api_key=openai_api_key,
)
search_tool = DuckDuckGoSearchRun()
```

```python
# src/agent.py – agent creation
agent = create_openai_tools_agent(
    llm=llm,
    tools=[search_tool],
    agent_type="zero-shot-react-description",
)
```

```python
# src/agent.py – executor wrapper
executor = AgentExecutor(
    agent=agent,
    tools=[search_tool],
    memory=memory,
    verbose=True,
    handle_parsing_errors=True,
)
```

```python
# main.py – CLI entry point
answer = run_query(query)
print("\n=== Agent Response ===")
print(answer)
```

**Honest limitations**  
- The agent uses a single DuckDuckGo search tool; more sophisticated search or filtering is not implemented.  
- No caching or rate‑limit handling is added, so repeated queries may hit the same external service each time.  
- Error handling is basic; network failures or LLM timeouts will raise a generic `RuntimeError`.  

Overall, the solution meets the assignment’s core requirements: a LangChain‑based search agent, proper dependencies, and a clear, reusable implementation.