# LangChain deep market research

- **Agent pattern:** Plan-and-research agent
- **Search surfaces:** Web and news
- **Saved artifact:** `langchain-market-research.md`


# What you'll build

A LangChain agent that produces a source-backed market brief instead of a one-shot answer. It plans the investigation, separates durable sources from recent reporting, compares independent evidence, and identifies unanswered questions.

**Default brief:** Assess commercial battery recycling in the United States. Finish with market signals, risks, open questions, and source URLs after comparing at least three independent sources.


# How the agent works

1.  **Plan the questions.** The system prompt asks for a short research plan before the first search.
2.  **Split durable and current.** Web search gathers background evidence; news search tracks recent market developments.
3.  **Compare before concluding.** The agent reconciles independent sources and marks risks or missing evidence in the final brief.


# Core agent setup

This excerpt shows the LangChain agent's research contract and tool set. The full script also configures the OpenAI-compatible model, environment, and Markdown report.

``` python
agent = create_agent(
    model=model,
    tools=[
        web_search(
            provider="langchain",
            allowed_engines=["google_light", "bing"],
        ),
        news_search(provider="langchain"),
    ],
    system_prompt=(
        "Make a short plan before searching, distinguish current reporting "
        "from durable background sources, and never present unsupported claims as fact."
    ),
)
result = agent.invoke({"messages": [{"role": "user", "content": PROMPT}]})
```

[View the complete `main.py` →](https://github.com/serpapi/serpapi-search-tools-python/blob/main/cookbook/langchain/main.py)


# Run the recipe

Set `SERPAPI_API_KEY` and `XAI_API_KEY` in the repository-root `.env`, then run:

``` bash
uv run --isolated --no-project --with 'serpapi-search-tools[langchain]' --with python-dotenv --with langchain-openai \
  cookbook/langchain/main.py
```

Optional controls: `XAI_MODEL`, `XAI_BASE_URL`, `COOKBOOK_PROMPT`, and `COOKBOOK_OUTPUT_DIR`.

**OpenAI-compatible model:** The recipe uses LangChain's `ChatOpenAI` client with xAI by default. Change the model or base URL through the environment without touching the search tools.


# Inspect the result

The recipe writes `cookbook-output/langchain-market-research.md`. Expect a decision-ready brief with durable context, recent signals, source comparisons, market risks, unanswered questions, and direct URLs.

Set `COOKBOOK_PROMPT` to reuse the same research discipline for another market.


# SerpApi tools used

[web_search](../../reference/web_search.md#serpapi_search_tools.web_search) gathers durable market and company sources, while [news_search](../../reference/news_search.md#serpapi_search_tools.news_search) finds recent developments. Their separate schemas help the agent plan each search against the right evidence type and compare sources before writing the brief.

**Framework pattern:** The research workflow follows LangChain's Deep Agents from scratch guide and uses SerpApi web and news tools.

- [Open the runnable recipe](https://github.com/serpapi/serpapi-search-tools-python/tree/main/cookbook/langchain)
- [View LangChain's source guide](https://docs.langchain.com/oss/python/langchain/deep-agent-from-scratch)