Financial Research Agent Example

This example shows how you might compose a richer financial research agent using the Agents SDK. The pattern is similar to the research_bot example, but with more specialized sub‑agents and a verification step.

The flow is:

1. Planning: A planner agent turns the end user’s request into a list of search terms relevant to financial analysis – recent news, earnings calls, corporate filings, industry commentary, etc.
2. Search: A search agent uses the built‑in WebSearchTool to retrieve terse summaries for each search term. (You could also add FileSearchTool if you have indexed PDFs or 10‑Ks.)
3. Sub‑analysts: Additional agents (e.g. a fundamentals analyst and a risk analyst) are exposed as tools so the writer can call them inline and incorporate their outputs.
4. Writing: A senior writer agent brings together the search snippets and any sub‑analyst summaries into a long‑form markdown report plus a short executive summary.
5. Verification: A final verifier agent audits the report for obvious inconsistencies or missing sourcing.

You can run the example with:

python -m examples.financial_research_agent.main

and enter a query like:

Write up an analysis of Apple Inc.'s most recent quarter.

Starter prompt

The writer agent is seeded with instructions similar to:

You are a senior financial analyst. You will be provided with the original query
and a set of raw search summaries. Your job is to synthesize these into a
long‑form markdown report (at least several paragraphs) with a short executive
summary. You also have access to tools like `fundamentals_analysis` and
`risk_analysis` to get short specialist write‑ups if you want to incorporate them.
Add a few follow‑up questions for further research.

You can tweak these prompts and sub‑agents to suit your own data sources and preferred report structure.