Adds example for financial agent (#255)

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 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.

Author: rm-openai

examples/financial_research_agent/README.md

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+# 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:
+
+```bash
+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.

examples/financial_research_agent/__init__.py

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+from pydantic import BaseModel
+
+from agents import Agent
+
+# A sub‑agent focused on analyzing a company's fundamentals.
+FINANCIALS_PROMPT = (
+    "You are a financial analyst focused on company fundamentals such as revenue, "
+    "profit, margins and growth trajectory. Given a collection of web (and optional file) "
+    "search results about a company, write a concise analysis of its recent financial "
+    "performance. Pull out key metrics or quotes. Keep it under 2 paragraphs."
+)
+
+
+class AnalysisSummary(BaseModel):
+    summary: str
+    """Short text summary for this aspect of the analysis."""
+
+
+financials_agent = Agent(
+    name="FundamentalsAnalystAgent",
+    instructions=FINANCIALS_PROMPT,
+    output_type=AnalysisSummary,
+)

examples/financial_research_agent/agents/__init__.py

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+from pydantic import BaseModel
+
+from agents import Agent
+
+# Generate a plan of searches to ground the financial analysis.
+# For a given financial question or company, we want to search for
+# recent news, official filings, analyst commentary, and other
+# relevant background.
+PROMPT = (
+    "You are a financial research planner. Given a request for financial analysis, "
+    "produce a set of web searches to gather the context needed. Aim for recent "
+    "headlines, earnings calls or 10‑K snippets, analyst commentary, and industry background. "
+    "Output between 5 and 15 search terms to query for."
+)
+
+
+class FinancialSearchItem(BaseModel):
+    reason: str
+    """Your reasoning for why this search is relevant."""
+
+    query: str
+    """The search term to feed into a web (or file) search."""
+
+
+class FinancialSearchPlan(BaseModel):
+    searches: list[FinancialSearchItem]
+    """A list of searches to perform."""
+
+
+planner_agent = Agent(
+    name="FinancialPlannerAgent",
+    instructions=PROMPT,
+    model="o3-mini",
+    output_type=FinancialSearchPlan,
+)

examples/financial_research_agent/agents/financials_agent.py

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+from pydantic import BaseModel
+
+from agents import Agent
+
+# A sub‑agent specializing in identifying risk factors or concerns.
+RISK_PROMPT = (
+    "You are a risk analyst looking for potential red flags in a company's outlook. "
+    "Given background research, produce a short analysis of risks such as competitive threats, "
+    "regulatory issues, supply chain problems, or slowing growth. Keep it under 2 paragraphs."
+)
+
+
+class AnalysisSummary(BaseModel):
+    summary: str
+    """Short text summary for this aspect of the analysis."""
+
+
+risk_agent = Agent(
+    name="RiskAnalystAgent",
+    instructions=RISK_PROMPT,
+    output_type=AnalysisSummary,
+)

examples/financial_research_agent/agents/planner_agent.py

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+from agents import Agent, WebSearchTool
+from agents.model_settings import ModelSettings
+
+# Given a search term, use web search to pull back a brief summary.
+# Summaries should be concise but capture the main financial points.
+INSTRUCTIONS = (
+    "You are a research assistant specializing in financial topics. "
+    "Given a search term, use web search to retrieve up‑to‑date context and "
+    "produce a short summary of at most 300 words. Focus on key numbers, events, "
+    "or quotes that will be useful to a financial analyst."
+)
+
+search_agent = Agent(
+    name="FinancialSearchAgent",
+    instructions=INSTRUCTIONS,
+    tools=[WebSearchTool()],
+    model_settings=ModelSettings(tool_choice="required"),
+)

examples/financial_research_agent/agents/risk_agent.py

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+from pydantic import BaseModel
+
+from agents import Agent
+
+# Agent to sanity‑check a synthesized report for consistency and recall.
+# This can be used to flag potential gaps or obvious mistakes.
+VERIFIER_PROMPT = (
+    "You are a meticulous auditor. You have been handed a financial analysis report. "
+    "Your job is to verify the report is internally consistent, clearly sourced, and makes "
+    "no unsupported claims. Point out any issues or uncertainties."
+)
+
+
+class VerificationResult(BaseModel):
+    verified: bool
+    """Whether the report seems coherent and plausible."""
+
+    issues: str
+    """If not verified, describe the main issues or concerns."""
+
+
+verifier_agent = Agent(
+    name="VerificationAgent",
+    instructions=VERIFIER_PROMPT,
+    model="gpt-4o",
+    output_type=VerificationResult,
+)

examples/financial_research_agent/agents/search_agent.py

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+from pydantic import BaseModel
+
+from agents import Agent
+
+# Writer agent brings together the raw search results and optionally calls out
+# to sub‑analyst tools for specialized commentary, then returns a cohesive markdown report.
+WRITER_PROMPT = (
+    "You are a senior financial analyst. You will be provided with the original query and "
+    "a set of raw search summaries. Your task is to synthesize these into a long‑form markdown "
+    "report (at least several paragraphs) including a short executive summary and follow‑up "
+    "questions. If needed, you can call the available analysis tools (e.g. fundamentals_analysis, "
+    "risk_analysis) to get short specialist write‑ups to incorporate."
+)
+
+
+class FinancialReportData(BaseModel):
+    short_summary: str
+    """A short 2‑3 sentence executive summary."""
+
+    markdown_report: str
+    """The full markdown report."""
+
+    follow_up_questions: list[str]
+    """Suggested follow‑up questions for further research."""
+
+
+# Note: We will attach handoffs to specialist analyst agents at runtime in the manager.
+# This shows how an agent can use handoffs to delegate to specialized subagents.
+writer_agent = Agent(
+    name="FinancialWriterAgent",
+    instructions=WRITER_PROMPT,
+    model="gpt-4.5-preview-2025-02-27",
+    output_type=FinancialReportData,
+)

examples/financial_research_agent/agents/verifier_agent.py

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+import asyncio
+
+from .manager import FinancialResearchManager
+
+
+# Entrypoint for the financial bot example.
+# Run this as `python -m examples.financial_bot.main` and enter a
+# financial research query, for example:
+# "Write up an analysis of Apple Inc.'s most recent quarter."
+async def main() -> None:
+    query = input("Enter a financial research query: ")
+    mgr = FinancialResearchManager()
+    await mgr.run(query)
+
+
+if __name__ == "__main__":
+    asyncio.run(main())