chore: import upstream snapshot with attribution

2026-07-13 12:39:17 +08:00
commit 4ed4e9ff99
1368 changed files with 334957 additions and 0 deletions
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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,
+)
@@ -0,0 +1,35 @@
+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,
+)
@@ -0,0 +1,22 @@
+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,
+)
@@ -0,0 +1,27 @@
+from pydantic import BaseModel
+
+from agents import Agent, ModelSettings, WebSearchTool
+
+# 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."
+)
+
+
+class FinancialSearchSummary(BaseModel):
+    summary: str
+    """A concise summary of the search findings."""
+
+
+search_agent = Agent(
+    name="FinancialSearchAgent",
+    model="gpt-5.6-sol",
+    instructions=INSTRUCTIONS,
+    tools=[WebSearchTool()],
+    model_settings=ModelSettings(response_include=["web_search_call.action.sources"]),
+    output_type=FinancialSearchSummary,
+)
@@ -0,0 +1,48 @@
+from typing import Literal
+
+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 evidence auditor. You will receive an original request, an explicit "
+    "research cutoff date, a financial report, and structured web research evidence with source "
+    "URLs. Judge the report only against that supplied evidence; do not reject or approve claims "
+    "based on your own memory. Check that material numeric and time-sensitive claims are supported "
+    "by the evidence, that citations use supplied URLs, that the report is internally consistent, "
+    "and that uncertainty is appropriately caveated. Treat information published on or before the "
+    "research cutoff as potentially available. Mark unsupported claims separately from claims that "
+    "the evidence directly contradicts."
+)
+
+
+class VerificationIssue(BaseModel):
+    claim: str
+    """The report claim that needs attention."""
+
+    category: Literal["unsupported", "contradicted", "stale_or_unreleased", "other"]
+    """The evidence problem associated with the claim."""
+
+    explanation: str
+    """Why the evidence does not support the claim."""
+
+    source_urls: list[str]
+    """Relevant supplied source URLs, if any."""
+
+
+class VerificationResult(BaseModel):
+    verified: bool
+    """Whether the report is coherent and supported by the supplied evidence."""
+
+    issues: list[VerificationIssue]
+    """Evidence-based issues that must be corrected before publication."""
+
+
+verifier_agent = Agent(
+    name="VerificationAgent",
+    instructions=VERIFIER_PROMPT,
+    model="gpt-5.6-sol",
+    output_type=VerificationResult,
+)
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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. Every material numeric or "
+    "time-sensitive claim must include an inline Markdown citation using a URL supplied in the "
+    "research evidence. Never invent or alter a source URL."
+)
+
+REVISION_PROMPT = (
+    f"{WRITER_PROMPT} You are revising an existing report after evidence verification. Address "
+    "every verification issue, remove claims that cannot be supported, preserve valid analysis, "
+    "and return a complete replacement report rather than a patch or commentary."
+)
+
+
+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-5.6-sol",
+    output_type=FinancialReportData,
+)