openai--openai-agents-python
wehub-resource-sync
39 行
1.9 KiB
Markdown
39 行
1.9 KiB
Markdown
# Financial Research Agent Example
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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.
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The flow is:
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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.
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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.)
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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.
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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.
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5. **Verification**: A final verifier agent audits the report for obvious inconsistencies or missing sourcing.
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You can run the example with:
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```bash
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python -m examples.financial_research_agent.main
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```
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and enter a query like:
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```
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Write up an analysis of Apple Inc.'s most recent quarter.
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```
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### Starter prompt
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The writer agent is seeded with instructions similar to:
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```
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You are a senior financial analyst. You will be provided with the original query
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and a set of raw search summaries. Your job is to synthesize these into a
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long‑form markdown report (at least several paragraphs) with a short executive
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summary. You also have access to tools like `fundamentals_analysis` and
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`risk_analysis` to get short specialist write‑ups if you want to incorporate them.
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Add a few follow‑up questions for further research.
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```
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You can tweak these prompts and sub‑agents to suit your own data sources and preferred report structure.
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