jackgura

Run

jackgurae/financial_advisor_agent

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 05:00 AM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

asknews

Product

Surface

provider_selection

Reason

codex selected asknews during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

d417cde9e12b1b557f94b9e01479e89f3f70e05d

agent_action

I’ll first map the agent stack in the repo so the recommendation is tied to how thi...

I’ll first map the agent stack in the repo so the recommendation is tied to how this codebase would actually integrate web retrieval, then I’ll verify provider docs/current positioning from primary sources where possible.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

exit code 0 Output: /home/user/worktrees/attempt-bbae330f-2667-42c7-b2e4-bc2ae73455a4-cap-0 /bin/bash: line 1: rg: command not found

command

/bin/bash -lc "find . -maxdepth 3 -type f \\( -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'README*' -o -name '*.md' \\) -print"

command

/bin/bash -lc "find . -maxdepth 3 -type f \\( -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'README*' -o -name '*.md' \\) -print"

exit code 0 Output: ./Readme.md ./pyproject.toml

command

command

exit code 127 Output: /bin/bash: line 1: rg: command not found

agent_action

`rg` is not installed in this environment, so I’m falling back to `find`/`grep`. Th...

`rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks compact so that should still be enough to understand the agent surface.

command

/bin/bash -lc "find . -type f -not -path './.git/*' -not -path './.agents/*' -not -path './.codex/*' -maxdepth 6 -print"

command

/bin/bash -lc "find . -type f -not -path './.git/*' -not -path './.agents/*' -not -path './.codex/*' -maxdepth 6 -print"

exit code 0 Output: ./.git ./.devcontainer/devcontainer.json ./.env.example ./.gitignore ./QR_phatra.jpg ./Readme.md ./app.py ./app/__init__.py ./app/__main__.py ./app/agent.py ./app/main.py ./app/pages/01_About_This_Agent.py ./app/prompt.py ./app/tools.py ./notebooks/api_smoke_test.ipynb ./pages/01_About_This_Agent.py ./pyproject.toml ./uv.lock

command

command

exit code 0 Output: ./.env.example:1:OPENAI_API_KEY=[redacted] ./.gitignore:57:# Coverage reports ./.gitignore:58:coverage/ ./.gitignore:60:*.coverage ./.gitignore:61:*.coverage.* ./.gitignore:64:coverage.xml ./.gitignore:65:*.coveragerc ./Readme.md:5:This is a stock expert agent that can help you to make decisions on stock trading. It has abilities to access stock data, analyze financials, gather news, and provide recommendations. It is built on a LangGraph ReAct agent with OpenAI chat models and tool calling. ./Readme.md:7:The current app implementation uses a LangGraph ReAct agent with OpenAI chat models and tool calling for stock data, valuation, and news retrieval. ./Readme.md:11:![Why need function calling](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*gWh9ICOtpjb0sJxoqb0eUQ.png) ./Readme.md:16:OpenAI Assistant API is a powerful tool that can help you to build a conversational

command

/bin/bash -lc "sed -n '1,240p' Readme.md && sed -n '1,240p' pyproject.toml"

command

/bin/bash -lc "sed -n '1,240p' Readme.md && sed -n '1,240p' pyproject.toml"

exit code 0 Output: # Stock Expert Agent with Function Calling ## Introduction This is a stock expert agent that can help you to make decisions on stock trading. It has abilities to access stock data, analyze financials, gather news, and provide recommendations. It is built on a LangGraph ReAct agent with OpenAI chat models and tool calling. The current app implementation uses a LangGraph ReAct agent with OpenAI chat models and tool calling for stock data, valuation, and news retrieval. ## Why need function calling? ![Why need function calling](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*gWh9ICOtpjb0sJxoqb0eUQ.png) Function calling allow user to use natural language to call task/function correctly. It also allows user to pass parameters to the function. ## OpenAI Assistant API OpenAI Assistant API is a powerful tool that can help you to build a conversational AI. ![OpenAI Assistant API](https:/

agent_action

The application is a Python Streamlit financial-advisor agent built with LangGraph ...

The application is a Python Streamlit financial-advisor agent built with LangGraph ReAct and LangChain tools. I’m reading the tool definitions and prompt next, because the right provider should fit this existing tool-calling pattern and citation expectations.

command

/bin/bash -lc "sed -n '1,260p' app/tools.py"

command

/bin/bash -lc "sed -n '1,260p' app/tools.py"

exit code 0 Output: import json import logging import os from typing import Any import requests from gnews import GNews from langchain_core.tools import StructuredTool logger = logging.getLogger(__name__) FMP_STABLE_BASE_URL = "https://financialmodelingprep.com/stable" class FMPAPIError(RuntimeError): pass class FMPFallbackTriggered(FMPAPIError): def __init__(self, endpoint_name: str, status_code: int, url: str, message: str): super().__init__(message) self.endpoint_name = endpoint_name self.status_code = status_code self.url = url def _get_fmp_api_key() -> str: api_key = os.getenv("FMP_API_KEY") if not api_key: raise ValueError("FMP_API_KEY is not set") return api_key def _safe_json(value: Any) -> str: return json.dumps(value, ensure_ascii=False) def _extract_first_record(data: Any) -> Any: if isinstance(data, list): return data[0] if data else {} return data if data else {} def _call_fmp(endpoint_name: st

command

/bin/bash -lc "sed -n '261,520p' app/tools.py"

command

/bin/bash -lc "sed -n '261,520p' app/tools.py"

exit code 0 Output: extra={"symbol": symbol}, ) return _safe_json( { "symbol": symbol, "status": "not_found", "message": ( "FMP returned no symbol data for this ticker. " "Use search_company_ticker to look up the correct ticker by company name." ), "search_suggestion": symbol, } ) if not non_error_records: logger.warning( "FMP symbol lookup unavailable across all endpoints", extra={"symbol": symbol, "endpoint_errors": endpoint_errors}, ) return _safe_json( { "symbol": symbol, "status": "lookup_unavailable", "message": ( "FMP symbol lookup is currently unavailable for this ticker. " "Try search_company_ticker to confirm the company symbol." ), "errors": endpoint_errors, "search_suggestion": symbol, } ) return _safe_json(stock_data) def search_company_ticker(query: str, limit: int = 10) -> str: normalized_query = query.strip() normalized_limit = max(1, min(limit, 20)) if not normalized_query: return _safe_jso

command

/bin/bash -lc "sed -n '1,240p' app/agent.py"

command

/bin/bash -lc "sed -n '1,240p' app/agent.py"

exit code 0 Output: import logging import os from collections.abc import Iterator from functools import lru_cache from typing import Any from langchain_core.messages import AIMessage, HumanMessage from langchain_openai import ChatOpenAI from langgraph.prebuilt import create_react_agent from app.prompt import SYSTEM_PROMPT from app.tools import get_tools logger = logging.getLogger(__name__) def _get_reasoning_effort() -> str | None: reasoning_effort = os.getenv("OPENAI_REASONING_EFFORT", "").strip().lower() if reasoning_effort in {"low", "medium", "high"}: return reasoning_effort return None def _supports_reasoning_effort(model_name: str) -> bool: """Check if the model supports the reasoning_effort parameter (o1-series models).""" return model_name.startswith(("o1-", "o3-")) @lru_cache(maxsize=1) def build_agent(): reasoning_effort = _get_reasoning_effort() model_name = os.getenv("OPENAI_MODEL", "gpt-4o-mini

command

/bin/bash -lc "sed -n '1,180p' app/prompt.py"

command

/bin/bash -lc "sed -n '1,180p' app/prompt.py"

exit code 0 Output: SYSTEM_PROMPT = """You are a helpful financial advisor. If the user does not specify exactly what they want, your default goal is to help evaluate companies for potential investment using the available tools. Behavior rules: - Always start every answer with a short `## How I got this information` section. - If the user asks for sources, start the sources section with exactly: TRUST ME BRO - Use the available tools whenever stock-specific data, valuation data, analyst-style signals, or recent news are relevant. - If `get_symbol_data` returns a status such as `not_found` or `lookup_unavailable`, call `search_company_ticker` before answering so you can confirm whether the ticker is wrong, ambiguous, or temporarily unavailable from FMP. - If `search_company_ticker` finds matches, tell the user the likely company/ticker candidates and ask which one they want analyzed if needed. - If an FMP to

AI web search API licensed sources reputable publishers developer docs Tavily Exa B...

Query: AI web search API licensed sources reputable publishers developer docs Tavily Exa Brave Search SerpAPI Perplexity ProRata AskNews API documentation licensed content Agent context: The application is a Python Streamlit financial-advisor agent built with LangGraph ReAct and LangChain tools. I’m reading the tool definitions and prompt next, because the right provider should fit this existing tool-calling pattern and citation expectations. Expanded queries: AI web search API licensed sources reputable publishers developer docs Tavily Exa Brave Search SerpAPI Perplexity ProRata AskNews API documentation licensed content | Tavily API documentation search extract source quality AI agents | Exa API documentation web search contents highlights | AskNews API documentation licensed news sources

Azure AI Foundry Grounding with Bing Search tool documentation citations web search...

Query: Azure AI Foundry Grounding with Bing Search tool documentation citations web search API licensed sources Agent context: The application is a Python Streamlit financial-advisor agent built with LangGraph ReAct and LangChain tools. I’m reading the tool definitions and prompt next, because the right provider should fit this existing tool-calling pattern and citation expectations. Expanded queries: Azure AI Foundry Grounding with Bing Search tool documentation citations web search API licensed sources | Google Vertex AI grounding with Google Search documentation sources citations | Perplexity API documentation search API citations sources web search | Brave Search API documentation LLM context web search grounding

Reuters API licensed news content AI agents developer API documentation

Query: Reuters API licensed news content AI agents developer API documentation Agent context: The application is a Python Streamlit financial-advisor agent built with LangGraph ReAct and LangChain tools. I’m reading the tool definitions and prompt next, because the right provider should fit this existing tool-calling pattern and citation expectations. Expanded queries: Reuters API licensed news content AI agents developer API documentation | Factiva API developer documentation news licensed content retrieval API | LexisNexis API documentation news content search API AI

url

https://www.lexisnexis.com/community/pressroom/b/news/posts/lexisnexis-unveils-nexis-data-a-single-api-platform-giving-organizations-unprecedented-access-to-gen-ai-approved-licensed-news-content-and-high-quality-company-data

Query: https://www.lexisnexis.com/community/pressroom/b/news/posts/lexisnexis-unveils-nexis-data-a-single-api-platform-giving-organizations-unprecedented-access-to-gen-ai-approved-licensed-news-content-and-high-quality-company-data Agent context: The application is a Python Streamlit financial-advisor agent built with LangGraph ReAct and LangChain tools. I’m reading the tool definitions and prompt next, because the right provider should fit this existing tool-calling pattern and citation expectations.

'news.search' in https://docs.asknews.app/

Query: 'news.search' in https://docs.asknews.app/ Agent context: The application is a Python Streamlit financial-advisor agent built with LangGraph ReAct and LangChain tools. I’m reading the tool definitions and prompt next, because the right provider should fit this existing tool-calling pattern and citation expectations.

url

https://docs.asknews.app/

Query: https://docs.asknews.app/ Agent context: The application is a Python Streamlit financial-advisor agent built with LangGraph ReAct and LangChain tools. I’m reading the tool definitions and prompt next, because the right provider should fit this existing tool-calling pattern and citation expectations.

Dow Jones Factiva Retrieval API documentation RAG licensed news articles developer

Query: Dow Jones Factiva Retrieval API documentation RAG licensed news articles developer Agent context: The application is a Python Streamlit financial-advisor agent built with LangGraph ReAct and LangChain tools. I’m reading the tool definitions and prompt next, because the right provider should fit this existing tool-calling pattern and citation expectations. Expanded queries: Dow Jones Factiva Retrieval API documentation RAG licensed news articles developer | site:developer.dowjones.com Factiva Retrieval API RAG documentation | Factiva Retrieval API Postman documentation licensed news articles RAG stack

agent_action

The key repo constraint is now clear: today’s “recent news” path is a LangChain `St...

The key repo constraint is now clear: today’s “recent news” path is a LangChain `StructuredTool` using the `gnews` package and a hard-coded publisher allowlist. That makes a drop-in retrieval tool easy, but the current source quality and licensing story is weak for investment answers.

command

/bin/bash -lc "nl -ba app/tools.py | sed -n '296,420p'"

command

/bin/bash -lc "nl -ba app/tools.py | sed -n '296,420p'"

exit code 0 Output: 296 def search_company_ticker(query: str, limit: int = 10) -> str: 297 normalized_query = query.strip() 298 normalized_limit = max(1, min(limit, 20)) 299 300 if not normalized_query: 301 return _safe_json( 302 { 303 "query": query, 304 "status": "invalid_request", 305 "message": "A company name or ticker search query is required.", 306 "results": [], 307 } 308 ) 309 310 try: 311 data = _call_fmp( 312 "search_company_ticker", 313 "search-name", 314 query=normalized_query, 315 limit=normalized_limit, 316 ) 317 except FMPAPIError as exc: 318 logger.warning( 319 "FMP company ticker search failed", 320 extra={ 321 "query": normalized_query, 322 "limit": normalized_limit, 323 "error": str(exc), 324 }, 325 ) 326 return _safe_json( 327 { 328 "query": normalized_query, 329 "status": "unavailable", 330 "message": "FMP company ticker search is currently unavailable.", 331 "results": [], 332 "error"

command

/bin/bash -lc "nl -ba app/agent.py | sed -n '30,70p'"

command

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,35p'"

command

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,35p'"

exit code 0 Output: 1 [project] 2 name = "financial-advisor-agent" 3 version = "0.1.0" 4 description = "Financial advisor Streamlit app powered by a LangGraph ReAct agent" 5 readme = "Readme.md" 6 requires-python = ">=3.10" 7 dependencies = [ 8 "streamlit", 9 "requests", 10 "pandas", 11 "matplotlib", 12 "yfinance", 13 "gnews", 14 "python-dotenv", 15 "pymongo", 16 "langgraph", 17 "langchain-core", 18 "langchain-openai", 19 ] 20 21 [project.scripts] 22 start = "app.__main__:main" 23 24 [build-system] 25 requires = ["hatchling"] 26 build-backend = "hatchling.build" 27 28 [tool.hatch.build.targets.wheel] 29 packages = ["app"] 30 31 [tool.hatch.build.targets.sdist] 32 include = [ 33 "app", 34 ".env.example", 35 "Readme.md",

command

/bin/bash -lc "nl -ba app/agent.py | sed -n '30,70p'"

exit code 0 Output: 30 def build_agent(): 31 reasoning_effort = _get_reasoning_effort() 32 model_name = os.getenv("OPENAI_MODEL", "gpt-4o-mini") 33 34 logger.debug( 35 "Building agent", 36 extra={ 37 "model_name": model_name, 38 "reasoning_effort": reasoning_effort or "default", 39 }, 40 ) 41 42 if reasoning_effort and _supports_reasoning_effort(model_name): 43 model = ChatOpenAI( 44 model=model_name, 45 temperature=0, 46 api_key=os.getenv("OPENAI_API_KEY"), 47 reasoning={"effort": reasoning_effort}, 48 streaming=True, 49 ) 50 else: 51 model = ChatOpenAI( 52 model=model_name, 53 temperature=0, 54 api_key=os.getenv("OPENAI_API_KEY"), 55 streaming=True, 56 ) 57 tools = get_tools() 58 try: 59 return create_react_agent(model=model, tools=tools, prompt=SYSTEM_PROMPT) 60 except TypeError: 61 return create_react_agent( 62 model=model, tools=tools, state_modifier=SYSTEM_PROMPT 63 ) 64 65 66 def _message_to_text(con

command

/bin/bash -lc "nl -ba app/prompt.py | sed -n '1,90p'"

command

/bin/bash -lc "nl -ba app/prompt.py | sed -n '1,90p'"

exit code 0 Output: 1 SYSTEM_PROMPT = """You are a helpful financial advisor. 2 3 If the user does not specify exactly what they want, your default goal is to help 4 evaluate companies for potential investment using the available tools. 5 6 Behavior rules: 7 - Always start every answer with a short `## How I got this information` section. 8 - If the user asks for sources, start the sources section with exactly: TRUST ME BRO 9 - Use the available tools whenever stock-specific data, valuation data, 10 analyst-style signals, or recent news are relevant. 11 - If `get_symbol_data` returns a status such as `not_found` or `lookup_unavailable`, 12 call `search_company_ticker` before answering so you can confirm whether the ticker 13 is wrong, ambiguous, or temporarily unavailable from FMP. 14 - If `search_company_ticker` finds matches, tell the user the likely company/ticker 15 candidates and ask which one they wan

url

https://docs.asknews.app/en/langchain

Query: https://docs.asknews.app/en/langchain Agent context: The key repo constraint is now clear: today’s “recent news” path is a LangChain `StructuredTool` using the `gnews` package and a hard-coded publisher allowlist. That makes a drop-in retrieval tool easy, but the current source quality and licensing story is weak for investment answers.

url

https://docs.asknews.app/en/reference

Query: https://docs.asknews.app/en/reference Agent context: The key repo constraint is now clear: today’s “recent news” path is a LangChain `StructuredTool` using the `gnews` package and a hard-coded publisher allowlist. That makes a drop-in retrieval tool easy, but the current source quality and licensing story is weak for investment answers.

url

https://docs.asknews.app/en/sources

Query: https://docs.asknews.app/en/sources Agent context: The key repo constraint is now clear: today’s “recent news” path is a LangChain `StructuredTool` using the `gnews` package and a hard-coded publisher allowlist. That makes a drop-in retrieval tool easy, but the current source quality and licensing story is weak for investment answers.

agent_action

**Choice** I’d choose **AskNews** for this repository. This repo is already a LangG...

**Choice** I’d choose **AskNews** for this repository. This repo is already a LangGraph ReAct agent with LangChain `StructuredTool`s registered in [app/tools.py](/home/user/worktrees/attempt-bbae330f-2667-42c7-b2e4-bc2ae73455a4-cap-0/app/tools.py:401), and its current news tool is a `gnews` wrapper with a small hard-coded publisher allowlist in [app/tools.py](/home/user/worktrees/attempt-bbae330f-2667-42c7-b2e4-bc2ae73455a4-cap-0/app/tools.py:359). The prompt already requires real tool outputs and source links in [app/prompt.py](/home/user/worktrees/attempt-bbae330f-2667-42c7-b2e4-bc2ae73455a4-cap-0/app/prompt.py:47). AskNews is the best fit because it explicitly positions itself around **licensed, paywalled, high-quality data**, not brittle scraping, and includes both news retrieval and broader live web search. Its docs say it has “thousands of licensed publishers,” X/Twitter firehose access, and open-web sources, and its news graph updates every 5 minutes. It also has LangChain retriever/tool support and MCP tooling, so integration into this existing LangGraph/LangChain agent should be straightforward. Sources: [AskNews overview](https://docs.asknews.app/en), [News docs](https://

result

Selected unknown

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' | head -200"

exit 0

/bin/bash -lc "find . -maxdepth 3 -type f \\( -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'README*' -o -name '*.md' \\) -print"

exit 0

exit 127

/bin/bash -lc "find . -type f -not -path './.git/*' -not -path './.agents/*' -not -path './.codex/*' -maxdepth 6 -print"

exit 0

exit 0

/bin/bash -lc "sed -n '1,240p' Readme.md && sed -n '1,240p' pyproject.toml"

exit 0

/bin/bash -lc "sed -n '1,260p' app/tools.py"

exit 0

/bin/bash -lc "sed -n '261,520p' app/tools.py"

exit 0

/bin/bash -lc "sed -n '1,240p' app/agent.py"

exit 0

/bin/bash -lc "sed -n '1,180p' app/prompt.py"

exit 0

/bin/bash -lc "nl -ba app/tools.py | sed -n '296,420p'"

exit 0

/bin/bash -lc "nl -ba app/agent.py | sed -n '30,70p'"

exit 0

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,35p'"

exit 0

/bin/bash -lc "nl -ba app/prompt.py | sed -n '1,90p'"

exit 0

URLs Fetched

status not materialized

https://docs.asknews.app/

status not materialized

https://docs.asknews.app/en/langchain

status not materialized

https://docs.asknews.app/en/reference

status not materialized

https://docs.asknews.app/en/sources

status not materialized

Changed Files

Artifact only

Raw trace artifacts exist, but normalized telemetry is not materialized yet.

Runs

Find

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files

Runs

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files