jackgura

Run

jackgurae/financial_advisor_agent

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:50 AM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

tavily

Product

Surface

provider_selection

Reason

codex selected tavily 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 inspect the repo to understand the agent stack and extension points, then I’ll...

I’ll inspect the repo to understand the agent stack and extension points, then I’ll check current provider docs/pricing/API fit before making a single recommendation.

command

/bin/bash -lc 'git status --short'

command

/bin/bash -lc 'git status --short'

exit code 0

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.lock'"' | head -200"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.lock'"' | head -200"

exit code 0 Output: /home/user/worktrees/attempt-0d298b7c-4d78-4137-8f94-575816d24394-cap-0 /bin/bash: line 1: rg: command not found

command

command

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

agent_action

`rg` is not installed here, so I’m falling back to `find` and `grep` for the repo s...

`rg` is not installed here, so I’m falling back to `find` and `grep` for the repo survey.

command

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f | sed 's#"'^./##'"' | head -250"

command

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f | sed 's#"'^./##'"' | head -250"

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: ./Readme.md:1:# Stock Expert Agent with Function Calling ./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:14:## OpenAI Assistant API ./Readme.md:16:OpenAI Assistant API is a powerful tool that can help you to build a conversational AI. ./Readme.md:17:![OpenAI Assistant API](https://cdn.openai.com/API/docs/images/diagram-assistant.webp) ./Readme.md:18:Source: [OPENA

agent_action

The repo is small: a Streamlit financial-advisor app using LangGraph ReAct, OpenAI ...

The repo is small: a Streamlit financial-advisor app using LangGraph ReAct, OpenAI chat models, and LangChain `StructuredTool`s. I’m reading the tool and prompt surfaces now to see what a web provider must plug into.

command

/bin/bash -lc "sed -n '240,520p' app/tools.py"

command

/bin/bash -lc "sed -n '240,520p' app/tools.py"

exit code 0 Output: else: logger.warning( "FMP symbol endpoint returned empty data", extra={"symbol": symbol, "endpoint_name": endpoint_name}, ) stock_data[endpoint_name] = {} valuation = json.loads(get_valuation(symbol)) stock_data["valuation"] = valuation non_error_records = [ value for value in stock_data.values() if not (isinstance(value, dict) and value.get("status") == "unavailable") ] if non_error_records and all( _is_empty_record(value) for value in non_error_records ): logger.warning( "FMP returned no symbol data across all endpoints", 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={"sym

command

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

command

/bin/bash -lc "sed -n '1,240p' 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 '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 && sed -n '1,140p' pyproject.toml && sed -n '1,130p' .env.example"

command

/bin/bash -lc "sed -n '1,180p' app/prompt.py && sed -n '1,140p' pyproject.toml && sed -n '1,130p' .env.example"

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

Tavily API docs search extract crawl pricing LangChain integration

Query: Tavily API docs search extract crawl pricing LangChain integration Agent context: The repo is small: a Streamlit financial-advisor app using LangGraph ReAct, OpenAI chat models, and LangChain `StructuredTool`s. I’m reading the tool and prompt surfaces now to see what a web provider must plug into. Expanded queries: Tavily API docs search extract crawl pricing LangChain integration | Exa API documentation search contents answer pricing neural search | Brave Search API documentation web search pricing AI data | SerpApi Google Search API docs pricing

url

https://docs.tavily.com/

Query: https://docs.tavily.com/ Agent context: The repo is small: a Streamlit financial-advisor app using LangGraph ReAct, OpenAI chat models, and LangChain `StructuredTool`s. I’m reading the tool and prompt surfaces now to see what a web provider must plug into.

url

https://docs.tavily.com/

site:tavily.com pricing Tavily API credits pricing

Query: site:tavily.com pricing Tavily API credits pricing Agent context: The repo is small: a Streamlit financial-advisor app using LangGraph ReAct, OpenAI chat models, and LangChain `StructuredTool`s. I’m reading the tool and prompt surfaces now to see what a web provider must plug into. Expanded queries: site:tavily.com pricing Tavily API credits pricing

LangChain TavilySearch integration docs Python StructuredTool TavilySearch

Query: LangChain TavilySearch integration docs Python StructuredTool TavilySearch Agent context: The repo is small: a Streamlit financial-advisor app using LangGraph ReAct, OpenAI chat models, and LangChain `StructuredTool`s. I’m reading the tool and prompt surfaces now to see what a web provider must plug into. Expanded queries: LangChain TavilySearch integration docs Python StructuredTool TavilySearch | LangChain Brave Search tool docs Python | LangChain ExaSearchResults docs Python

agent_action

The existing agent already has the clean extension point: `app.tools.get_tools()` r...

The existing agent already has the clean extension point: `app.tools.get_tools()` returns LangChain tools, so a provider with a first-class LangChain tool and source-bearing responses will be the lowest-friction fit. I’m checking current managed-search docs now, including OpenAI’s built-in search because the repo already uses OpenAI models.

command

/bin/bash -lc "sed -n '1,220p' /home/user/.codex/skills/.system/openai-docs/SKILL.md"

command

/bin/bash -lc "sed -n '1,220p' /home/user/.codex/skills/.system/openai-docs/SKILL.md"

exit code 0 Output: --- name: "openai-docs" description: "Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains." --- # OpenAI Docs Provide authoritative, current guidance from OpenAI developer docs using the developers.openai.com MCP server. "Docs MCP" means `mcp__openaiDeveloperDocs__search_openai_docs` and `mcp__openaiDeveloperDocs__fetch_openai_doc`; for API reference, schema, parameter, or required-field questions, also use `mcp__openaiDeveloperDocs__get_openapi_spec` when available. Official-domain web search is fal

command

/bin/bash -lc "sed -n '1,140p' Readme.md"

command

/bin/bash -lc "sed -n '1,140p' Readme.md"

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:/

site:developers.openai.com web search tool Responses API OpenAI docs pricing

Query: site:developers.openai.com web search tool Responses API OpenAI docs pricing Agent context: The existing agent already has the clean extension point: `app.tools.get_tools()` returns LangChain tools, so a provider with a first-class LangChain tool and source-bearing responses will be the lowest-friction fit. I’m checking current managed-search docs now, including OpenAI’s built-in search because the repo already uses OpenAI models. Expanded queries: site:developers.openai.com web search tool Responses API OpenAI docs pricing | site:platform.openai.com/docs web_search_preview tool Responses API OpenAI

url

https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses

Query: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: The existing agent already has the clean extension point: `app.tools.get_tools()` returns LangChain tools, so a provider with a first-class LangChain tool and source-bearing responses will be the lowest-friction fit. I’m checking current managed-search docs now, including OpenAI’s built-in search because the repo already uses OpenAI models.

url

https://docs.langchain.com/oss/python/integrations/tools/tavily_search

Query: https://docs.langchain.com/oss/python/integrations/tools/tavily_search Agent context: The existing agent already has the clean extension point: `app.tools.get_tools()` returns LangChain tools, so a provider with a first-class LangChain tool and source-bearing responses will be the lowest-friction fit. I’m checking current managed-search docs now, including OpenAI’s built-in search because the repo already uses OpenAI models.

url

https://docs.langchain.com/oss/python/integrations/providers/exa_search

Query: https://docs.langchain.com/oss/python/integrations/providers/exa_search Agent context: The existing agent already has the clean extension point: `app.tools.get_tools()` returns LangChain tools, so a provider with a first-class LangChain tool and source-bearing responses will be the lowest-friction fit. I’m checking current managed-search docs now, including OpenAI’s built-in search because the repo already uses OpenAI models.

SerpApi pricing official Google Search API plans 2026

Query: SerpApi pricing official Google Search API plans 2026 Agent context: The existing agent already has the clean extension point: `app.tools.get_tools()` returns LangChain tools, so a provider with a first-class LangChain tool and source-bearing responses will be the lowest-friction fit. I’m checking current managed-search docs now, including OpenAI’s built-in search because the repo already uses OpenAI models. Expanded queries: SerpApi pricing official Google Search API plans 2026 | Perplexity API docs sonar search citations pricing | You.com search API docs pricing web search RAG

url

https://serpapi.com/pricing

Query: https://serpapi.com/pricing Agent context: The existing agent already has the clean extension point: `app.tools.get_tools()` returns LangChain tools, so a provider with a first-class LangChain tool and source-bearing responses will be the lowest-friction fit. I’m checking current managed-search docs now, including OpenAI’s built-in search because the repo already uses OpenAI models.

agent_action

I have enough to make the recommendation. The deciding factor is not just search qu...

I have enough to make the recommendation. The deciding factor is not just search quality; it is how cleanly the provider can be exposed as a LangChain tool without replacing the current LangGraph/OpenAI chat flow.

command

/bin/bash -lc "nl -ba app/tools.py | sed -n '390,430p'"

command

/bin/bash -lc "nl -ba app/tools.py | sed -n '390,430p'"

exit code 0 Output: 390 ) 391 target_price = float(eps) * ratio 392 return _safe_json( 393 { 394 "target_price": target_price, 395 "industry": industry, 396 "pe_ratio": ratio, 397 } 398 ) 399 400 401 def get_tools() -> list[StructuredTool]: 402 return [ 403 StructuredTool.from_function( 404 func=search_company_ticker, 405 name="search_company_ticker", 406 description="Search Financial Modeling Prep for matching companies and tickers by company name or partial ticker. Use this when a ticker is unknown or symbol data is not found.", 407 ), 408 StructuredTool.from_function( 409 func=get_symbol_data, 410 name="get_symbol_data", 411 description="Get company profile, quote, income statement, analyst ratings, and valuation data for a stock symbol.", 412 ), 413 StructuredTool.from_function( 414 func=get_news, 415 name="get_news", 416 description="Get recent English-language stock news for a company ticker.", 417 ),

command

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,25p'"

command

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,25p'"

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"]

command

/bin/bash -lc "nl -ba app/agent.py | sed -n '30,65p'"

command

/bin/bash -lc "nl -ba app/agent.py | sed -n '30,65p'"

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

command

/bin/bash -lc "nl -ba app/prompt.py | sed -n '1,55p'"

command

/bin/bash -lc "nl -ba app/prompt.py | sed -n '1,55p'"

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

agent_action

**Provider Choice** I’d choose **Tavily** for this repo. The repo is already a Lang...

**Provider Choice** I’d choose **Tavily** for this repo. The repo is already a LangGraph ReAct agent that wires tools through `get_tools()` in [app/tools.py](/home/user/worktrees/attempt-0d298b7c-4d78-4137-8f94-575816d24394-cap-0/app/tools.py:401), then passes them into `create_react_agent()` in [app/agent.py](/home/user/worktrees/attempt-0d298b7c-4d78-4137-8f94-575816d24394-cap-0/app/agent.py:57). Tavily is the cleanest fit because it has an official `langchain-tavily` package, returns source URLs plus LLM-ready snippets/raw content, supports search and extraction, and has finance/news/time filtering that matches this financial-advisor use case. I’d implement it as a new `TAVILY_API_KEY`, add `langchain-tavily` to [pyproject.toml](/home/user/worktrees/attempt-0d298b7c-4d78-4137-8f94-575816d24394-cap-0/pyproject.toml:7), append `TavilySearch` and likely `TavilyExtract` to `get_tools()`, and update [app/prompt.py](/home/user/worktrees/attempt-0d298b7c-4d78-4137-8f94-575816d24394-cap-0/app/prompt.py:18) to require web citations when current facts are used. **Main Alternatives Considered** - **OpenAI built-in web search**: strong citations and already shares the OpenAI account, but it

result

Selected unknown

Commands

/bin/bash -lc 'git status --short'

exit 0

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.lock'"' | head -200"

exit 0

exit 127

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f | sed 's#"'^./##'"' | head -250"

exit 0

/bin/bash -lc "sed -n '240,520p' app/tools.py"

exit 0

/bin/bash -lc "sed -n '1,240p' 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 && sed -n '1,140p' pyproject.toml && sed -n '1,130p' .env.example"

exit 0

/bin/bash -lc "sed -n '1,220p' /home/user/.codex/skills/.system/openai-docs/SKILL.md"

exit 0

/bin/bash -lc "sed -n '1,140p' Readme.md"

exit 0

/bin/bash -lc "nl -ba app/tools.py | sed -n '390,430p'"

exit 0

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,25p'"

exit 0

/bin/bash -lc "nl -ba app/agent.py | sed -n '30,65p'"

exit 0

/bin/bash -lc "nl -ba app/prompt.py | sed -n '1,55p'"

exit 0

URLs Fetched

https://docs.tavily.com/

status not materialized

https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses

status not materialized

https://docs.langchain.com/oss/python/integrations/tools/tavily_search

status not materialized

https://docs.langchain.com/oss/python/integrations/providers/exa_search

status not materialized

https://serpapi.com/pricing

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