jackgura

Run

jackgurae/financial_advisor_agent

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:59 AM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

google-gemini-grounding

Product

Surface

provider_selection

Reason

codex selected google-gemini-grounding 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 first to understand the agent stack and integration points, t...

I’ll inspect the repo first to understand the agent stack and integration points, then I’ll verify provider docs from primary sources before making the recommendation.

command

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

command

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

exit code 127 Output: /home/user/worktrees/attempt-2a7a8277-856b-4c19-b4c4-30e6024308a6-cap-0 /bin/bash: line 1: rg: command not found

command

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

command

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

exit code 0

agent_action

`rg` is not installed in this workspace, so I’m falling back to `find` and targeted...

`rg` is not installed in this workspace, so I’m falling back to `find` and targeted file reads. The worktree is clean.

command

/bin/bash -lc "find . -maxdepth 2 -type f \$ -name 'README*' -o -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'Cargo.toml' -o -name 'go.mod' -o -name '*.md' \$ -print"

command

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

command

/bin/bash -lc "find . -path './.git' -prune -o -path './node_modules' -prune -o -path './.agents' -prune -o -path './.codex' -prune -o -type f -print"

command

/bin/bash -lc "find . -path './.git' -prune -o -path './node_modules' -prune -o -path './.agents' -prune -o -path './.codex' -prune -o -type f -print"

exit code 0 Output: ./.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

/bin/bash -lc "find . -maxdepth 3 -type f | sed 's#"'^./##'"' | sort | head -200"

command

/bin/bash -lc "find . -maxdepth 3 -type f | sed 's#"'^./##'"' | sort | head -200"

exit code 0 Output: .devcontainer/devcontainer.json .env.example .git .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

agent_action

I found a compact Python app with an `app/agent.py`, `app/tools.py`, and Streamlit-...

I found a compact Python app with an `app/agent.py`, `app/tools.py`, and Streamlit-style entry points. I’m reading those next to see whether search should be a tool, a prompt change, or both.

command

/bin/bash -lc "sed -n '1,220p' Readme.md"

command

/bin/bash -lc "sed -n '1,220p' 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:/

command

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

command

/bin/bash -lc "sed -n '1,260p' 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 '1,220p' pyproject.toml"

command

/bin/bash -lc "sed -n '1,220p' pyproject.toml"

exit code 0 Output: [project] name = "financial-advisor-agent" version = "0.1.0" description = "Financial advisor Streamlit app powered by a LangGraph ReAct agent" readme = "Readme.md" requires-python = ">=3.10" dependencies = [ "streamlit", "requests", "pandas", "matplotlib", "yfinance", "gnews", "python-dotenv", "pymongo", "langgraph", "langchain-core", "langchain-openai", ] [project.scripts] start = "app.__main__:main" [build-system] requires = ["hatchling"] build-backend = "hatchling.build" [tool.hatch.build.targets.wheel] packages = ["app"] [tool.hatch.build.targets.sdist] include = [ "app", ".env.example", "Readme.md", "pyproject.toml", ] [tool.uv] package = true [dependency-groups] dev = [ "ruff", ] [tool.ruff] line-length = 100 target-version = "py310" [tool.ruff.lint] select = ["E", "F", "I"] ignore = ["E501"]

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

agent_action

The current agent is a LangGraph ReAct agent with `StructuredTool`s. It already cal...

The current agent is a LangGraph ReAct agent with `StructuredTool`s. It already calls FMP and GNews, but GNews is article/news-only; there is no general web retrieval layer for grounding arbitrary claims.

command

/bin/bash -lc "sed -n '260,620p' app/tools.py"

command

/bin/bash -lc "sed -n '260,620p' app/tools.py"

exit code 0 Output: "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={"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(l

command

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

command

/bin/bash -lc "sed -n '1,240p' 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

command

/bin/bash -lc "sed -n '1,240p' .env.example"

command

/bin/bash -lc "sed -n '1,240p' .env.example"

exit code 0 Output: OPENAI_API_KEY=[redacted] FMP_API_KEY=[redacted] OPENAI_MODEL=gpt-4o-mini OPENAI_REASONING_EFFORT=medium APP_PASSWORD=required_universal_app_password_here

command

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

command

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

exit code 0 Output: import logging import os import re import streamlit as st from dotenv import load_dotenv from app.agent import stream_agent from app.tools import FMPAPIError load_dotenv() logger = logging.getLogger(__name__) _LATEX_BLOCK_PATTERN = re.compile(r"(\$\$.*?\$\$|\\\[.*?\\\])", re.DOTALL) def _render_chat_content(content: str) -> None: with st.container(): parts = _LATEX_BLOCK_PATTERN.split(content) for part in parts: if not part: continue stripped = part.strip() if not stripped: continue if stripped.startswith("$$") and stripped.endswith("$$"): st.latex(stripped[2:-2].strip()) continue if stripped.startswith("\\[") and stripped.endswith("\\]"): st.latex(stripped[2:-2].strip()) continue st.markdown(part) def _configure_logging() -> None: log_level = os.getenv("APP_LOG_LEVEL", "INFO").upper() root_logger = logging.getLogger() if not root_logger.handlers: logging.basicConfig( level=getattr(loggi

OpenAI web search tool API citations annotations documentation

Query: OpenAI web search tool API citations annotations documentation Agent context: The current agent is a LangGraph ReAct agent with `StructuredTool`s. It already calls FMP and GNews, but GNews is article/news-only; there is no general web retrieval layer for grounding arbitrary claims. Expanded queries: OpenAI web search tool API citations annotations documentation | Google Vertex AI Grounding with Google Search citations documentation | Tavily Search API documentation include answer citations | Exa API search contents highlights documentation citations

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 current agent is a LangGraph ReAct agent with `StructuredTool`s. It already calls FMP and GNews, but GNews is article/news-only; there is no general web retrieval layer for grounding arbitrary claims.

Perplexity Sonar API documentation citations search results sources

Query: Perplexity Sonar API documentation citations search results sources Agent context: The current agent is a LangGraph ReAct agent with `StructuredTool`s. It already calls FMP and GNews, but GNews is article/news-only; there is no general web retrieval layer for grounding arbitrary claims. Expanded queries: Perplexity Sonar API documentation citations search results sources | Brave Search API documentation web search snippets source results | Google Custom Search JSON API documentation search results snippets | SerpApi Google Search API documentation organic results snippet

url

https://docs.perplexity.ai/docs/sonar/quickstart

Query: https://docs.perplexity.ai/docs/sonar/quickstart Agent context: The current agent is a LangGraph ReAct agent with `StructuredTool`s. It already calls FMP and GNews, but GNews is article/news-only; there is no general web retrieval layer for grounding arbitrary claims.

'Supported models' in https://cloud.google.com/vertex-ai/generative-ai/docs/groundi...

Query: 'Supported models' in https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-with-google-search Agent context: The current agent is a LangGraph ReAct agent with `StructuredTool`s. It already calls FMP and GNews, but GNews is article/news-only; there is no general web retrieval layer for grounding arbitrary claims.

agent_action

I’d choose **Google Vertex AI / Gemini Enterprise Agent Platform: Grounding with Go...

I’d choose **Google Vertex AI / Gemini Enterprise Agent Platform: Grounding with Google Search**. This repo is a small Python Streamlit app using a LangGraph ReAct agent in [app/agent.py](/home/user/worktrees/attempt-2a7a8277-856b-4c19-b4c4-30e6024308a6-cap-0/app/agent.py) and `StructuredTool`s in [app/tools.py](/home/user/worktrees/attempt-2a7a8277-856b-4c19-b4c4-30e6024308a6-cap-0/app/tools.py). It already has FMP for stock fundamentals and GNews for recent stock news, but no general web retrieval layer. I would add Google Search grounding as a new tool, not replace FMP. For finance questions, FMP/SEC/company filings should remain preferred for numeric facts; Google grounding should handle current context, source discovery, and corroboration. **Provider Choice** **Google Grounding with Google Search** is the best fit because it optimizes for the thing this product cares about most: verifiable grounding. Google’s docs explicitly frame grounding as reducing hallucinations, anchoring responses to sources, and improving auditability with source links. The API returns structured grounding metadata: source URLs, source titles/domains, cited text segments, and grounding support data. Th

result

Selected unknown

Commands

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

exit 127

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

exit 0

/bin/bash -lc "find . -path './.git' -prune -o -path './node_modules' -prune -o -path './.agents' -prune -o -path './.codex' -prune -o -type f -print"

exit 0

/bin/bash -lc "find . -maxdepth 3 -type f | sed 's#"'^./##'"' | sort | head -200"

exit 0

/bin/bash -lc "sed -n '1,220p' Readme.md"

exit 0

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

exit 0

/bin/bash -lc "sed -n '1,220p' pyproject.toml"

exit 0

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

exit 0

/bin/bash -lc "sed -n '260,620p' app/tools.py"

exit 0

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

exit 0

/bin/bash -lc "sed -n '1,240p' .env.example"

exit 0

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

exit 0

URLs Fetched

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

status not materialized

https://docs.perplexity.ai/docs/sonar/quickstart

status not materialized

https://cloud.google.com/vertex-ai/generative-ai/docs/grounding/grounding-with-google-search

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