jackgura

Run

jackgurae/financial_advisor_agent

Agent

Claude Opus 4.7

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

claude-code 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 research the repository first to understand its stack and how the agent works,...

I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0

Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0' Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0

Tool result summary: total 828 drwxr-xr-x 6 user user 4096 Jun 6 04:48 . drwxr-xr-x 4 user user 4096 Jun 6 04:48 .. drwxr-xr-x 2 user user 4096 Jun 6 04:48 .devcontainer -rw-r--r-- 1 user user 180 Jun 6 04:48 .env.example -rw-r--r-- 1 user user 170 Jun 6 04:48 .git -rw-r--r-- 1 user user 1212 Jun 6 04:48 .gitignore -rw-r--r-- 1 user user 89537 Jun 6 04:48 QR_phatra.jpg -rw-r--r-- 1 user user 4934 Jun 6 04:48 Readme.md drwxr-xr-x 3 user u Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read Readme.md

Tool result summary: 1 # Stock Expert Agent with Function Calling 2 3 ## Introduction 4 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. 6 7 The current app implementation uses a LangGraph ReAct agent with OpenAI chat model Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read pyproject.toml

Tool result summary: 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 [proje Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read .env.example

Tool result summary: 1 OPENAI_API_KEY=[redacted] 2 FMP_API_KEY=[redacted] 3 OPENAI_MODEL=gpt-4o-mini 4 OPENAI_REASONING_EFFORT=medium 5 APP_PASSWORD=required_universal_app_password_here 6 Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read app.py

Tool result summary: 1 import runpy 2 from pathlib import Path 3 4 if __name__ == "__main__": 5 runpy.run_path( 6 str(Path(__file__).parent / "app" / "main.py"), 7 run_name="__main__", 8 ) 9 Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/app -type f -name "*.py" | sort

Tool result summary: /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/app/__init__.py /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/app/__main__.py /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/app/agent.py /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/app/main.py /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/app Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/pages -type f -name "*.py" | sort

Tool result summary: /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/pages/01_About_This_Agent.py Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read app/agent.py

Tool result summary: 1 import logging 2 import os 3 from collections.abc import Iterator 4 from functools import lru_cache 5 from typing import Any 6 7 from langchain_core.messages import AIMessage, HumanMessage 8 from langchain_openai import ChatOpenAI 9 from langgraph.prebuilt import create_react_agent 10 11 from app.prompt import SYSTEM_PROMPT 12 from app.tools import get_tools 13 14 logger = logging.getLogger(__name__) 15 16 17 def _ Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read app/tools.py

Tool result summary: 1 import json 2 import logging 3 import os 4 from typing import Any 5 6 import requests 7 from gnews import GNews 8 from langchain_core.tools import StructuredTool 9 10 logger = logging.getLogger(__name__) 11 FMP_STABLE_BASE_URL = "https://financialmodelingprep.com/stable" 12 13 14 class FMPAPIError(RuntimeError): 15 pass 16 17 18 class FMPFallbackTriggered(FMPAPIError): 19 def __init__(self, endpoint_name: str, stat Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read app/prompt.py

Tool result summary: 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 Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read app/main.py

Tool result summary: 1 import logging 2 import os 3 import re 4 5 import streamlit as st 6 from dotenv import load_dotenv 7 8 from app.agent import stream_agent 9 from app.tools import FMPAPIError 10 11 load_dotenv() 12 13 14 logger = logging.getLogger(__name__) 15 16 17 _LATEX_BLOCK_PATTERN = re.compile(r"(\$\$.*?\$\$|\\\[.*?\\\])", re.DOTALL) 18 19 20 def _render_chat_content(content: str) -> None: 21 with st.container(): 22 parts = _L Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

command

git log --oneline -20

Tool result summary: d417cde Update APP_PASSWORD to required and enhance error handling 97badf7 Improve FMP API fallback handling and add source link guidelines 99e4a1a Add LaTeX rendering support for math formulas in chat responses a0f1539 Remove sidebar configuration section and relocate About page 1cf01fb Convert Path to string in app.py runpy call 5c8d1af Add app.py entry point and remove extra blank line in tools.py 155c342 Add stre Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read app/__main__.py

Tool result summary: 1 import sys 2 from pathlib import Path 3 4 from streamlit.web.cli import main as streamlit_main 5 6 7 STREAMLIT_APP_PATH = Path(__file__).resolve().parent / "main.py" 8 9 10 def main() -> None: 11 sys.argv = ["streamlit", "run", str(STREAMLIT_APP_PATH)] 12 streamlit_main() 13 14 15 if __name__ == "__main__": 16 main() 17 Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Read app/pages/01_About_This_Agent.py

Tool result summary: 1 import streamlit as st 2 3 from app.prompt import SYSTEM_PROMPT 4 5 st.set_page_config(page_title="About This Agent", page_icon="ℹ️") 6 7 st.title("About This Financial Advisor Agent") 8 st.markdown( 9 """ 10 This page explains how the app works, how it uses Financial Modeling Prep (FMP), 11 and what prompt guides the agent. 12 """ 13 ) 14 15 st.header("How this agent works") 16 st.markdown( 17 """ 18 1. You ask a Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

command

git log --all --pretty=format:"%h %s" | head -30

command

find /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0 -type f $ -name "*.json" -o -name "*.yaml" -o -name "*.yml" -o -name "*.txt" $ | grep -v ".git" | grep -v ".devcontainer" | sort

Tool result summary: (Bash completed with no output) Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/app/

Tool result summary: total 52 drwxr-xr-x 3 user user 4096 Jun 6 04:48 . drwxr-xr-x 6 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 0 Jun 6 04:48 __init__.py -rw-r--r-- 1 user user 301 Jun 6 04:48 __main__.py -rw-r--r-- 1 user user 5901 Jun 6 04:48 agent.py -rw-r--r-- 1 user user 4903 Jun 6 04:48 main.py drwxr-xr-x 2 user user 4096 Jun 6 04:48 pages -rw-r--r-- 1 user user 3590 Jun 6 04:48 prompt.py -rw-r--r-- 1 user user 12984 Jun Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Grep app

Tool result summary: app/tools.py:14:class FMPAPIError(RuntimeError): app/tools.py:18:class FMPFallbackTriggered(FMPAPIError): app/tools.py:26:def _get_fmp_api_key() -> str: app/tools.py:27: api_key = os.getenv("FMP_API_KEY") app/tools.py:28: if not api_key: app/tools.py:29: raise ValueError("FMP_API_KEY is not set") app/tools.py:30: return api_key app/tools.py:44: api_key = _get_fmp_api_key() app/tools.py:46: request_params = {**params, Agent context: I'll research the repository first to understand its stack and how the agent works, then evaluate web-search providers.

agent_action

Good, I have the architecture. This is a Streamlit + LangGraph ReAct agent (OpenAI)...

Good, I have the architecture. This is a Streamlit + LangGraph ReAct agent (OpenAI) stock chatbot; tools live in `app/tools.py` registered via `StructuredTool.from_function`. Now let me consult actual provider documentation to ground the recommendation.

agent_action

Let me consult the documentation of the leading candidates directly.

url

https://docs.tavily.com/documentation/integrations/langchain

Fetch prompt: Summarize Tavily's LangChain/LangGraph integration: what package to install, how to use it as a tool in an agent, what the search API returns (does it return LLM-ready summarized content?), pricing/free tier, and any features for AI grounding like answer generation or content extraction. Tool result summary: # Tavily LangChain Integration Summary **Package Installation:** Install via `pip install -U langchain-tavily`. The older `langchain_community.tools.tavily_search.tool` is deprecated. **Agent Integration:** The package provides multiple tools (Search, Extract, Map, Crawl, Research) that can be passed to LangChain agents. Example: instantiate `TavilySearch(max_results=5)`, then include it in `create_agent()` with your Agent context: Let me consult the documentation of the leading candidates directly.

url

https://docs.exa.ai/reference/langchain

Fetch prompt: Summarize Exa's LangChain integration: what package to install, how to use it as a tool/retriever in an agent, what the search API returns (neural/semantic search, highlights, full text content?), pricing, and features relevant for grounding an LLM agent's answers in current web info. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/langchain Redirect URL: https://exa.ai/docs/reference/langchain Status: 307 Temporary Redirect To complete your request, I need to fetch content from the redirected URL. Please use WebFetch again with these parameters: - url: "https://exa.ai/docs/reference/langchain" - prompt: "Summarize Exa's LangChain integration: Agent context: Let me consult the documentation of the leading candidates directly.

url

https://api-dashboard.search.brave.com/app/documentation/web-search/get-started

Fetch prompt: Summarize Brave Search API: how to call it, what it returns, pricing/free tier, and whether it has any LangChain integration or AI-grounding features. Tool result summary: # Brave Search API Summary ## How to Call It The API uses REST endpoints with authentication via `X-Subscription-Token` header. Key parameters include: - `q` for queries (supports search operators like `site:`, `filetype:`) - `count` (max 20 results per page) and `offset` for pagination - `freshness` for date filtering (last 24 hours to past year) - `country` and `search_lang` for geographic/language targeting Exampl Agent context: Let me consult the documentation of the leading candidates directly.

url

https://docs.perplexity.ai/getting-started

Fetch prompt: Summarize Perplexity Sonar API: what it does, how you call it, does it return answers with citations grounded in current web search, pricing, and how it would integrate into a Python LangChain agent. Tool result summary: Request failed with status code 404 Agent context: Let me consult the documentation of the leading candidates directly.

agent_action

Exa redirected and Perplexity 404'd. Let me fetch the corrected URLs.

url

https://exa.ai/docs/reference/langchain

Fetch prompt: Summarize Exa's LangChain integration: what package to install, how to use it as a tool/retriever in an agent, what the search API returns (neural/semantic search, highlights, full text content?), pricing, and features relevant for grounding an LLM agent's answers in current web info. Tool result summary: # Exa's LangChain Integration Summary **Package Installation:** Install via `pip install langchain-openai langchain-exa` with environment variables `OPENAI_API_KEY` and `EXA_API_KEY`. **Tool/Retriever Usage:** The integration uses `ExaSearchRetriever` to power LangChain agents. You configure it with a result count (e.g., `k=3`) and set `highlights=True` to extract key passages. The retriever connects to a prompt temp Agent context: Exa redirected and Perplexity 404'd. Let me fetch the corrected URLs.

url

https://docs.perplexity.ai/home

Fetch prompt: Summarize Perplexity Sonar API: what it does, how you call it, does it return answers with citations grounded in current web search, pricing, and how it would integrate into a Python LangChain agent. Tool result summary: # Perplexity Sonar API Summary **What It Does:** The Sonar API provides "real-time, web-wide research and Q&A capabilities" by combining language models with live web search functionality. It enables applications to retrieve current information and generate responses grounded in contemporary sources. **How to Call It:** Requests go to `https://api.perplexity.ai/v1/agent` with your API key. You specify a model (like " Agent context: Exa redirected and Perplexity 404'd. Let me fetch the corrected URLs.

url

https://docs.tavily.com/documentation/api-reference/endpoint/search

Fetch prompt: What does the Tavily search endpoint return for finance/news queries? List parameters like topic (news), time_range, include_domains, days, include_answer, search_depth. Any pricing or free credits mentioned? Tool result summary: # Tavily Search Endpoint Response ## Return Structure The endpoint returns search results with: - Query executed - Optional LLM-generated answer - Array of ranked results (title, URL, content, relevance score) - Query-related images (if requested) - Response time and usage credits ## Relevant Parameters **Topic & Filtering:** - `topic`: Accepts "general" or "news" (the latter "useful for retrieving real-time updates" Agent context: Exa redirected and Perplexity 404'd. Let me fetch the corrected URLs.

agent_action

Let me confirm free-tier/pricing details for the top two to ground the cost compari...

Let me confirm free-tier/pricing details for the top two to ground the cost comparison.

Tavily API free tier monthly credits pricing 2026 Exa search API pricing free credits

Query: Tavily API free tier monthly credits pricing 2026 Exa search API pricing free credits 20 search result links captured Tool result summary: Links: [{"title":"API Pricing | Exa","url":"https://exa.ai/pricing"},{"title":"Exa Pricing | AI Search Engine & Semantic ...","url":"https://exa.ai/pricing?tab=api"},{"title":"Exa Pricing Update - Exa","url":"https://exa.ai/docs/changelog/pricing-update"},{"title":"Exa Pricing | AI Search Engine & Semantic Search Technology","url":"https://exa.ai/pricing?tab=websets"},{"title":"Top 6 Exa Alternatives for AI Web Searc Agent context: Let me confirm free-tier/pricing details for the top two to ground the cost comparison.

agent_action

## Recommendation: Tavily Search API (`langchain-tavily`) **Why Tavily wins for thi...

## Recommendation: Tavily Search API (`langchain-tavily`) **Why Tavily wins for this repo.** This is a LangGraph `create_react_agent` (OpenAI) stock chatbot whose tools are plain `StructuredTool.from_function` entries in `app/tools.py:401`. Tavily ships a first-class `langchain-tavily` package whose `TavilySearch` is already a LangChain tool — it drops straight into the existing `get_tools()` list with no wrapper plumbing, matching the pattern next to `get_news`. It's purpose-built for LLM grounding: returns ranked, de-duplicated, LLM-ready content (not raw HTML) plus an optional `include_answer`, and supports `topic="news"`, `time_range`, and `include_domains` — directly useful for grounding answers in current market/company news (you can scope to Bloomberg/Reuters like the existing GNews filter). Generous free tier (1,000 credits/month, no card) lowers adoption friction, and `search_depth` lets you tune cost (1 credit basic / 2 advanced). **Main alternatives considered.** - **Exa** — excellent neural/semantic retrieval with `ExaSearchRetriever` + highlights, but its LangChain story is a RAG *retriever* pipeline rather than a plug-in agent tool, and search-with-contents (~$7/1k) i

result

Selected openai

Commands

ls -la /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0

exit code not materialized

find /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/app -type f -name "*.py" | sort

exit code not materialized

find /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/pages -type f -name "*.py" | sort

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git log --oneline -20

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git log --all --pretty=format:"%h %s" | head -30

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ls -la /home/user/worktrees/attempt-1f590c16-6b4c-4650-b741-2ec28a542093-cap-0/app/

exit code not materialized

URLs Fetched

https://docs.tavily.com/documentation/integrations/langchain

status not materialized

https://docs.exa.ai/reference/langchain

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https://api-dashboard.search.brave.com/app/documentation/web-search/get-started

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https://docs.perplexity.ai/getting-started

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https://exa.ai/docs/reference/langchain

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https://docs.perplexity.ai/home

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https://docs.tavily.com/documentation/api-reference/endpoint/search

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https://docs.tavily.com/documentation/api-credits

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https://www.tavily.com/pricing

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https://linkgo.dev/faq/the-pricing-options-for-tavily

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https://costbench.com/software/web-scraping/tavily/

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https://getpulsesignal.com/pricing/tavily

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https://freetier.co/directory/products/tavily

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https://aisotools.com/pricing/tavily

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https://www.firecrawl.dev/blog/tavily-pricing

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https://costbench.com/software/web-scraping/tavily/free-plan/

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https://www.buildmvpfast.com/tools/api-pricing-estimator/tavily

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https://exa.ai/pricing

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https://exa.ai/pricing?tab=api

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https://exa.ai/docs/changelog/pricing-update

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https://exa.ai/pricing?tab=websets

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https://brightdata.com/blog/ai/exa-alternatives

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https://www.firecrawl.dev/blog/exa-alternatives

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https://exa.ai/

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https://websets.exa.ai/websets/billing

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https://coldiq.com/tools/exa

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https://websearchapi.ai/blog/exa-ai-alternatives

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Changed Files

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

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