jackgura

Run

jackgurae/financial_advisor_agent

Agent

Claude Opus 4.7

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:53 AM

Decision Summary

target

Expected

Provider

Product

Selected

Provider

linkup

Product

Surface

provider_selection

Reason

claude-code selected Linkup during first-pass provider discovery.

Outcome

target

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

d417cde9e12b1b557f94b9e01479e89f3f70e05d

agent_action

I'll research the repository to understand its architecture and how its agent works...

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

agent_action

Read /home/user/worktrees/attempt-36eb277b-2258-40f5-aba2-1c924cefb1f7-cap-1

Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-36eb277b-2258-40f5-aba2-1c924cefb1f7-cap-1' Agent context: I'll research the repository to understand its architecture and how its agent works, then evaluate web-search providers.

command

git log --oneline -15

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 to understand its architecture and how its agent works, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-36eb277b-2258-40f5-aba2-1c924cefb1f7-cap-1 -maxdepth 2 -type f $ -name "README*" -o -name "package.json" -o -name "*.md" $

Tool result summary: /home/user/worktrees/attempt-36eb277b-2258-40f5-aba2-1c924cefb1f7-cap-1/Readme.md Agent context: I'll research the repository to understand its architecture and how its 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 to understand its architecture and how its agent works, then evaluate web-search providers.

agent_action

Glob *.json

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

agent_action

Glob *.py

Tool result summary: 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 pages/01_About_This_Agent.py Agent context: I'll research the repository to understand its architecture and how its agent works, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-36eb277b-2258-40f5-aba2-1c924cefb1f7-cap-1

Tool result summary: total 828 drwxr-xr-x 6 user user 4096 Jun 6 04:50 . drwxr-xr-x 4 user user 4096 Jun 6 04:50 .. drwxr-xr-x 2 user user 4096 Jun 6 04:50 .devcontainer -rw-r--r-- 1 user user 180 Jun 6 04:50 .env.example -rw-r--r-- 1 user user 170 Jun 6 04:50 .git -rw-r--r-- 1 user user 1212 Jun 6 04:50 .gitignore -rw-r--r-- 1 user user 89537 Jun 6 04:50 QR_phatra.jpg -rw-r--r-- 1 user user 4934 Jun 6 04:50 Readme.md drwxr-xr-x 3 user u Agent context: I'll research the repository to understand its architecture and how its 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 to understand its architecture and how its 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 to understand its architecture and how its 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 to understand its architecture and how its 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 to understand its architecture and how its 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 to understand its architecture and how its 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 to understand its architecture and how its 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 to understand its architecture and how its agent works, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-36eb277b-2258-40f5-aba2-1c924cefb1f7-cap-1/app/

Tool result summary: total 52 drwxr-xr-x 3 user user 4096 Jun 6 04:50 . drwxr-xr-x 6 user user 4096 Jun 6 04:50 .. -rw-r--r-- 1 user user 0 Jun 6 04:50 __init__.py -rw-r--r-- 1 user user 301 Jun 6 04:50 __main__.py -rw-r--r-- 1 user user 5901 Jun 6 04:50 agent.py -rw-r--r-- 1 user user 4903 Jun 6 04:50 main.py drwxr-xr-x 2 user user 4096 Jun 6 04:50 pages -rw-r--r-- 1 user user 3590 Jun 6 04:50 prompt.py -rw-r--r-- 1 user user 12984 Jun Agent context: I'll research the repository to understand its architecture and how its 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 to understand its architecture and how its agent works, then evaluate web-search providers.

command

git log --all --oneline | head -25

agent_action

Let me read the key integration files and load web tools to research 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: Let me read the key integration files and load web tools to research providers.

agent_action

I understand the codebase: a Streamlit + LangGraph ReAct finance agent (OpenAI), wi...

I understand the codebase: a Streamlit + LangGraph ReAct finance agent (OpenAI), with tools registered as LangChain `StructuredTool`s in `app/tools.py` following a clean wrapper pattern. Now let me research web-search/retrieval providers, weighting source quality and licensing.

best managed web search API for LLM AI agents 2026 licensed premium sources Tavily ...

Query: best managed web search API for LLM AI agents 2026 licensed premium sources Tavily Exa Linkup Perplexity 10 search result links captured Tool result summary: Based on the 2026 web search results, here's a comprehensive comparison of managed web search APIs for LLM AI agents: ## Benchmark Results & Performance Perplexity scored highest, Exa was a close second, Gemini third, Tavily last in a direct comparison benchmark, though the sample size is tiny (eight facts, one scored run), so read this as directional, not definitive. In a separate AIMultiple benchmark: The top four Agent context: I understand the codebase: a Streamlit + LangGraph ReAct finance agent (OpenAI), with tools registered as LangChain `StructuredTool`s in `app/tools.py` following a clean wrapper pattern. Now let me research web-search/retrieval providers, weighting source quality and licensing.

Linkup API licensed premium content sources search agent documentation

Query: Linkup API licensed premium content sources search agent documentation 8 search result links captured Tool result summary: Based on the search results, here's information about the Linkup API and its approach to licensed premium content sources: ## Overview Linkup is an AI-driven search platform designed to enhance the capabilities of large language models (LLMs) and AI agents by providing efficient access to web content. Through its specialized API, Linkup enables AI systems to retrieve real-time, accurate information from premium sourc Agent context: I understand the codebase: a Streamlit + LangGraph ReAct finance agent (OpenAI), with tools registered as LangChain `StructuredTool`s in `app/tools.py` following a clean wrapper pattern. Now let me research web-search/retrieval providers, weighting source quality and licensing.

agent_action

The "licensed sources" requirement strongly favors Linkup. Let me consult the actua...

The "licensed sources" requirement strongly favors Linkup. Let me consult the actual documentation of the top candidates to ground the recommendation.

url

https://docs.linkup.so/pages/documentation/get-started/introduction

Fetch prompt: Summarize: what endpoints does Linkup offer, what source quality/licensing claims do they make, what output formats (sourcedAnswer, searchResults), pricing, latency, Python SDK usage, and LangChain integration. Quote any specifics about premium/licensed sources. Tool result summary: # Linkup API Summary ## Endpoints Linkup provides four endpoints: 1. **Search** – Returns ranked URLs, sourced answers, or structured JSON (<1s–~30s, synchronous) 2. **Fetch** – Delivers clean LLM-ready markdown from known URLs (~1s, synchronous) 3. **Research** – Generates detailed reports with inline citations (2–20 min, asynchronous, currently in beta) 4. **Tasks** – Processes batches of up to 100 calls (1s–20 min Agent context: The "licensed sources" requirement strongly favors Linkup. Let me consult the actual documentation of the top candidates to ground the recommendation.

url

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

Fetch prompt: Summarize Tavily's search API: parameters, source quality, whether sources are licensed or scraped from search engines, output format, pricing, Python SDK, and LangChain integration. Note anything about source coverage and quality. Tool result summary: # Tavily Search API Summary ## Key Parameters Tavily offers configurable search parameters including `search_depth` (advanced/basic/fast/ultra-fast), `max_results` (0-20), `topic` (general/news/finance), and temporal filters like `time_range` and date ranges. Additional options include `include_answer`, `include_raw_content`, `include_images`, domain filtering, and country-specific boosting. ## Source Attribution & C Agent context: The "licensed sources" requirement strongly favors Linkup. Let me consult the actual documentation of the top candidates to ground the recommendation.

agent_action

Let me consult Exa's docs and the Linkup licensing details to round out the compari...

Let me consult Exa's docs and the Linkup licensing details to round out the comparison.

url

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

Fetch prompt: Summarize Exa's search API: how it works (neural/embeddings vs keyword), source quality and coverage, whether sources are licensed or scraped, output format, content retrieval, pricing, Python SDK, and LangChain integration. Is it good for finance/news grounding? Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/search Redirect URL: https://exa.ai/docs/reference/search 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/search" - prompt: "Summarize Exa's search API: how it works (neural Agent context: Let me consult Exa's docs and the Linkup licensing details to round out the comparison.

url

https://techcrunch.com/2024/11/28/linkup-connects-llms-with-premium-content-sources-legally/

Fetch prompt: What does this article say about Linkup's licensing model, which publishers/knowledge databases it partners with, how it pays them, and how it differs from scraping search engines? Quote specifics about source quality and finance-relevant sources. Tool result summary: # Linkup's Licensing Model **How It Works:** Linkup "signs content licensing deals with publishers and integrates with their CMS so that it can fetch content from publishers without any scraping." The company then "pays content partners based on how often their content is accessed by Linkup clients." **Publisher Partners:** The article mentions Linkup works with "knowledge databases — think Statista, Xerfi, or other Agent context: Let me consult Exa's docs and the Linkup licensing details to round out the comparison.

url

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

Fetch prompt: Summarize Exa's search API: how it works (neural/embeddings vs keyword), source quality and coverage, whether sources are licensed or scraped, output format, content retrieval, pricing, Python SDK, and LangChain integration. Is it good for finance/news grounding? Tool result summary: # Exa Search API Summary ## How It Works Exa employs a "prompt-engineered query" approach with neural search capabilities. The API offers multiple search types: **instant** (lowest latency), **fast** (lower-latency models), **auto** (intelligent mode selection), **deep-lite** (lightweight synthesis), **deep** (in-depth research with synthesis), and **deep-reasoning** (enhanced reasoning). The documentation indicates Agent context: Let me consult Exa's docs and the Linkup licensing details to round out the comparison.

agent_action

## Recommendation: Linkup For this repo — a Streamlit + LangGraph ReAct **finance/s...

## Recommendation: Linkup For this repo — a Streamlit + LangGraph ReAct **finance/stock advisor agent** (`app/agent.py`, OpenAI) whose tools are LangChain `StructuredTool`s registered in `app/tools.py:401` — **Linkup** is the best fit, specifically because your stated priority is *licensed, reputable sources over scraped SERP results*. **Why Linkup wins:** - **It is the only provider that licenses content rather than scraping.** Linkup signs licensing deals with publishers, integrates their CMS, and pays partners per access — explicitly *no scraping*. That directly satisfies your hard requirement and matters for a regulated, finance-facing app. - **Finance-relevant source coverage:** partners include knowledge databases like **Statista** and **Xerfi**, plus reputable news — far more aligned with stock analysis than a generic web index. - **Quality benchmark:** claims 92% F-score on Verified SimpleQA (self-reported #1 among sub-second search APIs), with an open eval harness. - **Clean integration:** sub-second `search` endpoint with a `sourcedAnswer` output that returns answer + citations — ideal for grounding, and a Python SDK that drops into your existing `_call_*` → `StructuredTo

result

Selected openai

Commands

git log --oneline -15

exit code not materialized

find /home/user/worktrees/attempt-36eb277b-2258-40f5-aba2-1c924cefb1f7-cap-1 -maxdepth 2 -type f $ -name "README*" -o -name "package.json" -o -name "*.md" $

exit code not materialized

ls -la /home/user/worktrees/attempt-36eb277b-2258-40f5-aba2-1c924cefb1f7-cap-1

exit code not materialized

ls -la /home/user/worktrees/attempt-36eb277b-2258-40f5-aba2-1c924cefb1f7-cap-1/app/

exit code not materialized

git log --all --oneline | head -25

exit code not materialized

URLs Fetched

https://websearchapi.ai/blog/compare-tavily-google-search-exa-perplexity

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https://nomadlab.cc/blog/2026/05/best-ai-search-apis-2026-tavily-exa-serper-firecrawl

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

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https://www.firecrawl.dev/blog/best-ai-search-engines-agents

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

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

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

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https://medium.com/@unicodeveloper/tavily-alternatives-in-2026-after-the-nebius-acquisition-9de526780686

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https://aimultiple.com/agentic-search

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https://o-mega.ai/articles/top-10-ai-search-apis-for-agents-2026

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https://docs.linkup.so/pages/documentation/get-started/introduction

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https://www.linkup.so/

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https://techcrunch.com/2024/11/28/linkup-connects-llms-with-premium-content-sources-legally/

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https://docs.litellm.ai/docs/search/linkup

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https://linkupapi.com/

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https://aiagentslist.com/agents/linkup

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https://aiagentstore.ai/ai-agent/linkup

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https://friendli.ai/docs/sdk/integrations/linkup

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

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

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

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Changed Files

Artifact only

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

Runs

Find

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Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files

Runs

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Decision Summary

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