alokiitd

Run

Alokiitdh/vector

Agent

Claude Opus 4.7

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:54 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

c817eb6c5d727b0d2deaabb628c284823abf24f0

agent_action

I'll research the repository structure first to understand the agent architecture b...

I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1 -maxdepth 2 -type f \( -name "*.md" -o -name "*.json" -o -name "*.toml" -o -name "*.yml" -o -name "*.yaml" \) | head -20

Tool result summary: /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/README.md /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/pyproject.toml Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read README.md

Tool result summary: <system-reminder>Warning: the file exists but is shorter than the provided offset (1). The file has 1 lines.</system-reminder> Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read pyproject.toml

Tool result summary: 1 [project] 2 name = "product-research-agent" 3 version = "0.1.0" 4 description = "Add your description here" 5 readme = "README.md" 6 requires-python = ">=3.13" 7 dependencies = [ 8 "ipython>=9.7.0", 9 "langchain-groq>=1.0.1", 10 "langchain-openai>=1.0.3", 11 "langgraph>=1.0.3", 12 "python-dotenv>=1.2.1", 13 ] 14 Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

ls -la /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/ | head -30

Tool result summary: total 208 drwxr-xr-x 3 user user 4096 Jun 6 04:51 . drwxr-xr-x 4 user user 4096 Jun 6 04:51 .. -rw-r--r-- 1 user user 170 Jun 6 04:51 .git -rw-r--r-- 1 user user 1540 Jun 6 04:51 .gitignore -rw-r--r-- 1 user user 5 Jun 6 04:51 .python-version -rw-r--r-- 1 user user 0 Jun 6 04:51 README.md -rw-r--r-- 1 user user 20137 Jun 6 04:51 graph_1.png -rw-r--r-- 1 user user 303 Jun 6 04:51 pyproject.toml drwxr-xr-x 5 user user Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src -type f -name "*.py" | head -30

Tool result summary: /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/graph/main_graph.py /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/graph/state.py /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/graph/testing.py /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/llm/llm_groq.py /home/user/worktrees/attempt-a0b86363-4804-49ab- Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1 -maxdepth 2 -type f \( -name "*.env*" -o -name "*config*" -o -name "*settings*" \)

Tool result summary: (Bash completed with no output) Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/main.py

Tool result summary: 1 def main(): 2 print("Hello from product-research-agent!") 3 4 5 if __name__ == "__main__": 6 main() 7 Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/graph/main_graph.py

Tool result summary: 1 from langgraph.graph import StateGraph, START, END 2 from src.graph.state import AgentState 3 from src.nodes.router import router 4 from src.nodes.specs_agent import specs_agent 5 from src.nodes.search_agent import search_agent 6 from src.nodes.review_agent import review_agent 7 from src.nodes.combine_results import comb_results 8 9 def build_graph(): 10 graph = StateGraph(AgentState) 11 12 graph.add_node("router", Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/graph/state.py

Tool result summary: 1 2 from typing import List, TypedDict, Literal, Optional 3 4 class ProductSpecs(TypedDict): 5 category: str 6 max_price: Optional[float] 7 min_price: Optional[float] 8 brand_preferences: List[str] 9 use_cases: List[str] 10 key_requirements: List[str] 11 12 13 class Product(TypedDict): 14 id: str 15 name: str 16 price: float 17 ratings: float 18 url: str 19 20 class ProductReviewSummary(TypedDict): 21 product_id: str Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/nodes/router.py

Tool result summary: 1 def router(): 2 pass Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/nodes/specs_agent.py

Tool result summary: 1 from src.graph.state import AgentState, ProductSpecs 2 from src.llm.llm_groq import llm_groq 3 from langchain_core.messages import SystemMessage 4 5 6 # We are using langchain wrapper for structured output. 7 llm_product_spec = llm_groq.with_structured_output(ProductSpecs) 8 9 def specs_agent(state: AgentState): 10 """Extracting the User expecatations from the query""" 11 user_query = state['user_query'] 12 system_ Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/nodes/search_agent.py

Tool result summary: 1 from src.graph.state import AgentState, ProductSpecs 2 from src.llm.llm_groq import llm_groq 3 from langchain_core.messages import SystemMessage 4 5 def search_agent(): 6 pass Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/nodes/review_agent.py

Tool result summary: 1 def review_agent(): 2 pass Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/nodes/combine_results.py

Tool result summary: 1 def comb_results(): 2 pass Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/llm/llm_groq.py

Tool result summary: 1 from langchain_groq import ChatGroq 2 import os 3 from dotenv import load_dotenv 4 5 load_dotenv(override=True) 6 7 llm_groq = ChatGroq( 8 groq_api_key= os.environ["GROQ_API_KEY"], 9 model="openai/gpt-oss-120b", # Can be replace with our preferred model 10 temperature=0.1 11 ) 12 13 # result = llm.invoke("Tell me about Groq and its AI accelerators.") 14 # print(result) Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/llm/llm_openai.py

Tool result summary: 1 from langchain_openai import ChatOpenAI 2 3 llm_openai = ChatOpenAI( 4 model = "gpt-4.1", 5 temperature= 0.1 6 ) Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

cat /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/.gitignore

Tool result summary: # Dependencies node_modules/ npm-debug.log* yarn-debug.log* yarn-error.log* package-lock.json yarn.lock # Environment variables .env .env.local .env.development.local .env.test.local .env.production.local # Database *.db *.sqlite *.sqlite3 # Logs logs *.log npm-debug.log* yarn-debug.log* yarn-error.log* lerna-debug.log* # Runtime data pids *.pid *.seed *.pid.lock # Coverage directory used by tools like istanbul cover Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

git log --oneline -15

Tool result summary: c817eb6 Spec Agent added 23acfea Vector Started .. Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

git diff HEAD~1 HEAD --name-only

Tool result summary: graph_1.png pyproject.toml src/graph/__pycache__/main_graph.cpython-313.pyc src/graph/__pycache__/state.cpython-313.pyc src/graph/__pycache__/testing.cpython-313.pyc src/graph/main_graph.py src/graph/testing.py src/llm/__pycache__/llm_groq.cpython-313.pyc src/llm/llm_groq.py src/nodes/__pycache__/combine_results.cpython-313.pyc src/nodes/__pycache__/review_agent.cpython-313.pyc src/nodes/__pycache__/router.cpython-31 Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/graph/testing.py

Tool result summary: 1 from src.graph.main_graph import build_graph 2 from IPython.display import Image 3 4 5 app = build_graph() 6 png_bytes = app.get_graph().draw_mermaid_png() 7 8 # Save to file 9 with open("graph_1.png", "wb") as f: 10 f.write(png_bytes) 11 12 print("Saved as graph.png") 13 14 15 16 Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

Read src/nodes/step_route.py

command

tree -L 3 /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src -I "__pycache__"

Tool result summary: Exit code 127 /bin/bash: line 1: tree: command not found Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src -type f -name "*.py" | sort

command

Tool result summary: (Bash completed with no output) Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

grep -r "tool\|function.*call\|structured.*output" /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src --include="*.py" -i

Tool result summary: /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/nodes/specs_agent.py:# We are using langchain wrapper for structured output. /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/nodes/specs_agent.py:llm_product_spec = llm_groq.with_structured_output(ProductSpecs) /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/nodes/specs_agent.py: print(" Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

git log --all --oneline

command

git show 23acfea --stat

Tool result summary: commit 23acfea555e586901e12e0680c3733a6080ce610 Author: Alok <144205496+Alokiitdh@users.noreply.github.com> Date: Mon Nov 24 17:18:53 2025 +0530 Vector Started .. .gitignore | 135 ++++++++ .python-version | 1 + README.md | 0 pyproject.toml | 11 + src/graph/main_graph.py | 0 src/graph/state.py | 38 +++ src/llm/llm_groq.py | 14 + src/llm/llm_openai.py | 6 + src/main.py | 6 + src/nodes/combine_results.py | 0 src/nodes/r Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

grep -r "environ\|getenv\|config\|api.*key" /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src --include="*.py" -B 2 -A 2

Tool result summary: /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/llm/llm_groq.py- /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/llm/llm_groq.py-llm_groq = ChatGroq( /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/llm/llm_groq.py: groq_api_key= os.environ["GROQ_API_KEY"], /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/llm Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

grep -A 20 "dependencies" /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/pyproject.toml

Tool result summary: dependencies = [ "ipython>=9.7.0", "langchain-groq>=1.0.1", "langchain-openai>=1.0.3", "langgraph>=1.0.3", "python-dotenv>=1.2.1", ] Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

grep -i "name = " /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/uv.lock | head -50

Tool result summary: name = "annotated-types" name = "anyio" { name = "idna" }, { name = "sniffio" }, name = "asttokens" name = "certifi" name = "charset-normalizer" name = "colorama" name = "decorator" name = "distro" name = "executing" name = "groq" { name = "anyio" }, { name = "distro" }, { name = "httpx" }, { name = "pydantic" }, { name = "sniffio" }, { name = "typing-extensions" }, name = "h11" name = "httpcore" { name = "certifi" } Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

grep "name = " /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/uv.lock | wc -l && grep "name = \"langchain\|name = \"langgraph" /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/uv.lock

Tool result summary: 155 name = "langchain-core" name = "langchain-groq" { name = "langchain-core" }, name = "langchain-openai" { name = "langchain-core" }, name = "langgraph" { name = "langchain-core" }, { name = "langgraph-checkpoint" }, { name = "langgraph-prebuilt" }, { name = "langgraph-sdk" }, name = "langgraph-checkpoint" { name = "langchain-core" }, name = "langgraph-prebuilt" { name = "langchain-core" }, { name = "langgraph-chec Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

Tool result summary: /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/graph/main_graph.py:from src.nodes.search_agent import search_agent /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/graph/main_graph.py: graph.add_node("search_agent", search_agent) /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src/graph/main_graph.py: graph.add_edge("router", "search_agen Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1 -name "__init__.py"

Tool result summary: (Bash completed with no output) Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

command

find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1 -type f ! -path "*/.git/*" ! -path "*/__pycache__/*" ! -name "*.pyc" ! -name "uv.lock" ! -name "graph_1.png" | sort

Tool result summary: /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/.git /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/.gitignore /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/.python-version /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/README.md /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/pyproject.toml /ho Agent context: I'll research the repository structure first to understand the agent architecture before recommending a provider.

agent_action

This is a LangGraph product-research agent (Python 3.13, Groq LLM, LangChain) with ...

This is a LangGraph product-research agent (Python 3.13, Groq LLM, LangChain) with an empty `search_agent` placeholder. Now I'll consult actual provider documentation before recommending. Let me load web tools.

agent_action

Now I'll research the leading managed search/retrieval providers. The "licensed, re...

Now I'll research the leading managed search/retrieval providers. The "licensed, reputable sources over scraped results" requirement is a strong signal — let me consult the actual docs for the top candidates in parallel.

best agentic web search API for LLM agents 2026 Tavily Exa Linkup licensed sources ...

Query: best agentic web search API for LLM agents 2026 Tavily Exa Linkup licensed sources comparison 10 search result links captured Tool result summary: Based on the search results, here's a comprehensive comparison of the best agentic web search APIs for LLM agents in 2026: ## Performance Benchmarks Brave Search leads with 14.89, but Firecrawl, Exa, and Parallel Search Pro are so close that the differences could be random variation. However, Brave consistently outperformed Tavily by about 1 point, a gap large enough to be meaningful rather than random chance. ## Key Agent context: Now I'll research the leading managed search/retrieval providers. The "licensed, reputable sources over scraped results" requirement is a strong signal — let me consult the actual docs for the top candidates in parallel.

url

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

Fetch prompt: What is Linkup? What sources does it cover, does it provide licensed/premium publisher content, what are its search capabilities (depth, structured output), pricing, and does it integrate with LangChain/LangGraph? Summarize source quality claims. Tool result summary: # Linkup: Web Search API for AI ## What is Linkup? Linkup is "the production-grade web search API for AI" designed for teams building AI applications. It's used by companies including Legora, Polymarket, Cohere, McKinsey, and SNCF. ## Source Coverage & Premium Content The documentation provided does not specify which sources Linkup covers or whether it includes licensed/premium publisher content. ## Search Capabiliti Agent context: Now I'll research the leading managed search/retrieval providers. The "licensed, reputable sources over scraped results" requirement is a strong signal — let me consult the actual docs for the top candidates in parallel.

url

https://docs.exa.ai/reference/getting-started

Fetch prompt: What is Exa search API? What kind of search (neural/embeddings/keyword), what sources does it index, source quality, content retrieval, structured answers, LangChain integration, pricing. Summarize. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/getting-started Redirect URL: https://exa.ai/docs/reference/getting-started 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/getting-started" - prompt: "What is Exa search AP Agent context: Now I'll research the leading managed search/retrieval providers. The "licensed, reputable sources over scraped results" requirement is a strong signal — let me consult the actual docs for the top candidates in parallel.

url

https://docs.tavily.com/welcome

Fetch prompt: What is Tavily? How does its search work for AI agents, what sources, source quality/filtering, LangChain/LangGraph integration, structured output, pricing. Summarize. Tool result summary: # Tavily Summary Based on the provided documentation excerpt, here's what can be determined: **What is Tavily:** The page describes it as offering "state-of-the-art web search" capabilities, positioned as a service for building search functionality. **Search Functionality for AI Agents:** The documentation emphasizes agent integration, directing AI systems to specific setup guides and routing options including "SDK/A Agent context: Now I'll research the leading managed search/retrieval providers. The "licensed, reputable sources over scraped results" requirement is a strong signal — let me consult the actual docs for the top candidates in parallel.

agent_action

Good signal — Linkup and Exa lead on quality. Let me dig into the "licensed/premium...

Good signal — Linkup and Exa lead on quality. Let me dig into the "licensed/premium sources" angle specifically, plus Exa's docs and an independent benchmark.

url

https://exa.ai/docs/reference/getting-started

Fetch prompt: What is Exa search API? What kind of search (neural/embeddings/keyword), what sources does it index, source quality, content retrieval, structured answers, LangChain integration, pricing. Summarize. Tool result summary: # Exa Search API Summary Based on the provided content, here's what can be determined: **What is Exa?** "Exa is a search engine made for AIs." It's specifically designed to help artificial intelligence systems find precise web content. **Core Functionalities:** The platform offers four main capabilities: - `/search` – Locates webpages using embeddings-based search and intelligent methods - `/contents` – Retrieves cle Agent context: Good signal — Linkup and Exa lead on quality. Let me dig into the "licensed/premium sources" angle specifically, plus Exa's docs and an independent benchmark.

Linkup search API licensed premium publisher content partnerships trusted sources

Query: Linkup search API licensed premium publisher content partnerships trusted sources 10 search result links captured Tool result summary: Based on the search results, here's what I found about Linkup's search API and its partnerships with licensed premium publisher content: ## Overview Linkup is a French startup building an API that lets developers access web content from premium, trusted sources and hand the results to a large language model (LLM) to enrich its answers. Through its specialized API, Linkup enables AI systems to retrieve real-time, accu Agent context: Good signal — Linkup and Exa lead on quality. Let me dig into the "licensed/premium sources" angle specifically, plus Exa's docs and an independent benchmark.

url

https://aimultiple.com/agentic-search

Fetch prompt: Summarize the benchmark results comparing agentic search APIs (Tavily, Exa, Linkup, Brave, Serper, Parallel, Firecrawl). Which scored highest on accuracy/answer quality? What does it say about source quality, licensed sources, and trustworthiness of each provider? Include any scores. Tool result summary: # Agentic Search API Benchmark Summary ## Top Performers The benchmark evaluated 8 search APIs across 100 real-world AI/LLM queries. **Brave Search led with an Agent Score of 14.89**, followed closely by Firecrawl (14.58), Exa (14.39), and Parallel Search Pro (14.21). These four APIs showed statistically indistinguishable performance differences. **Tavily ranked fifth** with a score of 13.67, showing the only meaning Agent context: Good signal — Linkup and Exa lead on quality. Let me dig into the "licensed/premium sources" angle specifically, plus Exa's docs and an independent benchmark.

agent_action

Linkup's licensing model directly matches the requirement. Let me verify its LangCh...

Linkup's licensing model directly matches the requirement. Let me verify its LangChain/Python integration since the repo is built on LangChain/LangGraph.

langchain-linkup python package LangChain integration LinkupSearchTool

Query: langchain-linkup python package LangChain integration LinkupSearchTool 9 search result links captured Tool result summary: Based on the search results, here's what I found about the langchain-linkup Python package: ## Overview The LinkupSearchTool makes available the Linkup API search as a LangChain tool. Linkup provides an API to connect LLMs to the web and the Linkup Premium Partner sources. ## Installation You can install the langchain-linkup package using pip: `pip install langchain-linkup` ## Key Components The package provides two Agent context: Linkup's licensing model directly matches the requirement. Let me verify its LangChain/Python integration since the repo is built on LangChain/LangGraph.

agent_action

## Recommendation: **Linkup** For this repo — a LangGraph product-research agent (G...

## Recommendation: **Linkup** For this repo — a LangGraph product-research agent (Groq LLM, LangChain, empty `search_agent` placeholder at `src/nodes/search_agent.py`) — **Linkup** is the best fit, specifically because you weighted licensed, reputable sources over scraped SERP results. **Why Linkup wins on your stated criterion:** It's the only provider whose core model *is* licensed content. It signs licensing deals with premium publishers and pulls via integrated CMS — no scraping — plus knowledge databases like Statista and Xerfi, paying partners per access ([TechCrunch](https://techcrunch.com/2024/11/28/linkup-connects-llms-with-premium-content-sources-legally/)). It also ranks #1 on Verified SimpleQA among sub-second search APIs (92% F-score). Crucially, it has a first-class LangChain integration (`pip install langchain-linkup`, `LinkupSearchTool` / `LinkupSearchRetriever`) that drops straight into your stack, with `depth` (standard/deep) and `structured` output that maps onto your existing `Product` TypedDict — and it reads `LINKUP_API_KEY` from env, matching your `dotenv` pattern. **Main alternatives considered:** - **Exa** — strongest *neural/semantic* search; best for conc

result

Selected unknown

Commands

find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1 -maxdepth 2 -type f \( -name "*.md" -o -name "*.json" -o -name "*.toml" -o -name "*.yml" -o -name "*.yaml" \) | head -20

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ls -la /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/ | head -30

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find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src -type f -name "*.py" | head -30

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find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1 -maxdepth 2 -type f \( -name "*.env*" -o -name "*config*" -o -name "*settings*" \)

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cat /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/.gitignore

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git log --oneline -15

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git diff HEAD~1 HEAD --name-only

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tree -L 3 /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src -I "__pycache__"

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find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src -type f -name "*.py" | sort

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grep -r "tool\|function.*call\|structured.*output" /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src --include="*.py" -i

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git log --all --oneline

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git show 23acfea --stat

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grep -r "environ\|getenv\|config\|api.*key" /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/src --include="*.py" -B 2 -A 2

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grep -A 20 "dependencies" /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/pyproject.toml

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grep -i "name = " /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1/uv.lock | head -50

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find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1 -name "__init__.py"

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find /home/user/worktrees/attempt-a0b86363-4804-49ab-9883-51c07dec0efc-cap-1 -type f ! -path "*/.git/*" ! -path "*/__pycache__/*" ! -name "*.pyc" ! -name "uv.lock" ! -name "graph_1.png" | sort

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https://aimultiple.com/agentic-search

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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/best-ai-search-engines-agents

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https://composio.dev/content/9-top-ai-search-engine-tools

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https://exa.ai/versus/tavily

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

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

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https://www.linkup.so/content-partners

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https://medium.com/@rutujadesai/linkup-bridging-the-gap-between-ai-and-premium-content-d3e8da809b71

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https://finance.yahoo.com/news/linkup-connects-llms-premium-content-162953702.html

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https://dig.watch/updates/linkup-builds-marketplace-for-ai-content-licensing

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

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https://www.linkup.so/

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https://linkupapi.com/

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https://aiagentsdirectory.com/agent/linkup

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https://www.linkedin.com/company/linkup-platform

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https://pypi.org/project/langchain-linkup/

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https://python.langchain.com/docs/integrations/providers/linkup/

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https://github.com/LinkupPlatform/langchain-linkup

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https://github.com/LinkupPlatform/langchain-linkup/blob/main/README.md

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https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/tools/linkup_search.ipynb

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https://pydigger.com/pypi/langchain-linkup

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https://aidoczh.com/langchain/docs/integrations/providers/linkup/linkup.html

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https://composio.dev/toolkits/linkup/framework/langchain

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https://www.langchain.com/

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