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 first to understand its architecture and how its agent...

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

command

find /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0 -type f -name "README*" -o -name "*.md" | head -20

Tool result summary: /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/README.md Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

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

command

ls -la /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/

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

command

find /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0 -type f \( -name "package.json" -o -name "pyproject.toml" -o -name "requirements.txt" -o -name "go.mod" -o -name "Cargo.toml" \)

Tool result summary: /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/pyproject.toml Agent context: I'll research the repository first 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 = "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 first to understand its architecture and how its agent works, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src -type f -name "*.py" | head -30

Tool result summary: /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph/main_graph.py /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph/state.py /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph/testing.py /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/llm/llm_groq.py /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9- Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

tree -L 3 /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src 2>/dev/null || find /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src -type d | head -20

Tool result summary: /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph/__pycache__ /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/llm /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/llm/__pycach Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

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

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

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

agent_action

Read src/nodes/router.py

Tool result summary: 1 def router(): 2 pass Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

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

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

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

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

command

find /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0 -type f -name ".env*" -o -name "*.env"

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

agent_action

Read src/nodes/review_agent.py

Tool result summary: 1 def review_agent(): 2 pass Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

agent_action

Read src/nodes/combine_results.py

Tool result summary: 1 def comb_results(): 2 pass Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

agent_action

Read src/nodes/step_route.py

agent_action

Read .gitignore

Tool result summary: 1 # Dependencies 2 node_modules/ 3 npm-debug.log* 4 yarn-debug.log* 5 yarn-error.log* 6 package-lock.json 7 yarn.lock 8 9 # Environment variables 10 .env 11 .env.local 12 .env.development.local 13 .env.test.local 14 .env.production.local 15 16 # Database 17 *.db 18 *.sqlite 19 *.sqlite3 20 21 # Logs 22 logs 23 *.log 24 npm-debug.log* 25 yarn-debug.log* 26 yarn-error.log* 27 lerna-debug.log* 28 29 # Runtime data 30 pi Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0 -type f -name "*.py" | sort

command

cat /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/.python-version

Tool result summary: 3.13 Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

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

command

head -100 /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/uv.lock

Tool result summary: version = 1 revision = 3 requires-python = ">=3.13" [[package]] name = "annotated-types" version = "0.7.0" source = { registry = "https://pypi.org/simple" } sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

agent_action

Grep tool|Tool|@tool|bind_tools|tool_choice

Tool result summary: No files found Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

agent_action

Grep requests|httpx|aiohttp|urllib|http

Tool result summary: No files found Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

grep -r "from langchain\|import.*groq\|import.*openai" /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src --include="*.py"

Tool result summary: /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/llm/llm_groq.py:from langchain_groq import ChatGroq /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/llm/llm_openai.py:from langchain_openai import ChatOpenAI /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/nodes/search_agent.py:from src.llm.llm_groq import llm_groq /home/user/worktrees/a Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

grep -r "invoke\|bind\|tool\|function" /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src --include="*.py" | head -40

Tool result summary: /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/llm/llm_groq.py:# result = llm.invoke("Tell me about Groq and its AI accelerators.") /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/nodes/specs_agent.py: response = llm_product_spec.invoke([system_prompt, user_query]) Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0 -type f ! -path '*/__pycache__/*' ! -path '*/.git/*' ! -name '*.png' -o -type d | head -50

Tool result summary: /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0 /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/.git /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/.gitignore /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/.python-version /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/README.md /home/user/worktre Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/

Tool result summary: total 24 drwxr-xr-x 5 user user 4096 Jun 6 04:51 . drwxr-xr-x 3 user user 4096 Jun 6 04:51 .. drwxr-xr-x 3 user user 4096 Jun 6 04:51 graph drwxr-xr-x 3 user user 4096 Jun 6 04:51 llm -rw-r--r-- 1 user user 100 Jun 6 04:51 main.py drwxr-xr-x 3 user user 4096 Jun 6 04:51 nodes Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0 -name "__init__.py" -o -type f -name "*.py" | xargs ls -lh

Tool result summary: -rw-r--r-- 1 user user 1.3K Jun 6 04:51 /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph/main_graph.py -rw-r--r-- 1 user user 896 Jun 6 04:51 /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph/state.py -rw-r--r-- 1 user user 256 Jun 6 04:51 /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph/testing.py -rw-r--r-- 1 user user Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

wc -l /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/**/*.py

Tool result summary: 40 /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph/main_graph.py 38 /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph/state.py 15 /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/graph/testing.py 13 /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0/src/llm/llm_groq.py 5 /home/user/worktrees/attempt-e6fe5 Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

git log --oneline --all | head -20

Tool result summary: c817eb6 Spec Agent added 23acfea Vector Started .. Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

git remote -v

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

command

git log -p --max-count=5 | head -200

Tool result summary: commit c817eb6c5d727b0d2deaabb628c284823abf24f0 Author: Alok <144205496+Alokiitdh@users.noreply.github.com> Date: Wed Nov 26 11:48:09 2025 +0530 Spec Agent added diff --git a/graph_1.png b/graph_1.png new file mode 100644 index 0000000..f130f95 Binary files /dev/null and b/graph_1.png differ diff --git a/pyproject.toml b/pyproject.toml index 2c3426a..97a2e94 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5 Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

command

git diff HEAD~1 HEAD 2>/dev/null | head -150

Tool result summary: diff --git a/graph_1.png b/graph_1.png new file mode 100644 index 0000000..f130f95 Binary files /dev/null and b/graph_1.png differ diff --git a/pyproject.toml b/pyproject.toml index 2c3426a..97a2e94 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,9 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.13" dependencies = [ + "ipython>=9.7.0", "langchain-groq>=1.0.1", "lan Agent context: I'll research the repository first to understand its architecture and how its agent works, then evaluate web-search providers.

agent_action

The repo is a Python 3.13 LangGraph product-research agent (LangChain + Groq), with...

The repo is a Python 3.13 LangGraph product-research agent (LangChain + Groq), with a stubbed `search_agent` node and no web retrieval yet. Now let me consult the actual documentation of the leading providers before recommending one. Let me load the web tools.

agent_action

Now let me consult the documentation of the leading providers, prioritizing those k...

Now let me consult the documentation of the leading providers, prioritizing those known for citation quality and factual grounding.

url

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

Fetch prompt: Describe Tavily's search/retrieval API: what it returns (sources, URLs, content snippets, raw content), citation support, factual accuracy/grounding features, score/relevance, Python SDK and LangChain integration, pricing tiers. Summarize the key fields in the response. Tool result summary: # Tavily Search API Overview ## What It Returns Tavily's search API delivers structured results including: - **Sources & URLs**: Each result contains a title and URL linked to the source - **Content Snippets**: Short descriptions and relevance scores (0-1 scale) for ranking - **Raw Content**: Optional cleaned HTML in markdown or plain text format via `include_raw_content` - **Images**: Query-related images plus per-s Agent context: Now let me consult the documentation of the leading providers, prioritizing those known for citation quality and factual grounding.

url

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

Fetch prompt: Describe Exa's search and retrieval API: neural/semantic search, what it returns (URLs, highlights, full text content, summaries), citation/source quality features, Python SDK and LangChain integration. How does it support grounding LLM answers with verifiable sources? Pricing. 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: "Describe Exa's search Agent context: Now let me consult the documentation of the leading providers, prioritizing those known for citation quality and factual grounding.

url

https://docs.perplexity.ai/api-reference/chat-completions-post

Fetch prompt: Describe Perplexity Sonar API: does it return citations/sources with answers? What grounding and factual accuracy features does it have? Does it return raw search results or only synthesized answers? Models available (sonar, sonar-pro). Pricing. Python/OpenAI-compatible SDK. Tool result summary: # Perplexity Sonar API Overview ## Citations & Sources Yes, the API returns citations with answers. The response includes a `citations` field containing "URLs of sources used to generate the response" and a `search_results` field with detailed search result objects. ## Grounding & Factual Accuracy Features The API provides several grounding mechanisms: - **Web search integration** across multiple modes (web, academic Agent context: Now let me consult the documentation of the leading providers, prioritizing those known for citation quality and factual grounding.

agent_action

Let me fetch the Exa redirect and the Linkup docs (a newer provider with strong fac...

Let me fetch the Exa redirect and the Linkup docs (a newer provider with strong factual-accuracy benchmark claims).

url

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

Fetch prompt: Describe Exa's search and retrieval API: neural/semantic search, what it returns (URLs, highlights, full text content, summaries), citation/source quality features, Python SDK and LangChain integration. How does it support grounding LLM answers with verifiable sources? Pricing. Tool result summary: # Exa Search and Retrieval API Overview Based on the documentation provided, here's what I can share about Exa: ## Core Capabilities Exa describes itself as **"a search engine made for AIs."** It offers four primary functionalities: 1. **Neural/Semantic Search** - The `/search` endpoint leverages embeddings-based search methods to locate webpages intelligently 2. **Content Retrieval** - The `/contents` endpoint deliv Agent context: Let me fetch the Exa redirect and the Linkup docs (a newer provider with strong factual-accuracy benchmark claims).

url

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

Fetch prompt: Describe Linkup's search/retrieval API: what it returns, citation support, factual accuracy benchmarks (do they claim SimpleQA or other benchmark results?), source quality, Python SDK and LangChain integration, pricing. Why is it positioned for factual grounding? Tool result summary: # Linkup Search/Retrieval API Overview ## What It Returns Linkup's **Search endpoint** transforms queries into "ranked URLs, sourced answer, or structured JSON" with latency under 1 second to ~30 seconds. The **Fetch endpoint** converts URLs into "clean LLM-ready markdown" in approximately one second. ## Citation & Accuracy The platform emphasizes factual grounding through verified benchmarks. Search ranks first amon Agent context: Let me fetch the Exa redirect and the Linkup docs (a newer provider with strong factual-accuracy benchmark claims).

Tavily vs Exa vs Perplexity Sonar vs Linkup factual accuracy SimpleQA benchmark web...

Query: Tavily vs Exa vs Perplexity Sonar vs Linkup factual accuracy SimpleQA benchmark web search API for agents 2026 10 search result links captured Tool result summary: Based on the search results, here's a comprehensive comparison of these web search APIs for agents on the SimpleQA benchmark in 2026: ## SimpleQA Benchmark Performance Linkup achieves state-of-the-art performance on SimpleQA with 90.10% F-Score, with Exa finishing close at 90.04%. However, more recent updates show Linkup scoring 91.0% F-Score on SimpleQA, while Perplexity Sonar achieved 77.3% F-Score compared to Sona Agent context: Let me fetch the Exa redirect and the Linkup docs (a newer provider with strong factual-accuracy benchmark claims).

agent_action

I have enough to make a recommendation. Let me note the key facts before they're cl...

I have enough to make a recommendation. Let me note the key facts before they're cleared. Benchmark picture (SimpleQA factuality, the metric most aligned with the stated priority): - **Linkup**: SOTA among sub-second APIs, ~90–91% F-score, open-source eval harness, source-trust controls, SOC2/ZDR/GDPR - **Exa**: ~90–91%, neural search + `/research` with citations - **Tavily**: dominant LangChain ecosystem but ~73% SimpleQA; being acquired by Nebius - **Perplexity Sonar**: built for consumer use, throttled, ~77–86% Let me save the durable accuracy constraint for future work on this repo.

agent_action

Read /home/user/.claude/projects/-home-user--morphiq-repo-cache-44907daf8206bc51e2fa76bc001db5bdfae379587558238e303f37c3454bf117/memory/MEMORY.md

Tool result summary: File does not exist. Note: your current working directory is /home/user/worktrees/attempt-e6fe5ffe-eef5-41f9-991e-6a5dd3ebe463-cap-0. Agent context: I have enough to make a recommendation. Let me note the key facts before they're cleared. Benchmark picture (SimpleQA factuality, the metric most aligned with the stated priority): - **Linkup**: SOTA among sub-second APIs, ~90–91% F-score, open-source eval harness, source-trust controls, SOC2/ZDR/GDPR - **Exa**: ~90–91%, neural search + `/research` with citations - **Tavily**: dominant LangChain ecosystem but ~73% Si

agent_action

## Recommendation: **Linkup** (with **Exa** as the very close runner-up) ### The re...

## Recommendation: **Linkup** (with **Exa** as the very close runner-up) ### The repo `product-research-agent` — Python 3.13, LangGraph + LangChain, Groq LLM. The `search_agent` node (`src/nodes/search_agent.py`) is a stub; there's no web retrieval yet. Config is `.env` + `python-dotenv`, so adding a `LINKUP_API_KEY` and a tool fits the existing pattern cleanly. ### Why Linkup Your stated priority is factual accuracy, source quality, and verifiable citations **above** latency, price, and setup. The benchmark that maps directly to that is **SimpleQA** (factuality), and Linkup is state-of-the-art among production search APIs (~90–91% F-score), shipping with an **open-source eval harness** so the claim is verifiable rather than marketing. It returns ranked URLs *and* a sourced answer (real citations), lets you **define which domains/sources to trust** (key for filtering low-quality product content), and offers Zero Data Retention / SOC 2 Type II / GDPR. That combination — top factuality + verifiable benchmark + source-trust controls — is the best fit for a product that can't tolerate hallucinations. ### Main alternatives considered - **Exa** — Essentially tied with Linkup on SimpleQA

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

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

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

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

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https://www.linkup.so/blog/linkup-establishes-sota-performance-on-simpleqa

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https://dev.to/valyuai/perplexity-sonar-alternatives-for-developers-2026-8j3

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

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https://www.humai.blog/ai-search-apis-compared-tavily-vs-exa-vs-perplexity/

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https://www.linkup.so/blog/perplexity-sonar-vs-linkup

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https://chatforest.com/guides/best-search-mcp-servers/

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

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https://alphacorp.ai/blog/perplexity-search-api-vs-tavily-for-rag-2026

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

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