alokiitd

Run

Alokiitdh/vector

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 05:09 AM

Decision Summary

target

Expected

Provider

Product

Selected

Provider

linkup

Product

Surface

provider_selection

Reason

codex 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 first map how the agent is structured and where retrieval would plug in, then ...

I’ll first map how the agent is structured and where retrieval would plug in, then I’ll check current provider docs so the recommendation is grounded in actual source coverage and integration fit.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.lock'"' | head -200"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.lock'"' | head -200"

exit code 0 Output: /home/user/worktrees/attempt-822c4ec7-1eb2-45b1-aa8e-11265b502d86-cap-0 /bin/bash: line 1: rg: command not found

command

command

exit code 127 Output: /bin/bash: line 1: rg: command not found

agent_action

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

`rg` is not installed in this workspace, so I’m falling back to `find` and `grep` for the repository pass.

command

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f | sed 's#"'^./##'"' | head -250"

command

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f | sed 's#"'^./##'"' | head -250"

exit code 0 Output: .git .gitignore .python-version README.md 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/state.py src/graph/testing.py src/llm/__pycache__/llm_groq.cpython-313.pyc src/llm/llm_groq.py src/llm/llm_openai.py src/main.py src/nodes/__pycache__/combine_results.cpython-313.pyc src/nodes/__pycache__/review_agent.cpython-313.pyc src/nodes/__pycache__/router.cpython-313.pyc src/nodes/__pycache__/search_agent.cpython-313.pyc src/nodes/__pycache__/specs_agent.cpython-313.pyc src/nodes/combine_results.py src/nodes/review_agent.py src/nodes/router.py src/nodes/search_agent.py src/nodes/specs_agent.py src/nodes/step_route.py uv.lock

command

command

exit code 0 Output: ./.gitignore:35:# Coverage directory used by tools like istanbul ./.gitignore:36:coverage/ ./.gitignore:39:# nyc test coverage ./.gitignore:42:# Grunt intermediate storage ./pyproject.toml:2:name = "product-research-agent" ./pyproject.toml:9: "langchain-groq>=1.0.1", ./pyproject.toml:10: "langchain-openai>=1.0.3", ./src/graph/main_graph.py:4:from src.nodes.specs_agent import specs_agent ./src/graph/main_graph.py:5:from src.nodes.search_agent import search_agent ./src/graph/main_graph.py:6:from src.nodes.review_agent import review_agent ./src/graph/main_graph.py:13: graph.add_node("specs_agent", specs_agent) ./src/graph/main_graph.py:14: graph.add_node("search_agent", search_agent) ./src/graph/main_graph.py:15: graph.add_node("review_agent", review_agent) ./src/graph/main_graph.py:21: # After riuter, we can fanout in parallerl to three agents ./src/graph/main_graph.py:25: graph.add_edge("

agent_action

The repository is a small Python LangGraph-style product research agent. I’m readin...

The repository is a small Python LangGraph-style product research agent. I’m reading the graph and node implementations now to understand whether the provider should be a direct API, a LangChain tool, or a custom node integration.

command

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

command

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

exit code 0 Output: [project] name = "product-research-agent" version = "0.1.0" description = "Add your description here" readme = "README.md" requires-python = ">=3.13" dependencies = [ "ipython>=9.7.0", "langchain-groq>=1.0.1", "langchain-openai>=1.0.3", "langgraph>=1.0.3", "python-dotenv>=1.2.1", ]

command

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

command

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

exit code 0

command

/bin/bash -lc "sed -n '1,240p' src/graph/state.py"

command

/bin/bash -lc "sed -n '1,240p' src/graph/state.py"

exit code 0 Output: from typing import List, TypedDict, Literal, Optional class ProductSpecs(TypedDict): category: str max_price: Optional[float] min_price: Optional[float] brand_preferences: List[str] use_cases: List[str] key_requirements: List[str] class Product(TypedDict): id: str name: str price: float ratings: float url: str class ProductReviewSummary(TypedDict): product_id: str pros: List[str] cons: List[str] overall_sentiment: Literal["positive", "neutral", "negative"] class AgentState(TypedDict): user_query: str # Agent Outputs product_specs: ProductSpecs product_list: List[Product] product_review: List[ProductReviewSummary] # Final Aggregated Recommendation final_recommendation: str # Control flags step:Literal["specs_generation", "product_search", "review_analysis", "final_recommendation"]

command

/bin/bash -lc "sed -n '1,220p' src/nodes/review_agent.py"

command

/bin/bash -lc "sed -n '1,220p' src/nodes/review_agent.py"

exit code 0 Output: def review_agent(): pass

command

/bin/bash -lc "sed -n '1,220p' src/nodes/search_agent.py"

command

/bin/bash -lc "sed -n '1,220p' src/nodes/search_agent.py"

exit code 0 Output: from src.graph.state import AgentState, ProductSpecs from src.llm.llm_groq import llm_groq from langchain_core.messages import SystemMessage def search_agent(): pass

command

/bin/bash -lc "sed -n '1,220p' src/graph/main_graph.py"

command

/bin/bash -lc "sed -n '1,220p' src/graph/main_graph.py"

exit code 0 Output: from langgraph.graph import StateGraph, START, END from src.graph.state import AgentState from src.nodes.router import router from src.nodes.specs_agent import specs_agent from src.nodes.search_agent import search_agent from src.nodes.review_agent import review_agent from src.nodes.combine_results import comb_results def build_graph(): graph = StateGraph(AgentState) graph.add_node("router", router) graph.add_node("specs_agent", specs_agent) graph.add_node("search_agent", search_agent) graph.add_node("review_agent", review_agent) graph.add_node("comb_results", comb_results) # Router is the fist node graph.add_edge(START, "router") # After riuter, we can fanout in parallerl to three agents # langgraph parallelism is driven by "updates" to the three shared states # When multiple nodes are reachable from the same previous node, # they can run in parallel, depending on the executor. graph.add

command

/bin/bash -lc "sed -n '1,220p' src/nodes/specs_agent.py"

command

/bin/bash -lc "sed -n '1,220p' src/nodes/specs_agent.py"

exit code 0 Output: from src.graph.state import AgentState, ProductSpecs from src.llm.llm_groq import llm_groq from langchain_core.messages import SystemMessage # We are using langchain wrapper for structured output. llm_product_spec = llm_groq.with_structured_output(ProductSpecs) def specs_agent(state: AgentState): """Extracting the User expecatations from the query""" user_query = state['user_query'] system_prompt = SystemMessage(content="you are an expert specification extraction agent from user query." \ "you have to extract the following:" \ "1. Category" \ "2. max_price" \ "3. min_price" \ "4. brand_preferences" \ "5. use cases" \ "6. Key requirements") response = llm_product_spec.invoke([system_prompt, user_query]) state["product_specs"] = response return state["product_specs"] if __name__ == "__main__": test_state: AgentState = { "user_query": "I want a lightweight laptop under $1200 for programming

command

/bin/bash -lc "sed -n '1,220p' src/nodes/combine_results.py && sed -n '1,160p' src/llm/llm_openai.py && sed -n '1,160p' src/llm/llm_groq.py"

command

/bin/bash -lc "sed -n '1,220p' src/nodes/combine_results.py && sed -n '1,160p' src/llm/llm_openai.py && sed -n '1,160p' src/llm/llm_groq.py"

exit code 0 Output: def comb_results(): passfrom langchain_openai import ChatOpenAI llm_openai = ChatOpenAI( model = "gpt-4.1", temperature= 0.1 )from langchain_groq import ChatGroq import os from dotenv import load_dotenv load_dotenv(override=True) llm_groq = ChatGroq( groq_api_key= os.environ["GROQ_API_KEY"], model="openai/gpt-oss-120b", # Can be replace with our preferred model temperature=0.1 ) # result = llm.invoke("Tell me about Groq and its AI accelerators.") # print(result)

agent_action

The current search and review nodes are stubs, and the state only has a product lis...

The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure.

command

/bin/bash -lc "sed -n '1,200p' src/nodes/router.py"

command

/bin/bash -lc "sed -n '1,200p' src/nodes/router.py"

exit code 0 Output: def router(): pass

command

/bin/bash -lc "sed -n '1,200p' src/nodes/step_route.py"

command

/bin/bash -lc "sed -n '1,200p' src/nodes/step_route.py"

exit code 0

command

/bin/bash -lc "find . -maxdepth 3 -type f -not -path './.git/*' -not -path './__pycache__/*' -print -exec wc -l {} \\;"

command

/bin/bash -lc "find . -maxdepth 3 -type f -not -path './.git/*' -not -path './__pycache__/*' -print -exec wc -l {} \\;"

exit code 0 Output: ./.git 1 ./.git ./.gitignore 134 ./.gitignore ./.python-version 1 ./.python-version ./README.md 0 ./README.md ./graph_1.png 73 ./graph_1.png ./pyproject.toml 13 ./pyproject.toml ./src/graph/main_graph.py 40 ./src/graph/main_graph.py ./src/graph/state.py 38 ./src/graph/state.py ./src/graph/testing.py 15 ./src/graph/testing.py ./src/llm/llm_groq.py 13 ./src/llm/llm_groq.py ./src/llm/llm_openai.py 5 ./src/llm/llm_openai.py ./src/main.py 6 ./src/main.py ./src/nodes/combine_results.py 1 ./src/nodes/combine_results.py ./src/nodes/review_agent.py 1 ./src/nodes/review_agent.py ./src/nodes/router.py 1 ./src/nodes/router.py ./src/nodes/search_agent.py 5 ./src/nodes/search_agent.py ./src/nodes/specs_agent.py 34 ./src/nodes/specs_agent.py ./src/nodes/step_route.py 0 ./src/nodes/step_route.py ./uv.lock 1051 ./uv.lock

managed web search retrieval API licensed sources AI agents provider documentation ...

Query: managed web search retrieval API licensed sources AI agents provider documentation Linkup Tavily Exa Brave Search Perplexity Grounding API Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure. Expanded queries: managed web search retrieval API licensed sources AI agents provider documentation Linkup Tavily Exa Brave Search Perplexity Grounding API | Linkup API licensed sources web search AI agents documentation | Tavily API search docs AI agents search depth include answer sources documentation | Exa API neural search contents docs source quality documentation

url

https://docs.linkup.so/

Query: https://docs.linkup.so/ Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure.

url

https://docs.linkup.so/

url

https://docs.linkup.so/pages/documentation/tutorials/filtering

Query: https://docs.linkup.so/pages/documentation/tutorials/filtering Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure.

Linkup licensed sources publisher partnerships API trusted sources licensed content

Query: Linkup licensed sources publisher partnerships API trusted sources licensed content Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure. Expanded queries: Linkup licensed sources publisher partnerships API trusted sources licensed content | site:linkup.so licensed sources publisher partners Linkup API | Linkup source coverage licensed content AI search API

Valyu API licensed sources web search AI agents docs

Query: Valyu API licensed sources web search AI agents docs Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure. Expanded queries: Valyu API licensed sources web search AI agents docs | Valyu Deepsearch API trusted sources documentation AI search | Parallel AI Search API premium data sources web research documentation | Perplexity Sonar API sources docs citations

url

https://docs.valyu.ai/deepsearch-api

Query: https://docs.valyu.ai/deepsearch-api Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure.

url

https://docs.valyu.ai/deepsearch-api

Linkup LangChain integration docs Python

Query: Linkup LangChain integration docs Python Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure. Expanded queries: Linkup LangChain integration docs Python | Linkup API integrations LangChain documentation

url

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

Query: https://docs.langchain.com/oss/python/integrations/providers/linkup Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure.

OpenAI Responses API web search tool citations documentation site:platform.openai.c...

Query: OpenAI Responses API web search tool citations documentation site:platform.openai.com/docs Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure. Expanded queries: OpenAI Responses API web search tool citations documentation site:platform.openai.com/docs

Google Vertex AI grounding with Google Search documentation citations API

Query: Google Vertex AI grounding with Google Search documentation citations API Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure. Expanded queries: Google Vertex AI grounding with Google Search documentation citations API | Google Gemini API grounding with Google Search documentation citations

You.com Search API AI web search documentation citations

Query: You.com Search API AI web search documentation citations Agent context: The current search and review nodes are stubs, and the state only has a product list plus review summaries. That means the retrieval provider should be able to return source metadata and snippets cleanly enough to add citations/source fields without fighting the existing structure. Expanded queries: You.com Search API AI web search documentation citations | you.com API search grounding docs AI agents trusted sources

agent_action

I’ve narrowed the tradeoff to AI-native search providers that return citations/snip...

I’ve narrowed the tradeoff to AI-native search providers that return citations/snippets or full extracted content, then checked whether they have source controls and a Python/LangChain path that matches this repo’s LangGraph setup.

command

/bin/bash -lc 'nl -ba src/nodes/search_agent.py'

command

/bin/bash -lc 'nl -ba src/nodes/search_agent.py'

exit code 0 Output: 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

command

/bin/bash -lc 'nl -ba pyproject.toml'

command

/bin/bash -lc 'nl -ba pyproject.toml'

exit code 0 Output: 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 ]

command

/bin/bash -lc 'nl -ba src/graph/state.py'

command

/bin/bash -lc 'nl -ba src/graph/state.py'

exit code 0 Output: 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 22 pros: List[str] 23 cons: List[str] 24 overall_sentiment: Literal["positive", "neutral", "negative"] 25 26 class AgentState(TypedDict): 27 user_query: str 28 29 # Agent Outputs 30 product_specs: ProductSpecs 31 product_list: List[Product] 32 product_review: List[ProductReviewSummary] 33 34 # Final Aggregated Recommendation 35 final_recommendation: str 36 37 # Control flags 38 step:Literal["specs_generation", "product_search", "review_analysis", "final_recommendation"]

command

/bin/bash -lc 'nl -ba src/graph/main_graph.py'

command

/bin/bash -lc 'nl -ba src/graph/main_graph.py'

exit code 0 Output: 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", router) 13 graph.add_node("specs_agent", specs_agent) 14 graph.add_node("search_agent", search_agent) 15 graph.add_node("review_agent", review_agent) 16 graph.add_node("comb_results", comb_results) 17 18 # Router is the fist node 19 graph.add_edge(START, "router") 20 21 # After riuter, we can fanout in parallerl to three agents 22 # langgraph parallelism is driven by "updates" to the three shared states 23 # When multiple nodes are reachable from the same previous node, 24 #

url

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

Query: https://www.linkup.so/content-partners Agent context: I’ve narrowed the tradeoff to AI-native search providers that return citations/snippets or full extracted content, then checked whether they have source controls and a Python/LangChain path that matches this repo’s LangGraph setup.

agent_action

**Recommendation** Choose **Linkup**. This repo is a small Python LangGraph/LangCha...

**Recommendation** Choose **Linkup**. This repo is a small Python LangGraph/LangChain-style product research agent: `search_agent` is currently a stub at [src/nodes/search_agent.py](/home/user/worktrees/attempt-822c4ec7-1eb2-45b1-aa8e-11265b502d86-cap-0/src/nodes/search_agent.py:5), the graph already has a dedicated search node at [src/graph/main_graph.py](/home/user/worktrees/attempt-822c4ec7-1eb2-45b1-aa8e-11265b502d86-cap-0/src/graph/main_graph.py:14), and dependencies already include LangChain/LangGraph at [pyproject.toml](/home/user/worktrees/attempt-822c4ec7-1eb2-45b1-aa8e-11265b502d86-cap-0/pyproject.toml:7). Linkup fits that shape directly via `langchain-linkup`. The decisive reason is source quality. Linkup’s LangChain docs explicitly describe it as connecting LLMs to the web **and Linkup Premium Partner sources**, which is closer to the “licensed/reputable sources, not generic scraped search results” requirement than Tavily, Exa, or Brave. Its own API supports `searchResults`, `sourcedAnswer`, and `structured` outputs, plus `standard`/`deep` retrieval modes, source snippets, inline citations, date controls, and domain include/exclude filters. Sources: [Linkup Search overv

result

Selected unknown

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.lock'"' | head -200"

exit 0

exit 127

/bin/bash -lc "find . -path './node_modules' -prune -o -path './.git' -prune -o -path './dist' -prune -o -path './build' -prune -o -type f | sed 's#"'^./##'"' | head -250"

exit 0

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

exit 0

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

exit 0

/bin/bash -lc "sed -n '1,240p' src/graph/state.py"

exit 0

/bin/bash -lc "sed -n '1,220p' src/nodes/review_agent.py"

exit 0

/bin/bash -lc "sed -n '1,220p' src/nodes/search_agent.py"

exit 0

/bin/bash -lc "sed -n '1,220p' src/graph/main_graph.py"

exit 0

/bin/bash -lc "sed -n '1,220p' src/nodes/specs_agent.py"

exit 0

/bin/bash -lc "sed -n '1,220p' src/nodes/combine_results.py && sed -n '1,160p' src/llm/llm_openai.py && sed -n '1,160p' src/llm/llm_groq.py"

exit 0

/bin/bash -lc "sed -n '1,200p' src/nodes/router.py"

exit 0

/bin/bash -lc "sed -n '1,200p' src/nodes/step_route.py"

exit 0

/bin/bash -lc "find . -maxdepth 3 -type f -not -path './.git/*' -not -path './__pycache__/*' -print -exec wc -l {} \\;"

exit 0

/bin/bash -lc 'nl -ba src/nodes/search_agent.py'

exit 0

/bin/bash -lc 'nl -ba pyproject.toml'

exit 0

/bin/bash -lc 'nl -ba src/graph/state.py'

exit 0

/bin/bash -lc 'nl -ba src/graph/main_graph.py'

exit 0

URLs Fetched

https://docs.linkup.so/

status not materialized

https://docs.linkup.so/pages/documentation/tutorials/filtering

status not materialized

https://docs.valyu.ai/deepsearch-api

status not materialized

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

status not materialized

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

status not materialized

Changed Files

Artifact only

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

Runs

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Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files

Runs

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Decision Summary

Trace Timeline

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Changed Files