alokiitd

Run

Alokiitdh/vector

Agent

GPT-5.3-Codex

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:56 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 existing agent is structured and where retrieval would plug ...

I’ll first map how the existing agent is structured and where retrieval would plug in, then I’ll verify current provider capabilities against primary docs before making a recommendation.

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.svg'"' | head -200"

command

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.svg'"' | head -200"

exit code 0 Output: /home/user/worktrees/attempt-240154db-5777-4994-8b82-45c0fd30746d-cap-0 /bin/bash: line 1: rg: command not found

command

command

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

command

/bin/bash -lc "find . -maxdepth 2 -type f \$ -name 'README*' -o -name 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'go.mod' -o -name 'Cargo.toml' -o -name '.env.example' \$ -print"

command

exit code 0 Output: ./README.md ./pyproject.toml

agent_action

`rg` is not available in this workspace, so I’m falling back to `find`/`grep`. The ...

`rg` is not available in this workspace, so I’m falling back to `find`/`grep`. The repo looks small so far: README plus a Python project file.

command

/bin/bash -lc "find . -type f -not -path './.git/*' -not -path './.agents/*' -not -path './.codex/*' -not -path '*/__pycache__/*' | sort"

command

/bin/bash -lc "find . -type f -not -path './.git/*' -not -path './.agents/*' -not -path './.codex/*' -not -path '*/__pycache__/*' | sort"

exit code 0 Output: ./.git ./.gitignore ./.python-version ./README.md ./graph_1.png ./pyproject.toml ./src/graph/main_graph.py ./src/graph/state.py ./src/graph/testing.py ./src/llm/llm_groq.py ./src/llm/llm_openai.py ./src/main.py ./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

/bin/bash -lc "sed -n '1,240p' README.md"

command

/bin/bash -lc "sed -n '1,240p' README.md"

exit code 0

command

command

/bin/bash -lc "sed -n '1,240p' pyproject.toml"

command

/bin/bash -lc "sed -n '1,240p' 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

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: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("router", "specs_agent") ./src/graph/main_grap

agent_action

This is a LangGraph-style Python agent with separate nodes for specs extraction, pr...

This is a LangGraph-style Python agent with separate nodes for specs extraction, product search, review analysis, and result combination. I’m reading those nodes now to understand whether the web provider should be a search API, a crawler/extractor, or a full answer API.

command

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

command

/bin/bash -lc "sed -n '1,240p' 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 '1,260p' src/graph/state.py"

command

/bin/bash -lc "sed -n '1,260p' 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 '1,260p' src/nodes/search_agent.py"

command

/bin/bash -lc "sed -n '1,260p' 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 '1,260p' src/nodes/specs_agent.py"

command

/bin/bash -lc "sed -n '1,260p' 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 '1,260p' src/nodes/combine_results.py"

command

/bin/bash -lc "sed -n '1,260p' src/nodes/combine_results.py"

exit code 0 Output: def comb_results(): pass

command

/bin/bash -lc "sed -n '1,260p' src/nodes/review_agent.py"

command

/bin/bash -lc "sed -n '1,260p' src/nodes/review_agent.py"

exit code 0 Output: def review_agent(): pass

command

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

command

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

exit code 0

command

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

command

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

exit code 0 Output: def router(): pass

OpenAI web search tool citations Responses API official docs

Query: OpenAI web search tool citations Responses API official docs Agent context: This is a LangGraph-style Python agent with separate nodes for specs extraction, product search, review analysis, and result combination. I’m reading those nodes now to understand whether the web provider should be a search API, a crawler/extractor, or a full answer API. Expanded queries: OpenAI web search tool citations Responses API official docs | Exa API search and contents official docs livecrawl contents citations | Tavily Search API official docs search depth raw content citations | Brave Search API official docs web search snippets

Google Custom Search JSON API official documentation snippets search results

Query: Google Custom Search JSON API official documentation snippets search results Agent context: This is a LangGraph-style Python agent with separate nodes for specs extraction, product search, review analysis, and result combination. I’m reading those nodes now to understand whether the web provider should be a search API, a crawler/extractor, or a full answer API. Expanded queries: Google Custom Search JSON API official documentation snippets search results | SerpApi Google Search API official documentation organic_results snippet source | Firecrawl search API official docs web search scrape markdown | Linkup API official docs search answer citations sources

url

https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses

Query: https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses Agent context: This is a LangGraph-style Python agent with separate nodes for specs extraction, product search, review analysis, and result combination. I’m reading those nodes now to understand whether the web provider should be a search API, a crawler/extractor, or a full answer API.

url

https://docs.linkup.so/

Query: https://docs.linkup.so/ Agent context: This is a LangGraph-style Python agent with separate nodes for specs extraction, product search, review analysis, and result combination. I’m reading those nodes now to understand whether the web provider should be a search API, a crawler/extractor, or a full answer API.

url

https://docs.linkup.so/pages/documentation/endpoints/search/overview

Query: https://docs.linkup.so/pages/documentation/endpoints/search/overview Agent context: This is a LangGraph-style Python agent with separate nodes for specs extraction, product search, review analysis, and result combination. I’m reading those nodes now to understand whether the web provider should be a search API, a crawler/extractor, or a full answer API.

'citations' in https://exa.ai/docs/reference/search

Query: 'citations' in https://exa.ai/docs/reference/search Agent context: This is a LangGraph-style Python agent with separate nodes for specs extraction, product search, review analysis, and result combination. I’m reading those nodes now to understand whether the web provider should be a search API, a crawler/extractor, or a full answer API.

url

https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results

Query: https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results Agent context: This is a LangGraph-style Python agent with separate nodes for specs extraction, product search, review analysis, and result combination. I’m reading those nodes now to understand whether the web provider should be a search API, a crawler/extractor, or a full answer API.

agent_action

**Provider Chosen** I’d choose **Linkup**, specifically: - `Search` with `depth="de...

**Provider Chosen** I’d choose **Linkup**, specifically: - `Search` with `depth="deep"` and `outputType="searchResults"` for agent-internal grounding. - `sourcedAnswer` only when directly returning a cited answer to a user. - `Research` for high-risk product comparisons where async latency is acceptable. Why: this repo is a product research agent with `search_agent`, `review_agent`, and `comb_results` currently stubbed out, so it needs source retrieval more than a generic “answer API” ([src/nodes/search_agent.py](/home/user/worktrees/attempt-240154db-5777-4994-8b82-45c0fd30746d-cap-0/src/nodes/search_agent.py:5)). Linkup’s docs are unusually aligned with that: ranked sources plus snippets, cited answers, structured output, source/domain filtering, date controls, multi-step search/scrape, and a separate deeper research mode. Their docs also publish benchmark claims for accuracy-oriented QA, which matters here more than latency or setup cost. Sources: [Linkup Search overview](https://docs.linkup.so/pages/documentation/endpoints/search/overview), [Linkup Research overview](https://docs.linkup.so/pages/documentation/endpoints/research/overview), [Linkup source filtering](https://docs.l

result

Selected unknown

Commands

/bin/bash -lc "pwd && rg --files -g '"'!*node_modules*'"' -g '"'!*.png'"' -g '"'!*.jpg'"' -g '"'!*.jpeg'"' -g '"'!*.gif'"' -g '"'!*.svg'"' | head -200"

exit 0

exit 127

exit 0

/bin/bash -lc "find . -type f -not -path './.git/*' -not -path './.agents/*' -not -path './.codex/*' -not -path '*/__pycache__/*' | sort"

exit 0

/bin/bash -lc "sed -n '1,240p' README.md"

exit 0

/bin/bash -lc "sed -n '1,240p' pyproject.toml"

exit 0

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

exit 0

/bin/bash -lc "sed -n '1,260p' src/graph/state.py"

exit 0

/bin/bash -lc "sed -n '1,260p' src/nodes/search_agent.py"

exit 0

/bin/bash -lc "sed -n '1,260p' src/nodes/specs_agent.py"

exit 0

/bin/bash -lc "sed -n '1,260p' src/nodes/combine_results.py"

exit 0

/bin/bash -lc "sed -n '1,260p' src/nodes/review_agent.py"

exit 0

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

exit 0

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

exit 0

URLs Fetched

https://platform.openai.com/docs/guides/tools-web-search?api-mode=responses

status not materialized

https://docs.linkup.so/

status not materialized

https://docs.linkup.so/pages/documentation/endpoints/search/overview

status not materialized

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

status not materialized

https://help.tavily.com/articles/3960993389-does-tavily-search-api-provide-citations-for-its-results

status not materialized

Changed Files

Artifact only

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

Runs

Find

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files

Runs

Run

Decision Summary

Trace Timeline

Commands

URLs Fetched

Changed Files