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

competitor

Expected

Provider

Product

Selected

Provider

tavily

Product

Surface

provider_selection

Reason

codex selected tavily during first-pass provider discovery. No expected provider was set for this prompt.

Outcome

competitor

Trace Timeline

agent_action

Prompt-lab work item started

agent_action

Pinned repository checkout verified

c817eb6c5d727b0d2deaabb628c284823abf24f0

agent_action

I’ll first map the repository’s agent stack and integration points, then I’ll verif...

I’ll first map the repository’s agent stack and integration points, then I’ll verify current provider docs before making a recommendation. After that I’ll give a concise provider choice with the alternatives and sources consulted.

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-13b985c7-b96c-4337-b9e6-ed4524fde8fe-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 'package.json' -o -name 'pyproject.toml' -o -name 'requirements*.txt' -o -name 'README*' -o -name '.env*' \$ -print"

command

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

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

agent_action

`rg` is not installed in this environment, so I’m falling back to `find`/`grep`. Th...

`rg` is not installed in this environment, so I’m falling back to `find`/`grep`. The repo looks small so that should still be enough to identify the agent architecture quickly.

command

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

command

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

exit code 0

command

/bin/bash -lc "find . -type f -not -path './.git/*' -not -path './.agents/*' -not -path './.codex/*' -not -path './__pycache__/*' -maxdepth 5 -print"

command

/bin/bash -lc "find . -type f -not -path './.git/*' -not -path './.agents/*' -not -path './.codex/*' -not -path './__pycache__/*' -maxdepth 5 -print"

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

/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

command

exit code 0 Output: ./.gitignore:35:# Coverage directory used by tools like istanbul ./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_graph.py:26: graph.add_edge("router", "search_agent") ./src/graph/main_graph.py:27: graph.add_edge("router", "r

agent_action

The project is a Python LangGraph product-research agent using LangChain chat model...

The project is a Python LangGraph product-research agent using LangChain chat models, currently with no retrieval/search dependency. I’m reading the graph state and nodes now to see where a provider would fit cleanly.

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/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 '1,240p' 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/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 '1,220p' src/nodes/router.py"

command

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

exit code 0 Output: def router(): pass

command

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

command

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

exit code 0 Output: 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)from langchain_openai import ChatOpenAI llm_openai = ChatOpenAI( model = "gpt-4.1", temperature= 0.1 )

Tavily API docs search extract LangChain integration

Query: Tavily API docs search extract LangChain integration Agent context: The project is a Python LangGraph product-research agent using LangChain chat models, currently with no retrieval/search dependency. I’m reading the graph state and nodes now to see where a provider would fit cleanly. Expanded queries: Tavily API docs search extract LangChain integration | Exa API docs search contents LangChain integration | Brave Search API docs web search API | SerpApi Google Search API docs

OpenAI web search tool Responses API docs

Query: OpenAI web search tool Responses API docs Agent context: The project is a Python LangGraph product-research agent using LangChain chat models, currently with no retrieval/search dependency. I’m reading the graph state and nodes now to see where a provider would fit cleanly. Expanded queries: OpenAI web search tool Responses API docs | Firecrawl search extract API docs LangChain integration | Tavily Extract API docs crawl map LangChain Python integration | LangChain Python TavilySearch tool docs

url

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

Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The project is a Python LangGraph product-research agent using LangChain chat models, currently with no retrieval/search dependency. I’m reading the graph state and nodes now to see where a provider would fit cleanly.

url

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

site:docs.tavily.com LangChain Python TavilySearch langchain-tavily

Query: site:docs.tavily.com LangChain Python TavilySearch langchain-tavily Agent context: The project is a Python LangGraph product-research agent using LangChain chat models, currently with no retrieval/search dependency. I’m reading the graph state and nodes now to see where a provider would fit cleanly. Expanded queries: site:docs.tavily.com LangChain Python TavilySearch langchain-tavily | site:python.langchain.com TavilySearchResults LangChain Python Tavily

url

https://docs.tavily.com/documentation/integrations/langchain

Query: https://docs.tavily.com/documentation/integrations/langchain Agent context: The project is a Python LangGraph product-research agent using LangChain chat models, currently with no retrieval/search dependency. I’m reading the graph state and nodes now to see where a provider would fit cleanly.

Tavily pricing API credits search extract pricing 2026

Query: Tavily pricing API credits search extract pricing 2026 Agent context: The project is a Python LangGraph product-research agent using LangChain chat models, currently with no retrieval/search dependency. I’m reading the graph state and nodes now to see where a provider would fit cleanly. Expanded queries: Tavily pricing API credits search extract pricing 2026 | Exa pricing API search contents cost docs | Brave Search API pricing data for AI 2026 | Firecrawl pricing API search pricing

url

https://docs.tavily.com/guides/api-credits

Query: https://docs.tavily.com/guides/api-credits Agent context: The project is a Python LangGraph product-research agent using LangChain chat models, currently with no retrieval/search dependency. I’m reading the graph state and nodes now to see where a provider would fit cleanly.

agent_action

The integration point is straightforward: `search_agent` is still a stub, while `Ag...

The integration point is straightforward: `search_agent` is still a stub, while `AgentState` already has a `product_list` output with URL/rating/price fields. I’m checking provider docs for search, extracted page content, LangChain fit, freshness controls, and whether citations/source metadata are easy to preserve.

command

/bin/bash -lc "nl -ba src/nodes/search_agent.py | sed -n '1,80p'"

command

/bin/bash -lc "nl -ba src/nodes/search_agent.py | sed -n '1,80p'"

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 | sed -n '1,80p'"

command

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,80p'"

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 | sed -n '1,120p'"

command

/bin/bash -lc "nl -ba src/graph/state.py | sed -n '1,120p'"

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 | sed -n '1,100p'"

command

/bin/bash -lc "nl -ba src/graph/main_graph.py | sed -n '1,100p'"

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

Query: https://docs.tavily.com/documentation/api-reference/endpoint/search Agent context: The integration point is straightforward: `search_agent` is still a stub, while `AgentState` already has a `product_list` output with URL/rating/price fields. I’m checking provider docs for search, extracted page content, LangChain fit, freshness controls, and whether citations/source metadata are easy to preserve.

agent_action

**Provider I’d choose: Tavily.** This repo is a Python `langgraph`/LangChain produc...

**Provider I’d choose: Tavily.** This repo is a Python `langgraph`/LangChain product-research agent. The graph already has a `search_agent` node wired in, but [src/nodes/search_agent.py](/home/user/worktrees/attempt-13b985c7-b96c-4337-b9e6-ed4524fde8fe-cap-0/src/nodes/search_agent.py:5) is still a stub, and [AgentState](/home/user/worktrees/attempt-13b985c7-b96c-4337-b9e6-ed4524fde8fe-cap-0/src/graph/state.py:13) expects product records with `price`, `ratings`, and `url`. The best fit is a model-agnostic search/retrieval API that returns structured URLs plus clean page content, not just snippets. Tavily is the best managed provider here because its official `langchain-tavily` package supports Search, Extract, Map, Crawl, and Research, which fits the repo’s existing LangChain/LangGraph shape directly. Tavily Search returns JSON results with `title`, `url`, `content`, `score`, and optional `raw_content`; it also supports date filters, topics like `general`, `news`, and `finance`, domain filters, country boosting, and cleaned parsed HTML in markdown/text via `include_raw_content`. Its Extract tool can then retrieve clean content from chosen URLs for review and product-detail grounding

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

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

exit 0

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

exit 0

/bin/bash -lc "find . -type f -not -path './.git/*' -not -path './.agents/*' -not -path './.codex/*' -not -path './__pycache__/*' -maxdepth 5 -print"

exit 0

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

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/graph/state.py"

exit 0

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

exit 0

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

exit 0

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

exit 0

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

exit 0

/bin/bash -lc "nl -ba src/nodes/search_agent.py | sed -n '1,80p'"

exit 0

/bin/bash -lc "nl -ba pyproject.toml | sed -n '1,80p'"

exit 0

/bin/bash -lc "nl -ba src/graph/state.py | sed -n '1,120p'"

exit 0

/bin/bash -lc "nl -ba src/graph/main_graph.py | sed -n '1,100p'"

exit 0

URLs Fetched

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

status not materialized

https://docs.tavily.com/documentation/integrations/langchain

status not materialized

https://docs.tavily.com/guides/api-credits

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