alokiitd

Run

Alokiitdh/vector

Agent

Claude Opus 4.7

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:53 AM

Decision Summary

competitor

Expected

Provider

Product

Selected

Provider

tavily

Product

Surface

provider_selection

Reason

claude-code 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 research the repository structure first, then evaluate web-search providers.

command

find . -type f -name "*.md" -o -name "*.json" -o -name "*.toml" -o -name "*.yaml" -o -name "*.yml" | head -30

Tool result summary: ./README.md ./pyproject.toml Agent context: I'll research the repository structure first, 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 structure first, 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 structure first, then evaluate web-search providers.

command

git log --oneline -20

Tool result summary: c817eb6 Spec Agent added 23acfea Vector Started .. Agent context: I'll research the repository structure first, then evaluate web-search providers.

command

find . -type f -name "*.py" | head -20

Tool result summary: ./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 Agent context: I'll research the repository structure first, then evaluate web-search providers.

command

git log --oneline -1 --format="%H %s" | cat && git diff HEAD~1 HEAD --stat

Tool result summary: c817eb6c5d727b0d2deaabb628c284823abf24f0 Spec Agent added graph_1.png | Bin 0 -> 20137 bytes pyproject.toml | 2 + src/graph/__pycache__/main_graph.cpython-313.pyc | Bin 0 -> 1618 bytes src/graph/__pycache__/state.cpython-313.pyc | Bin 0 -> 1964 bytes src/graph/__pycache__/testing.cpython-313.pyc | Bin 0 -> 674 bytes src/graph/main_graph.py | 40 +++ src/graph/testing.py | 15 + src/llm/__pycache__/llm_groq.cpython-313. Agent context: I'll research the repository structure first, 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 structure first, 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 structure first, 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 structure first, 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 structure first, then evaluate web-search providers.

agent_action

Read src/nodes/search_agent.py

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, 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 structure first, 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 structure first, 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 structure first, 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 structure first, then evaluate web-search providers.

command

find . -name ".env*" -o -name "config*" -o -name "*secret*" | grep -v ".git" | grep -v "__pycache__"

Tool result summary: (Bash completed with no output) Agent context: I'll research the repository structure first, then evaluate web-search providers.

command

git log --all --oneline

Tool result summary: c817eb6 Spec Agent added 23acfea Vector Started .. Agent context: I'll research the repository structure first, then evaluate web-search providers.

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, then evaluate web-search providers.

command

git show c817eb6 --stat

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 graph_1.png | Bin 0 -> 20137 bytes pyproject.toml | 2 + src/graph/__pycache__/main_graph.cpython-313.pyc | Bin 0 -> 1618 bytes src/graph/__pycache__/state.cpython-313.pyc | Bin 0 -> 1964 bytes src/graph/__pycache__/testing.cpython-313.pyc | Bin 0 -> 674 byte Agent context: I'll research the repository structure first, then evaluate web-search providers.

command

ls -la

Tool result summary: total 208 drwxr-xr-x 3 user user 4096 Jun 6 04:51 . drwxr-xr-x 3 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, 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 structure first, then evaluate web-search providers.

command

find . -type f \( -name "*.txt" -o -name "requirements*" \) | head -10

Tool result summary: (Bash completed with no output) Agent context: I'll research the repository structure first, then evaluate web-search providers.

command

grep -r "tool" --include="*.py" | head -20

Tool result summary: (Bash completed with no output) Agent context: I'll research the repository structure first, then evaluate web-search providers.

command

grep -r "def " --include="*.py" | grep -v "__pycache__"

Tool result summary: src/graph/main_graph.py:def build_graph(): src/main.py:def main(): src/nodes/combine_results.py:def comb_results(): src/nodes/review_agent.py:def review_agent(): src/nodes/router.py:def router(): src/nodes/search_agent.py:def search_agent(): src/nodes/specs_agent.py:def specs_agent(state: AgentState): Agent context: I'll research the repository structure first, then evaluate web-search providers.

command

grep -r "import" --include="*.py" | grep -v "__pycache__" | sort | uniq

Tool result summary: src/graph/main_graph.py:from langgraph.graph import StateGraph, START, END src/graph/main_graph.py:from src.graph.state import AgentState src/graph/main_graph.py:from src.nodes.combine_results import comb_results src/graph/main_graph.py:from src.nodes.review_agent import review_agent src/graph/main_graph.py:from src.nodes.router import router src/graph/main_graph.py:from src.nodes.search_agent import search_agent src Agent context: I'll research the repository structure first, then evaluate web-search providers.

agent_action

Let me verify the key files directly before researching providers.

agent_action

Read src/nodes/search_agent.py

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: Let me verify the key files directly before researching providers.

agent_action

Now I understand the architecture: a LangGraph product-research agent where `search...

Now I understand the architecture: a LangGraph product-research agent where `search_agent.py` is a stub needing live product/price data. Let me research providers and consult their docs.

agent_action

I'll consult provider documentation directly for the top candidates, focusing on fr...

I'll consult provider documentation directly for the top candidates, focusing on freshness/recency capabilities and LangChain integration.

url

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

Fetch prompt: Extract details about: search freshness/recency controls (time range, days, topic=news), real-time coverage, pricing, and whether results include scored content suitable for LLM agents. List any parameters related to recency. Tool result summary: # Tavily Search: Recency Controls & LLM-Suitability ## Freshness/Recency Parameters **Time Range Options:** - `time_range`: Filters by publish/update date with values: `day`, `week`, `month`, `year` (or abbreviated: `d`, `w`, `m`, `y`) - `start_date` & `end_date`: Precise date filtering in YYYY-MM-DD format **Real-Time Coverage:** The `topic` parameter supports `"news"` specifically for "retrieving real-time updates, Agent context: I'll consult provider documentation directly for the top candidates, focusing on freshness/recency capabilities and LangChain integration.

url

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

Fetch prompt: Extract details about: date/recency filters (start_published_date, end_published_date), live crawling / freshness, real-time coverage, pricing, and suitability for LLM agents grounding answers on current info. Tool result summary: REDIRECT DETECTED: The URL redirects to a different host. Original URL: https://docs.exa.ai/reference/search Redirect URL: https://exa.ai/docs/reference/search 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/search" - prompt: "Extract details about: date/recency filters (sta Agent context: I'll consult provider documentation directly for the top candidates, focusing on freshness/recency capabilities and LangChain integration.

url

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

Fetch prompt: Extract details about the Sonar API: search_recency_filter, real-time web access, citations, freshness of data, and pricing. Is it a search API or an LLM-answer API? Tool result summary: # Perplexity Sonar API Analysis ## API Classification The Sonar API is a **hybrid LLM-answer API with integrated web search capabilities**, not a pure search API. It generates conversational responses while optionally incorporating real-time web data. ## Key Features **Search Recency Filter:** Allows filtering by publication date using options like "hour," "day," "week," "month," or "year" to control result freshness Agent context: I'll consult provider documentation directly for the top candidates, focusing on freshness/recency capabilities and LangChain integration.

agent_action

Let me consult Exa (redirected) and Brave Search API docs to complete the comparison.

url

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

Fetch prompt: Extract details about: date/recency filters (start_published_date, end_published_date), live crawling / freshness, real-time coverage, pricing, and suitability for LLM agents grounding answers on current info. Tool result summary: # Exa Search API: Recency, Freshness & LLM Grounding Suitability ## Date/Recency Filters The API provides granular temporal filtering through four parameters: - **`startPublishedDate` / `endPublishedDate`**: Filter by content publication date in ISO 8601 format. Results include "links with a published date after/before this" threshold. - **`startCrawlDate` / `endCrawlDate`**: Filter by discovery date. "Crawl date ref Agent context: Let me consult Exa (redirected) and Brave Search API docs to complete the comparison.

url

https://api-dashboard.search.brave.com/app/documentation/web-search/get-started

Fetch prompt: Extract details about: freshness parameter, recency controls, real-time index coverage, news coverage, pricing, and suitability for grounding LLM answers on current information. Tool result summary: # Brave Web Search API: Freshness & LLM Grounding Analysis ## Freshness Parameters The API offers date-based filtering through the `freshness` parameter with these options: - **"Last 24 Hours"** (`pd`) - latest updates - **"Last 7 Days"** (`pw`) - weekly trends - **"Last 31 Days"** (`pm`) - monthly developments - **"Last Year"** (`py`) - annual scope - Custom date ranges (e.g., `2022-04-01to2022-07-30`) ## Index Cove Agent context: Let me consult Exa (redirected) and Brave Search API docs to complete the comparison.

agent_action

## Recommendation: Tavily **Repo context:** A LangGraph product-research agent (Gro...

## Recommendation: Tavily **Repo context:** A LangGraph product-research agent (Groq + OpenAI via LangChain). The `search_agent.py` stub needs to populate `Product` objects (current price, ratings, URL) — so live, fresh retrieval that an LLM can ground on is the core need. **Choice — Tavily** because it fits this stack on every axis that matters: - **Agent-native + LangChain integration.** Tavily bills itself as "a search engine for LLM agents" and ships a first-party `langchain-tavily` tool, so it drops into the existing `bind_tools()`/LangGraph pattern with minimal glue and matches the current `os.environ` + `dotenv` secrets convention. - **Recency/freshness (weighted heavily).** `time_range` (day/week/month/year), explicit `start_date`/`end_date`, and `topic="news"` for real-time current events. Returns cleaned, relevance-**scored** content (`include_raw_content`, `include_answer`) ready for grounding. - Simple per-credit pricing and a free tier. **Alternatives considered & docs I actually consulted:** - **Perplexity Sonar** (consulted `docs.perplexity.ai`) — strong `search_recency_filter`, but it's an *LLM-answer* API, not a retrieval primitive. The repo already has its own LLM

result

Selected openai

Commands

find . -type f -name "*.md" -o -name "*.json" -o -name "*.toml" -o -name "*.yaml" -o -name "*.yml" | head -30

exit code not materialized

git log --oneline -20

exit code not materialized

find . -type f -name "*.py" | head -20

exit code not materialized

git log --oneline -1 --format="%H %s" | cat && git diff HEAD~1 HEAD --stat

exit code not materialized

find . -name ".env*" -o -name "config*" -o -name "*secret*" | grep -v ".git" | grep -v "__pycache__"

exit code not materialized

git log --all --oneline

exit code not materialized

git show 23acfea --stat

exit code not materialized

git show c817eb6 --stat

exit code not materialized

ls -la

exit code not materialized

find . -type f \( -name "*.txt" -o -name "requirements*" \) | head -10

exit code not materialized

grep -r "tool" --include="*.py" | head -20

exit code not materialized

grep -r "def " --include="*.py" | grep -v "__pycache__"

exit code not materialized

grep -r "import" --include="*.py" | grep -v "__pycache__" | sort | uniq

exit code not materialized

URLs Fetched

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

status not materialized

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

status not materialized

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

status not materialized

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

status not materialized

https://api-dashboard.search.brave.com/app/documentation/web-search/get-started

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