alokiitd

Run

Alokiitdh/vector

Agent

Claude Opus 4.7

Status

completed

Trace

available

Telemetry

artifact only

Last Run

Jun 06, 04:51 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 to understand the agent architecture, ...

I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

pwd

Tool result summary: /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0 Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

agent_action

Read /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0

Tool result summary: EISDIR: illegal operation on a directory, read '/home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0' Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0

Tool result summary: total 208 drwxr-xr-x 3 user user 4096 Jun 6 04:48 . drwxr-xr-x 3 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 170 Jun 6 04:48 .git -rw-r--r-- 1 user user 1540 Jun 6 04:48 .gitignore -rw-r--r-- 1 user user 5 Jun 6 04:48 .python-version -rw-r--r-- 1 user user 0 Jun 6 04:48 README.md -rw-r--r-- 1 user user 20137 Jun 6 04:48 graph_1.png -rw-r--r-- 1 user user 303 Jun 6 04:48 pyproject.toml drwxr-xr-x 5 user user Agent context: I'll research the repository structure first to understand the agent architecture, 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 to understand the agent architecture, then evaluate web-search providers.

agent_action

Read .python-version

Tool result summary: 1 3.13 2 Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

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 structure first to understand the agent architecture, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src -type f -name "*.py" | head -20

Tool result summary: /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/graph/main_graph.py /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/graph/state.py /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/graph/testing.py /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/llm/llm_groq.py /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf- Agent context: I'll research the repository structure first to understand the agent architecture, 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 to understand the agent architecture, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src -type d

Tool result summary: /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/graph /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/graph/__pycache__ /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/llm /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/llm/__pycach Agent context: I'll research the repository structure first to understand the agent architecture, 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 to understand the agent architecture, 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 to understand the agent architecture, 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 to understand the agent architecture, 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 structure first to understand the agent architecture, 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 to understand the agent architecture, 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 to understand the agent architecture, 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 to understand the agent architecture, 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 to understand the agent architecture, 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 structure first to understand the agent architecture, 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 to understand the agent architecture, 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 to understand the agent architecture, then evaluate web-search providers.

command

grep -A 2 "\[\[package\]\]" /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/uv.lock | head -80

Tool result summary: [[package]] name = "annotated-types" version = "0.7.0" -- [[package]] name = "anyio" version = "4.11.0" -- [[package]] name = "asttokens" version = "3.0.1" -- [[package]] name = "certifi" version = "2025.11.12" -- [[package]] name = "charset-normalizer" version = "3.4.4" -- [[package]] name = "colorama" version = "0.4.6" -- [[package]] name = "decorator" version = "5.2.1" -- [[package]] name = "distro" version = "1.9 Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

Tool result summary: name = "groq" name = "langchain-core" name = "langchain-groq" { name = "groq" }, { name = "langchain-core" }, name = "langchain-openai" { name = "langchain-core" }, { name = "openai" }, { name = "langchain-core" }, { name = "langchain-core" }, { name = "langchain-core" }, name = "openai" { name = "langchain-groq" }, { name = "langchain-openai" }, { name = "langchain-groq", specifier = ">=1.0.1" }, { name = "langchain Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

grep "name = " /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/uv.lock | sort | uniq

Tool result summary: { name = "annotated-types" }, { name = "anyio" }, { name = "asttokens" }, { name = "certifi" }, { name = "charset-normalizer" }, { name = "colorama", marker = "sys_platform == 'win32'" }, { name = "decorator" }, { name = "distro" }, { name = "executing" }, { name = "groq" }, { name = "h11" }, { name = "httpcore" }, { name = "httpx" }, { name = "idna" }, { name = "ipython" }, { name = "ipython", specifier = ">=9.7.0" Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

find /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0 -type f \( -name "*.py" -o -name "*.json" -o -name "*.yaml" -o -name "*.yml" -o -name "*.env*" -o -name "config*" \) 2>/dev/null | grep -v __pycache__ | grep -v ".pyc" | sort

agent_action

Read src/nodes/step_route.py

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 to understand the agent architecture, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/

Tool result summary: total 24 drwxr-xr-x 5 user user 4096 Jun 6 04:48 . drwxr-xr-x 3 user user 4096 Jun 6 04:48 .. drwxr-xr-x 3 user user 4096 Jun 6 04:48 graph drwxr-xr-x 3 user user 4096 Jun 6 04:48 llm -rw-r--r-- 1 user user 100 Jun 6 04:48 main.py drwxr-xr-x 3 user user 4096 Jun 6 04:48 nodes Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/nodes/

Tool result summary: total 32 drwxr-xr-x 3 user user 4096 Jun 6 04:48 . drwxr-xr-x 5 user user 4096 Jun 6 04:48 .. drwxr-xr-x 2 user user 4096 Jun 6 04:48 __pycache__ -rw-r--r-- 1 user user 28 Jun 6 04:48 combine_results.py -rw-r--r-- 1 user user 28 Jun 6 04:48 review_agent.py -rw-r--r-- 1 user user 22 Jun 6 04:48 router.py -rw-r--r-- 1 user user 170 Jun 6 04:48 search_agent.py -rw-r--r-- 1 user user 1092 Jun 6 04:48 specs_agent.py -rw-r Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

ls -la /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/llm/

Tool result summary: total 20 drwxr-xr-x 3 user user 4096 Jun 6 04:48 . drwxr-xr-x 5 user user 4096 Jun 6 04:48 .. drwxr-xr-x 2 user user 4096 Jun 6 04:48 __pycache__ -rw-r--r-- 1 user user 372 Jun 6 04:48 llm_groq.py -rw-r--r-- 1 user user 111 Jun 6 04:48 llm_openai.py Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

git log --all --oneline

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 to understand the agent architecture, 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 to understand the agent architecture, then evaluate web-search providers.

command

git log --all --graph --decorate --oneline

Tool result summary: * c817eb6 (HEAD, main) Spec Agent added * 23acfea Vector Started .. Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

agent_action

Grep /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0

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

agent_action

Grep src

Tool result summary: src/nodes/specs_agent.py:20: response = llm_product_spec.invoke([system_prompt, user_query]) src/llm/llm_groq.py:8: groq_api_key= os.environ["GROQ_API_KEY"], src/llm/llm_groq.py:13:# result = llm.invoke("Tell me about Groq and its AI accelerators.") Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

head -100 /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-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 structure first to understand the agent architecture, then evaluate web-search providers.

agent_action

Read README.md

command

find /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0 -type f \( -name "*.txt" -o -name "*.md" -o -name "*.cfg" -o -name "*.ini" -o -name "*.toml" \) 2>/dev/null

Tool result summary: /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/README.md /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/pyproject.toml Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

grep -A 5 'name = "httpx"' /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/uv.lock

Tool result summary: { name = "httpx" }, { name = "pydantic" }, { name = "sniffio" }, { name = "typing-extensions" }, ] sdist = { url = "https://files.pythonhosted.org/packages/27/83/932f29aee838dac4ab77d5249c886159f88e157f925de214681cd54b0a1c/groq-0.36.0.tar.gz", hash = "sha256:766fa1ae34918552c6488767b16d4bd0ee3f0fb7809cd9f7760c90d13f32c63a", size = 144596, upload-time = "2025-11-20T02:30:05.446Z" } -- name = "httpx" version = "0.28.1" Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

grep -A 5 'name = "requests"' /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/uv.lock | head -20

Tool result summary: { name = "requests" }, { name = "requests-toolbelt" }, { name = "zstandard" }, ] sdist = { url = "https://files.pythonhosted.org/packages/03/e8/c91f5e34aa0e7cba90d31e3915866d655507732d0f67dd3986dbaf8eff63/langsmith-0.4.46.tar.gz", hash = "sha256:0b73d47ebd0a27ea10edec1717f36f6a865cb9ffc7f4e6a419e2cea1ab3c5b1e", size = 986987, upload-time = "2025-11-21T23:00:04.985Z" } wheels = [ -- name = "requests" version = "2.32.5 Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

grep -r "import" /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src --include="*.py" 2>/dev/null

Tool result summary: /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/graph/main_graph.py:from langgraph.graph import StateGraph, START, END /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/graph/main_graph.py:from src.graph.state import AgentState /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/src/graph/main_graph.py:from src.nodes.router import router /home/ Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

command

cat /home/user/worktrees/attempt-8f7d6ff2-fdde-4cdf-84d7-b7eefe9515fb-cap-0/pyproject.toml

Tool result summary: [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", ] Agent context: I'll research the repository structure first to understand the agent architecture, then evaluate web-search providers.

agent_action

This is a LangGraph/LangChain Python product-research agent with a stubbed `search_...

This is a LangGraph/LangChain Python product-research agent with a stubbed `search_agent`. Now let me consult provider documentation to make a grounded recommendation.

agent_action

I'll research the leading managed web-search/retrieval providers, focusing on LangC...

I'll research the leading managed web-search/retrieval providers, focusing on LangChain/LangGraph fit.

best web search API for LLM agents 2026 Tavily Exa Brave Serper comparison LangChain

Query: best web search API for LLM agents 2026 Tavily Exa Brave Serper comparison LangChain 10 search result links captured Tool result summary: Based on the search results, here's a comprehensive comparison of the best web search APIs for LLM agents in 2026: ## Top Performers An independent benchmark by AIMultiple evaluated 8 search APIs and found the top four providers statistically indistinguishable: Brave Search (14.89), Firecrawl (14.58), Exa (14.39), and Parallel Search Pro (14.21). However, Brave consistently outperformed Tavily by about 1 point, a gap Agent context: I'll research the leading managed web-search/retrieval providers, focusing on LangChain/LangGraph fit.

langchain-tavily integration LangGraph web search tool documentation 2026

Query: langchain-tavily integration LangGraph web search tool documentation 2026 8 search result links captured Tool result summary: Based on the search results, here's what I found about the LangChain-Tavily integration with LangGraph: ## Overview Tavily's Search API is a search engine built specifically for AI agents (LLMs), delivering real-time, accurate, and factual results at speed. The integration lives in the langchain-tavily package. ## Installation and Setup To get started, you need to install langchain-tavily with pip install -qU langcha Agent context: I'll research the leading managed web-search/retrieval providers, focusing on LangChain/LangGraph fit.

agent_action

Now let me consult the actual documentation for the top candidates — LangChain's Ta...

Now let me consult the actual documentation for the top candidates — LangChain's Tavily integration, Exa, and Brave Search API.

url

https://docs.langchain.com/oss/python/integrations/tools/tavily_search

Fetch prompt: Extract: how the langchain-tavily package is installed and used as a tool in a LangChain/LangGraph agent. What does the search return (content, raw content, extraction)? What parameters does it support? Any mention of a retrieve/extract endpoint? Pricing or free tier mentions? Tool result summary: # Tavily Search Integration in LangChain ## Installation & Setup The package is installed via `pip install -qU langchain-tavily`. An API key from [Tavily's site](https://app.tavily.com/sign-in) is required and set as the `TAVILY_API_KEY` environment variable. ## Search Returns The tool delivers several data types: "title, URL, content snippet, raw_content, answer, images". However, not all are always included—certain Agent context: Now let me consult the actual documentation for the top candidates — LangChain's Tavily integration, Exa, and Brave Search API.

url

https://github.com/tavily-ai/langchain-tavily

Fetch prompt: Extract: what tools does langchain-tavily provide (TavilySearch, TavilyExtract, TavilyCrawl, TavilyMap)? How are they used in a LangGraph agent? What does each do? Any code example showing tool binding to an agent. Tool result summary: # LangChain-Tavily Tools Overview ## Available Tools The langchain-tavily package provides four primary tools: 1. **TavilySearch** - "allows you to complete search queries using Tavily's Search API endpoint" 2. **TavilyExtract** - enables content extraction from specific URLs 3. **TavilyCrawl** - "allows you to crawl websites using Tavily's Crawl API endpoint" 4. **TavilyMap** - creates a "structured map of website U Agent context: Now let me consult the actual documentation for the top candidates — LangChain's Tavily integration, Exa, and Brave Search API.

url

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

Fetch prompt: Extract: what does the Exa search/retrieval API offer for LLM agents? Neural/semantic search, contents retrieval, highlights. Does it have a LangChain integration? Pricing/free tier. How is it used in Python? 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: "Extract: what does th Agent context: Now let me consult the actual documentation for the top candidates — LangChain's Tavily integration, Exa, and Brave Search API.

agent_action

I have strong Tavily detail. Let me get Exa's actual docs and Brave's to round out ...

I have strong Tavily detail. Let me get Exa's actual docs and Brave's to round out the comparison.

url

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

Fetch prompt: Extract: what does the Exa search/retrieval API offer for LLM agents? Neural/semantic search, contents retrieval, highlights, answer endpoint. Does it have a LangChain integration? Pricing/free tier. How is it used in Python? Tool result summary: # Exa Search API for LLM Agents Based on the documentation provided, here's what Exa offers: ## Core Capabilities Exa provides four main functionalities for AI agents: 1. **Neural/Semantic Search**: The "/search" endpoint uses "embeddings-based search and other intelligent methods" to find relevant webpages. 2. **Contents Retrieval**: The "/contents" endpoint delivers "clean, up-to-date, parsed HTML from Exa search r Agent context: I have strong Tavily detail. Let me get Exa's actual docs and Brave's to round out the comparison.

url

https://brave.com/search/api/

Fetch prompt: Extract: what does the Brave Search API offer? Independent index, freshness/goggles, AI/LLM grounding features, pricing/free tier, latency. Does it have a LangChain integration? Is there a content-extraction component or just SERP results? Tool result summary: # Brave Search API Overview ## Core Offerings The Brave Search API provides: - **Independent Index**: Powered by Brave's own web crawler with "over 30 billion pages, kept fresh by over 100 million page updates every day" - **AI/LLM Optimization**: Specialized endpoints including an "LLM Context" endpoint that delivers grounded search results with citations - **Custom Filtering**: "Search Goggles" feature enabling dom Agent context: I have strong Tavily detail. Let me get Exa's actual docs and Brave's to round out the comparison.

agent_action

## Recommendation: Tavily (`langchain-tavily`) For grounding this agent in current ...

## Recommendation: Tavily (`langchain-tavily`) For grounding this agent in current web information, I'd use **Tavily**. **Why it's the best fit for *this* repo:** The codebase is Python 3.13 on **LangGraph + LangChain** with a stubbed `search_agent.py`. Tavily ships an official `langchain-tavily` package whose `TavilySearch` tool binds directly into LangGraph's prebuilt agent pattern — it drops into the existing stub with minimal glue and matches how `specs_agent` already uses LangChain primitives. Critically, it returns **LLM-ready extracted content plus a synthesized answer and citations in a single call** (search → scrape → filter → rank), which is exactly what "ground answers in current web info" needs. Its `TavilyExtract`/`TavilyCrawl` tools also cover the planned `review_agent` (pulling product-page/review content). Free tier (1,000 searches/month) is enough to build on. **Main alternatives considered:** - **Exa** — neural/semantic search with `/contents`, `/answer`, `/research`. Excellent for research-grade RAG and has a LangChain integration; close second. Slightly more setup to assemble search+contents. - **Brave Search API** — independent 30B-page index, low latency (~669

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URLs Fetched

https://www.firecrawl.dev/blog/best-web-search-apis

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https://www.sona.com/blog/llm-search-api-best-options-for-developers-in-2026

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https://aimultiple.com/agentic-search

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https://brave.com/learn/best-search-api-2026/

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https://o-mega.ai/articles/top-10-ai-search-apis-for-agents-2026

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https://www.scrapingbee.com/blog/best-ai-search-api/

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https://michaellivs.com/blog/web-search-for-agents-2026/

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https://brightdata.com/blog/web-data/best-research-apis

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https://nomadlab.cc/blog/2026/05/best-ai-search-apis-2026-tavily-exa-serper-firecrawl

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https://www.firecrawl.dev/blog/best-ai-search-engines-agents

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https://docs.langchain.com/oss/python/integrations/tools/tavily_search

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https://www.youtube.com/watch?v=JtQEjfzE_Ko

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https://python.langchain.com/api_reference/community/tools/langchain_community.tools.tavily_search.tool.TavilySearchResults.html

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https://patotricks15.medium.com/building-a-smart-search-agent-with-langchain-and-tavily-search-6838076e35f1

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https://deepwiki.com/wanrengang/langchain-agent-framework/6.4-web-search-tools

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https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/integrations/tools/tavily_search.ipynb

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https://apxml.com/courses/building-llm-apps-with-langchain/chapter-6-developing-autonomous-agents/practice-web-searching-agent

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https://github.com/tavily-ai/langchain-tavily

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

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

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https://brave.com/search/api/

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