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Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40ddd7dd21d4-cap-1/README.md Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 A simple local Retrieval-Augmented Generation (RAG) chatbot that can answer to questions by acquiring information from personal pdf documents. 2 3 (please, if you find this content useful please consider leaving a star ⭐) 4 5 ## What is Retrieval-Augmented Generation (RAG)? 6 <div style="text-align: center;"> 7 <img src="https://miro.medium.com/v2/resize:fit:1400/1*J7vyY3EjY46AlduMvr9FbQ.png" alt="rag_pipeline" wid Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: total 80 drwxr-xr-x 8 user user 4096 Jun 6 04:48 . drwxr-xr-x 4 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 376 Jun 6 04:48 .env -rw-r--r-- 1 user user 170 Jun 6 04:48 .git -rw-r--r-- 1 user user 50 Jun 6 04:48 .gitignore -rw-r--r-- 1 user user 5123 Jun 6 04:48 README.md -rw-r--r-- 1 user user 4119 Jun 6 04:48 app.py drwxr-xr-x 2 user user 4096 Jun 6 04:48 data drwxr-xr-x 2 user user 4096 Jun 6 04:48 embeddi Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 pypdf 2 langchain==1.1.3 3 langchain-core==1.2.5 4 langchain-community==0.4.1 5 langchain-chroma==1.0.0 6 langchain-text-splitters==1.0.0 7 numpy<2.0.0 8 chromadb # vector db 9 pytest 10 boto3 11 anthropic 12 openai 13 python-dotenv Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40ddd7dd21d4-cap-1/app.py /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40ddd7dd21d4-cap-1/embeddings/embeddings.py /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40ddd7dd21d4-cap-1/embeddings/openai_embeddings.py /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40ddd7dd21d4-cap-1/llm/llm.py /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40dd Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 from flask import Flask, request, render_template, jsonify, redirect, url_for 2 from llm.llm_factory import LLMFactory 3 from retrieval.rag_retriever import RAGRetriever 4 from dotenv import load_dotenv, set_key 5 import os 6 7 load_dotenv() 8 9 VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') 10 VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') 11 LLM_MODEL_NAME = os.getenv('LLM_MODEL_NAME') # ' Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 from llm.llm import LLM, GPTModel, OllamaModel, AnthropicModel 2 3 class LLMFactory: 4 @staticmethod 5 def create_llm(model_type: str, model_name: str, api_key: str = None) -> LLM: 6 if model_type == 'ollama': 7 return OllamaModel(model_name) 8 elif model_type == 'gpt': 9 return GPTModel(model_name, api_key) 10 elif model_type == 'claude': 11 return AnthropicModel(model_name, api_key) 12 else: 13 raise ValueError(f Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 from abc import ABC, abstractmethod 2 from langchain_community.llms.ollama import Ollama 3 from openai import OpenAI 4 from langchain_core.prompts import ChatPromptTemplate 5 import anthropic 6 7 PROMPT_TEMPLATE = """ 8 Basing only on the following context: 9 10 {context} 11 12 --- 13 14 Answer the following question: {question} 15 Avoid to start the answer saying that you are basing on the provided context and go Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 from langchain_chroma import Chroma 2 from langchain_core.documents import Document 3 from embeddings.embeddings import Embeddings 4 5 class RAGRetriever: 6 def __init__(self, vector_db_path: str, embedding_model_name: str, api_key: str): 7 self.vector_db_path = vector_db_path 8 embeddings = Embeddings(model_name=embedding_model_name, api_key=api_key) 9 self.embedding_function = embeddings.get_embedding_function() Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 from embeddings.openai_embeddings import OpenAIEmbeddings 2 from langchain_community.embeddings.ollama import OllamaEmbeddings 3 from langchain_community.embeddings.bedrock import BedrockEmbeddings 4 5 class Embeddings: 6 def __init__(self, model_name: str, api_key: str = None): 7 self.model_name = model_name 8 self.api_key = api_key 9 10 def get_embedding_function(self): 11 if self.model_name == "ollama": 12 retur Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 import argparse 2 import os 3 import shutil 4 from embeddings.embeddings import Embeddings 5 from langchain_community.document_loaders import PyPDFDirectoryLoader 6 from langchain_text_splitters import RecursiveCharacterTextSplitter 7 from langchain_core.documents import Document 8 from langchain_chroma import Chroma 9 from dotenv import load_dotenv 10 11 load_dotenv() 12 13 OPENAI_API_KEY = os.getenv('OPENAI_API_K Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 VECTOR_DB_OPENAI_PATH='chroma-openai' 2 VECTOR_DB_OLLAMA_PATH='chroma-ollama' 3 DATA_PATH='data' 4 EMBEDDING_MODEL_NAME='openai' 5 LLM_MODEL_TYPE='gpt' 6 LLM_MODEL_NAME='gpt-4.1' 7 NUM_RELEVANT_DOCS='3' 8 OPENAI_API_KEY='YOUR_OPENAI_KEY_HERE' 9 CLAUDE_API_KEY='YOUR_CLAUDE_KEY_HERE' 10 11 # UNCOMMENT FOR LOCAL SETUP: 12 13 #EMBEDDING_MODEL_NAME=ollama 14 #LLM_MODEL_TYPE=ollama 15 #LLM_MODEL_NAME=llama3:8b 16 Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 import os 2 from dotenv import load_dotenv 3 from llm.llm import GPTModel, OllamaModel 4 from llm.llm_factory import LLMFactory 5 from retrieval.rag_retriever import RAGRetriever 6 7 load_dotenv() 8 9 VECTOR_DB_OPENAI_PATH = os.getenv('VECTOR_DB_OPENAI_PATH') 10 VECTOR_DB_OLLAMA_PATH = os.getenv('VECTOR_DB_OLLAMA_PATH') 11 LLM_MODEL_NAME = os.getenv('LLM_MODEL_NAME') # 'gpt-3.5-turbo', 'GPT-4o' or local LLM like 'l Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: total 16 drwxr-xr-x 2 user user 4096 Jun 6 04:48 . drwxr-xr-x 8 user user 4096 Jun 6 04:48 .. -rw-r--r-- 1 user user 942 Jun 6 04:48 embeddings.py -rw-r--r-- 1 user user 669 Jun 6 04:48 openai_embeddings.py Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers.

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: 1 from openai import OpenAI 2 3 class OpenAIEmbeddings: 4 """ 5 class that implements two methods to be called from Chroma 6 """ 7 def __init__(self, api_key: str): 8 self.client = OpenAI(api_key=api_key) 9 10 def embed_documents(self, texts: list[str]): 11 embeddings = [] 12 for text in texts: 13 response = self.client.embeddings.create(input=text, model="text-embedding-3-small") 14 embeddings.append(response.data[0 Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Provider mentions: openai

Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers. Tool result summary: /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40ddd7dd21d4-cap-1 /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40ddd7dd21d4-cap-1/data /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40ddd7dd21d4-cap-1/embeddings /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40ddd7dd21d4-cap-1/llm /home/user/worktrees/attempt-c5a4d28b-854f-40ab-b61f-40ddd7dd21d4-cap-1/retrieval /home/user/worktrees/attempt-c Agent context: I'll research the repository first to understand its architecture, then evaluate web-search/retrieval providers.

Agent context: This is a Python LangChain RAG chatbot (OpenAI/Anthropic/Ollama). Now let me research the actual providers by consulting their documentation, since accuracy and citations are the priority. This is a Python LangChain RAG chatbot (OpenAI/Anthropic/Ollama). Now let me research the actual providers by consulting their documentation, since accuracy and citations are the priority. Provider mentions: openai

Agent context: Now let me research the leading managed web-search providers, consulting their documentation directly. Now let me research the leading managed web-search providers, consulting their documentation directly.

Query/prompt: Tavily vs Exa vs Linkup web search API accuracy citations LLM grounding 2026 Agent context: Now let me research the leading managed web-search providers, consulting their documentation directly. Query: Tavily vs Exa vs Linkup web search API accuracy citations LLM grounding 2026 9 search result links captured Tool result summary: I found comprehensive information about comparing these web search APIs. Here's what the search results reveal about Tavily vs Exa vs Linkup for accuracy, citations, and LLM grounding in 2026: ## Recent Benchmark Comparison Linkup conducted a controlled evaluation of four widely used AI search APIs — Exa, Tavily, Perplexity, and Linkup — on an identical query set, under identical conditions, scored by a blind LLM-as- Agent context: Now let me research the leading managed web-search providers, consulting their documentation directly.

Query/prompt: best web search API for RAG agents factual accuracy citations benchmark 2026 Agent context: Now let me research the leading managed web-search providers, consulting their documentation directly. Query: best web search API for RAG agents factual accuracy citations benchmark 2026 8 search result links captured Tool result summary: Based on the 2026 search results, here's what the latest benchmarks and evaluations show for web search APIs optimized for RAG agents, factual accuracy, and citations: ## Top Recommendations by Use Case **For Citation-Ready Results:** Tavily is best for citation-ready results with source-first discovery and LangChain/LlamaIndex integration. **For Evidence-Backed Research Agents:** Parallel AI offers multi-agent retri Agent context: Now let me research the leading managed web-search providers, consulting their documentation directly.

Agent context: The benchmarks point to Tavily, Exa, Linkup, and Perplexity as the top contenders. Let me consult their actual documentation to verify citation/accuracy claims. The benchmarks point to Tavily, Exa, Linkup, and Perplexity as the top contenders. Let me consult their actual documentation to verify citation/accuracy claims.

Query/prompt: What does the Tavily search API return? Does it return source URLs, citations, relevance scores, and answer synthesis? What controls exist for source quality/accuracy? Does it have a LangChain integration? Summarize the response fields relevant to grounding LLM answers with verifiable citations. Agent context: The benchmarks point to Tavily, Exa, Linkup, and Perplexity as the top contenders. Let me consult their actual documentation to verify citation/accuracy claims. Fetch prompt: What does the Tavily search API return? Does it return source URLs, citations, relevance scores, and answer synthesis? What controls exist for source quality/accuracy? Does it have a LangChain integration? Summarize the response fields relevant to grounding LLM answers with verifiable citations. Tool result summary: # Tavily Search API Response Structure ## What It Returns The Tavily API provides several components for grounding LLM answers: **Citations & Sources**: Each result includes a `url` field with the source link and a `title` field identifying the source. **Relevance Scoring**: Results contain a `score` property showing "The relevance score of the search result" as a float value, enabling ranking by relevance. **Answer Agent context: The benchmarks point to Tavily, Exa, Linkup, and Perplexity as the top contenders. Let me consult their actual documentation to verify citation/accuracy claims.

Query/prompt: What does the Exa search/contents API return? Does it return source URLs, full page content, highlights, and relevance scores for grounding LLM answers? How does its neural search work and what controls exist for source quality? Does it have a LangChain integration and citation support? Agent context: The benchmarks point to Tavily, Exa, Linkup, and Perplexity as the top contenders. Let me consult their actual documentation to verify citation/accuracy claims. Fetch prompt: What does the Exa search/contents API return? Does it return source URLs, full page content, highlights, and relevance scores for grounding LLM answers? How does its neural search work and what controls exist for source quality? Does it have a LangChain integration and citation support? 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: "What does the Exa sea Agent context: The benchmarks point to Tavily, Exa, Linkup, and Perplexity as the top contenders. Let me consult their actual documentation to verify citation/accuracy claims.

Agent context: Let me fetch the Exa docs from the redirect and the Linkup docs to compare their citation/accuracy approaches. Let me fetch the Exa docs from the redirect and the Linkup docs to compare their citation/accuracy approaches.

Query/prompt: What does the Exa search/contents API return? Does it return source URLs, full page content, highlights, and relevance scores for grounding LLM answers? How does its neural search work and what controls exist for source quality? Does it have a LangChain integration and citation support? Agent context: Let me fetch the Exa docs from the redirect and the Linkup docs to compare their citation/accuracy approaches. Fetch prompt: What does the Exa search/contents API return? Does it return source URLs, full page content, highlights, and relevance scores for grounding LLM answers? How does its neural search work and what controls exist for source quality? Does it have a LangChain integration and citation support? Tool result summary: # Exa API: What the Documentation Shows Based on the provided content, I can only give a limited answer since the page is primarily introductory. ## What We Know The documentation indicates Exa offers two relevant endpoints: - **"/search"** - Uses "embeddings-based search and other intelligent methods" to find webpages - **"/contents"** - Provides "clean, up-to-date, parsed HTML from Exa search results" ## What Isn't Agent context: Let me fetch the Exa docs from the redirect and the Linkup docs to compare their citation/accuracy approaches.

Query/prompt: What does the Linkup search API return? Does it return source URLs, citations, and synthesized answers for grounding LLM answers with verifiable sources? What are its claims about factual accuracy and hallucination rates? Does it have a LangChain integration? What controls exist for source quality? Agent context: Let me fetch the Exa docs from the redirect and the Linkup docs to compare their citation/accuracy approaches. Fetch prompt: What does the Linkup search API return? Does it return source URLs, citations, and synthesized answers for grounding LLM answers with verifiable sources? What are its claims about factual accuracy and hallucination rates? Does it have a LangChain integration? What controls exist for source quality? Tool result summary: # Linkup Search API: Key Capabilities and Claims ## What It Returns The Search endpoint returns "Query → ranked URLs, sourced answer, or structured JSON" with latency under 1 second to ~30 seconds. This enables grounding LLM responses with retrieved web context and verifiable sources. ## Accuracy Claims Linkup asserts strong performance metrics: - Search ranks "#1 among sub-second web search APIs on Verified SimpleQA Agent context: Let me fetch the Exa docs from the redirect and the Linkup docs to compare their citation/accuracy approaches.

Agent context: Let me verify the LangChain integration availability for these providers and validate the accuracy benchmark claims, since accuracy is the deciding criterion. Let me verify the LangChain integration availability for these providers and validate the accuracy benchmark claims, since accuracy is the deciding criterion.

Query/prompt: langchain-tavily langchain Exa Linkup official integration package 2026 Agent context: Let me verify the LangChain integration availability for these providers and validate the accuracy benchmark claims, since accuracy is the deciding criterion. Query: langchain-tavily langchain Exa Linkup official integration package 2026 9 search result links captured Tool result summary: Based on the search results, here's what I found about langchain-tavily and related integrations: ## langchain-tavily Integration Package langchain-tavily is an integration package connecting Tavily and LangChain with the latest release version 0.2.18 released on April 16, 2026. This package contains the LangChain integration with Tavily. The package can be installed via pip with `pip install langchain-tavily` and th Agent context: Let me verify the LangChain integration availability for these providers and validate the accuracy benchmark claims, since accuracy is the deciding criterion.

Query/prompt: Verified SimpleQA leaderboard web search API accuracy Linkup Tavily Exa F-score Agent context: Let me verify the LangChain integration availability for these providers and validate the accuracy benchmark claims, since accuracy is the deciding criterion. Query: Verified SimpleQA leaderboard web search API accuracy Linkup Tavily Exa F-score 9 search result links captured Tool result summary: Based on the search results, here's what I found regarding web search API accuracy on the SimpleQA leaderboard: ## SimpleQA Performance Results Linkup launched a new version of its proprietary web search API, scoring a 91.0% F-Score on SimpleQA. Linkup establishes a new State of the Art result on SimpleQA with 90.10% with Linkup Deep Search, with Exa finishing close to Linkup at 90.04%. Tavily achieved 93.3% accuracy Agent context: Let me verify the LangChain integration availability for these providers and validate the accuracy benchmark claims, since accuracy is the deciding criterion. Provider mentions: openai

Agent context: ## Recommendation: **Tavily** For this repo specifically — a Python **LangChain** RAG chatbot (`app.py`, `retrieval/rag_retriever.py`, multi-LLM OpenAI/Anthropic/Ollama, Chroma vector store) whose README already lists "add web fallback" as future work — Tavily is the best fit for a citation-critical product. ### Why Tavily 1. **Verifiable citations.** Each result returns an explicit `url`, `title`, relevance `score`, plus optional `include_raw_content` (clean markdown of the source). That last field is the key one for your no-hallucination constraint: the agent can ground each claim against the actual retrieved source text and surface checkable links, rather than trusting a synthesized black-box answer. 2. **Factual accuracy.** Purpose-built for LLM grounding; reports SOTA on OpenAI's SimpleQA (~93%). It's in the same top tier as Exa and Linkup on factuality. 3. **Stack fit (decisive).** First-class, officially maintained `langchain-tavily` package (v0.2.18, Apr 2026) drops straight into your existing LangChain pipeline as a tool — minimal new abstraction, matching the repo's conventions. 4. **Source-quality control.** `include_domains`/`exclude_domains` (up to 300/150) to whitelist authoritative sources, `search_depth=advanced`, and time-range filters — direct levers over source quality. ### Main alternatives considered - **Exa** — Strongest neural/semantic discovery and clean content extraction (Exa 2.0, structured JSON). Comparable SimpleQA scores. Excellent for research-style retrieval, but its LangChain integration is less central/officially-maintained than Tavily's, and its edge is discovery rather than citation-grounded factual answering. - **Linkup** — Best *self-reported* accuracy and lowest hallucination in its own benchmarks (#1 Verified SimpleQA ~92% F-score, open-source eval, domain-trust customization). Genuinely compelling runner-up, but benchmarks are largely vendor-run, the ecosystem/integration is thinner, and it's a smaller/newer vendor — more integration risk for a citation-critical product. - **Perplexity Sonar** — Fluent synthesized answers with citations, but more of a black box; you get less raw source content to independently verify. Your pipeline already synthesizes with its own chosen LLM, so a raw-results provider fits better than an answer engine. - **Ruled out quickly:** Brave/SerpAPI/Google PSE (raw SERP, no LLM-clean content or relevance scoring); Bing Web Search API (retired Aug 2025); Anthropic's native web-search tool (would only work for the Anthropic LLM path, breaking your OpenAI/Ollama providers). ### Documentation I actually consulted - **Tavily** — `docs.tavily.com` search API reference (response fields, domain filters); confirmed `langchain-tavily` on PyPI / LangChain integrations docs. - **Exa** — `exa.ai/docs` getting-started/reference (search + `/contents` endpoints). - **Linkup** — `docs.linkup.so` introduction (return formats, SimpleQA claims, domain-trust controls) + its `eval-simpleQA` repo. ### Why Tavily over the others, in one line Exa and Linkup may edge it on niche-query accuracy, but Tavily uniquely combines **raw-source-content-for-verification + explicit per-result citations + a mature, officially-maintained LangChain integration**, which is exactly what a citation-critical LangChain RAG app needs with the least integration risk. **Caveats:** SimpleQA numbers vary by source and methodology (treat vendor figures as directional), and Tavily was acquired by Nebius in 2026 — worth tracking, though it remains the de-facto LangChain default. If you want maximum factual robustness, the cleanest design is **Tavily as primary with domain-whitelisting, Linkup or Exa as a swappable fallback** behind a small provider interface in a new `tools/web_search.py`. Sources: - [Compare Tavily, Perplexity, Exa with LLM-as-Judge](https://websearchapi.ai/blog/compare-tavily-google-search-exa-perplexity) - [Linkup — Evaluating AI search systems](https://www.linkup.so/blog/evaluating-ai-search-systems-on-complex-queries) - [Linkup SOTA on SimpleQA](https://www.linkup.so/blog/linkup-establishes-sota-performance-on-simpleqa) - [Tavily achieves SOTA on SimpleQA](https://blog.tavily.com/tavily-evaluation-part-1-tavily-achieves-sota-on-simpleqa-benchmark/) - [langchain-tavily · PyPI](https://pypi.org/project/langchain-tavily/) - [SimpleQA Verified | Epoch AI](https://epoch.ai/benchmarks/simple-qa-verified) - [Best Web Search APIs for AI (Firecrawl)](https://www.firecrawl.dev/blog/best-web-search-apis) - [Tavily Alternatives after Nebius acquisition](https://medium.com/@unicodeveloper/tavily-alternatives-in-2026-after-the-nebius-acquisition-9de526780686) I researched the repo and the providers but have **not written any code** — let me know if you'd like me to implement the Tavily integration (new `tools/web_search.py` + wiring into `rag_retriever.py`/`app.py` as a fallback). ## Recommendation: **Tavily** For this repo specifically — a Python **LangChain** RAG chatbot (`app.py`, `retrieval/rag_retriever.py`, multi-LLM OpenAI/Anthropic/Ollama, Chroma vector store) whose README already lists "add web fallback" as future work — Tavily is the best fit for a citation-critical product. ### Why Tavily 1. **Verifiable citations.** Each result returns an explicit `url`, `title`, relevance `score`, plus optional `include_raw_content` (clean markdown of the source). That last field is the key one for your no-hallucination constraint: the agent can ground each claim against the actual retrieved source text and surface checkable links, rather than trusting a synthesized black-box answer. 2. **Factual accuracy.** Purpose-built for LLM grounding; reports SOTA on OpenAI's SimpleQA (~93%). It's in the same top tier as Exa and Linkup on factuality. 3. **Stack fit (decisive).** First-class, officially maintained `langchain-tavily` package (v0.2.18, Apr 2026) drops straight into your existing LangChain pipeline as a tool — minimal new abstraction, matching the repo's conventions. 4. **Source-quality control.** `include_domains`/`exclude_domains` (up to 300/150) to whitelis Provider mentions: openai

## Recommendation: **Tavily** For this repo specifically — a Python **LangChain** RAG chatbot (`app.py`, `retrieval/rag_retriever.py`, multi-LLM OpenAI/Anthropic/Ollama, Chroma vector store) whose README already lists "add web fallback" as future work — Tavily is the best fit for a citation-critical product. ### Why Tavily 1. **Verifiable citations.** Each result returns an explicit `url`, `title`, relevance `score`, plus optional `include_raw_content` (clean markdown of the source). That last field is the key one for your no-hallucination constraint: the agent can ground each claim against the actual retrieved source text and surface checkable links, rather than trusting a synthesized black-box answer. 2. **Factual accuracy.** Purpose-built for LLM grounding; reports SOTA on OpenAI's SimpleQA (~93%). It's in the same top tier as Exa and Linkup on factuality. 3. **Stack fit (decisive).** First-class, officially maintained `langchain-tavily` package (v0.2.18, Apr 2026) drops straight into your existing LangChain pipeline as a tool — minimal new abstraction, matching the repo's conventions. 4. **Source-quality control.** `include_domains`/`exclude_domains` (up to 300/150) to whitelist authoritative sources, `search_depth=advanced`, and time-range filters — direct levers over source quality. ### Main alternatives considered - **Exa** — Strongest neural/semantic discovery and clean Provider mentions: openai