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Researchers at Sentient Foundation have released Open Deep Search (ODS), an open-source framework that can match the quality of proprietary AI search solutions such as Perplexity and ChatGPT Search. ODS equips large language models (LLMs) with advanced reasoning agents that can use web search and other tools to answer questions.
For enterprises looking for customizable AI search tools, ODS offers a compelling, high-performance alternative to closed commercial solutions.
The AI search landscape
Modern AI search tools like Perplexity and ChatGPT Search can provide up-to-date answers by combining the knowledge and reasoning capabilities of LLMs with web search. However, these solutions are typically proprietary and closed-source, making it difficult to customize them and adopt them for special applications.
“Most innovation in AI search has happened behind closed doors. Open-source efforts have historically lagged in usability and performance,” Himanshu Tyagi, co-founder of Sentient, told VentureBeat. “ODS aims to close that gap, showing that open systems can compete with, and even surpass, closed counterparts on quality, speed, and flexibility.”
Open Deep Search (ODS) architecture
Open Deep Search (ODS) is designed as a plug-and-play system that can be integrated with both open-source models like DeepSeek-R1 and closed models such as GPT-4o and Claude.
ODS comprises two core components, both leveraging the chosen base LLM:
Open Search Tool: This component takes a query and retrieves information from the web that can be given to the LLM as context. Open Search Tool performs a few key actions to improve search results and make sure it provides relevant context to the model. First, it rephrases the original query in different ways to broaden the search coverage and capture diverse perspectives. The tool then fetches results from a search engine, extracts context from the top results (snippets and linked pages), and applies chunking and re-ranking techniques to filter for the most relevant content. It also has custom handling for specific sources like Wikipedia, ArXiv and PubMed, and can be prompted to prioritize reliable sources when encountering conflicting information.
Open Reasoning Agent: This agent receives the user’s query and uses the base LLM and various tools (including the Open Search Tool) to formulate a final answer. Sentient provides two distinct agent architectures within ODS:
ODS-v1: This version employs a ReAct agent framework combined with Chain-of-T …