Infino Launched Open Source Agent Retrieval Platform
The new tool enables AI agents to query structured and unstructured data using a unified interface.
Updated on Oct. 7, 2026 in Artificial Intelligence
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Infino has launched an open source agent retrieval platform designed to simplify how AI agents access information. The system integrates inference models with a retrieval engine to support massive, multi-billion-document use cases.
Why it matters
Current data stacks are often fragmented across multiple search engines and databases, creating inefficiencies for AI agents. This platform addresses these bottlenecks by allowing agents to issue high volumes of simultaneous queries.
The platform utilizes a retrieval engine that embeds search indexes directly into Apache Parquet files on object storage. Developers ingest data by pointing the tool at existing Parquet or JSON files to enable agent-ready querying.
The players
Infino
Infino is a technology company founded by Ekechi Nwokah, Vinay Kakade, Asif Makhani, and Murali Krishna that focuses on data infrastructure.
Ekechi Nwokah
Ekechi Nwokah is a co-founder of the infrastructure firm Infino.
The details
The platform serves as a unified interface for data that is currently trapped in disparate search engines and vector databases. By storing data in Parquet files on object storage, it allows AI agents to interact with structured and unstructured information more efficiently than legacy systems.
Timeline
Infino formally launched the platform on October 7, 2026.
The Tech Race
The platform extends the utility of the Apache Parquet file format by embedding search indexes directly into the data structure. It signals a move away from fragmented vector databases toward native data-file querying architectures.
Developers and companies can reduce their data infrastructure costs by significantly cutting reliance on expensive search engines. The open source nature of the platform also allows for easier adoption and integration into existing data workflows.
The takeaway
The move toward embedding search capabilities directly into data files represents a significant shift for AI development efficiency. Organizations looking to lower costs should evaluate whether their data stacks can be consolidated into this new architecture.
Further reading
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Source note: This article includes information reported by The New Stack.
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