Infino Released Open Source Agent Retrieval Platform
The platform streamlines AI data access by indexing directly within storage files to reduce infrastructure complexity.
Updated on Oct. 7, 2026 in Artificial Intelligence
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Infino has launched an open source agent retrieval platform designed to allow AI agents to query structured and unstructured data through a unified interface. The system stores data in Apache Parquet files on object storage to support multi-billion-document use cases.
Why it matters
The platform addresses the fragmentation of current data stacks, which typically split resources across separate search engines, vector databases, and data warehouses. By consolidating these functions, it caters to the distinct way AI agents query data through simultaneous, high-frequency small requests.
The platform claims a 10x reduction in costs compared to traditional search infrastructure. It achieves this by embedding search indexes directly beside the footer within Apache Parquet files, allowing developers to query data by simply pointing the platform at existing JSON or Parquet stores.
The players
Infino
An infrastructure company focused on open source tools for AI agent data retrieval and query optimization.
Ekechi Nwokah
Founder of Infino who is developing tools to modernize data stack accessibility for AI models.
The details
The platform integrates inference models directly with its retrieval engine to bridge the gap between model processing and raw data. It utilizes Apache Parquet — a column-oriented file format that optimizes storage and retrieval speed — as its primary container. By indexing the data within the file footer, the system eliminates the need to move or duplicate data into secondary databases for agent access.
Timeline
October 7, 2026: Infino formally launched its agent retrieval platform.
The Tech Race
The platform marks a departure from the traditional use of Elasticsearch as the default search engine, shifting the architecture toward integrated file-based retrieval. It aims to overtake the fragmented data stack model by offering a streamlined path for agents to access multi-billion-document datasets.
Developers can implement this platform immediately by pointing it at their existing Apache Parquet or JSON data files. It is best suited for organizations managing high-volume, multi-billion-document datasets that currently struggle with the costs of maintaining separate search and database infrastructure.
The takeaway
Infino's open source approach simplifies the data-to-agent pipeline by removing the overhead of traditional search index management. Interested teams should monitor the platform's performance in multi-billion-document deployments to determine if it can sustain its claimed cost-efficiency at scale.
Further reading
For more on how modern systems manage massive data pools for machine learning, explore Artificial Intelligence.
Source note: This article includes information reported by The New Stack.
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