Chroma Context-1: Training a Self-Editing Search Agent

Chroma Context-1 is a 20 billion parameter agentic search model derived from gpt-oss-20B that addresses the inefficiencies of traditional retrieval pipelines, which typically operate in a single pass and struggle with multi-document or intermediate reasoning queries. Unlike frontier-scale LLMs, which achieve effective multi-hop retrieval through agentic search at a high cost and latency, Context-1 offers comparable retrieval performance at a significantly reduced cost and up to ten times faster inference speed. It functions as a subagent alongside a frontier reasoning model, capable of producing a ranked list of relevant documents in response to a query. The model is specifically trained to break down queries into subqueries, conduct iterative corpus searches, and selectively edit its own context to allow for further exploration, enhancing its efficiency and effectiveness in multi-hop retrieval scenarios.

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