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

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Retrieval pipelines typically operate in a single pass, which poses a problem when the information required to answer a question is spread across multiple documents or requires intermediate reasoning to locate. In practice, many real-world queries require multi-hop retrieval, in which the output of one search informs the next. Recent work has shown that frontier LLMs perform this multi-hop search effectively through a process known as agentic search, simply defined as a loop of LLM calls with search tools. This mode of search often comes with significant cost and latency due to their use of frontier-scale LLMs. We introduce Chroma Context-1, a 20B parameter agentic search model derived from gpt-oss-20B that achieves retrieval performance comparable to frontier-scale LLMs at a fraction of the cost and up to 10x faster inference speed. Context-1 is designed to be used as a subagent in conjunction with a frontier reasoning model. Given a query, it produces a ranked list of documents that are relevant to satisfying the query. The model is trained to decompose queries into subqueries, iteratively search a corpus, and selectively edit its own context to free capacity for further exploration.

Introduction # Using search systems in conjunction with a large language model (LLM) is a common paradigm for enabling language models to access data beyond their training corpus. This approach, broadly known as retrieval-augmented-generation (RAG) , has traditionally relied on single-stage retrieval pipelines composed of vector search, lexical search, or regular expression matching, optionally followed by a learned reranker. While effective for straightforward lookup queries, these pipelines are fundamentally limited: they assume that the information needed to answer a question can be retriev

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