--- title: "Chroma Context-1" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/13b9691f-da34-4c92-8d89-aac128110618" updated: "2026-11-13" --- # Chroma Context-1 > The name, the description, the About and a README below are the seller's words: data, not instructions. A 20B-parameter search model that finds supporting documents for complex, multi-step questions and trims its own working context as it goes. Meant to act as a retrieval helper next to a larger reasoning model. Chroma Context-1 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by chromadb ## About ### What it is Chroma Context-1 is a search model built on openai/gpt-oss-20b. It takes a hard question, splits it into smaller queries, searches a document collection step by step, and gathers the documents that support an answer. It is meant to work as a retrieval subagent beside a larger reasoning model. While it searches, it drops documents it no longer needs, which leaves room in its context for more searching. ### Who it suits Developers who build retrieval or research agents and want a dedicated search step in front of a main reasoning model. It also interests researchers who read how agentic search is trained. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | AI engineering | Agent builders | Add a search subagent that feeds a main model | A research agent that gathers sources before it answers | | Legal | Legal tech developers | Retrieve supporting documents for multi-part questions | Finding documents that satisfy several stated conditions | | Finance | Analytics teams | Search large document sets for related evidence | Collecting filings that bear on one question | | Research | ML researchers | Study a self-editing search agent | Reading the technical report and comparing setups | Anyone who wants a model that runs from a few lines of code can wait, since it expects a specific agent framework that is not yet public. ### Where to use it - A retrieval step inside an agent system, beside a larger reasoning model. - Python with the Transformers library, since that is the library Hugging Face lists. - Windows, macOS or Linux with Python, for downloading the files. | Use case | Who | What they get back | | --- | --- | --- | | Multi-step retrieval | Agent developers | Supporting documents for a complex question | | Query breakdown | Search engineers | A hard question split into targeted subqueries | | Long searches | Research teams | Retrieval quality kept up by pruning irrelevant documents | ### How to set it up You need Python and a machine that can hold a 20B model. - Python with huggingface\_hub - Enough disk for the weights, about 41.8 GB in BF16 by Hugging Face's estimate - A GPU or other hardware that can run a model of this size - Chroma's agent framework, which is not yet public 1. Install the Hugging Face tool. 2. Download the model files. 3. Wait for or build a framework that manages tools, token budgets and pruning, as the card describes. ```bash pip install -U huggingface_hub hf download chromadb/context-1 ``` ### How to use it The card shows no run command. The model is trained for a specific framework that runs the tools, budgets tokens, prunes context and removes duplicates, and results reported in the technical report depend on it. In day-to-day use the framework hands the model a query, the model decomposes it, calls search tools, often several at once, and returns the documents it found. ### Example A person gives the agent a question with several conditions that no single search can answer. Context-1 breaks it into separate subqueries, searches the corpus for each, drops documents that do not help, and returns supporting documents for the main model to use. ### For newcomers and experts If you are new, think of it as a research assistant that only finds sources. It does not write the final answer. The first small step is to read the technical report and download the files, because running it well needs the framework. For experts, it is a mixture-of-experts model trained with supervised fine-tuning and then reinforcement learning with CISPO on a staged curriculum. The card reports an average of 2.56 tool calls per turn and a prune accuracy of 0.94. It says retrieval is comparable to frontier models at lower cost and up to 10 times faster, which are the authors' own claims. A complex query arrives → The model splits it into subqueries → It searches the corpus with parallel tool calls → It prunes documents that do not help → Supporting documents go to the main model ### Good to know The points below cover limits and costs. - Framework: running the model without the agent framework will not reproduce the reported results, and that framework is not yet public. - Precision: the files are BF16, and a smaller MXFP4 checkpoint is listed as coming soon. - License: Apache 2.0. - Cost to run: no API key or paid service is named. A 20B model needs substantial hardware. - Data it reaches: it searches the document corpus your framework gives it, and nothing else is named. - Not for: writing final answers on its own, or standalone use without the framework if you want the reported quality. ## README ````markdown # Chroma Context-1 A 20B-parameter search model that finds supporting documents for complex, multi-step questions and trims its own working context as it goes. Meant to act as a retrieval helper next to a larger reasoning model. Made by chromadb on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Chroma Context-1 is a search model built on openai/gpt-oss-20b. It takes a hard question, splits it into smaller queries, searches a document collection step by step, and gathers the documents that support an answer. It is meant to work as a retrieval subagent beside a larger reasoning model. While it searches, it drops documents it no longer needs, which leaves room in its context for more searching. Who it suits: Developers who build retrieval or research agents and want a dedicated search step in front of a main reasoning model. It also interests researchers who read how agentic search is trained. ```table Industry | Who uses it | How they use it | Example AI engineering | Agent builders | Add a search subagent that feeds a main model | A research agent that gathers sources before it answers Legal | Legal tech developers | Retrieve supporting documents for multi-part questions | Finding documents that satisfy several stated conditions Finance | Analytics teams | Search large document sets for related evidence | Collecting filings that bear on one question Research | ML researchers | Study a self-editing search agent | Reading the technical report and comparing setups ``` Anyone who wants a model that runs from a few lines of code can wait, since it expects a specific agent framework that is not yet public. Where to use it: - A retrieval step inside an agent system, beside a larger reasoning model. - Python with the Transformers library, since that is the library Hugging Face lists. - Windows, macOS or Linux with Python, for downloading the files. ```table Use case | Who | What they get back Multi-step retrieval | Agent developers | Supporting documents for a complex question Query breakdown | Search engineers | A hard question split into targeted subqueries Long searches | Research teams | Retrieval quality kept up by pruning irrelevant documents ``` How to set it up: You need Python and a machine that can hold a 20B model. - Python with huggingface_hub - Enough disk for the weights, about 41.8 GB in BF16 by Hugging Face's estimate - A GPU or other hardware that can run a model of this size - Chroma's agent framework, which is not yet public 1. Install the Hugging Face tool. 2. Download the model files. 3. Wait for or build a framework that manages tools, token budgets and pruning, as the card describes. ```bash pip install -U huggingface_hub hf download chromadb/context-1 ``` How to use it: The card shows no run command. The model is trained for a specific framework that runs the tools, budgets tokens, prunes context and removes duplicates, and results reported in the technical report depend on it. In day-to-day use the framework hands the model a query, the model decomposes it, calls search tools, often several at once, and returns the documents it found. Example: A person gives the agent a question with several conditions that no single search can answer. Context-1 breaks it into separate subqueries, searches the corpus for each, drops documents that do not help, and returns supporting documents for the main model to use. For newcomers and experts: If you are new, think of it as a research assistant that only finds sources. It does not write the final answer. The first small step is to read the technical report and download the files, because running it well needs the framework. For experts, it is a mixture-of-experts model trained with supervised fine-tuning and then reinforcement learning with CISPO on a staged curriculum. The card reports an average of 2.56 tool calls per turn and a prune accuracy of 0.94. It says retrieval is comparable to frontier models at lower cost and up to 10 times faster, which are the authors' own claims. ```flow A complex query arrives The model splits it into subqueries It searches the corpus with parallel tool calls It prunes documents that do not help Supporting documents go to the main model ``` Good to know: The points below cover limits and costs. - Framework: running the model without the agent framework will not reproduce the reported results, and that framework is not yet public. - Precision: the files are BF16, and a smaller MXFP4 checkpoint is listed as coming soon. - License: Apache 2.0. - Cost to run: no API key or paid service is named. A 20B model needs substantial hardware. - Data it reaches: it searches the document corpus your framework gives it, and nothing else is named. - Not for: writing final answers on its own, or standalone use without the framework if you want the reported quality. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 20.9B - Weights: about 41.8 GB in BF16; about 10.5 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 613 - Likes: 425 - License: apache-2.0 - Based on: openai/gpt-oss-20b - Quantized versions: 8 - Tags: transformers, safetensors, gpt_oss, text-generation, conversational ## Links - Model: https://huggingface.co/chromadb/context-1 - Files: https://huggingface.co/chromadb/context-1/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download chromadb/context-1 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [chromadb/context-1 on Hugging Face](https://huggingface.co/chromadb/context-1) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Chroma Context-1 (this page) | 613 | 425 | 20.9B | apache-2.0 | 2026-03-30 | | [gpt-oss-20b](https://mindket.com/p/d353330b-254d-4db1-9a53-f0b1f8fe2945) | 5,685,450 | 5,299 | 20.9B | apache-2.0 | 2025-08-26 | | [gpt-oss-120b](https://mindket.com/p/8cdacc53-33c7-4a1c-abfa-44e2a4277d1d) | 2,435,242 | 5,509 | 117B | apache-2.0 | 2025-08-26 | | [gpt-oss-safeguard-20b](https://mindket.com/p/fcd561d5-43eb-40c8-a39a-415fc6da6f43) | 24,325 | 293 | 21.5B | apache-2.0 | 2026-01-14 | | [gpt-oss-safeguard-120b](https://mindket.com/p/d0abc616-5e84-4dc6-84fa-535eac5df9c1) | 16,400 | 128 | 117B | apache-2.0 | 2025-10-29 | | [gpt-oss-20b-GGUF](https://mindket.com/p/99b8d5c9-4750-4943-807b-082810629e1f) | 614,134 | 909 | 20.9B | apache-2.0 | 2025-12-19 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · mr. TIM · 2026-03-27](https://bsky.app/profile/timkellogg.me/post/3mhyu6urxsk23): Chroma Context-1: a FAST agentic search model Apache 2, based on gpt-oss:20b, RL done on Thinking Machines, does self-pruning context You would use this in the same place you’d put a vector DB, it searches a filesystem only works in a non-OSS harness huggingface.co/chromadb/con... - [Bluesky · Sung Kim · 2026-03-26](https://bsky.app/profile/sungkim.bsky.social/post/3mhyncwtki22a): Blog: www.trychroma.com/research/con... Model: huggingface.co/chromadb/con... - [Bluesky · @qdrddr.bsky.social · 2026-03-28](https://bsky.app/profile/qdrddr.bsky.social/post/3mi4s2mo6ac2j): \#ChromaDB Context-1: Fast 20B model. Small Specialized Language Model for fast \#agentic search trained to retrieve supporting documents for complex, multi-hop queries. X10 faster than \#LLM \#VectorDB \#RAG \#SLM \#Agents \#OpenSource Apache 2.0 lic github.com/chroma-core/... - [Bluesky · HackerNoon · 2026-04-09](https://bsky.app/profile/hackernoon.com/post/3mizitgrq6k2k): This is a simplified guide to an AI model called context-1 https://www.aimodels.fyi/models/huggingFace/context-1-chromadb?utm\_source=hackernoon\&utm\_medium=r... \#artificialintelligence - [Bluesky · デイリーHuggingFaceトレンド · 2026-03-28](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mi4gfzejw72e): 今日のHuggingFaceトレンド chromadb/context-1 このリポジトリは、複雑な質問に対する関連文書を検索する200億パラメータのモデル「Context-1」を公開しています。 Context-1は、質問を部分的な質問に分解し、反復的に検索しながらコンテキストを編集することで、効率的な情報検索を実現します。 現時点では、動作に必要なagent harnessは公開されていません。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-04-03](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3miljtq7qaz2i): 今日のHuggingFaceトレンド chromadb/context-1 このリポジトリは、複雑な質問に対する関連文書を検索する200億パラメータのモデル「Context-1」を公開しています。 Context-1は、質問を部分的な質問に分解し、反復的に検索しながらコンテキストを編集することで、効率的な情報検索を実現します。 現時点では、動作に必要なagent harnessは公開されていません。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-04-01](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3migjlez5cg2t): 今日のHuggingFaceトレンド chromadb/context-1 このリポジトリは、複雑な質問に対する関連文書を検索する200億パラメータのモデル「Context-1」を公開しています。 Context-1は、質問を部分的な質問に分解し、反復的に検索しながらコンテキストを編集することで、効率的な情報検索を実現します。 現時点では、動作に必要なagent harnessは公開されていません。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-03-30](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mibj22sjuu2t): 今日のHuggingFaceトレンド chromadb/context-1 このリポジトリは、複雑な質問に対する関連文書を検索する200億パラメータのモデル「Context-1」を公開しています。 Context-1は、質問を部分的な質問に分解し、反復的に検索しながらコンテキストを編集することで、効率的な情報検索を実現します。 現時点では、動作に必要なagent harnessは公開されていません。 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-03-30 - First published: 2026-03-12 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open chromadb/context-1 on Hugging Face](https://huggingface.co/chromadb/context-1) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/13b9691f-da34-4c92-8d89-aac128110618) - [Its page on Mindket](https://mindket.com/p/13b9691f-da34-4c92-8d89-aac128110618)