--- title: "chromem-go" type: "tool" slug: "philippgille-chromem-go" canonical_url: "https://www.graphcanon.com/tools/philippgille-chromem-go" github_url: "https://github.com/philippgille/chromem-go" homepage_url: null stars: 1025 forks: 70 primary_language: "Go" license: "MPL-2.0" categories: ["vector-databases", "data-retrieval"] tags: ["chroma", "go", "vector-database", "embeddable", "llm", "rag", "retrieval-augmented-generation", "golang"] updated_at: "2026-07-07T18:48:08.884045+00:00" --- # chromem-go > Embeddable vector database for Go with Chroma-like interface and zero third-party dependencies. chromem-go is a lightweight, embeddable vector database written in Go that provides an interface reminiscent of Chromadb. It supports in-memory operations and optional persistence to disk, ideal for adding retrieval-augmented generation features directly into Go applications without requiring external databases. Targeted towards simplicity and performance in common use cases. ## Facts - Repository: https://github.com/philippgille/chromem-go - Stars: 1,025 ยท Forks: 70 ยท Open issues: 17 ยท Watchers: 12 - Primary language: Go - License: MPL-2.0 - Last pushed: 2026-05-17T18:00:12+00:00 ## Categories - [Vector Databases](/categories/vector-databases.md) - [Data & Retrieval](/categories/data-retrieval.md) ## Tags chroma, go, vector-database, embeddable, llm, rag, retrieval-augmented-generation, golang ## Related tools - [transformers](/tools/huggingface-transformers.md) - ๐Ÿค— Transformers: the model-definition framework for state-of-the-art machine learning models (โ˜… 162,347) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (โ˜… 151,298) - [firecrawl](/tools/firecrawl-firecrawl.md) - The API to search, scrape, and interact with the web at scale. 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Because `chromem-go` is embeddable it enables you to add retrieval augmented generation (RAG) and similar embeddings-based features into your Go app *without having to run a separate database*. Like when using SQLite instead of PostgreSQL/MySQL/etc. It's *not* a library to connect to Chroma and also not a reimplementation of it in Go. It's a database on its own. The focus is not scale (millions of documents) or number of features, but simplicity and performance for the most common use cases. On a mid-range 2020 Intel laptop CPU you can query 1,000 documents in 0.3 ms and 100,000 documents in 40 ms, with very few and small memory allocations. See [Benchmarks](#benchmarks) for details. > โš ๏ธ The project is in beta, under heavy construction, and may introduce breaking changes in releases before `v1.0.0`. All changes are documented in the [`CHANGELOG`](./CHANGELOG.md). ## Contents 1. [Use cases](#use-cases) 2. [Interface](#interface) 3. [Features + Roadmap](#features) 4. [Installation](#installation) 5. [Usage](#usage) 6. [Benchmarks](#benchmarks) 7. [Development](#development) 8. [Motivation](#motivation) 9. [Related projects](#related-projects) ## Use cases With a vector database you can do various things: - Retrieval augmented generation (RAG), question answering (Q&A) - Text and code search - Recommendation systems - Classification - Clustering Let's look at the RAG use case in more detail: ### RAG The knowledge of large language models (LLMs) - even the ones with 30 billion, 70 billion parameters and more - is limited. They don't know anything about what happened after their training ended, they don't know anything about data they were not trained with (like your company's intranet, Jira / bug tracker, wiki or other kinds of knowledge bases), and even the data they *do* know they often can't reproduce it *exactly*, but start to *hallucinate* instead. Fine-tuning an LLM can help a bit, but it's more meant to improve the LLMs reasoning about specific topics, or reproduce the style of written text or code. Fine-tuning does *not* add knowledge *1:1* into the model. Details are lost or mixed up. And knowledge cutoff (about anything that happened after the fine-tuning) isn't solved either. => A vector database can act as the up-to-date, precise knowledge for LLMs: 1. You store relevant documents that you want the LLM to know in the database. 2. The database stores the *embeddings* alongside the documents, which you can either provide or can be created by specific "embedding models" like OpenAI's `text-embedding-3-small`. - `chromem-go` can do this for you and supports multiple embedding providers and models out-of-the-box. 3. Later, when you want to talk to the LLM, you first send the question to the vector DB to find *similar*/*related* content. This is called "nearest neighbor search". 4. In the question to the LLM, you provide this content alongside your question. 5. The LLM can take this up-to-date precise content into account when answering. Check out the [example code](examples) to see it in action! ## Interface Our original inspiration was the [Chroma](https://www.trychroma.com/) interface, whose core API is the following (taken from their [README](https://github.com/chroma-core/chroma/blob/0.4.21/README.md)):
Chroma core interface ```python import chromadb # setup Chroma in-memory, for easy prototyping. Can add persistence easily! client = chromadb.Client() # Create collection. get_collection, get_or_create_collection, delete_collection also available! collection = client.create_collection("all-my-documents") # Add docs to the collection. Can also update and delete. Row-based API coming soon! collection.add( documents=["This is document1", "This is document2"], # we handle tokenization, embedding, and indexing automatic ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/philippgille-chromem-go`](/api/graphcanon/tools/philippgille-chromem-go) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_