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chroma-core/chroma

Search infrastructure for AI

GraphCanon updated 3w · GitHub synced 3w

29k stars2.4k forksLast push 3w Rust Apache-2.0

Decision brief

Chroma is an open-source data infrastructure for AI designed to support vector, hybrid, and full-text search capabilities with high performance.

Good fit when

  • - When you require a high-performance data infrastructure that can handle complex query needs for AI applications. - If your project necessitates fast, cost-effective, and scalable serverless services
  • - You need an open-source solution with easy onboarding as Chroma offers in-memory setup for rapid prototyping.

Avoid when

  • - In scenarios where a more mature or enterprise-grade solution is required, as Chroma might be rapidly evolving and not yet fully stabilized.
  • - If your project requires extensive customization at the lower levels that the relatively new tool might not support comprehensively yet
Pricing:
freemium - The open-source version is free to use and modify; the hosted service (Chroma Cloud) has a freemium model offering $5 of initial credits.
Requirements:
Min 1 GB RAM

Observed Jul 11, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Very active (0d since push)
As of 3w
Provenance
Not a fork · Organization account
As of 3w
Security (OSV)
8 low (8 low)
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

cargo add chroma
crates.io

How it fits your stack(31)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Alternative

Integrates

Relationship graph

Optional deeper exploration of typed edges and category neighbours.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Chroma is an open-source data infrastructure designed to provide high-performance vector, hybrid, and full-text search capabilities for AI applications.

Capability facts

Deploy
Self-host

Source: dockerfile:Dockerfile · Jul 28, 2026

Docker
Dockerfile present

Source: dockerfile:Dockerfile · Jul 28, 2026

CLI
CLI entrypoint

Source: pyproject.toml:[project.scripts] · Jul 28, 2026

Languages
rust, python

Source: github.language+pyproject.toml · Jul 28, 2026

Categories

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Node.js runtimeNode.js

Source: README excerpt (regex_v1, Jul 28, 2026)

# for javascript, npm install chromadb!
Source link
Python runtimePython

Source: README excerpt (regex_v1, Jul 28, 2026)

pip install chromadb # python client
Source link

Tags

README

Chroma - the open-source data infrastructure for AI.

Discord | License | Docs | Homepage

pip install chromadb # python client
# for javascript, npm install chromadb!
# for client-server mode, chroma run --path /chroma_db_path

Chroma Cloud

Our hosted service, Chroma Cloud, powers serverless vector, hybrid, and full-text search. It's extremely fast, cost-effective, scalable and painless. Create a DB and try it out in under 30 seconds with $5 of free credits.

Get started with Chroma Cloud

API

The core API is only 4 functions (run our 💡 Google Colab):

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 automatically. You can skip that and add your own embeddings as well
    metadatas=[{"source": "notion"}, {"source": "google-docs"}], # filter on these!
    ids=["doc1", "doc2"], # unique for each doc
)

# Query/search 2 most similar results. You can also .get by id
results = collection.query(
    query_texts=["This is a query document"],
    n_results=2,
    # where={"metadata_field": "is_equal_to_this"}, # optional filter
    # where_document={"$contains":"search_string"}  # optional filter
)

Learn about all features on our Docs

Get involved

Chroma is a rapidly developing project. We welcome PR contributors and ideas for how to improve the project.

Release Cadence We currently release new tagged versions of the pypi and npm packages on Mondays. Hotfixes go out at any time during the week.

License

Apache 2.0

For agents

This page has a .md twin and JSON over the API.

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