RAG Frameworks · Cinnamon

kotaemon

Kotaemon is an open-source RAG (Retrieval-Augmented Generation) web application for chatting with documents. It provides both a user-friendly interface for document QA and a Python framework for developers to build custom RAG pipelines with support for multiple LLMs and embedding providers.

Talk to DEV.co Discuss Implementation

Source: GitHub — github.com/Cinnamon/kotaemon

25.5k

GitHub stars

2.1k

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	Cinnamon/kotaemon
Owner	Cinnamon
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	25.5k
Forks	2.1k
Open issues	235
Latest release	v0.12.0 (2026-05-31)
Last updated	2026-06-09
Source	https://github.com/Cinnamon/kotaemon

What kotaemon is

Python-based RAG platform built on Gradio offering hybrid retrieval (full-text + vector search), multi-modal document parsing, configurable LLM/embedding integrations (OpenAI, Azure, Ollama, local), and a modular pipeline architecture. Supports Docker deployment with lite/full variants and includes advanced features like GraphRAG indexing and agent-based reasoning.

Quickstart

Get the kotaemon source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/Cinnamon/kotaemon.git2cd kotaemon3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Self-hosted document QA for enterprises

Deploy on-premises to enable teams to ask questions across private document collections without sending data to third-party APIs. Multi-user login and collection management built-in.

Rapid RAG prototyping and customization

Developers can import kotaemo as a library to experiment with retrieval strategies, re-ranking, prompts, and agent reasoning without building UI from scratch.

Multi-modal document analysis with citations

Process PDFs with figures/tables and receive answers with detailed source citations viewable in an in-browser PDF viewer, suitable for research, compliance, or audit workflows.

Implementation considerations

Python 3.10+ required; Docker (lite/full variants) recommended to reduce dependency friction, but adds image size and orchestration complexity.
LLM/embedding model selection and cost optimization critical: local models (Ollama, llama-cpp-python) for privacy vs. API providers (OpenAI, Azure) for quality—no built-in cost controls.
Document parsing depends on file type; standard PDF/HTML/XLSX supported natively, but DOC/DOCX require Unstructured library with OS-specific installation steps.
Hybrid retrieval (full-text + vector) and re-ranking add latency; tune chunk size, embedding dimensions, and re-ranker threshold for your document corpus.
Multi-user login, private/public collections, and collaboration features exist but security hardening (auth, TLS, secret rotation) responsibility falls on deployer.

When to avoid it — and what to weigh

Vendor lock-in with commercial SaaS preferred — Kotaemo requires self-hosting and maintenance. If you need fully managed, turn-key document QA with SLA/support, consider commercial alternatives.
Real-time, sub-second latency requirements — No performance benchmarks or latency guarantees provided. Deployment complexity and inference costs depend heavily on your LLM choice and indexing strategy.
No Python ecosystem tolerance — The framework and deployment are Python-centric (3.10+). Teams without Python expertise or DevOps capacity may struggle with setup and customization.
Strict compliance with proprietary LLM policies — While local LLM support exists, many deployments rely on third-party API calls (OpenAI, Azure). Verify vendor terms for your data classification and geography.

License & commercial use

Apache License 2.0 (Apache-2.0). This is a permissive, OSI-approved open-source license allowing commercial use, modification, and redistribution with attribution and liability disclaimer.

Apache-2.0 permits commercial use without royalties or license fees. However, ensure compliance with linked dependencies (Gradio, LangChain, embedding libraries, LLM provider terms). No warranty or indemnity provided by licensor. If integrating proprietary LLM APIs, review their terms separately. Consider consulting legal review before production deployment.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Adequate
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

Kotaemo is a self-hosted framework; security posture depends entirely on deployment and configuration. Key concerns: (1) No audit logs, rate-limiting, or DLP controls mentioned—custom implementation required. (2) API keys for LLM providers and embeddings must be managed securely (env vars, secrets manager). (3) Multi-user mode lacks explicit RBAC or data isolation guarantees—review source before production. (4) Data residency: ensure local LLM or compliant cloud region for sensitive documents. (5) No formal security policy, CVE disclosure process, or penetration testing mentioned. Conduct threat modeling and access control review before handling confidential data.

Alternatives to consider

LlamaIndex (formerly GPT Index)

Mature, widely-adopted Python RAG framework with broader integrations and larger community. Less opinionated UI; requires more custom development but offers greater flexibility.

LangChain + LangServe

Enterprise-grade RAG orchestration with production tooling (tracing, caching, monitoring). Steeper learning curve; better for teams with DevOps/platform engineering resources.

Haystack by Deepset

European-backed, privacy-focused RAG framework with strong hybrid search and modular pipelines. Smaller community; good for on-prem deployments and compliance-heavy use cases.

Software development agency

Build on kotaemon with DEV.co software developers

Kotaemo is free and open-source. Download, deploy in Docker, or integrate the Python library into your pipeline. Start with a live demo or review the developer guide.

Talk to DEV.co

Related open-source tools

Surfaced by semantic similarity across the DEV.co open-source index.

rag-web-ui mini-rag ragapp

Related on DEV.co

Explore the category and the services that help you build with it.

RAG Frameworks AI development API development Cloud deployment Talk to DEV.co

kotaemon FAQ

Can I use Kotaemo without coding or API keys?

Yes, as an end user. Download the latest release, run via Docker, and upload your documents. However, you will need API keys (OpenAI, Azure, etc.) or a local LLM (Ollama) to generate answers. Developers building custom pipelines require Python.

Is my data secure and private if I self-host?

Self-hosting keeps data on your infrastructure, not sent to Cinnamon or external vendors by default. However, you remain responsible for securing the deployment (auth, TLS, secrets, access controls). If using external LLM APIs (OpenAI, Azure), your prompts and context go to their servers per their privacy terms.

What document formats are supported?

Natively: PDF, HTML, MHTML, XLSX. With Unstructured library (OS-specific install): DOC, DOCX, and others. Multi-modal parsing (tables, figures) is enabled by default for supported formats.

Can I use local LLMs to avoid API costs and data egress?

Yes. Kotaemo supports Ollama and llama-cpp-python for local inference. Docker image ghcr.io/cinnamon/kotaemo:main-ollama includes Ollama bundled. Trade-off: higher compute cost (GPU/CPU) and lower answer quality compared to large commercial models.

Software development & web development with DEV.co

Need help beyond evaluating kotaemon? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and rag frameworks integrations — and maintain them long-term.

Build or Deploy Your RAG System

Kotaemo is free and open-source. Download, deploy in Docker, or integrate the Python library into your pipeline. Start with a live demo or review the developer guide.

Talk to DEV.co Schedule a Consultation