AI ERP with agents inside the systems you run
AI phone and voice agents for real calls
LLM.coPrivate, self-hosted LLM deployments
Legal AI infrastructure for firms
AI RFP discovery and response drafting
Automatic.coBusiness process automation
CyberAttack.aiThreat detection and defense
Secure AI virtual data roomsragapp
RAGapp is a Docker-based platform for deploying Retrieval-Augmented Generation (RAG) agents in enterprise environments without coding. It provides a web UI for configuration, supports both hosted models (OpenAI, Gemini) and local models (Ollama), and exposes REST APIs for integration.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | ragapp/ragapp |
| Owner | ragapp |
| Primary language | TypeScript |
| License | Apache-2.0 — OSI-approved |
| Stars | 4.4k |
| Forks | 480 |
| Open issues | 67 |
| Latest release | v0.1.5 (2024-11-04) |
| Last updated | 2025-01-22 |
| Source | https://github.com/ragapp/ragapp |
What ragapp is
TypeScript/Python application built on LlamaIndex that packages agentic RAG workflows into containerized deployments. Exposes admin, chat, and API endpoints; authentication and authorization are delegated to upstream API gateways or infrastructure layers.
Get the ragapp source
Clone the repository and explore it locally.
git clone https://github.com/ragapp/ragapp.gitcd ragapp# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Authentication must be implemented upstream via API Gateway or reverse proxy; RAGapp has no native auth layer.
- Requires Docker; deployment to Kubernetes supported conceptually but K8S descriptors noted as 'coming soon'.
- Dynamic frontend code sourced from `create-llama` project; developers must run `make build-frontends` before committing changes.
- Model selection (OpenAI/Gemini vs. local Ollama) configured via Admin UI; test model availability and latency in target environment.
- Currently single RAGapp instance or multiple instances with management UI; horizontal scaling strategy not documented.
When to avoid it — and what to weigh
- Strict production authentication requirement — RAGapp itself has no built-in authentication. You must layer an external API Gateway, adding operational complexity and potential latency.
- Highly custom agent logic needed — If you require bespoke agentic workflows beyond RAG configuration, direct LlamaIndex/LangChain integration is more flexible than RAGapp's declarative model.
- Pre-release stability expectations — Latest release is v0.1.5 (Nov 2024); project is early-stage. Production deployments should plan for API changes and test thoroughly.
- Mature authorization/multi-tenancy — README states authorization support is a future feature; currently no built-in access control or role-based restrictions.
License & commercial use
Licensed under Apache License 2.0, a permissive OSI-approved license allowing commercial use, modification, and distribution under stated conditions (include license notice, state changes).
Apache-2.0 permits commercial use. However, verify compliance with any third-party dependencies (LlamaIndex, LLM provider SDKs) separately. No warranty or liability limit in RAGapp itself; review your liability and support needs for production deployments.
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 | Low |
| DEV.co fit | Good |
| Assessment confidence | High |
RAGapp intentionally has no authentication layer; security is delegated to infrastructure (API Gateway, reverse proxy, network policy). No built-in encryption, audit logging, or authorization mentioned. Verify that your deployment layer enforces TLS, rate limiting, and access controls. Review LlamaIndex and LLM provider SDK security advisories before production use. Admin UI should not be exposed to untrusted networks.
Alternatives to consider
LangChain / LangServe
Lower-level framework with more customization; requires more development effort but offers finer-grained control over agent logic and deployment.
Superagent / Flowise / n8n
Similar no-code/low-code RAG platforms; may offer more mature auth, multi-tenancy, or workflow builder UX, but less enterprise-focused than RAGapp.
Direct LlamaIndex integration
Skip the abstraction layer; build custom RAG agents directly if your team has Python/TypeScript expertise and non-standard requirements.
Build on ragapp with DEV.co software developers
RAGapp streamlines agentic RAG deployment with zero-code configuration and Docker portability. Verify authentication strategy, test model performance, and plan infrastructure integration before production rollout.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
ragapp FAQ
Do I need to write code to use RAGapp?
Is RAGapp suitable for production?
How do I add authentication?
Can I use private LLMs only (no OpenAI/Gemini)?
Senior engineers for your next build
ragapp rarely ships on its own. As a software development company, DEV.co handles the surrounding work — data modeling, service integration, deployment, and the rag frameworks glue code that production actually requires.
Evaluate RAGapp for Your Enterprise RAG Needs
RAGapp streamlines agentic RAG deployment with zero-code configuration and Docker portability. Verify authentication strategy, test model performance, and plan infrastructure integration before production rollout.