Onyx
A self-hostable application layer over your own documents: chat, connectors, agents and research reports
What is Onyx?
Formerly Danswer, this is the whole surface a company usually assembles by hand — a chat interface, indexing from more than fifty sources, custom agents with their own instructions and actions, a multi-step research mode that returns a report, web search, code execution and artifacts — deployable by one command and pointed at any model provider, self-hosted or proprietary. Two things to weigh: the full deployment is a stack of index, workers, inference servers, cache and blob store, and directories named ee carry a separate enterprise licence rather than MIT.
What can you do with Onyx?
- Index what the organisation already uses — More than fifty indexing connectors ship out of the box, and anything without one can be reached over MCP instead, so the knowledge base is assembled from existing systems rather than by uploading copies.
- Choose a size before you choose the features — Lite runs the chat interface and agents in under a gigabyte of memory for a quick trial; Standard adds the hybrid vector and keyword index, the background workers that keep connectors in sync and the model inference servers.
- Ask for a report, not an answer — A deep-research mode runs a multi-step flow and returns an in-depth document, which is a different operation from the single retrieval-and-answer turn the chat box performs.
- Build agents that can act — Custom agents carry their own instructions, knowledge and actions, and those actions reach external applications through MCP with configurable authentication.
- Keep the models wherever you keep them — Self-hosted providers such as Ollama, LiteLLM and vLLM sit alongside Anthropic, OpenAI and Gemini, so the deployment does not force a decision about where inference happens.
Before you choose Onyx
- Directories named ee are covered by the Onyx Enterprise License rather than MIT, so the line between the open product and the commercial one runs through paths inside the same repository.
- The Standard deployment is a stack rather than a service: a hybrid index, background sync workers, model inference servers, a cache and a blob store all have to run and be operated somewhere.
Star history
21 Aug to 28 Aug · +113
Frequently asked questions
Is Onyx free for commercial use?
Onyx is released under the MIT licence — OSI-approved open source, which permits commercial use.
How can Onyx be deployed?
Onyx is available as Self-hosted / Managed cloud.
Documentation
Reproduced from the onyx-dot-app/onyx README, published under UNKNOWN — read the LICENSE file. Read the original ↗
Onyx - The Open Source AI Platform
Onyx is the application layer for LLMs - bringing a feature-rich interface that can be easily hosted by anyone. Onyx enables LLMs through advanced capabilities like RAG, web search, code execution, file creation, deep research and more.
Connect your applications with over 50+ indexing based connectors provided out of the box or via MCP.
[!TIP] Deploy with a single command:
curl -fsSL https://onyx.app/install_onyx.sh | bash
⭐ Features
- 🔍 Agentic RAG: Get best in class search and answer quality based on hybrid index + AI Agents for information retrieval
- Benchmark to release soon!
- 🔬 Deep Research: Get in depth reports with a multi-step research flow.
- Top of leaderboard as of Feb 2026.
- 🤖 Custom Agents: Build AI Agents with unique instructions, knowledge, and actions.
- 🌍 Web Search: Browse the web to get up to date information.
- Supports Serper, Google PSE, Brave, SearXNG, and others.
- Comes with an in house web crawler and support for Firecrawl/Exa.
- 📄 Artifacts: Generate documents, graphics, and other downloadable artifacts.
- ▶️ Actions & MCP: Let Onyx agents interact with external applications, comes with flexible Auth options.
- 💻 Code Execution: Execute code in a sandbox to analyze data, render graphs, or modify files.
- 🎙️ Voice Mode: Chat with Onyx via text-to-speech and speech-to-text.
- 🎨 Image Generation: Generate images based on user prompts.
Onyx supports all major LLM providers, both self-hosted (like Ollama, LiteLLM, vLLM, etc.) and proprietary (like Anthropic, OpenAI, Gemini, etc.).
To learn more - check out our docs!
🚀 Deployment Modes
Onyx supports deployments in Docker, Kubernetes, Helm/Terraform and provides guides for major cloud providers. Detailed deployment guides found here.
Onyx supports two separate deployment options: standard and lite.
Onyx Lite
The Lite mode can be thought of as a lightweight Chat UI. It requires less resources (under 1GB memory) and runs a less complex stack. It is great for users who want to test out Onyx quickly or for teams who are only interested in the Chat UI and Agents functionalities.
Standard Onyx
The complete feature set of Onyx which is recommended for serious users and larger teams. Additional components not included in Lite mode:
- Vector + Keyword index for RAG.
- Background containers to run job queues and workers for syncing knowledge from connectors.
- AI model inference servers to run deep learning models used during indexing and inference.
- Performance optimizations for large scale use via in memory cache (Redis) and blob store (MinIO).
[!TIP]
To try Onyx for free without deploying, visit Onyx Cloud.
🏢 Onyx for Enterprise
Onyx is built for teams of all sizes, from individual users to the largest global enterprises:
- 👥 Collaboration: Share chats and agents with other members of your organization.
- 🔐 Single Sign On: SSO via Google OAuth, OIDC, or SAML. Group syncing and user provisioning via SCIM.
- 🛡️ Role Based Access Control: RBAC for sensitive resources like access to agents, actions, etc.
- 📊 Analytics: Usage graphs broken down by teams, LLMs, or agents.
- 🕵️ Query History: Audit usage to ensure safe adoption of AI in your organization.
- 💻 Custom code: Run custom code to remove PII, reject sensitive queries, or to run custom analysis.
- 🎨 Whitelabeling: Customize the look and feel of Onyx with custom naming, icons, banners, and more.
📚 Licensing
There are two editions of Onyx:
- Onyx Community Edition (CE) is available freely under the MIT license and covers all of the core features for Chat, RAG, Agents, and Actions.
- Onyx Enterprise Edition (EE) includes extra features that are primarily useful for larger organizations.
For feature details, check out our website.
👪 Community
Join our open source community on Discord!
💡 Contributing
Looking to contribute? Please check out the Contribution Guide for more details.
Other options in RAG & Retrieval
ragflowRAG & RetrievalRAGFlow is a server you run rather than a library you import: documents go into a dataset, it splits them into chunks, and you can read every chunk on screen and hand-correct the ones that came out wrong before any of it reaches a model, with chat answers citing the chunk they came from. Around that sit an agent canvas and an ingestion pipeline you can rebuild yourself, so retrieval, tools and MCP servers can be wired together without writing code. The optical character recognition, table-structure and layout models that make sense of scans run on PDFs and images only — Word, Excel and PowerPoint files are read structurally, and the docs tell you to convert a DOCX to PDF if you want the visual parser on it. The price of all this is weight: a Docker Compose stack with Elasticsearch, MySQL, MinIO and Redis behind it, 4 cores and 16 GB of RAM as the stated minimum, and parsing that the project's own FAQ concedes is slower than LangChain's. If your documents are already clean text and you want retrieval inside your own Python application, LlamaIndex or Haystack will fit better.
DoclingDataThe difference from a text extractor is that structure survives: page layout, reading order, table cells, code blocks and formulas come through as parts of a single document representation rather than as a flattened string, which is what decides whether a table in a report is still a table by the time an agent reads it. Everything can run locally, including in air-gapped environments. The cost of that fidelity is compute — machine-learning models run over each page, so this is a pipeline to budget for, not a fast converter to call inline.
AnythingLLMRAG & RetrievalAnythingLLM ships the whole retrieval stack as one installable application: a document collector, an embedding model, a vector database and the chat interface arrive together, so you drop files into a workspace and start asking questions without wiring services to each other. Documents attached to a chat go into the model's context in full by default, and only when they overflow the context window does the app offer to chunk and embed them, after which answers are assembled from a few retrieved snippets. New workspaces run in agent mode, so the same chat window can browse the web, query a SQL database, call MCP servers or run a flow you drew in the visual editor. The trade-off is that this is an application rather than a library — you extend it through its settings, its custom-skill plugin format and its REST API, never by importing it into a program of your own — and the three ways to run it are not the same product: the desktop build has no accounts, the Docker build has no bundled model, and the hosted tier has neither MCP nor custom skills.