Onyx

Rather than build chat, retrieval and agent plumbing in house, a team can stand up Onyx and get an application layer for large language models that it hosts itself. Company knowledge arrives through more than 50 indexing connectors or through MCP.

Model choice stays open. The platform works with self hosted runtimes such as Ollama, LiteLLM and vLLM as well as proprietary providers including Anthropic, OpenAI and Gemini.

Several capabilities sit on top of that foundation.

• Agentic RAG: a hybrid index paired with retrieval agents for search and answer quality.
• Deep research: multi step research flows that produce in depth reports.
• Custom agents: agents given their own instructions, knowledge and actions.
• Web search: an in house crawler plus support for Serper, Google PSE, Brave, SearXNG and others.
• Code execution: a sandbox for analysing data, rendering graphs or modifying files.

Deployment runs through Docker, Kubernetes, Helm or Terraform, with a single install command for a quick start. A Lite mode trims the stack to a chat interface under 1GB of memory, while the standard deployment adds the vector and keyword index, background job workers, model inference servers and caching for larger use.

The Community Edition is MIT licensed and covers chat, RAG, agents and actions, with an Enterprise Edition adding single sign on, role based access control, analytics, query history and whitelabeling.