A technique where an AI agent retrieves relevant documents or data before generating a response, grounding its answers in real information rather than relying solely on training data.
RAG solves the "hallucination problem" by giving the agent access to real data before it answers. Instead of guessing from training data, the agent searches a knowledge base, retrieves relevant documents, and uses them as context for its response.
In OpenClaw, RAG works through the agent's workspace and MCP tool integrations. The agent can search files in its workspace, query databases via MCP servers, or call search APIs to retrieve current information before responding.
RAG is essential for agents that need domain-specific knowledge — company policies, product documentation, customer records — that the base LLM was never trained on.
Without RAG, agents answer from training data alone and hallucinate when they lack knowledge. RAG grounds responses in your actual data, dramatically improving accuracy for domain-specific questions.
Clawctl supports RAG through workspace file access and MCP server integrations. Upload documents to the agent workspace. Connect knowledge bases via MCP. The agent retrieves and references real data in every response.
Try Clawctl — 60 Second DeployFiles in the agent workspace, databases via MCP servers, search APIs, and any tool that returns text content.
It reduces them significantly by grounding responses in real data. It does not eliminate them entirely — the agent can still misinterpret retrieved information.
Limited by the context window. The agent retrieves the most relevant chunks that fit within its token budget.
Agent Memory
The ability of an AI agent to remember information across conversations and sessions, building knowledge over time.
Tool Use
The ability of an AI agent to interact with external tools and APIs — reading data, calling functions, and taking actions in the real world.
MCP (Model Context Protocol)
An open protocol that lets AI agents connect to external tools and data sources through a standardized interface.
Context Window
The maximum amount of text an LLM can process in a single request — including the conversation history, system prompt, and tool results.