The Knowledge Library is a curated store of external content you want the agent to reference. Unlike Memory (which builds automatically from conversations), Knowledge is intentional — you choose what goes in.
Add files, folders, or URLs to the library. Crew chunks, indexes, and embeds them so the agent can search and reference the content during conversations. Use it for:
Open Knowledge from the dashboard sidebar. The panel lets you:
When you add a folder, Crew scans it recursively:
.DS_Store, *.tmp, *.swp, etc.)ignore_patterns for customizationCrew fetches the URL content, chunks it, and indexes it. Useful for documentation sites, API references, or articles you want the agent to draw from.
The agent searches the Knowledge Library through the local_knowledge_search MCP tool. This happens automatically when the agent determines it needs reference material — you don't need to invoke it manually.
Search uses vector similarity (embeddings) when available, with keyword fallback. Results include the source and relevant content chunks.
| Knowledge | Memory | |
|---|---|---|
| How content gets in | You add it manually (files, folders, URLs) | Automatic from conversations |
| What it stores | External documents and references | Preferences, history, lessons, facts, episodes |
| User action | Required (add sources, confirm) | None (self-learning) |
| Best for | "The agent should know about this documentation" | "The agent should remember what I prefer" |
| Panel | Dedicated Knowledge sidebar item | Inspectable via Settings → Memory |
Both contribute to what the agent knows, but they serve different purposes and are managed differently.
Knowledge items are embedded for semantic search using the same in-process embedding runtime as Memory. The model downloads automatically on first start (~610 MB). Until it lands, search falls back to keyword matching.
No setup needed — embeddings are always-on.
The agent searches Knowledge when:
local_knowledge_search tool fires based on relevance signalsIt doesn't dump the entire library into context — it retrieves the most relevant chunks for the current question.
Knowledge