---

name: memory-knowledge-and-rag description: "Guides agents using CrewAI memory, knowledge sources, RAG clients and loaders, embedding providers, vector stores, reset-memory commands, and optional retrieval dependencies." disable-model-invocation: true metadata: disco-role: operating license: MIT

Memory, Knowledge, and RAG

Use this sub-skill when work involves CrewAI Memory, Knowledge, knowledge_sources, memory scopes/slices, RAG vector clients, RagTool, official RAG loaders, embedding providers, ChromaDB/Qdrant/LanceDB storage, or crewai reset-memories behavior.

Route First

• For memory construction, recall depth, scopes, slices, crew/agent memory behavior, and reset commands, read references/memory-and-knowledge.md.
• For Knowledge, knowledge_sources, crew-level vs agent-level knowledge, source classes, storage collections, and score thresholds, read references/memory-and-knowledge.md.
• For direct RAG clients, RagTool, data type detection, official loaders, and network/file loader boundaries, read references/rag-loaders.md.
• For embedding provider specs, vector DB configuration, LanceDB/ChromaDB/Qdrant storage defaults, optional dependencies, and dimension-mismatch prevention, read references/embedding-and-storage.md.
• For common failures and fixes, including embedder dimension mismatch, missing optional dependencies, storage path confusion, reset target confusion, unsupported file types, and credential-bound loaders, read references/troubleshooting.md.
• To safely check installed memory, knowledge, RAG, and optional loader imports without LLMs, credentials, network, or destructive operations, run scripts/check_rag_imports.py with --help first.

Boundaries

• Stay here for retrieval architecture: Memory, Knowledge, RagTool, RAG clients, vector stores, embedders, loaders, reset commands, and optional retrieval dependencies.
• Use ../files-and-multimodal/SKILL.md for low-level file input resolution, MIME/provider constraints, multimodal task files, and non-RAG file processing.
• Use ../tools-and-mcp/SKILL.md for general tool wrappers, custom BaseTool design, MCP, and non-RAG crewai_tools exports.
• Use ../llm-and-providers/SKILL.md for LLM provider credentials, model IDs, base URLs, streaming, and chat/completion auth.
• Use ../core-runtime/SKILL.md for Agent, Task, Crew, kickoff, process, guardrail, callback, and output design that happens around retrieval.
• Return to ../../SKILL.md when a request spans multiple CrewAI capability areas and needs root routing context.

Safe Defaults

• Prefer local StringKnowledgeSource, TextFileKnowledgeSource, JSON/CSV sources, or local RagTool.add(path=...) when the user has not approved network retrieval.
• Do not instantiate loaders that fetch web pages, GitHub repositories, YouTube transcripts, or databases unless the user authorizes network/database access and provides credentials if needed.
• Keep embedding provider secrets in runtime environment or user config; never put real API keys, tokens, database URIs, or full local storage paths in generated content.
• Use one embedding model per existing collection. If the embedder changes, reset or rebuild the relevant memory/knowledge/RAG collection rather than mixing vector dimensions.

Usability Targets

• Diagnose a dimension mismatch after changing embedding providers and choose the smallest safe reset/rebuild target.
• Build a knowledge-enabled crew with local document loaders while avoiding accidental network retrieval.