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Import files, URLs, and text into OpenMemory and search the knowledge base.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "openmemory-rag-mcp" yet — see the docs or source repo.
Use openmemory-rag-mcp to import this local product specification into the OpenMemory knowledge base and return the import result.
A result or status message confirming the document was imported into the knowledge base.
Use openmemory-rag-mcp to import the content from this URL into the OpenMemory knowledge base for later retrieval.
A status message showing whether the URL content was imported successfully or failed.
Use openmemory-rag-mcp to search the OpenMemory knowledge base for content related to "user permission configuration" and list the matches.
Relevant matches returned from the knowledge base for the query.
Developers or product managers can import local files, web content, and text notes into OpenMemory to centralize knowledge for later retrieval and reuse.
Researchers or technical teams can load reference documents and web sources into the knowledge base first, then use search to retrieve relevant content for RAG-style workflows.
When data has already been ingested, users can search by topic or keyword to quickly locate needed information.
It is an MCP server for importing documents into the OpenMemory RAG knowledge base. Based on the provided description, it supports file uploads, URL imports, text ingestion, and knowledge base search.
According to the description, it supports three ingestion methods: file uploads, URL imports, and plain text ingestion. For specific formats or limits, see the source repository.
It does more than ingestion; it also supports searching the OpenMemory knowledge base. That means it can both add content and query previously stored information.
Add memory storage and retrieval to local or self-hosted LLM workflows.
Turn local notes into a private searchable knowledge base for AI assistants.
Connect Obsidian vaults for local-first AI memory search and knowledge management.
Manage, version, and query a governance-grade Markdown knowledge base.
Safely read, write, and search a local Markdown memory store via MCP.
Lets AI retain conversation memory for more consistent long-term collaboration.