Best for
- Claude users who need persistent memory across conversations
- Developers who want local persistent memory in their projects
- Users concerned about data privacy and prefer local storage over cloud
M
mcp-knowledge-graph
Community
Persistent memory for AI models through a local knowledge graph.
GitHub source repository ↗
56FMRS · C
This server offers a flexible and safe design for AI memory management, supporting project-local and global storage with multi-database features. Installation is straightforward, though compatibility with Claude API needs verification.
mcp-knowledge-graph is an MCP server that enables AI models (like Claude) to store and retrieve information across conversations. It uses entities, relations, and observations to build a knowledge graph, and supports multiple databases (master and named) as well as project-local or global storage. The server emphasizes safety with file markers (_aim) to prevent accidental overwrites of unrelated files. It is designed for local development, suitable for users who want their AI assistant to remember long-term information.
Tools
aim_memory_store
Stores new memories (people, projects, concepts) into the knowledge graph.
aim_memory_add_facts
Adds facts to existing memories.
aim_memory_link
Links two memories together.
aim_memory_search
Searches memories by keyword.
aim_memory_get
Retrieves specific memories by exact name.
aim_memory_read_all
Reads all memories in a database.
aim_memory_list_stores
Lists all available databases.
aim_memory_forget
Forgets memories.
aim_memory_remove_facts
Removes specific facts from a memory.
aim_memory_unlink
Removes links between memories.
Setup
Prerequisites: Node.js 22+. Setup: Add the MCP server configuration to your Claude client configuration file, specifying a local path (e.g., --memory-path), then start the client to automatically install and run.
claude_desktop_config.json
{
"mcpServers": {
"Aim-Memory-Bank": {
"command": "npx",
"args": [
"-y",
"mcp-knowledge-graph",
"--memory-path",
"/Users/yourusername/.aim"
],
"autoapprove": [
"aim_memory_search",
"aim_memory_get",
"aim_memory_read_all",
"aim_memory_list_stores"
]
}
}
}
Fit and risk
Not for
- Scenarios requiring distributed or high-concurrency storage
- Advanced security needs like built-in encryption or access control
- Users who wish to store memory files in the cloud managed by a service provider
Required permissions
- Read and write to the local file system (within specified --memory-path or project .aim directory)
- Run npx commands to start the server
Risks and side effects
- Local files might be accidentally deleted or overwritten (the system has _aim marker protection, but risk remains)
- Sensitive information stored locally could be exposed if not encrypted
- Dependency on Node.js version and npm package management may cause conflicts
Troubleshooting
- If you encounter 'File does not contain required _aim safety marker' error, ensure the file starts with the correct marker or delete the file and let the system recreate it.
- If memories appear in unexpected locations, check if you are in a project directory with a .aim folder, and use aim_memory_list_stores to see the current storage location.
- If too many similar databases, manually delete unwanted .jsonl files and encourage the AI to use consistent naming.
Use cases
Personal memory management: Let AI assistants remember your preferences, birthdays, project info, etc.
Project-local memory: Create a .aim folder in your project directory to automatically use project-level memory.
Cross-device sync: Use a cloud-synced directory like Dropbox to store memory files for multi-device access.
Supported clients
Claude DesktopFull support
Claude CodeFull support