Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@dejaview-mcp Remember that Alice is the lead on Project Atlas"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Persistent knowledge graph memory for AI agents — an MCP server backed by DejaView.
Connect Claude Desktop, Cursor, Windsurf, or any MCP-compatible host to a live knowledge graph. Your AI remembers people, projects, decisions, and relationships across every session.
Get your free API key at dejaview.io.
The fastest way to get started — no pip install, no local process.
{
"mcpServers": {
"dejaview": {
"type": "streamable-http",
"url": "https://api.dejaview.io/mcp",
"headers": {
"Authorization": "Bearer dv_your_key_here"
}
}
}
}Paste this into your Claude Desktop, Cursor, or Windsurf MCP config. Done.
Related MCP server: Oceanir Memory
If you prefer to run the server locally:
pip install dejaview-mcpAdd to your config:
{
"mcpServers": {
"dejaview": {
"command": "dejaview-mcp",
"env": {
"DEJAVIEW_API_KEY": "dv_your_key_here"
}
}
}
}Config file locations:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Linux: ~/.config/claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
DejaView gives your AI a persistent knowledge graph it can read from and write to across sessions. Unlike flat context windows that reset every chat, the graph grows over time.
Tool | What it does |
| Load a full memory summary at session start |
| Store a fact (subject, predicate, object) |
| Store multiple facts at once |
| Get everything known about an entity |
| Find entities by name |
| Natural language Q&A over the graph with citations |
| See recently stored facts |
| Entity and relationship counts |
| Generate a public shareable link for any entity |
| Remove a specific fact |
| Remove an entity and all its connections |
Once connected, just chat naturally:
"Remember that Alice is the lead on Project Atlas and prefers async communication."
The agent calls remember() automatically. Next session:
"What do I know about Alice?"
The agent calls recall("Alice") and tells you everything — including things you told it months ago.
Want to run your own DejaView instance? The API is open source: github.com/JakeC77/DejaView
Set DEJAVIEW_ENDPOINT to point at your instance:
DEJAVIEW_API_KEY=dv_... DEJAVIEW_ENDPOINT=https://your-instance.com dejaview-mcpWebsite: dejaview.io
API: api.dejaview.io/docs
GitHub: github.com/JakeC77/DejaView-MCP
This server cannot be deployed
Persistent knowledge graph for AI agents. Remember, recall, and forget facts.
Persistent knowledge graph for AI-augmented teams. Store decisions, findings, and standing rules across agent sessions with semantic search and typed connections. Includes cross-session memory, audit trail, workspace isolation, and secret detection. Built for teams running agents that need to remember. Free until launch with team tier as default, anon trial available.
Shared long-term memory for AI agents: save and recall context as a searchable knowledge graph.
Persistent memory for AI agents. Semantic search, memory graph, W3C DID identity.
