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Dejaview Mcp

Dejaview Mcp

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github.com/JakeC77Memory & Knowledge0Updated 34w ago
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About Dejaview Mcp

Dejaview Mcp connects MCP-compatible hosts to DejaView, a persistent knowledge graph for AI agents. It stores and retrieves facts about people, projects, decisions, and relationships across sessions, with cloud and local server options.

Key features

  1. 01Persistent graph memory across sessions
  2. 02Store single or multiple facts
  3. 03Recall entities and search by name
  4. 04Natural-language graph Q&A with citations
  5. 05View timelines and graph statistics
  6. 06Share or delete graph data

Use cases

  1. 01Carry project context between agent sessions
  2. 02Record decisions and relationships
  3. 03Look up stored facts about a person
  4. 04Remove outdated facts or entities

MCP support

It exposes DejaView graph operations as MCP tools for compatible hosts, including memory storage, retrieval, search, and deletion. It can connect through a cloud endpoint or run locally.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://api.dejaview.io/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

All endpoints
Streamable HTTPhttps://api.dejaview.io/mcpAuth required

Tools

11

This tool takes no parameters.

Details

From the project README

dejaview-mcp

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.


Option 1: Cloud (no install required) ⚡

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.


Option 2: Local (pip install)

If you prefer to run the server locally:

pip install dejaview-mcp

Add 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

What it does

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.

ToolWhat it does
agent_contextLoad a full memory summary at session start
rememberStore a fact (subject, predicate, object)
remember_manyStore multiple facts at once
recallGet everything known about an entity
searchFind entities by name
askNatural language Q&A over the graph with citations
timelineSee recently stored facts
graph_statsEntity and relationship counts
shareGenerate a public shareable link for any entity
forgetRemove a specific fact
forget_entityRemove an entity and all its connections

Example

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.


Self-hosting

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-mcp

Links

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.