Deja Vu

Local-first AI memory for agents and assistants.

Your AI tools forget everything between sessions. The ones that don't store your context on someone else's servers. Deja Vu is the third option — a memory layer that runs on your machine, in SQLite, and plugs into anything that speaks Python, REST, CLI, or MCP.

One memory store, every tool. Add a preference from the CLI, retrieve it in Claude Desktop, query it from a Python agent — same database, no sync, no account. Context built up in one tool is immediately available in the next.

Private by default. Memories live in ~/.dejavu on your machine. The only thing that leaves is the LLM call itself, routed through Venice's privacy-focused API. No telemetry, no hosted memory service, no vendor lock-in. Open the SQLite file with any client and inspect every byte.

Quick start

pip install dejavu-memory
dejavu init
dejavu add "I prefer concise technical explanations"
dejavu search "How should responses be written for me?"

That's it. Memories are saved to ~/.dejavu/ and searchable immediately.

Set your Venice API key

Deja Vu uses Venice for memory extraction and reasoning. Get a key at venice.ai and export it:

export VENICE_API_KEY="your-key"

Python

from dejavu import Memory

memory = Memory()
memory.add("I prefer local-first tools.", user_id="local_user")

results = memory.search("What tools do I prefer?", user_id="local_user")
print(results)

Local REST API

dejavu serve
curl http://127.0.0.1:8765/health

Runs on 127.0.0.1 by default — not exposed to your network.

How it works

Deja Vu is three layers: an interface layer (Python, CLI, REST, MCP), a memory engine that extracts and ranks what's worth remembering, and a local SQLite store. Venice handles the LLM calls for extraction and search — everything else runs on your machine.

When you add a memory, the engine asks Venice to pull out durable facts and preferences from the raw text, then writes them to SQLite with embeddings. When you search, it embeds the query, pulls the closest matches, and optionally re-ranks them through Venice for relevance.

Every interface hits the same engine and the same store. Add a memory through the CLI, retrieve it from Claude Desktop over MCP — same database, no sync.

MCP

Run Deja Vu as an MCP server so local agents and editors share the same memory store.

{
  "mcpServers": {
    "dejavu": {
      "command": "dejavu",
      "args": ["mcp"],
      "env": {
        "VENICE_API_KEY": "your-key"
      }
    }
  }
}

Drop this into your Claude Desktop, Cursor, or any MCP-compatible client config.

Interfaces

Interface	Use it for
Python SDK	Embedding memory directly into agents and scripts
CLI (dejavu)	Quick adds, searches, and inspection from the terminal
REST API	Language-agnostic access, local services, internal tools
MCP server	Sharing one memory store across Claude Desktop, Cursor, and other MCP clients

Privacy

• Memories stored locally under ~/.dejavu
• No hosted memory account required
• Telemetry off by default
• LLM calls go through Venice only

Project structure

Deja Vu writes everything to ~/.dejavu/:

~/.dejavu/
├── config.json   ← Venice key, model choices, local settings
├── memories.db   ← SQLite store: memories, embeddings, metadata
└── logs/         ← request logs (off by default, opt-in via config)

The repo itself:

dejavu/           ← core Python SDK and local memory engine
cli/              ← Python and Node CLIs
docs/             ← documentation
examples/         ← demo apps and integration samples
tests/            ← SDK and interface tests

Background

The goal is to make AI memory feel boring and dependable: easy to run, easy to inspect, and easy to move between tools. The design follows the white paper's long-term memory direction while keeping the first public version focused on the parts people can actually use right away.

Attribution

Deja Vu is based on the open-source work from mem0ai/mem0 and is licensed under Apache-2.0.