by JSingletonAI · MCP Server · ★ 501
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🔒 Is DejaVu safe to install? View the security audit →
DejaVu 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. DejaVu 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 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 That's it. Memories are saved to and searchable immediately. Set your Venice API key DejaVu uses Venice for memory extraction and reasoning. Get a k
| Stars | 501 |
| Forks | 9 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 66.3372781971754/100 |
| Last Updated | 2026-07-07 |
| Created | 2026-05-18 |
| Platforms | mcp, python |
| Est. Tokens | ~14k |
These tools work well together with DejaVu for enhanced workflows:
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DejaVu is DejaVu — private, portable AI memory powered by Venice and stored locally. It is categorized as a MCP Server with 501 GitHub stars.
DejaVu is primarily written in Python. It covers topics such as agents, ai, local-first.
You can find installation instructions and usage details in the DejaVu GitHub repository at github.com/JSingletonAI/DejaVu. The project has 501 stars and 9 forks, indicating an active community.
DejaVu is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to DejaVu on Agent Skills Hub include npcpy, roam-code, memov. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: