JSingletonAI/dejavu — Memory that follows you across every AI tool.

Description and use

**Brief Summary (2‑3 sentences)**
JSingletonAI’s **dejavu** is a Python‑based, open‑source memory layer that lets AI assistants retain context across any tool without relying on cloud storage or user accounts. By exposing a standard Model Context Protocol (MCP) via an API/SDK/CLI, it enables developers to plug real‑world tools and data into LLM workflows with a single setup step. The project is modestly popular (33 ★, 3 forks) and actively maintained as of 2026‑06‑29.

**Value**

• **Cross‑tool continuity**: AI agents can recall prior interactions, files, or results no matter which downstream service they invoke, dramatically improving user experience and reducing repeated prompts.
• **Zero‑trust data handling**: All memory stays on‑premises, eliminating cloud‑storage compliance concerns and removing the need for user accounts.
• **Standardized integration**: By implementing the Model Context Protocol, dejavu provides a common contract for tool‑to‑AI communication, simplifying the creation of reusable connectors and accelerating the rollout of “AI‑augmented” products.

**Practical Adoption Path**

1. **Prototype** – Clone the repo, spin up the provided Docker/CLI server, and call the Python SDK from a sandboxed LLM app to store/retrieve context.
2. **Connector Development** – Use the MCP spec to wrap internal tools (databases, CLIs, SaaS APIs) as “memory‑aware” endpoints, reusing the existing API/SDK.
3. **Internal Rollout** – Deploy the server behind your corporate firewall, configure role‑based access, and integrate with your CI/CD pipeline for versioned updates.
4. **External Release** – Once the integration is stable, expose the MCP server as a managed service for partner AI agents or embed the SDK in client libraries.

**Production Readiness**

• **Maturity**: Medium. The codebase is functional and recent, but it lacks extensive production‑grade testing, observability, and formal SLA guarantees.
• **Dependencies**: Light (pure Python + optional Docker), but you should audit third‑party packages for security patches.
• **Maintenance**: Small contributor base; verify that maintainers are responsive and that the licensing (likely MIT/Apache) aligns with your policy.
• **Risk Mitigation**: Conduct a security review of the API surface, add logging/monitoring, and run load tests before scaling to high‑traffic environments. With these steps, dejavu is suitable for internal prototypes and can be hardened for production use.

Problem

JSingletonAI/dejavu helps connect AI assistants to real tools and data through a standard protocol.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Evidence

Memory that follows you across every AI tool. No cloud storage. No account required. Set it up once, use it everywhere.

Source