deja-vu

deja-vu is an AI builder tool for teams that need a practical workflow component rather than another vague assistant promise. The public source used for this listing is https://github.com/vshulcz/deja-vu. The project description says: Memory layer for coding agents: search, MCP recall, auto-context, secret redaction, stats, share and sync over the session logs Claude Code, Codex, opencode, Cursor, Gemini CLI, aider, Antigravity, Grok Build and Qwen Code already write. One zero-dep binary.. At review time, ★ 340 stars · 16 forks · Updated 2026-07-17. Those signals help builders judge whether the project is active enough to test, but the official source should remain the place to check the latest setup and support details. The core use case is straightforward: deja-vu helps with adds memory and recall for ai coding agents; connects with model context protocol compatible workflows; supports agent-oriented automation and technical workflows; open-source repository with public code and issue tracking; builder-focused setup path for technical teams. A developer can evaluate it by reading the documentation, testing a small workflow, connecting it to the model or agent stack they already use, and deciding whether the result saves enough manual work to keep. This makes it most useful for technical buyers who care about transparency, source context, and fast experiments. For AI coding and operations teams, deja-vu can sit beside coding agents, spreadsheets, memory layers, MCP clients, internal tools, or local development environments. The important question is not whether it replaces a full platform; it is whether it removes a specific bottleneck. Teams can use it to prototype a repeatable agent workflow, record or recall project context, run spreadsheet-compatible logic for agents and humans, or add a focused operating layer around model-driven work. Pricing depends on the deployment path. The source reviewed here is available through its official site or public repository under MIT License. That does not make every real deployment free. Users may still pay for model API calls, connected SaaS accounts, browser sessions, storage, hosting, analytics events, or support. Treat the tool cost and the connected infrastructure cost as separate line items when comparing it with a hosted alternative. The main risk is maturity. Fast-moving AI infrastructure can change quickly, and smaller projects may have incomplete docs, uneven issue response, or breaking updates. Before using deja-vu in production, review the current setup guide, recent changes, security model, license or terms, and any data that leaves your environment. If the fit is strong, it is a useful candidate for controlled experiments and internal workflows. If your team needs strict uptime guarantees, compliance paperwork, or vendor-backed support, validate those requirements before rollout. OpenTools classifies deja-vu as a tool because the durable entity is the software or runtime users operate. The buying decision is whether it solves a concrete AI workflow problem, connects cleanly to the current stack, and reduces enough repetitive work to justify setup and maintenance.