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Turbo Quant Memory is the memory layer that makes AI agents feel like long-term teammates instead of short-term chat sessions.
If you use Claude Code, Codex, Cursor, OpenCode, Gemini CLI, or any MCP client, this is how you keep your institutional knowledge alive between tasks.
Most agent workflows fail in the same place: memory.
- Great insights disappear in chat history.
- Every new task restarts from zero.
- Teams re-explain the same architecture again and again.
Turbo Quant Memory fixes this by making your project knowledge persistent, searchable, and reusable.
| Typical AI workflow | With Turbo Quant Memory |
|---|---|
| Agents forget context between sessions | Agents can continue from saved project knowledge |
| Decisions stay buried in old threads | Decisions become reusable notes |
| Team knowledge stays inside one person's head | Knowledge becomes shared, searchable, and portable |
| Token budget is wasted on repeated reading | Context is loaded smarter, so more budget goes to reasoning |
Your agents stop behaving like temporary assistants and start behaving like members of the team.
- Local-first by design: your memory stays under your control.
- One memory layer for many clients: same knowledge, same standards, same outcomes.
- Built for real delivery: capture decisions, patterns, and handoffs that compound over time.
- Transparent and auditable: memory is explicit, structured, and easy to inspect.
Install:
uv tool install git+https://github.com/Lexus2016/turbo_quant_memory@v0.2.4
turbo-memory-mcp serveConnect in your client as MCP server tqmemory with command turbo-memory-mcp serve.
Client-specific setup files: CLIENT_INTEGRATIONS.md
- AI-first engineering teams
- Solo builders running multiple agents
- Product teams that want consistent AI execution quality
- Anyone tired of repeating context every day
Choose Turbo Quant Memory if you want:
- faster onboarding for every new task
- fewer repeated mistakes
- stronger continuity across sessions
- higher ROI from every agent run
- Client integrations: CLIENT_INTEGRATIONS.md
- Technical spec: TECHNICAL_SPEC.md
- Memory strategy: MEMORY_STRATEGY.md
- Benchmarks: benchmarks/latest.md