memorycrystal/memorycrystal

Every conversation remembered. Every decision recalled. Every session informed.
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Your AI forgets everything between sessions — who you are, what you decided, what failed, what works. Memory Crystal fixes that.
It captures each completed conversation once, distills durable knowledge only in bounded session-boundary or indexed nightly batches, and recalls the right context when an agent needs it. The turn write performs no inference. Capture stays automatic; recall defaults to deliberate tool calls so idle sessions cost nothing.
bash
curl -fsSL https://memorycrystal.ai/crystal | bash
## Works with everything
Install Memory Crystal on any MCP-compatible AI tool in one command:
| Platform | Install |
| Claude Code | curl -fsSL https://memorycrystal.ai/install-claude-mcp.sh \| bash |
| Codex CLI | curl -fsSL https://memorycrystal.ai/install-codex-mcp.sh \| bash |
| Factory Droid | curl -fsSL https://memorycrystal.ai/install-droid-mcp.sh \| bash |
| OpenClaw | curl -fsSL https://memorycrystal.ai/crystal \| bash |
| Claude Desktop | Add the MCP server in settings (guide) |
| Any MCP host | Point at https://api.memorycrystal.ai/mcp with a Bearer token |
The universal installer supports cloud, local, and self-hosted backends. It can register MCP clients, configure OpenClaw, install Codex/Claude hook assets, and stage a Dockerized local Convex backend for offline or self-hosted work.
## How it works
You send a message
┌─────────────────────────────────────────┐
│ CONTEXT ENGINE │
│ │
│ Vector search across durable LTM │
│ Lexical fallback over recent messages │
│ Deterministic bounded ranking │
│ Scope filtering + context budgeting │
│ → Inject top memories into context │
└─────────────────────────────────────────┘
AI responds with full context
┌─────────────────────────────────────────┐
│ MEMORY EXTRACTION │
│ │
│ Completed turn → STM (idempotent) │
│ Bounded extraction → durable LTM │
│ One authoritative LTM vector index │
└─────────────────────────────────────────┘
Every response is informed by what came before. Every conversation feeds the next one.
## Two memory layers
| Layer | Stores | Retention |
| Short-term (STM) | Recent raw messages, verbatim | Rolling window by tier |
| Long-term (LTM) | Facts, decisions, lessons, people, rules | Permanent, vector-indexed |
| Sensory raw content | Raw sensory payloads behind memory records | Summarized, tombstoned, or protected by policy |
STM gives recent continuity. LTM gives permanent knowledge. Sensory retention lets Memory Crystal keep durable recall value without keeping every raw payload forever.
## Five memory stores
| Store | Purpose | Example |
| sensory | Raw signals | "the user sounds frustrated about the deploy" |
| episodic | Events | "We shipped v2 on March 15" |
| semantic | Facts | "The API uses Convex for the backend" |
| procedural | How-to | "Deploy with npm run convex:deploy" |
| prospective | Plans | "Add billing webhooks next sprint" |
## Adaptive recall
Six modes, automatically selected:
| Mode | Prioritizes |
| General | Broad recall across STM + LTM |
| Decision | Decisions, lessons, and rules before risky changes |
| Project | Goals, workflows, and implementation context |