Persistent memory for AI agents.
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 conversations in real time, extracts durable knowledge, manages raw sensory retention, and injects the right context before every response. One install. No prompting gymnastics. Your AI just knows.
curl -fsSL https://memorycrystal.ai/crystal | bashInstall 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.
You send a message
│
▼
┌─────────────────────────────────────────┐
│ CONTEXT ENGINE │
│ │
│ Semantic search + BM25 across STM/LTM │
│ Knowledge graph boost │
│ Multi-signal reranker │
│ Diversity filter + context budgeting │
│ → Inject top memories into context │
└─────────────────────────────────────────┘
│
▼
AI responds with full context
│
▼
┌─────────────────────────────────────────┐
│ MEMORY EXTRACTION │
│ │
│ Raw message → Short-term memory │
│ LLM extracts facts/decisions → LTM │
│ Graph enrichment links related memories│
└─────────────────────────────────────────┘
Every response is informed by what came before. Every conversation feeds the next one.
| 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.
| 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" |
Memories don't exist in isolation. An async background job connects related memories — decisions link to the lessons that informed them, people link to their projects, rules link to the events that created them.
When the Context Engine searches, graph-connected memories rank higher. Your AI doesn't just remember facts — it understands relationships.
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 |
| People | Ownership, collaborators, and relationships |
| Workflow | Procedures, rules, and how-to memory |
| Conversation | Recent session context and continuity |
The Context Engine picks the right mode. You don't configure anything.
First-class immutable reference collections for docs, policies, runbooks, and imported source material. They sit alongside conversational memory so your agent can keep learned context and stable reference data separate.
- Immutable — imported chunks stay stable, not rewritten by conversation
- Scoped — tenant and scope filters keep KBs private to the right workspace
- Bulk import — standard import or high-volume bulk-insert without blocking on embedding
- Background enrichment — embedding and graph backfill run asynchronously
Every tool works in any MCP host or automatically within OpenClaw hooks.
| Tool | What it does |
|---|---|
crystal_recall |
Semantic search across all long-term memory |
crystal_remember |
Store a memory — decisions, facts, lessons |
crystal_what_do_i_know |
Everything known about a topic |
crystal_why_did_we |
Decision archaeology — why a past choice was made |
crystal_preflight |
Pre-flight check before risky actions |
crystal_search_messages |
Hybrid search over verbatim conversation history, with time-window (fromMs/toMs or ISO dates) and pagination |
crystal_checkpoint |
Snapshot memory state at a milestone |
crystal_wake |
Session startup — briefing and guardrails |
crystal_trace |
Trace a memory back to its source conversation |
crystal_who_owns |
Find ownership of a file, module, or area |
crystal_explain_connection |
Explain relationships between concepts |
crystal_dependency_chain |
Trace dependency chains between entities |
crystal_recent |
Recent messages for short-term context |
crystal_edit |
Update an existing memory |
crystal_forget |
Archive or delete a memory |
crystal_stats |
Memory and usage statistics |
crystal_set_scope |
Override channel scope for the session |
crystal_list_knowledge_bases |
List available knowledge bases |
crystal_query_knowledge_base |
Search a knowledge base |
crystal_import_knowledge |
Import reference chunks into a KB (upsert-by-dedupeKey replaces in place) |
crystal_list_knowledge_base_memories |
Enumerate a KB's chunks with cursor pagination + ids |
crystal_empty_knowledge_base |
Delete all chunks but keep the KB row/id/bindings |
crystal_ideas |
List active Organic ideas and discoveries |
crystal_idea_action |
Act on Organic ideas |
memory_search |
Search LTM and return crystal paths |
memory_get |
Read a full memory by ID or path |
Short-term message tools (crystal_search_messages, crystal_recent, recall message matches, and wake/session summaries derived from recent messages) redact likely secrets before returning tool output. A turn becomes searchable after capture finishes at turn completion; active same-turn tool calls should not expect the current prompt or response to appear yet.
All core operations available over authenticated HTTP:
POST /api/mcp/capture Create a memory
POST /api/mcp/recall Hybrid recall over all memory
POST /api/mcp/search-messages Search short-term history
GET /api/knowledge-bases List knowledge bases
POST /api/knowledge-bases Create a knowledge base
POST /api/knowledge-bases/:id/import Import chunks
POST /api/knowledge-bases/:id/bulk-insert High-volume migration
POST /api/knowledge-bases/:id/query Query a knowledge base
All endpoints require Authorization: Bearer <api-key>. Per-key rate limiting enforced.
memorycrystal/
├── plugin/ OpenClaw plugin — hooks into conversation lifecycle
├── plugins/shared/ Shared hook script for Claude Code, Codex, Factory
├── mcp-server/ MCP server — stdio/HTTP compatibility layer
├── packages/mcp-server/ Streamable HTTP MCP variant
├── convex/ Backend — schema, capture, recall, graph, retention
│ └── crystal/ All Memory Crystal Convex functions
├── apps/
│ ├── web/ Next.js 15 dashboard (Tailwind 4, Convex Auth)
│ └── docs/ Mintlify documentation site
├── scripts/ Install, bootstrap, doctor, enable/disable
└── assets/ Logos and brand assets
Contributors can run an opt-in Dockerized Convex backend and dashboard locally, then seed fixture data without touching the managed production deployment:
npm run convex:local:up
npm run convex:local:seed
npm run convex:local:doctorProvider keys are intentionally split: MEMORY_CRYSTAL_API_KEY is client bearer auth, GEMINI_API_KEY powers Gemini embeddings, and optional OPENROUTER_API_KEY powers organic model features.
The local stack uses http://127.0.0.1:3210 for Convex RPC, http://127.0.0.1:3211 for HTTP actions, and http://127.0.0.1:6791 for the dashboard. See the Local-First Setup guide for end-user setup, local endpoints, backup and rollback behavior, and troubleshooting.
Run everything on your own infrastructure:
git clone https://github.com/memorycrystal/memorycrystal.git
cd memorycrystal && npm install
# Deploy to your own Convex project
CONVEX_DEPLOYMENT=prod:your-project-123 npx convex deploy
# Configure
echo 'CONVEX_URL=https://your-project-123.convex.cloud' > mcp-server/.env
echo 'GEMINI_API_KEY=your-key' >> mcp-server/.env
# Enable and verify
npm run crystal:enable
npm run crystal:doctorFor install-script OpenClaw installs, verify the loaded plugin directly:
openclaw plugins info crystal-memory
openclaw crystal_statusFull guide: docs.memorycrystal.ai/configuration/self-hosting
- Multi-tenant isolation — owner checks on every retrieval, database-level separation
- Per-peer / channel isolation — on multi-client accounts, memories and knowledge-base chunks are scoped by peer channel and fail closed for broad/absent scopes; recall, wake, and graph tools never surface one client's data in another's session
- API keys — SHA-256 hashed at rest, plaintext never stored
- Content scanner — blocks prompt injection, encoded payloads, credential patterns
- Prompt injection mitigation — recalled memories injected as informational context only
- Rate limiting — per-key enforcement on all endpoints
- Audit logging — all actions logged to
crystalAuditLog - Device flow auth — RFC 8628-style for CLI key provisioning
- Local mode — SQLite fallback, data never leaves your machine
| Plan | Price | Memories | STM Retention |
|---|---|---|---|
| Free | $0/mo | 500 | 7 days |
| Pro | $29/mo | 25,000 | 30 days |
| Ultra | $79/mo | Unlimited | 90 days |
| Enterprise | Custom | Custom | Custom |
Self-hosting is always free. Paid plans are for the managed cloud at memorycrystal.ai.
Memory Crystal is MIT open source. PRs welcome.
git clone https://github.com/memorycrystal/memorycrystal.git
cd memorycrystal && npm install && npm run devMIT License — memorycrystal.ai — Operated by Illumin8 Inc.