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Two-layer memory MCP server for AI agents with 37 tools, RAG, graphs, wiki, auth
Two-layer memory MCP server for AI agents with 37 tools, RAG, graphs, wiki, auth
Valid MCP server (1 strong, 4 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-cipher208-ariel-memory": {
"args": [
"-y",
"mcp-ariel-memory"
],
"command": "npx"
}
}
}From the project's GitHub README.
Universal Two-Layer Memory MCP Server
A two-layer universal memory system for AI agents. Real MCP Python SDK, async, 37 tools, stdio + HTTP transports, dashboard, metrics, authentication, automatic backups, external wiki folders, read-only replica.
mcp-ariel-memory is a production-ready MCP (Model Context Protocol) server that provides persistent, searchable memory for AI agents. It implements a two-layer architecture:
The server is built with the official MCP Python SDK (FastMCP), supports both stdio and HTTP transports, and includes enterprise features like authentication, rate limiting, automatic backups, and a real-time dashboard.
npx mcp-ariel-memory --transport stdio
Requires Python 3.10+ on the system. The npm wrapper automatically installs the Python package.
pip install git+https://github.com/Cipher208/mcp-ariel-memory.git
python -m mcp_server --transport stdio
docker build -t ariel-memory .
docker run -p 8000:8000 ariel-memory
git clone https://github.com/Cipher208/mcp-ariel-memory.git
cd mcp-ariel-memory
pip install -e ".[all]"
python -m mcp_server --transport stdio
Add to claude_desktop_config.json:
{
"mcpServers": {
"ariel-memory": {
"command": "npx",
"args": ["mcp-ariel-memory", "--transport", "stdio"]
}
}
}
Or with Docker:
{
"mcpServers": {
"ariel-memory": {
"command": "docker",
"args": ["run", "--rm", "-i", "ariel-memory", "--transport", "stdio"]
}
}
}
# Start HTTP server
python -m mcp_server --transport http --port 8000
# Or with Docker
docker run -p 8000:8000 ariel-memory --transport http --port 8000
# Or with dashboard + metrics
docker run -p 8000:8000 ariel-memory --transport http --port 8000 --dashboard
docker-compose up
| Platform | Method | Notes |
|---|---|---|
| Windows | npm / pip / Docker | aiosqlite fallback (sync sqlite3 + to_thread) |
| Linux | npm / pip / Docker | aiosqlite (native async) |
| macOS | npm / pip / Docker | aiosqlite (native async) |
| Docker | Any | Works on all platforms with Docker |
| Feature | Description |
|---|---|
| 37 MCP Tools | User layer (10), Agent layer (10), Auth (3), Backup (4), Saga (2), Replica (1), Import/Export (3), Maintenance (1), Emergency (1), Search (1), Context (1) |
| Two-Layer Memory | L1 ReflexBuffer → L2 SessionStore → L3 EpisodicMemory → L4 CoreMemory |
| Hybrid Search | FTS5 + vector similarity via Reciprocal Rank Fusion (RRF) |
| Knowledge Graph | Epistemic graph (facts, decisions) + Temporal graph (timeline) |
| Wiki System | 14 types (7 user + 7 agent), .md files as source of truth, FTS5 index |
| 24 Hooks | 12 user hooks + 12 agent hooks, integrated into tool pipeline |
| Saga Pattern | Multi-step operations with compensation, timeout, watchdog |
| Dashboard | HTML dashboard with stats, facts, episodes, audit log |
| Auth | API keys + Bearer tokens, persistent storage |
| Backup | Auto-backups with jitter, restore, cleanup |
| Metrics | Prometheus-compatible metrics endpoint |
| Read-Only Replica | SQLite read-only replica for queries |
| Embeddings | Multilingual (100+ languages including Russian) |
Message → L1 (ReflexBuffer, ring buffer, 50 items)
→ ImportanceGate (noise filter, threshold 0.3)
→ L2 (SessionStore, SQLite, 100 sessions)
→ EmotionTrigger (emotional analysis)
→ L3 (EpisodicMemory, SQLite, 1000 episodes)
→ L4 (CoreMemory, key-value, 5000 facts)
| Table | Module | Purpose |
|---|---|---|
core_memory | core/memory.py | L4 key-value facts |
sessions | core/session.py | L2 session history |
episodes | core/episodic.py | L3 episodic memories |
staging_memories | shared/dream_buffer.py | Temporary staging |
archived_memories | shared/archived_memories.py | Archived memories |
audit_log | features/audit_trail.py | Audit trail |
rate_limits | features/rate_limiting.py | Rate limiting |
embedding_cache | shared/embeddings.py | Cached embeddings |
rag_pages | rag/engine.py | RAG document pages |
rag_chunks | rag/engine.py | RAG document chunks |
rag_relations | rag/engine.py | RAG relations |
epi_nodes | graph/epistemic.py | Epistemic graph nodes |
epi_edges | graph/epistemic.py | Epistemic graph edges |
temporal_events | graph/temporal.py | Temporal events |
temporal_links | graph/temporal.py | Temporal links |
user_wiki | wiki/user_wiki.py | User wiki entries |
agent_wiki | wiki/agent_wiki.py | Agent wiki entries |
wiki_index | wiki/file_wiki.py | Wiki FTS5 index |
memory_conflicts | rag/conflict.py | Memory conflicts |
migration_log | shared/migrations.py | Migration history |
api_keys | features/auth.py | API keys |
| # | Document | Description |
|---|---|---|
| 01 | Architecture | Stack, two-layer model, L1-L4, consolidation |
| 02 | MCP Tools | All 37 tools with parameters and examples |
| 03 | Core Memory | ReflexBuffer, SessionStore, EpisodicMemory, CoreMemory |
| 04 | Search (RAG) | FTS5 + fallback, RRF, RetrievalRouter, ConflictResolver |
| 05 | Knowledge Graph | EpistemicGraph, TemporalGraph |
| 06 | Lifecycle | Forgetting, EmotionTrigger, Consolidation |
| 07 | Hooks | 24 hooks (12 user + 12 agent) |
| 08 | Wiki | FileWiki (.md files + FTS5) |
| 09 | Features | Auth, Backup, Dashboard, Audit, RateLimit |
| 10 | Shared | Cache, Saga+Watchdog, Middleware, Embeddings, Metrics |
| 11 | Operations | Transports, Dashboard, Auth, Backup, Configuration |
| 12 | Testing | pytest, project structure |
# Run all tests (104 passed)
pytest tests/ -v
# Run only integration tests
pytest tests/test_integration.py -v
# Run with coverage
pytest tests/ --cov=. --cov-report=term-missing
# config.yaml (optional, mounted as volume)
layers: { user: { enabled: true }, agent: { enabled: true } }
limits: { l1_buffer_size: 50, l4_core_limit: 5000 }
hooks: { user: { message_received: true }, agent: { error_occurred: true } }
forgetting: { decay_rate: 0.01, archive_threshold_days: 90 }
rag: { fts_enabled: true, vec_enabled: true }
embeddings: { model: "BAAI/bge-small-en-v1.5" }
wiki:
user: { diary: true, external_dirs: ["/path/to/notes"] }
agent: { decision_log: true, external_dirs: ["/path/to/lore"] }
auth: { api_keys_enabled: true, bearer_token_enabled: true }
backup: { auto_backup: true, backup_interval_hours: 24 }
# Install dev dependencies
pip install -e ".[dev]"
# Run linter
ruff check .
# Format code
ruff format .
# Run tests
pytest tests/ -v
MIT License - see LICENSE for details.
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