Encrypted-first embedded database with vector search and agent memory, exposed as MCP tools
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
We haven't yet run this listing's install command through our automated sandbox check. This isn't a red flag β we're steadily working through the catalog.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Historical memory results and configurations
For semantic memory through MCP, install uv and pull the local embedder and optional cross-encoder reranker:
Set CITADEL_KEY to your vault passphrase (export CITADEL_KEY="your-passphrase"
on macOS/Linux or $env:CITADEL_KEY = "your-passphrase" in PowerShell), then start:
The server communicates over stdio. See MCP for client configuration. Model downloads do not need a vault key; serving does.
Install the published package with pip install citadeldb. See the
Python source-build and semantic-memory guide.
Embedders implement embed_with_cancel(texts, cancel_token) and check cancellation
between bounded batches. Local Candle models require the candle-embed build feature.
Uses citadeldb and citadeldb-mem with the candle-embed feature. This example
loads e5-large and a local cross-encoder reranker. Other presets or a custom
Embedder are supported.
Uses the citadeldb and citadeldb-sql crates - or try SQL with no install in the live playground.
A native desktop client for Windows, macOS, and Linux. Open encrypted vaults, browse tables and memory, run SQL with EXPLAIN and ANALYZE, and inspect vectors and integrity results.
See the Studio guide for screenshots and build instructions. Download Citadel Studio for Windows, macOS, or Linux.
The adapters implement framework-specific storage, session, and retrieval interfaces. Each requires an explicit embedder. See the package README for setup, search behavior, and supported filters.
| Framework | Package | Implements |
|---|---|---|
| LangGraph | citadeldb-langgraph | BaseStore |
| CrewAI | citadeldb-crewai | StorageBackend |
| OpenAI Agents SDK | citadeldb-openai-agents | Session |
| Google ADK | citadeldb-google-adk | BaseMemoryService |
| LlamaIndex | citadeldb-llamaindex | BasePydanticVectorStore |
| LangChain | citadeldb-langchain | VectorStore, BaseChatMessageHistory |
| Haystack | citadeldb-haystack | DocumentStore |
| Microsoft Agent Framework | citadeldb-ms-agent-framework | HistoryProvider, ContextProvider |
| Strands Agents | citadeldb-strands-agents | SessionRepository |
One database serves every adapter on the thread that opened it, so a graph's long-term
store and its session transcripts can share one encrypted file. See packaging/ for each
package's own README.
Serve an encrypted memory region to Claude Desktop or any MCP client. citadeldb-mcp is
published to PyPI and listed in the official MCP registry
as dev.citadeldb/mcp. Run it without installing through uvx.
For the recommended semantic-recall setup, pull the embedder and cross-encoder reranker once:
The pull commands do not need a vault key. Before starting the server, set CITADEL_KEY
to the vault passphrase: use export CITADEL_KEY="your-passphrase" on macOS/Linux or
$env:CITADEL_KEY = "your-passphrase" in PowerShell. Then run:
--db, --embedder, and CITADEL_KEY are required when serving. The reranker is optional,
but e5-large with ms-marco-minilm is the configuration used for the memory benchmarks.
To install the executable instead, run pip install citadeldb-mcp or
cargo install citadeldb-mcp. Pull the same models with citadeldb-mcp pull e5-large and
citadeldb-mcp pull ms-marco-minilm, then add it to claude_desktop_config.json:
Recorded LoCoMo and LongMemEval results are summarized below; their configurations and limitations predate the current memory-engine changes. SQL comparisons with unencrypted SQLite across 59 cases are under Speed benchmarks.
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