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Local SQLite file, no account.","maintainers":[{"name":"zensation-ai","email":"hello@zensation.ai"}],"readme":"# @zensation/mcp\n\n**Status: early release.** The tool surface is small on purpose and may still\nchange before 1.0. The memory layers underneath it are the same ones the\n[ZenBrain paper](https://arxiv.org/abs/2604.23878) describes and the\n[benchmarks](https://github.com/zensation-ai/zenbrain/blob/main/docs/benchmarks.md) measure.\n\nAn [MCP](https://modelcontextprotocol.io) server that gives any MCP client — Claude\nDesktop, Claude Code, Cursor, or your own — a memory that survives the conversation.\n\nFour tools, one local SQLite file, no account and no network call.\n\n| Tool | What it does |\n|---|---|\n| `zenbrain_store` | Write something into long-term memory. Routing is automatic: steps or instructions become a procedure, content with an emotional weight above 0.5 an episode, a confidence above 0.9 a pinned core memory, anything else a semantic fact. Each call adds a memory; the same core memory stored again is updated. |\n| `zenbrain_recall` | Search the episodic, semantic, procedural and core layers for what is relevant to a query; working memory only when you name it. Results come back ranked, each tagged with the layer it came from. |\n| `zenbrain_consolidate` | One sleep-like maintenance pass: episodes with an emotional weight above 0.5 become semantic facts, each only once, and stale working-memory slots decay. Deletes nothing from long-term memory. |\n| `zenbrain_health` | How full each layer is: slots in use, episodes, facts and how many are due for review, procedures, core blocks. |\n\n## Install\n\nRequires **Node.js 22 or newer**.\n\n```bash\nnpm install -g @zensation/mcp\n```\n\n## Configure your client\n\n```json\n{\n  \"mcpServers\": {\n    \"zenbrain\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@zensation/mcp\"],\n      \"env\": {\n        \"ZENBRAIN_DB\": \"~/.zenbrain/memory.db\"\n      }\n    }\n  }\n}\n```\n\n| Variable | Default | Meaning |\n|---|---|---|\n| `ZENBRAIN_DB` | `./zenbrain.db` | Path to the SQLite file. A leading `~/` means your home directory. A relative path is relative to the directory your client starts the server in — some clients, Claude Desktop among them, may start it in `/`, where nothing can be written — so give an absolute or `~/` path. `:memory:` gives a store that is discarded when the process exits. |\n| `ZENBRAIN_CONTEXTS` | `personal,work,learning,creative` | Comma-separated context domains for cross-context memory. |\n\nThe server speaks MCP over stdio. Stdout carries protocol traffic only; diagnostics go\nto stderr.\n\n## The seven layers\n\nStoring is not filing. Which layer a memory lands in decides how it decays, how it is\nretrieved, and whether it survives consolidation.\n\n| | Layer | Holds |\n|--:|---|---|\n| 7 | Cross-Context Memory | Shared knowledge across domains |\n| 6 | Core Memory | Pinned facts |\n| 5 | Procedural Memory | \"How to do X\" — skills and workflows |\n| 4 | Long-Term Semantic | Facts, with FSRS scheduling |\n| 3 | Episodic Memory | Concrete experiences and events |\n| 2 | Short-Term / Session | Current conversation context |\n| 1 | Working Memory | Active task focus, 7±2 items |\n\nEach layer has its own retention, consolidation and retrieval rules. Review scheduling\nfollows a forgetting curve rather than a fixed timer, and emotionally weighted content\nconsolidates more strongly; both are implemented in\n[`@zensation/algorithms`](https://www.npmjs.com/package/@zensation/algorithms) and can be\nread line by line.\n\nThis server configures **no LLM provider**, so nothing in it calls a model: routing on\nstore is a content heuristic, and consolidation runs without generated summaries.\n\n## What this release does not do\n\nWorth knowing before you wire it in:\n\n- **No embedding provider is configured by default, so recall is lexical, not semantic.**\n  Without vectors, recall ranks by folded token overlap — umlauts and accents are folded,\n  rarer words weigh more, and an adjacent phrase beats the same words scattered. It will\n  find \"Kieler Foerde\" in a note about the \"Kieler Förde\"; it will *not* connect \"car\" to\n  \"automobile\". Ranking runs over the most recent 500 entries per layer, and a query that\n  matches nothing falls back to recency with `score: 0`. Pass an `EmbeddingProvider`\n  through the library for true semantic matching — that is the configuration the published\n  benchmarks were measured with.\n- **SQLite similarity search is a full scan.** Fine for one person's memory; use\n  [`@zensation/adapter-postgres`](https://www.npmjs.com/package/@zensation/adapter-postgres)\n  for larger volumes.\n- **Consolidation is a tool call, not a schedule.** Nothing runs it for you.\n- **The store is a plain file.** It is not encrypted. Put it somewhere you would put a\n  notebook.\n\n## Using it as a library\n\nThe server factory is exported, so you can mount ZenBrain's tools on a server of your own\nor drive them in tests:\n\n```typescript\nimport { createZenBrainServer } from '@zensation/mcp/server';\nimport { MemoryCoordinator } from '@zensation/core';\nimport { SqliteAdapter } from '@zensation/adapter-sqlite';\n\nconst coordinator = new MemoryCoordinator({\n  storage: new SqliteAdapter({ filename: './memory.db' }),\n});\n\nconst server = createZenBrainServer(coordinator);\n// connect it to any transport you like — the caller owns the coordinator's lifecycle\n```\n\n## Zero-dependency, and where that stops\n\n`@zensation/algorithms` and `@zensation/core` pull nothing but each other. That claim is\n[checked in CI on every push](https://github.com/zensation-ai/zenbrain/blob/main/scripts/verify-zero-dependencies.sh)\nagainst the packed tarballs, not against the source tree.\n\n**This package is deliberately outside that boundary.** An MCP server needs the protocol\nSDK, so it carries one. Keeping it in its own package is what lets the core stay clean:\ninstalling `@zensation/core` never pulls the MCP SDK, and installing this never weakens\nthe claim the core makes.\n\n## About ZenBrain\n\nZenBrain is a seven-layer, neuroscience-derived memory architecture for LLM agents, built as\nzero-dependency TypeScript and published under Apache-2.0. The benchmark results, and the\nconfiguration they were measured in, are reported in the paper; the reproduction packages\nare on Zenodo.\n\nWorks out of the box without an embedding provider — lexical ranking, zero\ndependencies. The paper's measurements used `nomic-embed-text` as the embedding provider.\n\n- Source and issues: [github.com/zensation-ai/zenbrain](https://github.com/zensation-ai/zenbrain)\n- Paper: [arXiv:2604.23878](https://arxiv.org/abs/2604.23878) · Open-access archive: [10.5281/zenodo.19353663](https://doi.org/10.5281/zenodo.19353663)\n- Try it in the browser: [zensation.ai/en/playground](https://zensation.ai/en/playground?utm_source=npm&utm_medium=readme&utm_campaign=evergreen)\n- Model card: [huggingface.co/zensation-ai/zenbrain](https://huggingface.co/zensation-ai/zenbrain)\n- Packages: [`@zensation/algorithms`](https://www.npmjs.com/package/@zensation/algorithms) · [`@zensation/core`](https://www.npmjs.com/package/@zensation/core) · [`@zensation/adapter-postgres`](https://www.npmjs.com/package/@zensation/adapter-postgres) · [`@zensation/adapter-sqlite`](https://www.npmjs.com/package/@zensation/adapter-sqlite) · [`@zensation/mcp`](https://www.npmjs.com/package/@zensation/mcp) · [`@zensation/ai-sdk`](https://www.npmjs.com/package/@zensation/ai-sdk) · [`@zensation/cli`](https://www.npmjs.com/package/@zensation/cli)\n- Registry entry: `ai.zensation/zenbrain`\n\nLicense: Apache-2.0\n","readmeFilename":"README.md"}