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SQLite memory engine for Aery agents","contributors":[{"name":"Abdias J"}],"maintainers":[{"name":"paryee","email":"aryeebus337@gmail.com"}],"readme":"# @aryee337/aery-mnemopi\n\nLocal SQLite memory engine for Aery agents.\n\nThis package is the Bun/TypeScript port of the Mnemosyne memory engine. It provides:\n\n- `Mnemopi`, a small facade for remember/recall/stats/sleep workflows.\n- `BeamMemory`, the lower-level working/episodic memory engine.\n- MCP tool definitions and a dispatcher for host integrations.\n- Optional local ONNX embeddings through `fastembed` and optional OpenAI-compatible embedding/LLM endpoints.\n\nThe package does not bundle or download a local GGUF LLM. LLM paths are host-backend or OpenAI-compatible remote only; when no LLM is configured, deterministic heuristic paths are used.\n\n## Basic use\n\n```ts\nimport { Mnemopi } from \"@aryee337/aery-mnemopi\";\n\nconst memory = new Mnemopi({ dbPath: \"./mnemopi.db\", bank: \"project\" });\nconst id = memory.remember(\"The deployment target is stable-cluster.\", {\n\tsource: \"notes\",\n\timportance: 0.8,\n\tveracity: \"true\",\n});\n\nconst results = memory.recall(\"deployment target\", 5);\nconsole.log(id, results[0]?.content);\n\nmemory.close();\n```\n\n## Configuration\n\n`Mnemopi` accepts LLM and embedding options directly. `MNEMOPI_*` environment variables remain fallbacks/defaults when the matching constructor option is omitted.\n\n```ts\nimport { Mnemopi } from \"@aryee337/aery-mnemopi\";\nimport type { Model } from \"@aryee337/aery-ai\";\n\nconst ftsOnly = new Mnemopi({ noEmbeddings: true });\n\nconst remoteEmbeddings = new Mnemopi({\n\tembeddingModel: \"text-embedding-3-small\",\n\tembeddingApiUrl: \"https://api.openai.com/v1\",\n\tembeddingApiKey: process.env.OPENAI_API_KEY,\n});\n\nconst remoteLlm = new Mnemopi({\n\tllm: {\n\t\tbaseUrl: \"https://api.openai.com/v1\",\n\t\tapiKey: process.env.OPENAI_API_KEY,\n\t\tmodel: \"gpt-4.1-mini\",\n\t},\n\t// Equivalent aliases: llmBaseUrl, llmApiKey, llmModel.\n});\n\ndeclare const smolModel: Model;\nconst piAiLlm = new Mnemopi({ llm: smolModel });\nconst dynamicLlm = new Mnemopi({\n\tllm: async (prompt, opts) => {\n\t\tconst token = await getFreshOauthToken();\n\t\treturn await completeWithPiAi(prompt, {\n\t\t\ttoken,\n\t\t\tmaxTokens: opts?.maxTokens,\n\t\t\ttemperature: opts?.temperature,\n\t\t});\n\t},\n});\n```\n\n### Banks and host scoping\n\n`Mnemopi` itself exposes banks directly through constructor options such as `bank`; it does not hard-code coding-agent project scoping.\n\nThe Aery coding-agent wrapper adds `mnemopi.scoping` on top of those constructor options:\n\n- `global`: one shared bank\n- `per-project`: isolated project memory\n- `per-project-tagged`: project-local writes plus global recall visibility\n\nIn `per-project-tagged`, the wrapper is responsible for combining project-local retention with global recall visibility. The package still just exposes banks plus constructor-level LLM and embedding options.\n\nCommon environment fallbacks:\n\n- `MNEMOPI_DATA_DIR` / `MNEMOPI_DB_PATH`: default storage location.\n- `MNEMOPI_NO_EMBEDDINGS=1`: force FTS-only recall.\n- `MNEMOPI_EMBEDDING_MODEL`: defaults to `BAAI/bge-small-en-v1.5`.\n- `MNEMOPI_EMBEDDING_API_URL` and `MNEMOPI_EMBEDDING_API_KEY`: OpenAI-compatible embedding endpoint.\n- `MNEMOPI_LLM_ENABLED=1`, `MNEMOPI_LLM_BASE_URL`, `MNEMOPI_LLM_API_KEY`, `MNEMOPI_LLM_MODEL`: OpenAI-compatible LLM endpoint.\n\nLocal embeddings use the `fastembed` npm package. Its default `BGESmallENV15` model is 384-dimensional and uses the package's CLS pooling plus vector normalization path. Local GGUF LLMs are not available in this package.\n\n## Commands\n\n```sh\nmnemopi remember \"Use stable-cluster for production deploys\"\nmnemopi recall \"production deploy target\"\nmnemopi stats\nmnemopi sleep\n```\n\n## Tests\n\n```sh\nbun --cwd packages/mnemopi test\nbun --cwd packages/mnemopi run check\n```\n","readmeFilename":"README.md"}