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is an MCP server that provides markdown-based memory for any AI agent supporting MCP sampling*.","maintainers":[{"name":"arcticzeroo","email":"spencer@frozor.io"}],"readme":"# muse\r\n\r\nMuse is an MCP server that provides markdown-based memory for any AI agent supporting MCP sampling*.\r\n\r\nThe idea is that the agent builds up a memory over time, which essentially becomes documentation for your codebase.\r\nSince they're just markdown files, you can (and should) check them into your version control system to share with\r\nyour team.\r\nWhen memory files are created/deleted/updated outside the MCP server (aka by humans), muse will automatically ingest\r\nthose changes into its memory. It is a good idea to review all documentation generated by Muse and to make edits before\r\nchecking it in.\r\n\r\n*Note: MCP sampling is currently (as of writing) only supported in VSCode, and occasionally has some bugs\r\n(e.g. you may have to restart VSCode every so often in case the server starts getting timeouts from VSCode). This\r\nproject _heavily_ uses MCP sampling, so you should probably only use models that don't count towards your\r\nrequest budget. There is no model hint provided by this project at the moment.\r\n\r\nSampling also tends to be somewhat slow in VSCode right now, but that will probably improve over time. IMO it is still\r\nworth waiting the ~30s for querying or ingestion since the memory is so useful.\r\n\r\n## Installation\r\n\r\nAdd an MCP server with the command `npx -y @arcticzeroo/muse <memory directory>`. Optionally you can also include a \r\ncontext file with `npx -y @arcticzeroo/muse <memory directory> --context <context file>`.\r\n\r\nIn `mcp.json`, this would look like:\r\n\r\n```json\r\n{\r\n  \"servers\": {\r\n    \"muse\": {\r\n      \"type\": \"stdio\",\r\n      \"command\": \"npx\",\r\n      \"args\": [\r\n        \"-y\",\r\n        \"@arcticzeroo/muse\",\r\n        \"<memory directory>\"\r\n      ]\r\n    }\r\n  }\r\n}\r\n```\r\n\r\nAdding a context file can help a lot if your codebase has a lot of invented terminology.\r\n\r\nFurther, it is probably a good idea to add a line to your `copilot-instructions.md` (or equivalent) that says something like:\r\n\r\n```\r\nIMPORTANT: Always query memory in muse before searching/writing code. Once you're done with your task, make sure to ingest into muse's memory with any new information you learned.\r\n```\r\n\r\n## Alternative Installation: use as a library\r\n\r\nMuse can be used as a library instead. Check how it is used in `server/tools` as an example.\r\n\r\n```typescript\r\nimport { MemorySession } from '@arcticzeroo/muse/session';\r\nimport { MCP_SERVER } from './mcp-server.js';\r\nimport z from 'zod';\r\n\r\nconst session = MemorySession.createAsync({\r\n    mcpServer: MCP_SERVER, // should be your MCP server instance from the MCP SDK\r\n    outputDirectory: '<memory directory>',\r\n    contextFile: '<context file>', // optional\r\n});\r\n\r\nMCP_SERVER.registerTool(\r\n    'query',\r\n    {\r\n        inputSchema: z.string()\r\n    },\r\n    async ({ query }) => {\r\n        const result = await session.queryMemory(query);\r\n        return [{\r\n            type: 'text',\r\n            text: result\r\n        }];\r\n    }\r\n);\r\n\r\nawait MCP_SERVER.connect(transport);\r\n\r\nawait session.initializeAfterMcpServerStarted();\r\n```\r\n\r\n## Usage\r\n\r\nOnce you have the MCP server running, you can use it in your AI agent however you like. You can do things like:\r\n- Ask the AI to investigate some feature, architecture, language patterns, etc. and ingest it into memory.\r\n- Ask the AI to go through your existing copilot-instructions file, investigate the related code, and ingest findings into memory.\r\n- Ask the AI to query memory for some topic.\r\n\r\n## Docs\r\n\r\nDocs in this repo `/docs` are generated by muse and slightly edited.","readmeFilename":"README.md"}