Neural memory
About
A memory for AI, without cloud service or fee, everything local (Most useful for coding)
Details
- Author
- drakonkat
- Categories
- Developer Tools, AI
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Setup
Install Neural memory in your MCP client (Claude Desktop, Cursor, Windsurf, and others).
Repository: https://github.com/drakonkat/neural-memory
Follow the installation instructions in the repository README, then restart your MCP client.
⚠️IMPORTANT NOTICE: This is anexperimental MCP (Model Context Protocol)tool. While it has shown promising results when used locally, it is still under active development and may evolve over time.Feedback and contributions are highly appreciated!Please report any issues, suggestions, or feature requests on ourGitHub Issuespage. Your input helps make this tool better for everyone! nya~
Model Context Protocol (MCP) system for knowledge memorization and contextual retrieval.
Want to try it right away in your Cline? Add this configuration to yourcline_mcp_settings.json:
{ "mcpServers": { "neural-memory": { "timeout": 120, "type": "stdio", "command": "npx", "args": [ "-y", "@drakonkat/neural-memory@latest" ] } } }
{ "mcpServers": { "neural-memory": { "timeout": 120, "type": "stdio", "command": "node", "args": [ "./neural-memory/src/index.js" ] } } }
⚠️Note:Replace the path with your local project path!
- No more separate databases per project!All memory resides in a single SQLite database
- Faster, simpler, zero configuration
- Rigid schemafor registering skills with consistent structure
- framework,language,filePattern,learnSteps,useCases
- Automatic context-based suggestions
- Generate visual reports of your memory
- Statistics, most used skills, recent work
import { initialize, handleMcpRequest } from 'neural-memory'; // Initialize the service await initialize(); // Add a node await handleMcpRequest('add_node', { keywords: ['fastify', 'middleware', 'auth'], content: 'Implementation of JWT middleware...', type: 'generic' }); // Register a skill await handleMcpRequest('register_skill', { name: 'Fastify CRUD API', framework: 'fastify', language: 'javascript', filePattern: '/.service.js', learnSteps: [ '1. Initialize project', '2. Create validation schema', '3. Implement handler' ], useCases: [ 'Create REST API', 'Handle standardized errors' ] }); // Search for skills const skills = await handleMcpRequest('suggest_skills', { currentKeywords: ['fastify', 'api'], domain: 'javascript' }); // Save context await handleMcpRequest('save_context_snapshot', { summary: 'Refactoring 80% complete', pendingTasks: ['Integration testing', 'Deploy to staging'], learnings: ['Prisma requires explicit migrations'] }); // Generate report const report = await handleMcpRequest('get_memory_report', { format: 'html' });
The system calculates aconfidence score (0.0-1.0)for each result based on:
- BM25 Score(35%) - Full-text search ranking
- Keyword Match(25%) - Keywords overlap
- Recency Bonus(10%) - Recent nodes preferred
- Type Score(15%) - Skills/errors are more important
- Weight(15%) - Manual weight
{ "name": "Skill Name", "framework": "fastify|react|prisma|...", "language": "javascript|typescript|python|...", "filePattern": "/.service.js", "learnSteps": ["Step 1", "Step 2"], "useCases": ["Case 1", "Case 2"], "implementation": "// Code...", "examples": ["file1.js"], "prerequisites": ["Prerequisite 1"] }
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