How do I use Self-Learning MCP?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Self-Learning MCP Search memory for recent debugging notes on race conditions" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Self-Learning MCP

by abhraweb-boop

Overview Schema Source Related Servers Discussions Admin

TypeScript

Self-Learning MCP

An MCP (Model Context Protocol) server that gives AI coding assistants durable long-term memory, reusable skills, a knowledge graph, post-task reflection, and evidence-based decision intelligence. Runs fully offline with no external API keys.

Created by Abhradeep Paul Chowdhury.

Features

Memory Engine — Store decisions, bugs, fixes, conventions, architecture notes, and prompt outcomes. Semantic search with deterministic hash-based embeddings (256-dim, cosine similarity).
Skill Engine — Create versioned, executable engineering workflows. Auto-learning from repeated successful outcomes.
Knowledge Graph — Nodes and edges across 11 entity types and 7 relationship types. Multi-hop BFS traversal.
Reflection Engine — Post-task analysis with heuristic root-cause categorization. Automatically creates skills from patterns that succeed 3+ times.
Planner — Decompose goals into ordered multi-step plans (build, debug, deploy, refactor, research). Execution loop with auto-retry and reflection.
Decision Intelligence Engine — Evidence-backed decisions with a 9-tier evidence hierarchy. Separates verified facts from assumptions. Refuses high-impact decisions when confidence falls below threshold.
Workspace & Knowledge Management — Project tracking, deployment history, code pattern analysis, full JSON export/import.
Web Dashboard — Next.js UI to visualize and operate all engines (8 tabs: Overview, Memory, Skills, Graph, Reflections, Planner, Decisions, Connect).

Related MCP server: knitbrain

Architecture

                   ┌──────────────────────────────────────────┐
AI Clients ───────>│  MCP stdio bridge (mcp-stdio.ts)         │
  (Claude Code,    │  JSON-RPC over stdin/stdout              │
   Cursor,         └─────────────────────┬────────────────────┘
   OpenCode,                            │
   VS Code)                             │ shares engines
                                        ▼
                   ┌──────────────────────────────────────────┐
Dashboard ────────>│  In-process API (engineApi.ts)           │
  (Next.js,        │  or standalone HTTP server (index.ts)    │
   port 3000)      │     ┌──────────────────────────────────┐ │
                   │     │ memory · skill · graph           │ │
                   │     │ reflection · planner · workspace │ │
                   │     │ decision                         │ │
                   │     └──────────────────────────────────┘ │
                   │              │                           │
                   │         ┌────▼─────┐                    │
                   │         │  SQLite  │                    │
                   │         │ (Prisma) │                    │
                   │         └──────────┘                    │
                   └──────────────────────────────────────────┘

The server runs in two modes:

In-process — All engines load inside the Next.js dashboard process (default, recommended)
Standalone — Hono HTTP server on port 3003 + separate MCP stdio bridge for AI clients

Both modes share the same engines and the same SQLite database.

26 MCP Tools

Category	Tools
Memory	`remember`, `recall`, `search_memory`, `store_memory`, `update_memory`, `delete_memory`
Skill	`learn_skill`, `run_skill`, `search_skills`
Reflection	`reflect`, `analyze_failure`
Graph	`graph_store`, `graph_query`, `graph_update`, `find_related`
Workspace	`project_summary`, `workspace_analysis`, `code_pattern_analysis`, `deployment_history`
Knowledge	`knowledge_export`, `knowledge_import`
Planner	`plan`, `execute_task`
Decision	`decide`, `record_decision_outcome`, `compare_decision_history`

Quick Start

# Prerequisites: Bun >= 1.3, Node.js >= 20
git clone https://github.com/abhraweb-boop/Self-Learning-MCP.git
cd Self-Learning-MCP

# Install root dependencies (dashboard + Prisma)
bun install

# Install MCP server dependencies
cd mini-services/mcp-server && bun install && cd ../..

# Copy environment config
cp .env.example .env

# Push database schema
bun run db:push

# (Optional) Seed demo data
cd mini-services/mcp-server && bun run seed && cd ../..

# Start the standalone HTTP server
cd mini-services/mcp-server && bun run dev

In a second terminal, start the dashboard:

cd Self-Learning-MCP
bun run dev

Open http://localhost:3000 to view the dashboard.

Environment Variables

Variable	Default	Description
`DATABASE_URL`	`file:./db/custom.db`	SQLite database path (relative to `prisma/schema.prisma`)
`LOG_LEVEL`	`info`	Logging verbosity: `trace`, `debug`, `info`, `warn`, `error`
`NODE_ENV`	`development`	Set to `production` for production builds

Connecting AI Clients

Configure your MCP client to spawn bun run mcp from the mini-services/mcp-server directory:

{
  "mcpServers": {
    "self-learning": {
      "command": "bun",
      "args": ["run", "mcp"],
      "cwd": "/absolute/path/to/mini-services/mcp-server"
    }
  }
}

Works with Claude Code, Cursor, OpenCode, VS Code (with an MCP extension), and any MCP-compatible client.

Production Build

bun run build

Creates a standalone .next/ bundle with all static assets. Serve with:

NODE_ENV=production bun .next/standalone/server.js

For production deployments:

Use a reverse proxy (Caddy, Nginx) with TLS
Set NODE_ENV=production
Back up the SQLite database regularly
Use knowledge_export for portable JSON backups

Project Structure

├── prisma/schema.prisma        # Database schema (SQLite)
├── src/                        # Next.js dashboard + in-process engine API
│   ├── app/                    # Dashboard pages and API routes
│   ├── components/             # shadcn/ui dashboard components
│   └── lib/
│       ├── engines/            # Core engines
│       ├── mcp-utils/          # Shared utilities (db, vector, logger)
│       ├── engineApi.ts        # In-process API dispatcher
│       └── mcp.ts              # Dashboard API client
├── mini-services/mcp-server/   # Standalone MCP server
│   ├── src/
│   │   ├── index.ts            # Hono HTTP server (port 3003)
│   │   ├── mcp-stdio.ts        # MCP stdio bridge
│   │   ├── seed.ts             # Demo data seeder
│   │   └── engines/            # Re-exports for standalone use
│   └── docs/                   # Documentation
├── Caddyfile                    # Optional reverse-proxy config
└── .env.example                 # Environment variable template

Technology Stack

Layer	Technology
Runtime	Bun 1.3+
Framework (dashboard)	Next.js 16, React 19
HTTP server (standalone)	Hono 4
Database	SQLite via Prisma 6
UI	shadcn/ui, Tailwind CSS 4
MCP protocol	Model Context Protocol SDK
Validation	Zod
Logging	Pino

Example Usage

Remember a decision:

User: Remember that we use App Router for all pages.
Agent: [calls remember with type=decision, title="Use App Router"...]

Evaluate a decision with evidence:

User: Should we switch from SQLite to PostgreSQL?
Agent: [calls decide with facts about traffic, cost, migration effort...]
        Returns: Refused — insufficient evidence (confidence 35%, threshold 60%).
        Requests more information: traffic metrics, cost comparison, migration plan.

Analyze a failure and auto-learn:

User: The deployment failed because the build ran out of memory.
Agent: [calls analyze_failure with goal="Deploy API" and error details]
        Creates: reflection + prompt_failure memory.
        After 3 similar failures: auto-creates a "deploy-api" skill with extra swap.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Author

Abhradeep Paul Chowdhury

GitHub: @abhradeep-paul-chowdhury

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Maintenance

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Resources

GitHub Repository

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Self-Learning MCP

Features

Architecture

26 MCP Tools

Quick Start

Environment Variables

Connecting AI Clients

Production Build

Project Structure

Technology Stack

Example Usage

License

Author

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Awareness Local

knitbrain

Lockstep Core

local-agent-context