Self-Learning MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Self-Learning MCPStart a new task to track my code review today"
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 Server
Self-improving memory for AI agents — Antigravity-native MCP server
A persistent memory system that lets AI agents learn from their own work. Records tasks, extracts patterns, detects mistakes, and proactively surfaces insights — all using the agent's own model through a cooperative intelligence pattern.
Quick Start (Antigravity)
# 1. Clone and build
git clone <repo-url> && cd Self-Learning-MCP
npm install && npm run build
# 2. Register in Antigravity
node dist/src/cli.js initThat's it. No API keys. No model config. No env vars. The server uses Antigravity's own model for all reasoning.
Related MCP server: Task Context MCP Server
How It Works
Agent-Cooperative Intelligence
Unlike traditional memory systems that need their own LLM, this server uses a cooperative pattern:
Server handles storage, retrieval, and structuring (SQLite + FTS5)
Agent (running on Antigravity's model) does all reasoning and synthesis
Agent commits learned patterns back to the server
Agent does work → calls mem_end_task → server returns synthesis context
→ agent reasons over it → calls mem_commit_synthesis → patterns stored
→ next task: mem_get_briefing → patterns influence approachThe Learning Loop
┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐
│ Record │────▶│ Recall │────▶│Synthesize│────▶│ Proactive│
│ │ │ │ │ │ │ │
│ Tasks │ │ Briefings│ │ Patterns │ │ Insights │
│ Steps │ │ Context │ │ Anti-pat │ │ Drift │
│ Errors │ │ Wiki │ │ Wiki │ │ Risks │
└──────────┘ └──────────┘ └──────────┘ └──────────┘Compact Wire Codec
All tool outputs use a token-efficient format (~77% smaller than verbose JSON):
Verbose: {"type":"pattern","description":"validate webhooks","confidence":0.95,"tags":["api","security"]}
Compact: {"t":"P","d":"validate webhooks","c":95,"ta":"api|security"}Tools Reference
Record (6 tools)
Tool | Description |
| Begin a task trace |
| Record a step |
| Record a correction |
| Close trace |
| Store entity |
| Create edge |
Recall (6 tools)
Tool | Description |
| Full-text search |
| Context packet for topic |
| Retrieve wiki |
| Structured query |
| Entity details |
| Pre-task intelligence |
Cooperative (3 tools)
Tool | Description |
| Commit learned patterns |
| Save wiki sections |
| Gather wiki context |
Proactive (3 tools)
Tool | Description |
| Active insights |
| Dismiss insight |
| Set watch |
Compact Codec Decoder Ring
Short Key | Full Name |
| type |
| description |
| confidence (0-100) |
| hit count |
| project |
| name |
| content |
| count / total |
| task ID |
| success |
| timestamp |
| section |
Entity Types: T=task, P=pattern, E=error, S=solution, J=project, C=code, R=person
Relationships: RB=resolved_by, DP=depends_on, CB=caused_by, IB=improved_by, FB=followed_by, TF=transferred_from, EF=extracted_from, UI=used_in
Configuration
All optional, via environment variables:
Variable | Default | Description |
|
| Database path |
|
| Minutes between proactive analysis |
|
| Days before pattern flagged stale |
|
| Wire format: |
|
| Log level |
Architecture
src/
├── server.ts # MCP entry point + proactive engine startup
├── config.ts # Env-var configuration
├── cli.ts # Init command for Antigravity setup
├── codec/ # Token-efficient wire format
│ ├── types.ts # Type codes, field maps
│ ├── encoder.ts # Internal → compact
│ ├── decoder.ts # Compact/verbose → internal
│ └── index.ts # Public API
├── db/
│ ├── schema.sql # SQLite schema (13 tables + 5 FTS5)
│ └── database.ts # Database class (SQL embedded)
├── tools/
│ ├── record.ts # 6 recording tools
│ ├── recall.ts # 6 recall tools
│ ├── cooperative.ts # 3 synthesis tools
│ └── proactive.ts # 3 proactive tools
├── wiki/
│ └── generator.ts # Wiki context gathering
└── proactive/
├── engine.ts # Hybrid scheduler orchestrator
├── staleness-detector.ts
├── drift-detector.ts
├── risk-forecaster.ts
├── opportunity-surfacer.ts
├── briefing-assembler.ts
└── index.tsGeneric MCP Usage
Works with any MCP client, not just Antigravity. Add to your MCP config:
{
"mcpServers": {
"self-learning-mcp": {
"command": "node",
"args": ["/absolute/path/to/Self-Learning-MCP/dist/src/server.js"]
}
}
}The difference: without instructions.md, the client agent needs to know when to call the memory tools on its own.
License
MIT
This server cannot be installed
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