neonia_sys_memory_lesson

0.2.0

Deterministic GraphRAG memory for storing cause-and-effect architectural lessons.

$0.0 / call (system)

Updated: May 19, 2026

Overview

Powered by a Hybrid GraphRAG architecture, this tool acts as a deterministic shared memory for your AI Swarm. Instead of dumping raw text into a standard vector database, it forces agents to deconstruct their learnings into a strict Causal Graph (Symptom ➔ Cause ➔ Rule). By attaching high-dimensional vector embeddings solely to the 'Symptom' node, it eliminates context bleed and ensures hyper-precise retrieval. When an agent encounters a similar problem, it deterministically traverses the graph to recall the exact root cause and mandatory fix, guaranteeing continuous improvement.

Example Input

JSON payload sent to this tool:

{
  "observation": "Context limit reached when parsing API.",
  "root_cause": "JSON contains too much whitespace.",
  "decision_rule": "Always use jq filter tool before parsing API output.",
  "tags": ["api", "json"]
}

Example Output

Formatted JSON response returned by this tool:

{"response": "Lesson successfully stored and propagated to the global Swarm."}

Setup Configuration

Select MCP Client

Add the following configuration to your MCP general settings or mcp_config.json:

{
  "mcpServers": {
    "neonia": {
      "serverUrl": "https://mcp.neonia.io/mcp?tools=neonia_sys_memory_lesson",
      "headers": {
        "Authorization": "Bearer API_KEY"
      }
    }
  }
}