Horizon Fidelity Monitor

"Quality is not a model property — it is a conversation property."

Horizon is a real-time conversation health monitor for AI agents. It tracks the structural dynamics of multi-turn conversations — semantic drift, information gain, ontological gap width, temporal desynchronisation, circadian cognitive load, conversation velocity, and causal reachability — dimensions that every LLM is architecturally blind to.

Based on the Trans-Horizon Communication Protocol (THCP) research. Three independent no-go theorems prove why no LLM can self-monitor these properties from the inside.

Why this exists

Multi-turn AI agents lose accuracy. Our market research puts the number at 39% accuracy degradation after 5 turns — a structural property of conversations that standard observability tools (LangSmith, RAGAS, DeepEval) cannot see because they measure responses, not conversations.

Horizon was built to close that gap. In A/B experiments across four scenarios, adding Horizon monitoring produced +15.7% composite quality lift and 87% fewer hallucination events when Horizon events triggered interventions. The math is grounded in 173 academic references across information theory, cognitive science, category theory, and Lorentzian geometry.

• Read the demand proof → docs/research/market-demand.md
• Read the category argument → docs/content/naming-the-category-conversation-dynamics-monitoring.md
• Read the engineering case → docs/content/why-every-production-agent-needs-conversation-dynamics-monitoring.md

Getting started

Three paths — pick the one that fits your workflow:

Path 1 — Hosted MCP (fastest, zero install)

The fastest way to add Horizon to any Cursor, VS Code, or Claude Desktop workspace. No Python required.

Request an alpha key → open a Discussion, then add the config for your client:

Cursor (~/.cursor/mcp.json):

{
  "mcpServers": {
    "horizon": {
      "url": "https://horizon.leocelis.com/sse",
      "headers": { "Authorization": "Bearer YOUR_KEY_HERE" }
    }
  }
}

VS Code / GitHub Copilot (.vscode/mcp.json in your workspace):

{
  "servers": {
    "horizon": {
      "type": "http",
      "url": "https://horizon.leocelis.com/sse",
      "headers": { "Authorization": "Bearer YOUR_KEY_HERE" }
    }
  }
}

VS Code note: Use "servers" (not "mcpServers") and "type": "http" — VS Code tries Streamable HTTP first and falls back to SSE automatically, so "type": "http" works with the /sse URL.

Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "horizon": {
      "url": "https://horizon.leocelis.com/sse",
      "headers": { "Authorization": "Bearer YOUR_KEY_HERE" }
    }
  }
}

That's it. Reload your MCP client and three tools appear: new_conversation, process_turn, configure_session.

Alpha access: Horizon's hosted endpoint is in private alpha. Keys are distributed to agent developers who want to monitor real projects. Open a Discussion to request one — describe your use case and we'll send a key.

Path 2 — pip install (library integration)

pip install horizon-monitor

Verify your install (exercises the full pipeline on 5 canonical scenarios, ~25s):

horizon-validate

Path 3 — MCP server from source

pip install 'horizon-monitor[mcp]'
horizon serve                             # stdio — for Cursor, Claude Desktop
horizon serve --transport sse --port 3847 # SSE — for web/team deployments

Add to ~/.cursor/mcp.json:

{


... [View full README on GitHub](https://github.com/leocelis/horizon#readme)