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 LLMs do not reliably surface from inside the conversation.

Why an external monitor? LLMs have limited and unreliable self-knowledge: introspection research shows partial self-access that is brittle and degrades on complex or out-of-distribution tasks (Binder et al. 2024; arXiv:2512.12411). So rather than depend on a model reporting its own conversation dynamics, Horizon measures them externally with cheap, deterministic, always-on arithmetic that does not call the model at all.

---

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. It is observability first: it surfaces conversation dynamics that response-level tools miss, using cheap deterministic arithmetic with zero model calls. In four controlled A/B scenarios where Horizon events drove a re-grounding intervention we measured a +15.7% composite quality lift and 87% fewer hallucination events — but those are synthetic, scripted scenarios with a hand-tuned controller, not a production result. Treat them as promising in-house evidence, not a guaranteed outcome (see Validation and LEGAL.md §5). Each signal reduces to a standard information-theory measure; the relativity / Lorentzian framing is design metaphor, not a physical claim (see 4D Spacetime Signals).

• 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:

{
  "mcpServers": {
    "horizon": { "command": "horizon", "args": ["serve"] }
  }
}

Full Cursor and Claude Desktop setup guides: docs/integrations/

---

What it monitors

Standard observability tools evaluate individual response quality. Horizon evaluates conversation quality — a structurally different problem:

Tool	What it sees	What it misses
LangSmith, Braintrust	Latency, cost, per-response quality	Drift across turns
RAGAS, DeepEval	Faithfulness, relevance per turn	Temporal desync, cognitive load
Human raters	Subjective quality	Systematic structural decay
Horizon	Conversation dynamics	Intentionally nothing

Horizon does not replace per-response quality tools. It adds the dimension they all lack.

---

Quickstart

from horizon import FidelityMonitor
from datetime import datetime, timezone

monitor = FidelityMonitor()
session_id = monitor.new_conversation(metadata={"domain": "technical"})

result = monitor.process_turn(
    session_id,
    human_message="How does Python handle memory management?",
    agent_response="Python uses reference counting and a cyclic garbage collector...",
    timestamp=datetime.now(timezone.utc).isoformat(),
)

print(f"Fidelity:         {result.fidelity_score:.2f}")
print(f"Health:           {result.health_status}")
print(f"Circadian factor: {result.circadian_factor:.2f}")
print(f"Causal horizon:   {result.reachable_turns} reachable turns")
for event in result.events:
    print(f"  Event: {event.type} (confidence={event.confidence:.2f})")

---

Framework integrations

OpenAI SDK

from openai import OpenAI
from horizon import FidelityMonitor

monitor = FidelityMonitor()
session_id = monitor.new_conversation()
client = monitor.wrap(OpenAI(), session_id)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Tell me about quantum computing."}]
)

traj = monitor.get_trajectory(session_id)
print(f"Fidelity: {traj.current_fidelity:.2f}  T*: {traj.estimated_t_star}")

monitor.wrap() accepts custom timestamp and context providers for testing and replay.

Anthropic SDK

from anthropic import Anthropic
from horizon import FidelityMonitor

monitor = FidelityMonitor()
session_id = monitor.new_conversation()
client = monitor.wrap(Anthropic(), session_id)

response = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Explain RLHF."}]
)

LangChain

from langchain_openai import ChatOpenAI
from horizon import FidelityMonitor
from horizon.integrations.langchain import HorizonCallback

monitor = FidelityMonitor()
session_id = monitor.new_conversation()
callback = HorizonCallback(monitor, session_id)

llm = ChatOpenAI(callbacks=[callback])
llm.invoke("Explain the CAP theorem.")
print(f"Fidelity: {callback.last_result.fidelity_score:.2f}")

OpenAI Agents SDK

from openai_agents import Agent, Runner
from horizon import FidelityMonitor

monitor = FidelityMonitor()
session_id = monitor.new_conversation()
agent = Agent(name="assistant", model="gpt-4o-mini", instructions="You are helpful.")

for user_message in conversation:
    result = Runner.run_sync(agent, user_message)
    monitor.process_turn(session_id, human_message=user_message,
        agent_response=result.final_output, timestamp=datetime.now(timezone.utc).isoformat())

---

4D Spacetime Signals

"Spacetime" here is a metaphor, not physics. The relativity vocabulary (Minkowski interval, light cone, proper time) is design inspiration — it shaped which quantities we compute. Every signal below reduces to a standard information-theory or arithmetic measure on text embeddings and timestamps, listed in the Plain definition column. Nothing in Horizon's behavior or validation depends on the analogy being literally true, and the Lorentzian interval_class is emitted as descriptive metadata only — no event or score depends on it.

Every process_turn() returns a TurnResult with 29 fields across five signal families:

Core (always present)

Signal	Description
fidelity_score	Composite conversation health [0, 1]
igt_value	Information Gain per Turn — semantic novelty
divergence_score	Jensen-Shannon proxy for intent/response gap
twr_value	Token Waste Ratio — semantic redundancy
consistency_score	Bipredictability — structural coherence
epsilon_t	Estimated ontological gap width [0, 1]
health_status	healthy / degrading / critical / converged
conversation_mode	execute / explore / refine / learn (auto-detected)

Temporal (requires timestamp)

Signal	Description
gap_seconds	Wall-clock gap since last turn
estimated_retention	Human memory retention (Ebbinghaus half-life model)
circadian_factor	Human cognitive capacity at this hour [0.3, 1.0]
temporal_asymmetry	Penalty for temporal desync
resumption_cost	none / low / medium / high / extreme
temporal_references	Resolved deictic expressions ("yesterday", "last week")

Pace (requires timestamp + turn ≥ 2)

Signal	Description
conversation_velocity	Semantic displacement / proper time
conversation_acceleration	Velocity delta (requires turn ≥ 3)

Spacetime (requires timestamp + turn ≥ 2) — descriptive metadata only

Signal	Description (metaphor)	Plain definition (what it computes)
spacetime_interval	ds² with Minkowski-like signature (−,+,+,+)	A 4-term weighted distance: ds² = −α·log(1+Δt)² + β·ΔD_JS² + γ·Δε² + δ·ΔC². The minus sign on the time term is a convention, not a physical law.
interval_class	timelike / spacelike / lightlike	The sign bucket of ds² (< −ε, > ε, else lightlike). Emitted as metadata only — no event or fidelity score consumes it.

Causal (requires timestamp)

Signal	Description (metaphor)	Plain definition (what it computes)
reachable_turns	Turns still inside the causal light cone	Count of prior turns where in_context × retention(Δt) × cosine_similarity > θ — still in-window, not yet memory-decayed, and topically related.
reachable_fraction	Fraction of history still causally reachable	reachable_turns / (turn − 1).

Spatial (requires client_context)

Signal	Description
location_class	home / office / mobile_transit / unknown
spatial_constraint	Attention budget, screen capacity, max response length
spatial_frame_shift	Context switch magnitude

---

14 Event Types

All events default to observe mode (emitted, not acted on). Enable active mode via configure() once your event achieves ≥ 0.7 precision/recall on your domain.

Event	Fires when
checkpoint.clarification	D_JS above clarification threshold
checkpoint.comprehension	Consistency drops below threshold
alert.drift	Fidelity declining for drift_window consecutive turns
alert.contradiction	Bipredictability below consistency threshold
alert.verbosity	Token Waste Ratio above verbosity threshold
signal.convergence	IGT trend consistently low — natural endpoint approaching
signal.optimal_length	T* (estimated optimal length) reached
signal.horizon_widening	IGT trend strongly positive — conversation expanding
signal.session_reset	Large temporal gap with low retention
signal.temporal_desync	Gap + retention drop below desync threshold
signal.broken_reference	Reachable fraction drops below broken-reference threshold
signal.frame_shift	Spatial constraint shifts significantly
signal.pace_shift	Conversation acceleration above pace threshold
signal.light_cone_collapse	Reachable fraction below light-cone threshold

---

Configure

# Per-session override
monitor.configure(
    session_id=session_id,
    clarification_threshold=0.25,           # tighter D_JS gate
    event_modes={"alert.drift": "active"},  # activate one event
)

# Compound weight override
monitor.configure(
    fidelity_weights={"alpha": 0.35, "lambda_r": 0.12, "lambda_i": 0.28, "beta": 0.25},
    temporal_weights={"gamma": 0.08, "delta": 0.04},
    spacetime_coefficients={"alpha": 1.0, "beta": 1.0, "gamma": 0.8, "delta_st": 0.5},
)

---

Export

# JSON
result = monitor.export_to(session_id, target="json")

# LangSmith / Langfuse / OpenTelemetry / Arize
result = monitor.export_to(session_id, target="langsmith",
    connection={"api_key": "ls__..."})

pip install horizon-monitor[langsmith]   # or langfuse, otel, arize

---

Architecture

Input: plain strings (human_message, agent_response, optional timestamp, optional client_context)

Core pipeline (< 50ms on CPU):
  1. Embed both turns (local sentence-transformers, lazy-loaded)
  2–6.  IGT · D_JS · TWR · Bipredictability · Epsilon
  7. Temporal signals  — gap, retention, circadian, deictic
  8. Fidelity dynamics — composite score
  9. Health classification
 10. Pace signals       — velocity, acceleration
 11. Spacetime interval — ds² and interval class
 12. Causal reachability — light-cone membership
 13. Spatial signals    — device, location, frame shift
 14. Mode detection     — auto-classify conversation type
 15. Event evaluation   — 14 threshold checks
 16. Optional: SQLite persistence

Output: TurnResult dataclass (29 fields)

Design constraints (all test-enforced):

• Zero LLM calls — pure arithmetic and local embeddings
• Zero external network calls by default — fully local
• Zero transitive framework dependencies in core
• < 50ms core pipeline on CPU
• < 100MB memory for 100-turn conversations
• All events observe-by-default — never interferes unless explicitly configured

---

Validation

What is proven, and what is not. Horizon's signals are correlational, in-domain measurements that track human quality ratings well. They are observability, not a proven outcome guarantee. Here is the honest status of each claim:

Claim	Status	Where
Fidelity correlates with human ratings (in-domain)	✅ measured (ρ ≈ 0.6–0.7)	gates below
Signal beats naive heuristics	✅ measured	V3
Holds on a third-party corpus (out-of-domain)	❌ tested — ρ = 0.039 on MT-Bench expert judgments (n=80; below 0.3 floor); needs direct quality labels	V0_2_0_EVIDENCE.md §Fix 4, adapt_external_corpus.py
Events predict degradation (leading, not lagging)	⚠️ tested on MT-Bench — insufficient-data (2-turn chats; events rarely fire); tool works	leading_indicator.json, measure_leading_indicator.py
Acting on events improves outcomes (+15.7%)	⚠️ synthetic A/B only; needs an independent corpus	run_interventional_ab.py, LEGAL.md §5

The four gates below pass on a labelled 5,602-record corpus (not bundled — see the evidence pack; scripts/build_validation_corpus.py regenerates a synthetic corpus that exercises the gate logic, not these exact numbers):

Gate	Constraint	v0.2.0
V1 — proxy correlation	per-conv ρ ≥ 0.6, per-turn ρ ≥ 0.5	0.685 / 0.659
V2 — per-event P/R	every event P ≥ 0.7 AND R ≥ 0.7	all 16 events ≥ 0.70 / 0.70
V3 — beats heuristics	rho lift > 25%, structural P ≥ 0.6	+202.4% lift, P=R=1.00
V5 — cross-domain	per-turn ρ ≥ 0.4 AND per-conv ρ ≥ 0.48	min 0.517 / 0.718

Cross-embedding stability: ρ_conv spread 0.026, ρ_turn spread 0.018 across three sentence-transformer backends (22M / 33M / 110M params). The fidelity signal lives in conversational structure, not in the embedding manifold. (Note: cross-embedding stability on the same corpus is distinct from cross-corpus OOD — first third-party run on MT-Bench pairwise labels gave ρ = 0.039; see evidence pack §Fix 4.)

Remediation handoff: docs/reviews/DESIGN_FIXES_HANDOFF.md · gaps source: DESIGN_FIXES_redteam_remediation.md

Full evidence pack: docs/reviews/V0_2_0_EVIDENCE.md

---

Deployment

Self-hosted Docker (MCP server on port 3847)

cd deploy/docker
docker compose up

Horizon serves the MCP API via SSE. Point .cursor/mcp.json to http://localhost:3847/sse. The Dockerfile pre-caches the all-MiniLM-L6-v2 weights at build time — zero cold start.

Hosted (DigitalOcean App Platform)

The official hosted endpoint is live at https://horizon.leocelis.com. It runs on DigitalOcean App Platform (single instance, Redis-backed session resumability) and requires a Bearer token. See Path 1 above.

---

Development

git clone https://github.com/leocelis/horizon.git
cd horizon
python -m venv .venv && source .venv/bin/activate
pip install -r requirements-dev.txt

pytest tests/ -v                         # full suite
pytest tests/unit tests/integration tests/e2e -v   # fast path (~6 min)
ruff check src/ tests/
black --check src/ tests/

---

Repository layout

horizon/
├── src/horizon/         # package source (PEP 517/518 src/ layout)
│   ├── engines/         # IGT, D_JS, TWR, coherence, fidelity, epsilon, mode
│   ├── spacetime/       # temporal, circadian, deictic, velocity, interval, light cone, spatial
│   ├── events/          # 14-event evaluator
│   ├── integrations/    # OpenAI, Anthropic, LangChain, export targets
│   ├── mcp/             # MCP server + CLI
│   └── storage/         # optional SQLite persistence
├── tests/               # unit / integration / e2e / perf / validation
├── examples/            # runnable framework demos
├── deploy/              # Procfile, build.sh, runtime.txt, docker/
├── docs/
│   ├── research/        # market-demand.md + THCP theoretical framework
│   ├── content/         # published pieces on conversation dynamics monitoring
│   ├── integrations/    # Cursor / Claude Desktop / Copilot setup guides
│   ├── spec/            # HORIZON_TECH_SPEC.md + intent.yaml
│   └── reviews/         # E2E reviews, validation evidence
└── pyproject.toml

---

Background

Horizon's design was inspired by the Trans-Horizon Communication Protocol (THCP), a speculative framework that maps human–AI communication onto general-relativity metaphors. The five THCP "conjectures" are design intuitions, not proven laws — each is useful only because it pointed at a concrete, computable signal:

THCP conjecture (metaphor)	Computable signal it inspired
THCP-1 — irreducible ontological loss ε > 0	epsilon_t — estimated intent/response gap width [0, 1]
THCP-2 — an optimal length T* exists beyond which fidelity decays	IGT-trend convergence detection (signal.convergence, estimated_t_star)
THCP-3 — communication requires encode/decode adjunction	consistency_score — bidirectional embedding predictability
THCP-4 — global coherence requires "sheaf gluing" across turns	cross-turn contradiction / claim-consistency checks
THCP-5 — optimal trajectories lie near the "light cone"	reachable_fraction — retention × similarity over prior turns

The metaphors are not load-bearing: drop the physics vocabulary and the signals are exactly the same standard measures. See the status / retraction note in the THCP essay for why the framework is positioned as motivation, not proof.

---

Community

• Request alpha access: Open a Discussion →
• Ask a question: GitHub Discussions
• Bug reports: GitHub Issues
• Contributing: CONTRIBUTING.md

---

License

MIT — see LICENSE.

---

Legal

Document	Purpose
LEGAL.md	Full legal notices: what Horizon is/is not, high-stakes domain warnings, performance claim scope, EU AI Act classification, grounding hook privacy, limitation of liability
TERMS_OF_SERVICE.md	Binding terms governing hosted server access and commercial use
PRIVACY_POLICY.md	GDPR Art. 13 compliant privacy notice — what data is collected and your rights
DATA_PROCESSING_AGREEMENT.md	GDPR Art. 28 DPA template for EU enterprise users (request via email)
SECURITY.md	Responsible disclosure policy; known self-hosted security considerations

Performance claims: The +15.7% quality lift and 87% fewer hallucination events figures in this README are from controlled A/B experiments on a 5,602-record labelled corpus. Results may vary by domain, model, and deployment configuration. Do not use these figures in external marketing without conducting your own domain-specific evaluation. See LEGAL.md §5 for full scope and evidentiary basis.

High-stakes domains: Do not enable event types in active mode in healthcare, legal, financial, or emergency service contexts without domain-specific validation and human oversight. See LEGAL.md §4.