Chronohorn

Chronohorn

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> Family-agnostic experiment tracker and architecture-search runtime for predictive descendants. > Built on the shared decepticons kernel.

What It Does

• Tracks experiments from any model family, stores them in SQLite, and keeps legality/trust state attached to results
• Analyzes curves and frontiers with saturation, marginal ranking, velocity, and ablation-board views
• Runs the search loop through manifest-driven fleet dispatch, drain, result pull-back, and auto-deepen/control surfaces
• Exposes runtime state to agents through 64 MCP tools, a terminal observer, and an HTTP runtime dashboard

Chronohorn is family-agnostic at the runtime layer. Family-specific mutation policy lives under python/chronohorn/families//; the shared mechanism layer stays in the decepticons kernel.

Quick Start

# Install from PyPI (decepticons kernel pulled automatically)
python3 -m pip install chronohorn

# Or monorepo dev install: shared kernel first, then runtime
python3 -m pip install -e ../decepticons
python3 -m pip install -e .

# Ingest results and view the observer/dashboard
chronohorn observe serve --result-dir out/results

# Emit a family-owned scan manifest
chronohorn fleet emit-family-matrix --family causal-bank --regime gated-retention

# Full daemon: drain + fleet probe + observer + MCP
chronohorn runtime --manifest manifests/frontier_gated_retention.jsonl

# CLI help
chronohorn --help

MCP Integration

Chronohorn exposes a stateful MCP surface for experiment querying, frontier analysis, ablation tracking, fleet control, saturation detection, learning-curve comparison, and manifest/runtime management. The exact tool set changes with the runtime; the live registry is in [python/chronohorn/mcp.py](./python/chronohorn/mcp.py). Run chronohorn mcp for stdio transport. See [.mcp.json](./.mcp.json) for a client configuration example.

Repo Boundary

The intended split is:

decepticons -> chronohorn -> heinrich
kernel         runtime       evidence / audit

• decepticons owns reusable mechanisms, config validation, and export-friendly kernel surfaces
• chronohorn owns training, replay, scoring, scan emission, fleet execution, and runtime control
• heinrich is outside the runtime path and owns external evidence packaging

See [docs/REPOBOUNDARY.md](./docs/REPOBOUNDARY.md) and [docs/STACK.md](./docs/STACK.md) for the promoted boundary.

Repo Guide

The repo has a few different kinds of material that matter for different reasons:

• [docs/README.md](./docs/README.md) points to canonical live docs vs historical docs
• [manifests/README.md](./manifests/README.md) explains named regimes, generated queue files, and archive intent
• [state/README.md](./state/README.md) explains the tracked runtime snapshot and handoff files
• [scripts/README.md](./scripts/README.md) explains which scripts are wrappers vs maintenance utilities

Adding a Model Family

Create a package at python/chronohorn/families// implementing the FamilyTrainingAdapter protocol. The registry auto-discovers it — no manual registration. See CLAUDE.md for the full protocol reference and conventions.

Current Focus

The active causal-bank search is organized around cheap O(n) architecture screening before promotion:

• 10k rapid ablation lanes for mechanism screening
• scale/context survival rows aimed at pushing the frontier toward 1.0
• primary learned-substrate experiments around gated_delta
• VRAM-tier-aware fleet placement so small CUDA rows can prefer the smallest sufficient GPU lane

Current manifests live under [manifests/](./manifests/), and current results/launch state live under [out/results/](./out/results/) and [out/fleet/](./out/fleet/).

License

MIT — see [LICENSE](LICENSE).

Source & license

This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: asuramaya
• Source: asuramaya/chronohorn
• License: MIT
• Homepage: https://chronohorn.com

Install and usage instructions live in the source repository linked above.